This study examines luxury e-pricing strategies across different e-commerce platforms in China and Italy. It analyzes price fluctuations over 55 days and finds that: 1) Italian multi-brand platforms employ a "fixed pricing" strategy while Chinese platforms use a "shifting pricing" strategy with daily price changes. 2) Shifting prices negatively impact Chinese consumers' brand perceptions and willingness to buy by lowering their "customer centricity". 3) Cultural characteristics moderate the effect of shifting prices on willingness to buy, with less impact on multicultural Chinese consumers. The study concludes shifting prices should be avoided as they undermine brands while fixed pricing maintains integrity and positively influences consumers.

1. I
East and West Luxury e-pricing Strategy
An empirical study on the implications of a Global Mandate
Roberto Leumann
Double Degree In International Management
Bocconi University – Fudan School of Management － 复旦管理学院
Empirical Thesis
Tutor: Ferdinando Pennarola
Discussant: Paola Cillo

2. II

3. III
Acknowledgements
I dedicate this paper first and foremost to my parents, who gave me the opportunity to pave
my own path through life, supporting me whenever I needed.
I wish to express my deep gratitude for Professors Ferdinando Pennarola and Paola
Cillo for the indispensable assistance provided during the execution and actual analyses of
this empirical research.
I also want to sincerely thank Carlo Moltrasio, who helped me a great deal, especially
during the origination of the idea behind this paper.
In conclusion, I want to thank all of my friends, old ones and new ones, for the
enormous support they gave me during these years and remarkably Veronica Hong, with
whom I share a strong passion for linguistics.

4. IV

5. V
Table of Contents
Acknowledgements.................................................................................................. III
Abstract...................................................................................................................... 1
中文摘要 ...................................................................................................................... 2
1. Introduction and Research Objectives................................................................ 7
1.1 Introduction ................................................................................................................................ 7
1.2 Research Objectives.................................................................................................................. 8
1.2.1 Assumptions and Hypotheses .............................................................................................. 9
2. Literary Review.................................................................................................... 10
3. The Luxury e-Commerce Industry - a Background......................................... 12
3.1 The Luxury Market and e-Pricing strategies ......................................................................... 12
3.1.1 The Luxury Market – fundamentals, channels and online opportunities............................. 12
3.1.2 e-Pricing strategies in the Luxury Market: differentials, variability and harmonization ....... 14
3.2 The Luxury Customer and Brand Dynamics ......................................................................... 15
3.2.1 The Luxury equation: the paramount importance of Pricing ............................................... 15
3.2.2 China: Generational Research and Cultural Traits ............................................................. 17
4. Methodology ........................................................................................................ 18
4.1 e-Pricing Strategy Investigation ............................................................................................. 19
4.1.1 Price Extractor Macro (PEM) – Preliminary Design............................................................ 20
4.1.2 Hypothesis Testing and Other Data Analyses .................................................................... 27
4.1.3 Validation: Exchange Rate Correlation............................................................................... 32
4.2 Effects on Brand and Willingness to Buy (WTB) .................................................................. 33
4.2.1 Brand Impact Survey .......................................................................................................... 34
4.2.2 Moderated Regression Analysis ......................................................................................... 39
5. Results ................................................................................................................. 45
5.1 e-Pricing Strategy Investigation ............................................................................................. 45
5.1.1 ANOVA Analyses................................................................................................................ 45
5.1.2 Pivot Analysis ..................................................................................................................... 49
5.1.3 Validation: Exchange Rate (ER) Correlation ...................................................................... 53
5.2 Moderated Regressions - Effects on Brand and WTB......................................................... 54
5.2.1 Regression Set 1 - The effect of Variability on WTB .......................................................... 54
5.2.2 Regression Set 2 - The Moderation effect of Customer Centricity (CC)............................. 58
5.3 Overall Summary of Results ................................................................................................... 66
5.4 One-to-One Interviews............................................................................................................. 66
6. Conclusions and Recommendations ................................................................ 67
6.1 Main Conclusions on Price Shifting....................................................................................... 67
6.2 Conclusions on Chinese Monocultural and Multicultural.................................................... 68
6.3 Recommendations for Brands................................................................................................ 68
Appendix 1 – Brand selection within Clusters ..................................................... 70
Appendix 2 – Final Panel Selection ....................................................................... 71
Appendix 3 – PEM Coding...................................................................................... 74
Appendix 4 – Currency Conversion ...................................................................... 77
Appendix 5 – Survey Design .................................................................................. 79
References ............................................................................................................... 83

6. 1
Abstract
Technological breakthroughs completely transformed most of the existent markets,
and those markets that entailed retail operations have been shaken in a profound way
more often than not.
The luxury market, given its intrinsic characteristics, has been one of those markets
whose transition to e-commerce has come with greater delay but the deep changes
are now becoming more and more visible.
Pricing strategies, being at the heart of the so-called luxury equation, had to adapt as
well, with consequences not always straight-forward to determine.
In this paper, luxury e-pricing strategies of different e-commerce platforms are studied
in order to understand them in depth and clarify the alterations that distinguish them
across nations or, more broadly, across continents.
The effects of different strategies on final customers is also of paramount importance
and it is therefore researched in a second moment.
The whole analysis sheds a light on the unstable equilibrium and the threats. which
characterize luxury companies when they decided to sell their goods all over the world
and thus taking a global mandate.

7. 2
中文摘要
緒論及研究目標
此研究計畫起始於對各國奢侈品於網路平台販售之不同訂價策略的發現。舉例
來說，義大利的電商使用「固定價格」（price fixing）策略，然而在中國，「浮動定價」
（price shifting）是較常被使用的策略。此研究希望透過實證觀察及分析來驗證以上所
述之觀察到的現象，並且探討這些策略對於消費者的影響。
此實證調查使用迭代架構來分析西方與中國電商所使用的奢侈品定價策略的不
同，並於此分析後深入了解各個策略對於消費者在品牌觀感與購買意願上的影響。

8. 3
此研究分為兩部分：
1. 電商定價策略調查：旨在探討中國與義大利不同電商平台所制訂的定價策略
並觀察其價格的變動模式。最主要的研究假說是，義大利電商採用「固定價
格」（price fixing）策略，即維持奢侈品的定價不變直到折扣季；中國的電
商則採用「浮動定價」（price shifting）策略，同樣商品的價格幾乎每一天都
在改變。
• 匯率的變動在此研究中是被納入考量的，以利評估價格的變動是受
到匯差或是受到市場價格競爭的影響。
2. 消費者品牌觀感與購買意願調查：對於中國及意大利定價策略的假說驗證為
真後，接下來最重要的課題就在於探討不停變動的價格對奢侈品市場的消費
者的影響，探討同時也將消費者不同的文化背景與消費行為模式納入考量。
這部分的假說是浮動的價格對各個消費者的影響不同，而當中的不同與其文
化背景息息相關。
研究假設
電商定價策略調查是設計來測試：
假設 #1（assumption）: 商品價格變動與匯率變動無關
假說 （hypothesis)
0. 初步假說：所有單一品牌的電商平台都採用「固定價格」（price fixing）策略。
1. 研究假說＃1: 意大利的多品牌電商採用「固定價格」（price fixing）策略，中國的多
品牌電商採用「浮動定價」（price shifting）策略。

9. 4
消費者品牌認知與購買意願調查的部分將基於一個基本假設測試兩個假說。
假設 #2（assumption）: 浮動定價對於品牌觀感與購買意願有統計上顯著的影響。
此部分的假說（hypothesis）則為：
2. 研究假說＃2：浮動定價對購買意願的影響取決於受試者的文化特性。
3. 研究假說 ＃3：消費者的購買意願取決於其對品牌的觀感，而其對品牌的觀感身受價
格浮動與否影響。
研究方法
1. 電商定價策略調查
此第一部分的研究期望能盡可能蒐集到所有電商的定價模式。
主要目的在於追蹤和比較不同電商的針對特定奢侈品品牌商品在義大利與中國
的定價。此處所指的電商包括單一品牌（品牌直營）及多品牌（獨立經營）的電商。
為了能取得每一天的價格變化這樣龐大的資訊，一個“Price Extractor Macro”
(PEM) 程式被應用於下載各個電商平台的價格資訊，且這些資訊被儲存於一個資料庫。
PEM 集結了 55 天1
的資訊。這 55 天正好能觀察到兩個不同階段的價格變動資訊：非折
扣季（截至五月底）與折扣季（六月的第一週）。
PEM 資料庫所取得的資料能透過敘述統計來分析各個不同電商與地理位置的變
異。ANOVA 分析則用來驗證統計的顯著性。樞軸分析也用來更深入的分析不同商品的
價格變異。
1
⾃自 2016 年 4 ⽉月 14 ⽇日⾄至 2016 年 6 ⽉月 7 ⽇日⽌止。

10. 5
2. 消費者品牌觀感與購買意願調查
第一部份的研究證實中國的多品牌奢侈品電商，不同於義大利的單一品牌及多
品牌電商採用「固定價格」（price fixing）的策略，採用「浮動定價」（price shifting）
策略後，第二部分研究則針對定價策略對消費者的影響進行問卷調查。此部分的研究
透過 1）品牌衝擊調查，與 2）調節迴歸來衡量價格變異對品牌及購買意願的影響。
結論
第一部分：電商定價策略調查
初步假說：所有單一品牌的電商平台都採用「固定價格」（price fixing）策略。
研究假說＃1: 意大利的多品牌電商採用「固定價格」（price fixing）策略，中國的多品
牌電商採用「浮動定價」（price shifting）策略。

11. 6
第二部分：消費者品牌觀感與購買意願調查
研究假說＃2：浮動定價對購買意願的影響取決於受試者的文化特性。
研究假說 ＃3：消費者的購買意願取決於其對品牌的觀感，而其對品牌的觀感身受價格
浮動與否影響。
第一部分的研究證實「浮動定價」（price shifting）策略只被中國的多品牌奢侈
品電商採用，所有其他電商平台則採用「固定價格」（price fixing）策略。
第二部分的研究顯示「浮動定價」（price shifting）策略影響消費的對品牌的觀
感，更間接影響了消費者的購買意願（消費者中心指數（Customer Centricity Index）
可詮釋此發現）。
「浮動定價」（price shifting）策略的對消費者中心指數（ＣＣ）形成直接效應，
並對購買意願（WTB）造成間接效應。這兩個效應都經過文化特質的調節已過濾掉消
費者對價格浮動及其細微的反應。
西方人與受多元文化影響的中國人有許多相同之處。價格變異對他們的消費者
中心指數有負的影響(∆CC_West = -‐4.5/10 scores; ∆CC_Multi = -‐1.5/10 scores)，此情形
同時一致的與購買意願有正向相關，出於同樣的原因使整體的購買意願下降。
單一文化下的中國人不受此影響。價格變異不影響消費者中心指數，且消費者
中心指數似乎不影響購買意願。然而，價格變動正向且直接的影響了購買意願（提升
了約莫 4 個級分）。在進行與單一文化下的中國人的一對一訪談後發現，由於此類受
訪者的價格敏感度高，他們對價格變動評價是正面的。這樣正面的評價出自於他們能
夠追蹤商品的價格、與單一品牌電商的價格做比較、並於多品牌電商的商品價格達到
最低點時購買商品。

12. 7
1. Introduction and Research Objectives
1.1 Introduction
The idea behind the entire empirical study comes from the preliminary realization that
Luxury pricing dynamics of online e-commerce platforms seem to be different across
continents.
After having noticed this phenomenon, the researcher had the primary intent to find
evidence for this preliminary realization, through a structured analysis of online pricing.
The Luxury Market has been one of those markets in which e-commerce penetration
has come with largest delay. This mainly because this market was originally believed
to be one were the physical in-shop experience was paramount for the consumption
itself. However, the rapid growth experienced by luxury e-tailers in 2016 is a sufficient
proof that that e-commerce revolution is already happening also in this market.
Given the complex distribution structure of the luxury market (retail – wholesale), it was
fundamental to study and highlight commonalities and dissimilarities between the
pricing strategies of different channels and understand if other than the channel, also
geography was an important determinant of pricing.
Other than the Distribution structure complexity, the fashion luxury market has other
peculiarities to keep in mind. The paramount importance of pricing (together with
quality and scarcityscarsity) as one of the main components of the luxury experience
is one of them. So to say, it was not enough to validate the initial intuition that pricing
was different across continents and channels, it was also crucial to establish the
psychological relation between price and customers and assess what consequences
that different pricing strategies could have on the final customers.
The research objectives of the study are presented, together with assumptions and
hypotheses, in the next paragraph.

13. 8
1.2 Research Objectives
The empirical Investigation presented here has been structured in an iterative way in
order to analyze the different luxury e-pricing approaches adopted in the West and in
China and, later on, study the effects of these approaches on Brand perception and on
customers’ Willingness to Buy (from now on, WTB).
The iterative research is divided in two parts:
1. e-Pricing Strategy Investigation: The aim is to shed a light on the different e-
pricing strategy applied by several e-commerce platforms in Italy and China and
compare the behavior of prices. The main hypothesis to verify is that, while in

14. 9
Italy a fix-pricing strategy is applied, keeping the prices of luxury good fixed until
the sale season, in China, prices shifts constantly (almost on a daily basis).
• At this point and Exchange Rate Validation is performed, in order to
assess if the price shift is related to ER fluctuations or simply to
competitive pressures on the market.
2. Effects on Brand Perception and Consumers’ WTB: Once the characteristics of
the two different strategies are empirically verified, it is of major importance to
assess the effect that a constant price shifting in the Luxury Market has on
Consumers with different cultural background and consumption behaviors. The
main hypothesis presented here is that Price shifting have a different consumer
impact depending on the cultural background of the consumer.
1.2.1 Assumptions and Hypotheses
Overall, the research is based on 2 main assumption and 3 hypotheses, which are
summarized in a structured way hereafter:
The “e-Pricing Strategy Investigation” part is designed to test a:
Assumption #1: The Price variability does not depend on currency fluctuation
Hypotheses:
0. Preliminary Hypothesis: Monobrand platforms apply Price Fixing everywhere
1. Research Hypothesis #1: Multibrand platforms in Italy apply Price Fixing,
Multibrand platforms in China apply Price Shifting (Price Variability).
The “Effects on Brand Perception and WTB” part is designed to test 2 Hypothesis
under a Fundamental Assumption:
Assumption #2: Price Shifting (Price Variability) has a statistically significant impact
on both WTB and Brand Perception.
Hypotheses were instead that:
2. Research Hypothesis #2: The impact of Price Shifting (Price Variability) on
WTB varies according to (or is moderated by) the cultural traits of the
respondent.
3. Research Hypothesis #3: The impact on WTB is created (or moderated by)
the Brand Perception, which is directly impacted by Price Shifting (Variability),

15. 10
2. Literary Review
Taking into consideration that the afore-mentioned research objectives range over two
main areas, this paper will try to enrich the academic inquiry regarding those two,
namely 1) luxury e-commerce evolution and pricing and 2) Brand effect of Pricing on
customers with different cultural backgrounds.
Even though existing literature is often tangent to most of the subjects analyzed in this
paper, empirical analyses on price behaviors are mostly done by Think Tanks and
Consulting companies; their reports are also used as guidelines for the current analysis.
The first area has mostly been tackled by Academia in terms of the relationship
between Luxury players and e-commerce or, more broadly, with the digital
environment and the internet.
Uchè Okonkwo, a world-wide known pioneer of luxury business strategy and
industry expert has produced countless articles on this topic. in Sustaining the luxury
brand on the Internet (2009)2
has summed up the three main waves of thought of
Academics regarding the relationship between the Luxury market and the internet: a
threat, a channel and a huge opportunity. Moreover, the paper describes the cautious
steps of Luxury Players towards the establishment of a digital brand interface and
seven challenges that Luxury players are doomed to face in order to succeed in this
venture. The 6th
challenge refers to the smart use of e-commerce and articulates the
problem of stock, global demand, universal transparency (regarding product quality)
and product selection.
The aim of this paper is to elaborate on the possible challenges and threats that Global
Luxury companies encounter when dealing with e-commerce pricing strategies. More
specifically it is important to explain the threat of online price setting, especially when
prices become globally transparent and customers’ information asymmetry starts to
fade away.
In his book Luxury Online (2010)3
, Okonkwo describes the new paradigm of Luxury.
The outdated “top-down” approach in which the Luxury brands “talk AT consumers”3
,
2
Brand Management 2009, 16(5/6), pp.302–310.
3
Okonkwo, U. (2010). Luxury online. Basingstoke: Palgrave Macmillan.

16. 11
should be replaced with more conversational approaches, where brands “talk WITH
consumers” 3
. The author emphasizes the importance of customer centricity, price
harmonization and transparency, underlining that “advertising dressed as something
else [trying to make customers believe something not factually verified] can only result
in backlash”3
.
Regarding this issues, the aim of this paper is to bring to the surface a price tactic
applied by Multibrand Platforms4
in one region (Price shifting in China) that might
become a threat to the brand perception of consumers everywhere.
Exane Paribas and Contactlab have published in October 2015 their annual
Digital Luxury: Online Pricing Landscape5
. The report analyzes in depth the item
selection and price range of the ENTIRE collection presented online by many Luxury
brands. The report underlines many of strategies and decisions embarked by Luxury
companies online but does not take into account the possible Inter-period price
variability which is the main research objective of this paper.
Regarding the second area, the existing literature mainly focuses on Generational
Researches that try to explain consumption decisions of luxury consumers towards
luxury goods.
Xu, Y. and Giovannini, S. in Luxury fashion consumption and Generation Y
consumers6
investigates the consumption dynamics of American Generation Yers with
a strong focus on Luxury consumption.
Liu, Q. in An Empirical Research On Online Luxury Goods Buying intention of
Generation Y in China 7
evaluates the same fundamentals, focusing on Chinese
Generation Yers.
This research paper starts from the Generational Research made by existing literature,
namely the distinction between Generation X and Y. Later on, it tries to understand the
impact of pricing on the Brand perception of consumers introducing Cultural Traits and,
more specifically, Cultural Homogeneity and Heterogeneity. Cultural traits are
additional with respect to Generational Traits.
4
For more info about “Multibrand”, check the Section 3.1.1 below.
5
Exane Paribas - ContactLab, (2015). Digital Luxury: Online Pricing Landscape SS15.
Luxury Goods. Milano: Exane Paribas, ContactLab.
6
Journal of Fashion Marketing and Management, 19(1), pp.22-40.
7
Master of Science Dissertation. Bocconi University.

17. 12
In addition, the next Section (3. The Luxury e-Commerce Industry - a Background)
contains data from other Industry experts, Consulting companies and Think Tanks,
namely Bain, Altagamma, McKinsey and Exane Paribas.
3. The Luxury e-Commerce Industry - a Background
The following two sections contain some background information to help the reader
going through the two segments of the empirical research. The first one concentrates
on the generalities of the luxury market and on the main characteristics of pricing in
this market. The second one focuses instead on the luxury equation and the
psychological components of luxury products which defines them as such; of course,
pricing is one important component for the research purposes and will receive close
attention.
3.1 The Luxury Market and e-Pricing strategies
An overview of the Market’s generalities, features and latest characteristics, is
definitely useful to clarify the set-up of the first segment of the empirical analysis.
3.1.1 The Luxury Market – fundamentals, channels and online opportunities
The Fashion Luxury market, which is the focus of study in this research, lies within the
Personal Luxury Goods market. This industry is very dynamic and fluid and it has been
experiencing major changes during the latest years.
In 2015, the Personal Luxury market accounted for €253 billion8
in size (Revenues)
and was growing at a real rate9
of 1-2% from 2014. Regarding geographies and key
locations, Europe and Americas remain the largest selling regions, the ones able to
attract luxury consumers the most. Even though the key selling locations are Western,
consumers mainly come from China (31%) where a big percentage of the world’s high
net-worth individuals is concentrated and where a large middle class is developing.
8
Altagamma-Bain Report on Luxury 2015 and McKinsey.
9
The growth rate is adjusted considering the exchange rate fluctuations.

18. 13
It is clear that the whole industry is highly dependent on China and on Chinese
consumers, this explains why it is interesting to study this region and its differences
with more traditional Luxury geographies.
The channels available to luxury market incumbents are various. The market is mainly
dependent on wholesale, while there are several types of retail options possible for
brands. Monobrand retailers are those stores which are managed by the luxury brand
and sell only the brand’s product. In the last 10 years, the opening of proprietary
retailers, especially in double-digit growing markets like China, has been one of the
main sources of growth for luxury brands. Opposite to Monobrand stores, there are
Multibrand stores which sell different types of brands within a certain category.
This same structure (Monobrand versus Multibrand) can be found both in the brick-
and-mortar environment and in the online one.
During the latest years the Luxury Market has experienced a tremendous increase in
the e-commerce penetration, with revenues online totaling €16.8 billions8
in 2015, with
a 30% CAGR with respect to 2012.
Overall, 7% of the whole Personal Luxury Revenues are obtained through e-commerce
channels. As mentioned before, e-commerce has reached the luxury market with a
greater delay with respect to other markets and it is widely believed that luxury e-
commerce is nearing its tipping point in 2016. The tipping point is the time-point that
precedes a remarkable scaling up of operations, the moment in which the investments
starts adding up at a faster pace. Indeed, it is forecasted that by 2025 luxury e-
commerce will make around 20-25% of Personal Luxury Revenues, thus nearly tripling
its current penetration and totaling €70 billion8
(Revenues).
Digital investments are becoming the real arena on which luxury brands are competing
against each other and it is already possible to profile winners and losers on this
battlefield. Burberry, for instance, already being one of the established luxury e-
commerce leaders, has committed to invest £10 million10
in 2016 and £25 million each
year after in “retail, digital and enhancing critical capabilities”.
10
Business of Fashion.

19. 14
3.1.2 e-Pricing strategies in the Luxury Market: differentials, variability and
harmonization
As mentioned by Bain & Company, 2015 and 2016 will be the years in which “The main
challenge facing most luxury brands [will be] establishing the right pricing model”11
.
This because of two different elements of the market which are emerging and lately
coming into collision:
• Cross-country price differentials
• Digital and e-commerce transparency
Cross-country Price Differential is an element that has strongly possessed the luxury
strategy only in the latest decades. These differentials, which could be as high as
40%12
higher prices exclude tax effect, have become popular when Brands started
adapting their pricing model to emerging countries, especially China. The initial aim of
these differentials was to extract the highest possible Willingness To Pay (WTP) from
consumers assuming the high demand of new riches for Luxury products. This system
was sustainable since the regions where differentials were applied were also
characterized by a high degree of information asymmetry. As time went by, China (and
other emerging markets were differentials were applied) underwent a digital revolution
and a progressive development of the middle class, together with a higher and higher
openness to trade and travel. All these elements gave to the market an unprecedented
level of Transparency which made possible for shoppers to compare before and after-
tax prices worldwide and make consumption decisions differently. Phenomena like
“Shopping Travelling” in European Capitals and 代购 (“Daigou”, Chinese shopping
agents purchasing luxury items in Europe and reselling them in China making a profit
out of the price differential) quickly started to spread.
In order to invert this trend, major Brands are fine-tuning their price models with the
aim of harmonizing world-wide price lists13
. This approach is definitely good in the
long run to avoid value confusion in consumers coming from different regions; however,
11
Bain & Company. (2016). LUXURY GOODS WORLDWIDE MARKET STUDY - FW 2015.
12
Dilen Schneider. (2016). Luxury Consumer Trends Report Q12015
13
Luxury Daily (2016). Chanel aligns prices to prepare for future.

20. 15
the short run effect is a sharp price increase in some areas where prices were lower
and decrease in others, with possible momentary demand shifts. The pioneer in this
“alignment experiment” has been Chanel which in June 2015 started normalizing the
price of three signature models, causing a 20% downshift in China and a 20% upshift
in Europe for these three.
Given the enormous pressure and attention that revolves around pricing and price lists,
Brands normally follow a “Standard Period – Sale Period” timing, which is also reflected
in the contractual agreements they have with wholesalers. Prices are decided before
putting the products on the market, left “fixed” during the standard period and rebated
during the sale period.
To prevent the incurrence of “luxury value confusion” or “brand dilution”, price shifting
and variability during Standard periods in normally avoided. This is why the study of
possible “price shifting” strategies is an element of interest of the research.
3.2 The Luxury Customer and Brand Dynamics
A deeper understanding of the psychological impact of pricing as an ingredient of the
Luxury Formula and the evolution of the Luxury Customer Culture will be fundamental
to better understand the second segment of the empirical analysis.
3.2.1 The Luxury equation: the paramount importance of Pricing
In order to understand the psychological importance of luxury pricing, it is essential to
start from the definition of luxury goods and understand the ingredients that come
together to make a product a “Luxury product”.
A Luxury Product “is a good for which demand increases more than proportionally as
income rises”14
. Price, together with product scarcity (exclusivity coupled with strict
timing) and superior quality is one of the main dimensions of the so-called Luxury
equation, which is lately said to be in crisis because of the profound changes that the
industry is experiencing15
.
14
From Heine, Klaus: (2011) The Concept of Luxury Brands. Luxury Brand Management, No.
1, ISSN 2193-1208
15
From Business of Fashion, Italian Industry Debates Luxury Equation in Crisis.

21. 16
The price applied on Luxury goods is defined as “Premium”, meaning that it is kept
artificially high in order to give favorable perceptions to consumers, increase exclusivity
and communicate the higher intangible value that the product conveys (value
encapsulated in heritage, handcraft techniques, design, first-class materials, and so
forth).
This psychological role of price predisposes and accompanies the customer along the
journey which will lead to the final acquisition of ownership. Pricing is an important, yet
also collateral feature when the Luxury deal is completed in a brick-and-mortar
environment, but it becomes supremely important when the deal is concluded online,
because it turns out to be one of the major “luxury value conveyors”.
This is the reason why Luxury brand normally strategize very carefully the price model
they apply on their Monobrand stores and the price lists that they pass on to Multibrand
stores.

22. 17
3.2.2 China: Generational Research and Cultural Traits
Especially in countries like China, which went through a process of rapid development
where the consumption behavior of individuals has been abruptly revolutionized,
Generational Research has attempted to capture the characteristics of a specific group
of people extrapolating generalizations useful to describe an entire Generation.
The classic Generational segmentation highlights the differences between generations
X and Y, meaning those who were born before and after the Digital revolution (before
and after the late 90s).
In China, this demarcation comes with an even greater intensity since generation Yers
are the first generation born under the One-Child Policy and bearing an even more
distinctive set of characteristics: “higher personal value, higher level of attention from
parents, higher internet dependency on both daily and leisure decisions”16
. In 2015,
Generation Yers were approximately 18% of the total Chinese population16
.
These labels however are not absolutely self-sufficient and, especially in China, they
fail to account for the members of generation Y that were largely exposed, since their
early childhood to Western Values and Culture This exposure has been possible due
to the higher openness of China, to the greater penetration of the internet and to the
rising middle class which has given the possibility to gain experience and education
abroad to a higher percentage of the young population.
These subsets of the Generation Y should be regarded as “Monocultural Yers”,
Mandarin Chinese speakers only and mainly exposed to Chinese and pan-Asian
values, and “Multicultural Yers”, fluent in English and exposed over long periods of
time to Western Culture and Values.
16
Lu (2005), pg. 7-10.

23. 18
4. Methodology
As previously explained, the empirical research is structured in a two-pronged iterative
way. For each of the Research objectives (1. And 2.), a structured approach has been
designed to gather data, investigate and come to an answer that would clarify the
whole phenomenon. The whole process is summarized here:
1. E-Pricing Strategy Investigation
a. Price Extractor Macro (PEM) Design and Execution
b. ANOVA and Pivot analysis
1+ Validation: Exchange Rate Correlation
2. Effects on Brand and WTB
a. Design of a Comprehensive Survey
b. Moderated Regression Analysis

24. 19
4.1 e-Pricing Strategy Investigation
The first step of the research process had the aim to gather as much data as possible
about the pricing schemes applied by different e-commerce platforms.
The main objective is to define a panel of comparable items of some luxury brand and
monitor the prices applied in Italy and China by different e-commerce channels – both
proprietary ones (mono-brands) and independent ones (Multibrand).
In order to gather data on a DAILY BASIS, it was important to automate the process
of “price extraction” or “price pulling”, that is the process by which the prices are
downloaded from different e-commerce platform and collected into a database.
For this reason, a “Price Extractor Macro” (PEM) has been designed and
programmed. The Macro has been ran for 55 days17
in order to be able to monitor the
price pattern of different platforms during two different periods: standard period (until
the end of May) and sale period (The first week of June).
After the data from the PEM has been collected, Descriptive statistics have been
calculated to analyze the variability of different e-commerce platforms and across
geographies.
An ANOVA analysis has been run in order to verify the statistical significance of
results.
The overall results were also aggregated through a Pivot Analysis in order to analyze
the price variability more in depth.
17
From April 14th 2016 to June 7th 2016

25. 20
4.1.1 Price Extractor Macro (PEM) – Preliminary Design
4.1.1.1 PEM – Panel Design
The initial step for the creation of the PEM has been to select a panel of items to
monitor in order to have a complete panorama of the pricing scheme applied in the two
geographical Areas Selected, namely Italy and China.
The most important factors that had to be defined in order to select the panel were:
1. Which Brand to monitor,
2. How many and which e-commerce platforms to monitor
3. How Many and which Items to monitor
1. Brand Panel Selection
The Brand selection has been done according to 2 criteria:
• Mirror the Luxury Pyramid (Altagamma Bain 2015)
• Select Brands 1) with strong and efficient proprietary e-commerce in order to
compare them with multi-brand, 2) offering many different Items (Apparel,
Shoes, Bags etc.).
The Luxury Pyramid information has been derived by the Bain Altagamma Monitor
2015, which contains the following breakdown for Brands: 38% Accessible brands (the
base of the pyramid, with lower prices and weaker brand reputation), 36% Accessible
Brands (middle of the pyramid) and 26% Absolute Brands (Top of the pyramid, with
higher prices and stronger brand reputation).
In order to mirror the Luxury Pyramid, 5 clusters of Brands18
have been selected:
Luxury Pyramid Cluster calculation Cluster name
Absolute 26% ≈ 20%*5 à 1 Cluster 1
Aspirational 36% ≈ 40%*5 à 2 Cluster 2 and 3
Accessible 38% ≈ 40% à 2 Cluster 4 and 5
18
Clusters may contain items from one or more brands inside

26. 21
In order to select which brands would populate each cluster, the criteria presented
above (e-commerce efficiency and completeness of the product offer) have been used.
For further details about the Brand selection within the clusters, please take
Appendix 1 – Brand selection within Clusters as reference.
The final Brand Selection is normally composed by one Brand only, with the exception
of Cluster 5 which contains 3 brands (each representing only its distinctive item).
The Final Selection is presented hereafter:
Cluster Luxury Pyramid Brand
Cluster 1 Absolute Valentino
Cluster 2 Aspirational Stella McCartney
Cluster 3 Aspirational Dolce & Gabbana
Cluster 4 Accessible Burberry
Cluster 5 Accessible Moschino (Apparel only)
Michael Kors (Bags only)
Tory Burch (Shoes Only)
2. e-Commerce Platform Panel Selection
The e-Commerce Platforms to be monitored were selected following two sets of criteria.
The set #1 includes the necessity to gather enough data to compare the characteristics
of Monobrand (proprietary) and Multi-brand (independent) e-commerce Platforms.
This is mainly due to the fact that the research needed data coming both from brand-
managed platforms and non brand-managed platforms.
The set #2 includes the following criteria:
• Focus on Luxury Goods (specific and non-generic, like Amazon, for instance)
• Operations in China-only or both of the geographies under study (Italy and
China)
• Delivery of 100% real goods19
19
This criterion has been added especially in the selection of Chinese e-commerce
Platforms. The ones selected have a “100%正品保证“ guarantee (Guaranteed and
certified quality and originality of the products).

27. 22
The Final Selection is presented hereafter:
Type Name Geographies
Mono-brand Valentino Store Italy & China
Stella McCartney Store Italy & China
Dolce & Gabbana Store Italy & China
Burberry Store Italy & China
Michael Kors Store Italy & China
Moschino Store Italy & China
Tory Burch Store Italy & China
Multi-Brand Luisa Via Roma Italy & China
Farfetch Italy & China
MyTheresa Italy & China
Net-A-Porter Italy & China
尚品网 (Shangpin wang) China Only
天猫 (Tianmao – T-mall) China Only
走秀网 (Zouxiu wang – Xiu) China Only
3. Item Panel Selection
The Item Selection has been performed taking into account the following criteria:
• Mirror the sales breakdown of different types of items within the Fashion Luxury
Market (Data from Bain Altagamma Monitor 2015).
• Ensure that the selected items are sold on ALL of the selected e-Commerce
platforms in order to ensure homogeneity of results.
• Price Range Homogeneity
For each Brand, a total of 5 items were selected for the monitoring. To determine the
number of item from each product category (Apparel, Bags and Shoes – excluding
“Accessorizes” for simplicity) the Luxury Sale Breakdown Information (Altagamma
Report 2015) has been used. The final panel of items is presented hereafter:
Product Category Portion of Total Luxury Sales Number or items (Total: 5)
Apparel 40% 40%*5 = 2
Bags 44%≈40% 40%*5 = 2
Shoes 15%≈20% 20%*5 = 1

28. 23
During the actual selection on the e-Commerce Platforms, the following rule has been
followed to ensure Price Range Homogeneity:
Product Category Item number Price Range
Apparel Apparel 1 Third highest Decile (Mid-High Price)
Apparel 2 First Highest Decile (Highest Price)
Bags Bag 1 Third highest Decile (Mid-High Price)
Bag 2 First Highest Decile (Highest Price)
Shoes Shoes Average Brand Price for shoes
Final Aggregated Panel
Aggregating all the Brand Cluster, e-Commerce Platforms and Item Category
selections presented above, the Final Panel database has been populated with 325
single items, as explained hereafter
Type Name China Italy Subtotal
Mono-brand Valentino Store 5 5 10
Stella McCartney Store 5 5 10
Dolce & Gabbana Store 5 5 10
Burberry Store 5 5 10
Michael Kors Store 2 2 4
Moschino Store 2 2 4
Tory Burch Store 1 1 2
Multi-Brand Luisa Via Roma 25 25 50
Farfetch 25 25 50
MyTheresa 25 25 50
Net-A-Porter 25 25 50
尚品网 (Shangpin wang) 25 x 25
天猫 (Tianmao – T-mall) 25 x 25
走秀网 (Zouxiu wang – Xiu) 25 x 25
Grand Total 325
For further details about the final Panel selection and Item Taxonomy, please take
Appendix 2 – Final Panel Selection as reference.

29. 24
4.1.1.2 PEM – From Panel to Dataset
In order to extract valuable insights from the e-commerce platforms and thus shed light
on the e-Pricing strategy applied, it was crucial to create a LARGE DATABASE, by
extracting the price of each single item on a DAILY basis.
Given the large amount of single items (325), it was necessary to build a Macro to
automate the process of daily price extraction.
A Macro is a “sequence of computing instructions available to the programmer as a
single program statement”20
. More specifically, it is a series of pre-defined statements
which can be combined to form a program structure which functions automatically,
when run.
In order for Macros to work, they have to be coded in a tailor-made fashion, in order to
suit the specific function that the programmer wants the Macro to complete.
The following sections explain in details the 2 main processes that compose the “life”
of the PEM, namely: 1) the Coding and 2) The Execution.
20
Oxford Dictionary, Computer Sciences Section, 2016
,

30. 25
4.1.1.3 PEM – Coding
The PEM is fundamentally an aggregation of three different Macros which are operated
simultaneously. The three different Macros were named: 1) Download HTML, 2)
Extract and 3) Populate.
For further information on the overall PEM’s Control Panel and the exact coding of the
three sub-macros, take Appendix 3 – PEM Coding as reference.
1. Download HTML
Each one of the 325 single observations, were coupled with an unique link21
. The
“Download” Macro opens each single link and downloads the HTML code connected
to it. The HTML code is a “standard systematic code” used to write World Wide Web
Pages.
Once the HTML code of the single item’s page has been downloaded, it is saved on
the Device’s Hard Disk with a single unique name, which correspond to the Item code22
.
2. Extract Prices
Once the “Download” Macro has terminated, the “Extract Prices” Macro starts. After
having uploaded on an Excel Sheet the entire content of the HTML code file which
resides in the Device’s Hard Disk, this Macro scans it to find the Price information
which is contained within the code.
This Macro had to be customized for every different e-Commerce platform because
the way the price information is coded within the page’s HTML differs depending on
the website design.
All in all, The macro looks for the hotwords23
which precedes the Price information and
extracts the price.
21
Example: http://www.toryburch.it/sandalo-­‐cecile/51158723.html?cgid=shoes-­‐
heels&start=11&dwvar_51158723_color=001
22
The Item code is the unique code assigned to each item of the Database. For further
information, take Section 4.1 and Appendix 2 (Taxonomy) as reference.
23
Examples of hotwords are “price”, “dailyprice”, “prodprice”, “itemprice” etc.

31. 26
3. Populate Database
Once the “Extract Prices” Macro has terminated, the last part of the PEM, namely the
“Populate Database” Macro starts.
This Macro simply collects the data extracted during the second sub-process and
arranges it in a database tidily and in an organized way.
4.1.1.4 PEM – Execution
The PEM was operated for 55 days 24
at the same hour 25
in order to ensure
homogeneity of results.
Even though the PEM process is automated, that does not mean that it is
instantaneous. Indeed, it took approximately 75 minutes every day to successfully run
the macros and collect the data, plus an extra 10 minutes a day to solve minor errors
in the Macros.
The 75 minutes were spread unevenly between the Sub-Macros: The “Download
HTML” was by far the longest of the three, lasting approximately 98% (73 minutes) of
the overall PEM functioning.
Overall, the PEM has successfully extracted and collected 17,875 single
observations (325 single items * 55 days of research).
24
From April 14th 2016 to June 7th 2016
25
Approximately at 11:00AM, Rome Time (GMT+2)
equivalent to 5PM, Shanghai Time (GMT+8)

32. 27
4.1.2 Hypothesis Testing and Other Data Analyses
The overall data collection obtained with the PEM was instrumental for the hypotheses
testing that triggered the overall research. These hypotheses are summed up hereafter:
• Preliminary Hypothesis: Monobrand platforms apply Price Fixing everywhere
• Research Hypothesis #1: Multibrand platforms in Italy apply Price Fixing,
Multibrand platforms in China apply Price Shifting (Price Variability).
After the successful collection of the 17,875 daily prices, the bulk of data was in
different currencies (USD, Euro and Chinese RMB). Therefore, before running any
type of analysis on them, they were all converted to Euro to ensure homogeneity. For
Further information about the Currencies and the Exchange rates used for conversion,
take Appendix 4 – Currency Conversion, as reference.
After the conversion, for each of the 325 single items, the following metrics have been
calculated:
• Mean
• Variance
• Standard Deviation
• Coefficient of Variation
• Minimum Price
• Maximum Price
• Number of Price Changes: How many times the price has changed during the
research period
These metrics will be used to perform 2 types of analyses:
1. ANOVA: where the two hypotheses are tested and some additional information
about multibrand platforms in China are gathered.
2. Pivot Analysis: where more detailed information about the price decisions are
highlighted.

33. 28
4.1.2.1 One-way ANOVA analysis of St. Deviations
The aim of the ANOVA analysis is to test the research hypotheses set at the beginning.
This will be done studying the variability of different subsets of data and compare them
to each others in order to assess if the difference in the variability is statistically
significant. The metric used to assess the variability is the Standard Deviation (sigma)
calculated, for each one of the 325 items, along the course of the 55 days of data
collection.
The ANOVA analysis is broken down in 3 single ANOVA analyses. At each round, the
subset that is found to have null variability (sigma=0, Price Fixing) is discarded, and
further analysis is done on smaller subsets to identify exactly which subset shows a
Positive variability (sigma>0, Price Shifting).

34. 29
1. Full Dataset - Monobrand vs. Multibrand (Preliminary Hypothesis)
The very first round of ANOVA contains the whole bulk of data collected. All the 325
single items are used to validate the Preliminary Hypothesis that Monobrand platforms,
everywhere in the world apply Price Fixing (sigma=0).
For this purpose, the variability of the following groups is compared:
• Monobrand (50 items): Contains 25 items from Monobrand platforms selling in
Italy and 25 items in Monobrand platforms selling in China
• Multibrand (275 items): Contains 100 items from Multibrand platforms selling in
Italy and 175 items in Multibrand platforms selling in China.
The COMPLETE dataset is the following:
Type Name China Italy Subtotal
Mono-brand Valentino Store 5 5 10
Stella McCartney Store 5 5 10
Dolce & Gabbana Store 5 5 10
Burberry Store 5 5 10
Michael Kors Store 2 2 4
Moschino Store 2 2 4
Tory Burch Store 1 1 2
Multi-Brand Luisa Via Roma 25 25 50
Farfetch 25 25 50
MyTheresa 25 25 50
Net-A-Porter 25 25 50
尚品网 (Shangpin wang) 25 x 25
天猫 (Tianmao – T-mall) 25 x 25
走秀网 (Zouxiu wang – Xiu) 25 x 25
Grand Total 325

35. 30
2. Multibrand – Italy vs. China (Research Hypothesis #1)
After the first round is completed, the Preliminary hypothesis has been confirmed,
Monobrand platforms shows null variability (sigma=0, Price Fixing) when compared
to Multibrand platforms.
The second round analyses Multibrand-platforms data only, testing the Research
Hypothesis #1 that Multibrand platforms in Italy show null variability (sigma=0, Price
Fixing), while Multibrand platforms in China show Positive variability (sigma>0,
Price Shifting).
The MULTIBRAND dataset is the following:
Type Name China Italy Subtotal
Mono-brand Valentino Store 0 0 0
Stella McCartney Store 0 0 0
Dolce & Gabbana Store 0 0 0
Burberry Store 0 0 0
Michael Kors Store 0 0 0
Moschino Store 0 0 0
Tory Burch Store 0 0 0
Multi-Brand Luisa Via Roma 25 25 50
Farfetch 25 25 50
MyTheresa 25 25 50
Net-A-Porter 25 25 50
尚品网 (Shangpin wang) 25 x 25
天猫 (Tianmao – T-mall) 25 x 25
走秀网 (Zouxiu wang – Xiu) 25 x 25
Grand Total 275
3. Multibrand in China – e-Commerce Platform Variability
After the second round is completed, the Research hypothesis #1 has been confirmed,
Multibrand platforms in Italy show null variability (sigma=0, Price Fixing) when
compared to Multibrand platforms in China.
The third round is analyses data coming from Multibrand platforms in China only,
testing the possible differences among different platforms.

36. 31
The MULTIBRAND/CHINA dataset is the following:
Type Name China Italy Subtotal
Mono-brand Valentino Store 0 0 0
Stella McCartney Store 0 0 0
Dolce & Gabbana Store 0 0 0
Burberry Store 0 0 0
Michael Kors Store 0 0 0
Moschino Store 0 0 0
Tory Burch Store 0 0 0
Multi-Brand Luisa Via Roma 25 0 25
Farfetch 25 0 25
MyTheresa 25 0 25
Net-A-Porter 25 0 25
尚品网 (Shangpin wang) 25 x 25
天猫 (Tianmao – T-mall) 25 x 25
走秀网 (Zouxiu wang – Xiu) 25 x 25
Grand Total 175
4.1.2.2 Pivot Analysis
After having validated the statistical significance of the Hypotheses, the bulk of data
has been manipulated through Pivot analysis to shed light on the Price Differential and
the Price Variations detectable in the sample.
This analysis can be split in 3 parts:
1. The Big Picture and the Sale Period Behavior
2. Price Differential Analysis: China vs. Italy
3. Coefficient of Variation and Number of Changes
1. The Big Picture and the Sale Period Behavior
In this part, the whole bulk of data is aggregated to visualize the differences in Price
Behavior between the Italian and Chinese Markets, especially regarding the two main
periods of observation: Standard and Sale Period.

37. 32
2. Price Differential Analysis: China vs. Italy
This analysis focuses on the average price differentials between the two markets under
analysis (Italy and China). The data breakdown is structured in two ways:
• Retailer Type Breakdown (Monobrand vs. Multibrand)
3. Coefficient of Variation and Number of Changes
The analysis focuses on the Coefficient of Variation and on the number of changes.
The Data analyzed in this section does not include the Monobrand data, since we
already found that it has Null Variability.
Data breakdown is presented in 2 ways:
• Luxury Pyramid Breakdown (Absolute, Aspirational, Accessible)
• Product Category Breakdown (Apparel, Bags, Shoes)
4.1.3 Validation: Exchange Rate Correlation
After having proved the hypotheses true, it is important to validate Assumption #1:
“The Price variability does not depend on currency fluctuation”.
This additional validation is crucial to understand if the price behavior is strictly
decisional of the e-commerce platforms or if it is instead mandated by the need to curb
the Exchange Rate effect between Euro, Dollar and Yuan.
Most probably, only some minor portion of price variations in China will depend on the
Exchange Rate variability, while the majority of it will probably depend on the e-
commerce platform decisions.
The Validation process is simple in its form. The 55 daily prices for all of the China-
based items will be inserted in a Correlation Matrix (containing Pearson’s r or
Correlation coefficients) against the 55 daily Exchange Rates (Euro/Yuan, Dollar/Yuan)
for the period.

38. 33
4.2 Effects on Brand and Willingness to Buy (WTB)
After having statistically proved that Multibrand Platforms operating in China, unlike
Multibrand platforms operating in Italy and Monobrand platforms, adopt a Price Shifting
Strategy on Luxury goods, it is important to determine the effect of these practices on
final consumers.
This empirical analysis is done through 1) a Brand Impact Survey, which will provide
data to perform several 2) Moderated regressions to assess the impact of Price
Variability on Brand and Willingness to Buy (WTB).
Test of Hypotheses
Test of Assumption

39. 34
4.2.1 Brand Impact Survey
The Survey has been active for a period of 15 days26
and has been directed to a
selected pool of respondent which could meet the desired target characteristics.
4.2.1.1 Survey’s Objectives, Main Assumption and Hypotheses
The survey has been conceived keeping in mind two sets of objectives:
1. Make sure that Price Shifting (Price Variability) has a statistically significant
impact on customers,
2. Elucidate on the effect Price Shifting (Price Variability) practices may have on:
• Brand Perception and Recognition
• Willingness to Buy (WTB)
The design of the survey and the selection of target respondents was articulated
around Assumption #2, which states that: “Price Shifting has an impact on both WTB
and Brand Perception”.
The Hypotheses were instead that:
2. Research Hypothesis #2: The impact of Price Shifting (Price Variability) on
WTB varies according to (or is moderated by) the cultural traits of the
respondent.
3. Research Hypothesis #3: The impact on WTB is created (or moderated by)
the Brand Perception, which is directly impacted by Price Shifting (Variability),
26
From May 30th to June 14th 2016.

40. 35
4.2.1.2 Survey Target Selection and Design
Regarding the Target selection, the main indexes used where:
1. Potential Luxury Customer Traits
• Age - Above 20 years old, to ensure potentiality to buy and/or possess
• Propensity to possess and/or consume Fashion Luxury Product
2. Desired Cultural Traits, either:
• Western Culture: People selected were born and raised in Central
Europe, Eastern Europe and main Anglophone Countries (US, UK,
Australia)
• Chinese Multi-culture: People selected were born and raised in China but
had a long term foreign experience and spoke more than one language
fluently
• Chinese Mono-culture: People selected were born and raised in China,
only had few experiences abroad and could only speak Mandarin
Chinese fluently.
In order to assess the Brand Effects of Price Shifting (Price Variability) in an accurate
way and avoid respondent confusion the survey has been designed in a simple and
straight-forward way.
For further information about the Survey Design, take Appendix 5 – Survey Design
as Reference.
Selection of the Brand to be surveyed
In order to avoid complexity, out of the 7 brands analyzed in the Price Extractor Macro
(PEM) part, only one has been selected. The selection has been done from data about
the “Interest Over Time” 27
.The metric used was the “Average Interest over time” which
made “Valentino” stand out as the one attracting most interest on average.
27
Data coming from the comparison of the Brand Names on “Google Trends” (2004-
2016)

41. 36
Survey Questions
Regarding the actual survey questions, the Occam Razor technique has prevailed,
resulting in the compilation only 5 questions, other than the ordinary profiling questions
(regarding age, sex, luxury consumption propensity and cultural background).
These 5 questions were designed to assess 5 Dimensions to study:
1. Willingness to buy (WTB)
2. Brand Perception – Exclusivity
3. Brand Perception – Authenticity
4. Brand Perception – Creativity
5. Brand Perception – Customer Centricity
The 5 Questions have been asked BEFORE and AFTER a short video of 30s
explaining the behavior of prices, in order to obtain 2 scores:
• “before-video” score
• “after-video” score
to be used for comparison.
Survey Versions and Languages
In order to control for the effective impact of price variability, two additional precautions
have been taken:
• The survey has been realized in two version: 1) a control version, which
displayed a video where prices were constant (Price Fixing), and
2) an experiment version, which displayed a video where prices were shifting
(Price Shifting).
• The survey has been realized in two different languages: 1) English (For
westeners) and 2) Mandarin Chinese (for Chinese people).
A total of 4 Versions of the survey have thus been dispatched:
• Chinese-Experiment,
• Chinese-Control,
• English-Experiment
• English Control.

42. 37
4.2.1.2 Sample Demographics and Validation
This section presents the fundamentals (total number of respondents, sex and age) of
the Survey’s data and checks for the validity of age distribution.
Respondents – an Outlook
The table below shows the overall number of respondents broken down by type of
survey and cultural background. Given the preciseness of the targeting, the data
distribution is quite even among the categories.
The overall number of respondents is 175.
Sample Demographics – Sex and Age Validation
Regarding sex, it was a conscious decision to break down the sample unevenly, given
the characteristics of the product. As such, fashion luxury brand appeals women more
than men.
The following table and pie chart describe the sex distribution (overall, M:F=64%:34%).
Count
of
Respondents
Control
86
China_Monoculture
9
China_Multiculture
25
Western_Culture
52
Experiment
89
China_Monoculture
17
China_Multiculture
18
Western_Culture
54
Grand
Total
175
China
Western
Grand
Total
Control
Experiment
Control
Experiment
Female
58,82%
54,29%
65,38%
72,22%
64,00%
Male
41,18%
45,71%
34,62%
27,78%
36,00%
64%
36%
Sex
Breakdown
-­‐ Grand
Total
Female Male

43. 38
Regarding Age instead, a sample validation has been run to make sure that the sub-
samples were homogeneous. The results follow:
ANOVA Tablea
Sum of
Squares df
Mean
Square F Sig.
Age *
Experiment_Control
Between
Groups
(Combined)
28.394 1 28.394 2.142 .145
Within Groups 2293.126 173 13.255
Total 2321.520 174
ANOVA Tablea
Sum of
Squares df
Mean
Square F Sig.
Age *
Geography
Between
Groups
(Combined)
30.940 1 30.940 2.337 .128
Within Groups 2290.580 173 13.240
Total 2321.520 174
Since in both of the Cases, the F-test failed (non significant), this means that the means
of the subsamples are not statistically different, making the sample homogeneous from
an age point of view. Also graphing the distribution of the 4 sub-samples, it is clear that
they are fairly homogeneous.
0
2
4
6
8
10
12
20 22 23 24 25 26 27 28 29 30 31 33 56
Age
Distribution
China
Control China
Experiment Western
Control Western
Experiment

44. 39
4.2.1.3 Data Cleansing – Attention Test
Since the compilation of the survey, an Attention test has been designed as a
necessary condition for usability of the respondent’s data. Right after the explanatory
video – where the behavior of price, either fixing or shifting, was described – a question
regarding the price behavior was presented to make sure that the respondent was
aware of the phenomenon described.
Out of 175 respondents, 11 (10 Western and 1 Chinese) got the answer wrong, leaving
the sample with 164 responses.
An additional response was excluded because of the fact that most of the responses
were far different from those of the others (outlier test).
Therefore, the final sample used for the regression analysis was composed by 163
usable responses.

45. 40
4.2.2 Moderated Regression Analysis
4.2.2.1 Intro – Assumption and Hypotheses
Before starting with the Regression description, it is important to recap the Assumption
#2 and Hypotheses behind Regression analysis.
The Research Assumption #2: “Price Shifting has a statistically significant impact on
both Brand Perception and WTB”.
The Hypotheses were instead that:
2. Research Hypothesis #2: The impact of Price Shifting (Price Variability) on
WTB varies according to (or is moderated by) the cultural traits of the
respondent.
3. Research Hypothesis #3: The impact on WTB is created (or moderated by)
the Brand Perception, which is directly impacted by Price Shifting (Variability),
The Regression Analysis that follow will disentangle the intricate relationship that exists
between the variables obtained through the survey.
This process will be done in three steps:
1. Mean Comparison: To verify the Assumption #2 and select the variables
2. Regression Set 1: To test the Hypothesis #1
3. Regression Set 2: To test the Hypothesis #2

46. 41
4.2.2.2 Mean Comparison – Assumption #2 Validation and Variable Exclusion
In order to verify if the price Variability had an impact on all of the 5 dimensions28
extrapolated through the survey, the delta of each of the dimensions has been
calculated in this way:
∆Dimension = After-video score – Before-video score
Subsequently, a Descriptive statistics and a t-Test were run to verify if there was a
statistically significant difference between the Experiment Group (which witnessed the
Price Shifting) and the Control Group (which did not).
The Results of the Group Descriptive Statistics are presented here after:
Dummy_
Variability N Mean Std. Deviation Std. Error Mean
Delta_Exclusivity Control 80 -.25 .436 .049
Experiment 84 -.38 .599 .065
Delta_Creativity Control 80 -.29 1.009 .113
Experiment 84 -.01 2.027 .221
Delta_Authenticity Control 80 -.21 .441 .049
Experiment 84 -.36 .573 .063
Delta_Customer_Centricity Control 80 -.01 .921 .103
Experiment 84 -3.75 1.913 .209
Delta_WTB Control 80 -.36 1.105 .124
Experiment 84 -2.86 3.170 .346
These group Statistics highlight already that only three dimensions seem to have a
mean that is sensibly different in the two subgroups (Creativity, Customer Centricity
and WTB).
28
The 5 dimensions will be summed up here:
1. Willingness To buy (WTB)
2. Brand Perception – Authenticity
3. Brand Perception – Exclusivity
4. Brand Perception – Creativity
5. Brand Perception – Customer Centricity (CC)

47. 42
We have to verify the results of the t-Test to make sure of the statistical significance of
these differences:
Independent Samples T-Test
Levene's Test for Equality of
Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Delta_Exclusivity Equal variances assumed 12.052 .001 1.594 162 .113 .131
Equal variances not assumed 1.606 151.633 .110 .131
Delta_Creativity Equal variances assumed 21.175 .000 -1.094 162 .276 -.276
Equal variances not assumed -1.110 123.042 .269 -.276
Delta_Authenticity Equal variances assumed 11.991 .001 1.804 162 .073 .145
Equal variances not assumed 1.815 155.262 .071 .145
Delta_Customer_Centricity Equal variances assumed 67.366 .000 15.815 162 .000 3.738
Equal variances not assumed 16.059 120.787 .000 3.738
Delta_WTB Equal variances assumed 42.638 .000 6.662 162 .000 2.495
Equal variances not assumed 6.791 103.756 .000 2.495
Only the highlighted ∆Dimensions passed the test with a confidence level of 95%
(α=0.05). This means that there is not always evidence of statistically significant
difference between the mean of the 2 subsets (Experiment and Control). Therefore the
Regression Analysis will only include:
• ∆Customer Centricity (From now on, “∆CC”): The only brand perception
indicator that is significantly affected by Variability
• ∆WTB
4.2.2.3 Regression Variables Explanation
From the results of the t-Test the only two dimensions left to study are: ∆CC and ∆WTP,
which will be used together with other dummies (regarding demographics and cultural
traits) to study the Brand and Consumption effects of Price variability.
This section will sum up the whole bulk of variables that will be used in the following
two sets of Regressions and should be regarded as a taxonomy or legend for
understanding:

48. 43
List and explanation of the variables:
# Name Type of Variable
1 Delta_WTB Numerical (Ordinal)
2 Delta_Customer_Centricity Numerical (Ordinal)
3 Dummy_Variability Dummy
0=Control (Fixing)
1=Experiment (Shifting)
4 Dummy_Monoculture Dummy
0=Other
1=Chinese Monoculture
5 Dummy_Multiculture Dummy
0=Other
1=Chinese Multiculture
6 Dummy_WestCulture Dummy
0=Other
1=Westculture
7 Mod_Variability_CMono Interaction #3*#4 = DUMMY*DUMMY
8 Mod_Varibility_CMulti Interaction #3*#5 = DUMMY*DUMMY
9 Mod_Variability_West Interaction #3*#6 = DUMMY*DUMMY
10 Mod_Multiculture_zCC Interaction #2*#4 = DUMMY*ORDINAL
11 Mod_Monoculture_zCC Interaction #2*#5 = DUMMY*ORDINAL
12 Mod_WestCulture_zCC Interaction #2*#6 = DUMMY*ORDINAL
13 Trimod_Variability_zCC_Mono Interaction #2*#3*#4 = DUMMY**DUMMY*ORDINAL
14 Trimod_Variability_zCC_Multi Interaction #2*#3*#5 = DUMMY**DUMMY*ORDINAL
15 Trimod_Variability_zCC_West Interaction #2*#3*#6 = DUMMY**DUMMY*ORDINAL
It is important to notice that Variables from #10 to #15 contain Standardized (z-scored)
values for Customer Centricity. Centered variables (z-scored) are useful when dealing
with interaction variables because they avoid Multi-collinearity with the base variable
when inserted in the Model together.

49. 44
4.2.2.4 Regression Set 1 – The effect of Variability on WTB
This model is a preliminary model, created to verify the solidity of the Research
Hypothesis #2:
In order to do so, the following Regressions have been set up:
Regression Dependent Variable Independent Variables
1 Delta_WTB Dummy Variability
2 Delta_WTB Mod_Variability_Multi
Mod_Varibility_Mono
Mod_Variability_West
4.2.2.5 Regression Set 2 – The Moderation effect of Customer Centricity (CC)
This model is the final model, created to verify the solidity of Research Hypothesis #3:
In order to do so, the following Regressions have been set up:
Regression Dependent Variable Independent Variables
1 Delta_WTB Dummy_Variability
Mod_Variability_CMono
Mod_Variability_CMulti
Mod_Variability_West
Delta_Customer_Centricity
TriMod_Variability_zCC_Mono
TriMod_Variability_zCC_Multi
TriMod_Variability_zCC_West
2 Delta_Customer_Centricity Dummy_Variability
Mod_Variability_CMono
Mod_Variability_CMulti
3 Delta_WTB Delta_Customer_Centricity
3+ Delta_WTB Delta_Customer_Centricity
Mod_Multiculture_zCC
Mod_WestCulture_zCC

50. 45
5. Results
5.1 e-Pricing Strategy Investigation
5.1.1 ANOVA Analyses
The ANOVA Analyses were designed to test both the:
• Preliminary Hypothesis: Monobrand platforms apply Price Fixing everywhere
• Research Hypothesis #1: Multibrand platforms in Italy apply Price Fixing,
Multibrand platforms in China apply Price Shifting (Price Variability).
5.1.1.1 ANOVA1 – Mono-Multi (325 items) – Preliminary Hypothesis
The ANOVA1 output is presented hereafter. Remember that the descriptive statistics
are calculated on the entire dataset (325) and are done on the Standard Deviations of
the single items, so all the descriptive indicators describe variability.
Descriptives
Std_Dev
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
Multibrand 275 26.5946 77.20394 4.65557 17.4294 35.7598 .00 535.41
Monobrand 50 .0000 .00000 .00000 .0000 .0000 .00 .00
Total 325 22.5031 71.64483 3.97414 14.6847 30.3215 .00 535.41
ANOVA 1
Std_Dev
Sum of Squares df Mean Square F Sig.
Between Groups 29923.072 1 29923.072 5.918 .006
Within Groups 1633162.764 323 5056.231
Total 1663085.837 324
It can be noticed that the 50 Monobrand had null variability (sigma=0, Price Fixing),
while Multibrand Platforms experienced a positive variability of 26€ (St. Dev. average)
on average, with peaks of approximately 535€ (St. Dev. maximum).
The ANOVA F-Test is significant signaling a statistically significant difference between
the means of the St. Dev.s of the two sub-samples.
Preliminary Hypothesis is tested and successfully proved correct.

51. 46
5.1.1.2 ANOVA2 – Geography (275 items) – Research Hypothesis #1
The analysis of Price variation continues with the exclusion of Monobrand platform
data which had null variability (sigma=0, Price Fixing).
ANOVA2 is performed on the Multibrand portion of Dataset (275) and concentrates on
the distinction between Price variability in Italy and Price Variability in China. The
results are presented hereafter.
It can be noticed that the 100 Multibrand platforms in Italy had null variability
(sigma=0, Price Fixing), while Multibrand Platforms experienced a positive
variability of 41€ (average St. Dev.) on average, with peaks of approx. 535€ (St. Dev.
maximum).
The ANOVA F-Test is significant, signaling a statistically significant difference between
the means of the St. Dev.s of the two sub-samples.
Research Hypothesis #1 is tested and successfully proved correct.
Descriptives
St. Dev
N Mean
Std.
Deviation
Std.
Error
95% Confidence
Interval for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
Std_Dev Italy 100 .0000 .00000 .00000 .0000 .0000 .00 .00
China 175 41.7915 93.52669 7.06995 27.8376 55.7454 .00 535.41
Total 275 26.5946 77.20394 4.65557 17.4294 35.7598 .00 535.41
ANOVA 2
St. Dev
Sum of Squares df Mean Square F Sig.
Std_Dev Between Groups 111142.840 1 111142.840 19.935 .000
Within Groups 1522019.924 273 5575.165
Total 1633162.764 274

52. 47
5.1.1.3 ANOVA3 – e-Commerce Platforms (175 items)
The third ANOVA is a way to study the variability of the sample in even further detail.
Multibrand platforms in Italy have been discarded because they show null variability
(sigma=0, Price Fixing), and an analysis of both the Mean Price and the St.
Deviations of Multibrand platforms in China (sample: 175) follows. The analysis is
carried out for every platform one-by-one.
ANOVA
Sum of Squares df Mean Square F Sig.
Mean Price Between Groups 9341555.389 6 1556925.898 2.131 .052
Within Groups 122769484.475 168 730770.741
Total 132111039.864 174
Std_Dev Between Groups 88828.823 6 14804.804 1.735 .116
Within Groups 1433191.102 168 8530.899
Total 1522019.924 174
Descriptives, ____ = Highest, _____ = Lowest
N Mean
Std.
Deviation Std. Error
95% Confidence Interval
for Mean
Minimum Maximum
Lower
Bound
Upper
Bound
Mean
Price
LuisaViaRoma 25 1385.68 972.16 194.43 984.39 1786.97 316.41 3841.65
Farfetch 25 1261.51 911.22 182.24 885.37 1637.64 181.57 3160.98
MyTheresa 25 862.76 614.04 122.80 609.30 1116.22 .00 2099.00
Net-a-Porter 25 1422.86 976.56 195.31 1019.75 1825.96 173.24 3361.48
Xiu 25 1410.07 857.40 171.48 1056.14 1763.99 230.77 3597.30
TianMao 25 1466.32 972.46 194.49 1064.91 1867.73 223.06 4311.47
Shangpin 25 929.24 573.05 114.61 692.69 1165.78 165.06 2307.70
Total 175 1248.35 871.35 65.86 1118.34 1378.35 .00 4311.47
Std_Dev LuisaViaRoma 25 2.33 4.38 .87 .52 4.14 .00 15.07
Farfetch 25 27.23 37.25 7.45 11.85 42.61 .00 127.60
MyTheresa 25 40.46 84.39 16.87 5.62 75.30 .00 348.79
Net-a-Porter 25 37.36 125.36 25.07 -14.38 89.11 .00 535.41
Xiu 25 77.43 110.31 22.06 31.90 122.97 .00 333.05
TianMao 25 63.89 145.27 29.05 3.92 123.86 .00 526.02
Shangpin 25 43.80 46.87 9.37 24.45 63.15 .00 192.92
Total 175 41.79 93.52 7.06 27.83 55.74 .00 535.41

53. 48
Aggregating the data from the tables and the graphs, we can make conclusions on average
prices and variability.
Regarding Average Price it can be seen that MyTheresa and Shangpin have the lowest
average prices for homogeneous products, while TianMao has the highest.
Regarding Price Variability (St. Dev), LuisaViaRoma is the one which more closely
resembles null variability (lowest variability), while Xiu is the one with highest variability.

54. 49
5.1.2 Pivot Analysis
This analysis has been carried out to extrapolate as much insights as possible from
the data available. The analysis can be split in 3 parts which are presented here after.
5.1.2.1 The Big Picture on Variability and Sale Period Behavior
The two graphs above display the whole bulk of data available for the Multibrand
platforms. It is clear that there is a much higher Price Heterogeneity and Variability in
China. Moreover, while the Standard Period (April-May) and the Sale Period (June)
are clearly demarked in Italy, Sales and rebates are presented continuously in China.
0
1000
2000
3000
4000
Aggregate
ITALY
-­‐ Multibrand
(Euro)
0
1000
2000
3000
4000
Aggregate
CHINA
-­‐ Multibrand
(Converted
Euro)
April 14th 2016 June 7th 2016
April 14th 2016 June 7th 2016
Standard
Period
Sale
Period

55. 50
5.1.2.2 Price Differential Analysis: China vs. Italy
This analysis aims at clarifying the main price differentials between Italy and China.
Legend: ___ = High, ___ = Low, ___ = Overall
Average
Prices
China
Italy
%China
Monobrand
€1.351,74
€931,29
31%
Burberry_Store
€1.246,16
€922,00
26%
Dolce&Gabbana_Store
€1.751,14
€1.023,00
42%
Michael_Kors_Store
€247,62
€247,62
0%
Moschino_Store
€413,45
€298,50
28%
Stella_Mccartney_Store
€1.460,64
€1.142,00
22%
Tory_Burch_Store
€258,50
€285,00
-­‐10%
Valentino_Store
€1.984,63
€1.294,00
35%
Multibrand
€1.248,35
€936,23
25%
Farfetch
€1.261,51
€940,36
25%
Luisa_Via_Roma
€1.385,68
€922,56
33%
Mytheresa
€862,76
€946,40
-­‐11%
Net-­‐a-­‐Porter
€1.422,86
€935,60
34%
Shangpin
€929,24
x
TianMao
€1.466,33
x
Xiu
€1.410,07
x
Grand
Total
€1.261,27
€935,24
26%
Prices of Fashion Luxury Goods in the selected product categories are overall higher
by 26% in China with respect to Italy.
Monobrand platforms’ prices are on average 31% higher in China, with
Dolce&Gabbana being the Brand charging more to Chinese customers (44% more)
and Tory Burch being the one charging less (Prices are 10% lower in China).
Monobrand platforms’ prices are on average 25% higher in China, with LuisaViaRoma
being the e-Commerce charging more to Chinese customers (33% more) and
MyTheresa being the one charging less (Prices are 11% lower in China).

56. 51
5.1.2.3 Coefficient of Variation and Number of Changes
This analysis aims at describing the Average Coefficient of Variation (St.
Dev/Mean) and number of changes. Beginning with the first topic:
Legend: ___ = High, ___ = Low
Regarding the Luxury Pyramid, Aspirational brands are the ones that on average
display higher variability followed by Accessible and Absolute. Brand-wise, Dolce and
Gabbana is the one with highest variation (6.11%), while Burberry is the one with
lowest.
Regarding Product Category, shoes show the highest variability , followed by Apparel
and Bags.
Luxury
Pyramid
Average
of
Coefficient
of
Variation
Absolute
2,48%
Valentino
2,48%
Aspirational
4,33%
Dolce&Gabbana
6,11%
Stella_McCartney
2,55%
Accessible
2,82%
Burberry
1,61%
Micheal_Kors
4,11%
Moschino
3,21%
Tory_Burch
5,56%
Grand
Total
3,36%
Product
Category
Average
of
Coefficient
of
Variation
Apparel
3,55%
Burberry
1,01%
Dolce&Gabbana
7,66%
Moschino
3,21%
Stella_McCartney
3,67%
Valentino
2,22%
Bag
2,93%
Burberry
1,35%
Dolce&Gabbana
4,88%
Micheal_Kors
4,11%
Stella_McCartney
2,12%
Valentino
2,19%
Shoes
3,82%
Burberry
3,32%
Dolce&Gabbana
5,44%
Stella_McCartney
1,17%
Tory_Burch
5,56%
Valentino
3,59%
Grand
Total
3,36%

57. 52
With respect to the average amount of times that the price has changed during the
55-day period of research. The tables only contain data from Multibrand Platforms in
China.
Legend: ___ = High, ___ = Low
Regarding the Luxury Pyramid breakdown, Dolce&Gabbana is the brand that has
overall changed more frequently, with 79 total price changes, with an average of almost
2 price changes per day.
On the other hand, Product Category breakdown shows that “Bags” experienced more
changes, followed by Apparel and Shoes. Within Bags, Michael Kors is the one that
has varied more frequently (41 times) and Valentino the one that has varied less
frequently (15 times). Within Apparels, Dolce&Gabbana has varied the most (36 times)
and Valentino the least (8 times). At last, within Shoes, Tory Burch is the most varying
(21 times) and Valentino and Dolce&Gabbana the least (10 times each).
China
Product
Category
Sum
of
Number
of
Changes
Apparel
114
Burberry
24
Dolce&Gabbana
36
Moschino
29
Stella_McCartney
17
Valentino
8
Bag
138
Burberry
21
Dolce&Gabbana
33
Micheal_Kors
41
Stella_McCartney
28
Valentino
15
Shoes
74
Burberry
19
Dolce&Gabbana
10
Stella_McCartney
14
Tory_Burch
21
Valentino
10
Grand
Total
326
China
Luxury
Pyramid
Sum
of
Number
of
Changes
Absolute
33
Valentino
33
Aspirational
138
Dolce&Gabbana
79
Stella_McCartney
59
Accessible
155
Burberry
64
Micheal_Kors
41
Moschino
29
Tory_Burch
21
Grand
Total
326

58. 53
5.1.3 Validation: Exchange Rate (ER) Correlation
The tests of the Exchange Rate Correlation Validation were overall passed, in the
sense that ER does not seem to have a significant and systematic correlation with
price variability. The correlation coefficient (r) is calculated between:
• The Daily prices of the 175 Items sold in China (the one that show variability)
• The Daily Exchange Rates29
during the 55 days of empirical research.
The following are the tables which summarize the results:
It is clear from the tables that:
• Only 15 items out of 175 (9%) show a significant
correlation with the exchange rate, considering a 95%
Confidence Interval.
• The Correlated items are all concentrated on the
Shangpin Platform, which has 9 out 25 items (36%)
with significant correlation. However, when analyzing
them item-by-item, it is possible to notice that some
of them (eg. SP_2) display a positive correlation,
while some of them (eg. SP_23) display a negative
correlation, so it is overall difficult to understand if
there’s a strong relation between the ER and price.
Overall, there is not enough evidence to confirm that the
variability is generated by Exchange Rate Fluctuation.
29
Extracted from OANDA Currency Exchange Rate.
Number
of
items
Items
with
sig.
correlation
Percentage
of
total
Average
Correlation
LuisaViaRoma
25
0
0%
0%
Farfetch
25
3
12%
14%
MyTheresa
25
0
0%
0%
Net-­‐A-­‐Porter
25
0
0%
0%
Shangpin
25
9
36%
43%
TianMao
25
1
4%
-­‐32%
Xiu
25
2
8%
17%
Overall
175
15
9%
9%
Code
Pearson’s
r
Sig.
(2-­‐
tailed)
1
FF_13
-­‐34%
0,012
2
FF_4
38%
0,013
3
FF_5
38%
0,013
4
SP_13
82%
0,039
5
SP_18
31%
0,015
6
SP_19
72%
0,047
7
SP_2
72%
0,047
8
SP_20
1%
0,027
9
SP_23
-­‐72%
0,047
10
SP_4
95%
0,031
11
SP_7
-­‐72%
0,047
12
SP_9
86%
0,004
13
TM_21
-­‐32%
0,017
14
X_18
11%
0,002
15
X_22
22%
0,017

59. 54
5.2 Moderated Regressions - Effects on Brand and WTB
The Moderated Regression Analyses were conducted to test both the:
• Assumption #2: Price Shifting (Price Variability) has a statistically significant
impact on both WTB and Brand Perception. (Tested in the Mean Comparison
Analysis, refer to Section 4.2.2.2)
• Hypotheses were instead that:
2. Research Hypothesis #2: The impact of Price Shifting (Price
Variability) on WTB varies according to (or is moderated by) the
cultural traits of the respondent.
à Tested in Regression Set 1
3. Research Hypothesis #3: The impact on WTB is created (or
moderated by) the Brand Perception, which is directly impacted by
Price Shifting (Price Variability).
à Tested in Regression Set 2
5.2.1 Regression Set 1 - The effect of Variability on WTB
The aim of the first set of regressions is to verify that the impact of price shifting on
WTB. For this reason, two regressions have been set up with the following
characteristics.
Regression Dependent Variable Independent Variables
1 Delta_WTB Dummy Variability
2 Delta_WTB Mod_Variability_Multi
Mod_Varibility_Mono
Mod_Variability_West
The next step is analyzing 1) The Models Summary and ANOVA, 2) The Coefficient
report and 3) a Scatter Plot Analysis.

60. 55
5.2.1.1 Models Summary and ANOVA
Model Summary
Model R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .464b
.215 .210 2.397 .215 44.383 1 162 .000
2 .829c
.687 .681 1.522 .687 117.207 3 160 .000
a. Dependent Variable: Delta_WTB
b. Predictors: (Constant), Mod_Variability_CMulti, Dummy_Variability,
Mod_Variability_CMono
c. Predictors: (Constant), Mod_Variability_CMono, Mod_Variability_CMulti,
Mod_Variability_West
ANOVAa,
Model Sum of Squares df Mean Square F Sig.
1 Regression 255.001 1 255.001 44.383 .000b
Residual 930.773 162 5.746
Total
1185.774 163
2 Regression 814.946 3 271.649 117.207 .000c
Residual 370.828 160 2.318
Total 1185.774 163
a. Dependent Variable: Delta_WTB
b. Predictors: (Constant), Dummy_Variability
d. Predictors: (Constant), Mod_Variability_CMono, Mod_Variability_CMulti,
Mod_Variability_West
Analyzing the Coefficient of Determination in its adjusted version (Adjusted R
Square), it is clear that the second model is way more solid than the first one. Indeed,
while in the First Regression approximately 20% of the variation in the dependent
variable is explained by a variation in the independent variables, in the Second one
this score dramatically rose to approximately 70%.
The ANOVA table shows that both of the Models have passed the F-Test. Therefore,
their results are overall statistically Significant.

61. 56
5.2.1.2 Coefficient Report
Analyzing the Coefficient Report, the first thing to notice is that both the constant and
the variables added are statically significant with a 95% confidence level.
Model 1 shows the relation between the ∆WTB and the general Dummy for Variability
shedding light on the general impact of Price Variability (Price Shifting) on the
willingness to pay of customers.
The Constant represents the reaction of the Control group and, so to say, the effects
of Price Fixing on WTB. The constant is slightly negative but very close to 0 and we
can definitely conclude that Price Fixing has no effect on WTB.
As captured by Dummy_Variability, Price shifting has an overall strong negative effect
on WTB, making its score decline by approximately 2.5/10 points.
Model 2 shows that the relation between Price Variability and ∆WTB is indeed
moderated by (or comes from the interaction between Price Volatility and) Cultural
Traits.
The Constant (Price Fixing) is still quite close to 0.
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) -.363 .268 -1.353 .008
Dummy_Variability -2.495 .374 -.464 -6.662 .000
2 (Constant) -.545 .205 -2.657 .009
Mod_Variability_CMulti -1.082 .310 -.177 -3.493 .001
Mod_Variability_West
-3.924 .299 -.668
-
13.121
.000
Mod_Variability_CMono 3.487 .422 .395 8.253 .000
a. Dependent Variable: Delta_WTB

62. 57
The other three interaction variables show that:
• Western Culture (Mod_Variability_West) and Chinese Multiculture
(Mod_Variability_CMulti) both create a NEGATIVE impact to ∆WTB from price
variability; A decrease of around 4.5/10 points in the case of West Culture and
1.5/10 on the Chinese Multiculture
• Chinese Monoculture (Mod_Variability_CMono) instead has a POSITIVE
impact on ∆WTB of 3/10, meaning that differently from the other two cultural
groups, Chinese Monoculture people enjoy price shifting.
5.2.1.3 Scatter Plot Analysis
Also from the Scatter Plot, which graphs the ∆WTB on the Y-axis and the Dummy
Variability on the X-axis, highlighting data for the 3 different cultural traits, it is clear
that:
• West Culture and Chinese Multiculture are impacted in a similar and negative
way,
• China Monoculture is impacted in a dissimilar and positive way

63. 58
5.2.2 Regression Set 2 - The Moderation effect of Customer Centricity (CC)
This second Set of Regressions aims at clarifying the relation between the Price
Variability (Price Shifting) and ∆WTB, proving that the relation is actually moderated
by a third variable, Customer Centricity (CC) which is impacted by the Price Variability.
For this reason, 3 Regression models have been set-up. They are going to be
analyzed one-by-one.
Regression Dependent Variable Independent Variables
1 Delta_WTB Dummy_Variability
Mod_Variability_CMono
Mod_Variability_CMulti
Mod_Variability_West
Delta_Customer_Centricity
TriMod_Variability_zCC_Mono
TriMod_Variability_zCC_Multi
TriMod_Variability_zCC_West
2 Delta_Customer_Centricity Dummy_Variability
Mod_Variability_CMono
Mod_Variability_CMulti
3 Delta_WTB Delta_Customer_Centricity
3+ Delta_WTB Delta_Customer_Centricity
Mod_Multiculture_zCC
Mod_WestCulture_zCC

64. 59
5.2.2.1 Regression Model 1 – Complete Regression
The aim of the First model is trying to validate in a straightforward way the relation
contained in the Second Hypothesis:
To do so, a complete Regression model has been set-up, containing 8 Variables (Refer
to the table in 5.2.2). Hereafter are presented the results as in 1) The Models Summary
and ANOVA, 2) The Coefficient report and 3) a Scatter Plot Analysis.
Model Summary and ANOVA
Model Summary
Model R
R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .913a
.834 .825 1.127 .834 97.296 8 155 .000
a. Predictors: (Constant), TriMod_Variability_zCC_West, TriMod_Variability_zCC_Mono, TriMod_Variability_zCC_Multi,
Mod_Variability_CMulti, Dummy_Variability, Mod_Variability_CMono, Delta_Customer_Centricity, Mod_Variability_West
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 988.858 8 123.607 97.296 .000bù
Residual 196.917 155 1.270
Total 1185.774 163
a. Dependent Variable: Delta_WTB
b. Predictors: (Constant), TriMod_Variability_zCC_West, TriMod_Variability_zCC_Mono, TriMod_Variability_zCC_Multi,
Mod_Variability_CMulti, Dummy_Variability, Mod_Variability_CMono, Delta_Customer_Centricity, Mod_Variability_West
The overall solidity (R Square) of the model is very high, with nearly 83% of the depend
variable variance explained by the independent variable. In this case, since the model
contains a large number of variables, it is very important to check for the Adjusted R
Square which should correct the R Square score if there are redundant variables.
The ANOVA F-Test was passed meaning that the model is statistically significant.

65. 60
Coefficient Report
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) -.542 .152 -3.566 .000
Dummy_Variability -4.610 .791 -.857 -5.827 .000
Mod_Variability_CMono 6.302 1.101 .714 5.725 .000
Mod_Variability_CMulti .567 .272 .093 2.084 .039
Mod_Variability_West .084 .867 .014 .097 .923
Delta_Customer_Centricity -.185 .138 -.165 -1.343 .181
TriMod_Variability_zCC_Mono -3.023 1.377 -.205 2.196 .030
TriMod_Variability_zCC_Multi 5.141 .785 .017 .180 .857
TriMod_Variability_zCC_West 5.236 .466 .052 .506 .614
a. Dependent Variable: Delta_WTB
The first 4 variables tell a very similar story with respect to Regression Set 1, Price
fixing (Constant) has nearly no effect on consumers, Price Varibility (Shifting) has an
overall negative impact on WTB (approx. 5/10 decline in WTB Score), Chinese
Multicultural people and Western people have a similar negative reaction, while
Chinese Monocultural People enjoy a positive impact of variability on WTB (approx.
6.3 – 4.1≈2/10 WTB score incretion).
The Last 4 variables are introduced to understand if the Variability - ∆WTB relation is
mediated by the brand effect, the effect of variability on the only indicator of Brand
Perception which is impacted by variability, Customer Centricity (CC).
Their relation is summed up hereafter:
Mono à -2 - 1*CC, Variability positively affects WTB but WTB has a one-to-one
negative relation with CC.
Multi à -5 + 1*CC, Variability strongly negatively affects WTB but WTB has a
one-to-one positive relation with CC.
West à -5 + 1*CC, Variability strongly negatively affects WTB but WTB has a
one-to-one positive relation with CC

66. 61
However, all of them are strongly non-significant, so their result might be misleading.
In order to capture the relation we can picture it through a Scatter Plot (next Section)
and try to disentangle the effect using other two distinct Regressions.
Scatter Plot Analysis
It is clear that that while:
• Western and Multicultural Chinese experience positive relation between
Variability on WTB, moderated by Customer Centricity. This means that when
the price varies, an increase In CC is coupled with an increase in WTB and a
decrease in CC by a decrease in WTB (Positive correlation).
• Monocultural Chinese look to be totally disentangled by this kind of influence.
CC does not moderate the effect of Variability on WTB.
To grasp this moderation effect, it is necessary to disentangle the Regression in steps:
• Regression 2: shows the effect of Price Variability on ∆CC
• Regression 3-4: shows the effect of ∆CC on ∆WTB

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5.2.2.2 Regression Model 2 – The Effect of Price Variability in Customer
Centricity (CC)
From the Scatter Plot showed in the previous section, it was clear that the impact of
Variability on WTV is moderated by Customer Centricity only for Multicultural Chinese
and Westerners, while Monocultural Chinese do not experience this moderation effect.
However, Regression 1 was non-significant so 2 more regressions are set up.
Regression2 will cover the first segment of the relation.
Model Summary and ANOVA
Model Summary
Model R
R
Square
Adjusted
R Square
Std. Error of
the
Estimate
Change Statistics
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .913a
.834 .831 .990 .834 267.532 3 160 .000
a. Predictors: (Constant), Mod_Variability_CMulti, Dummy_Variability,
Mod_Variability_CMono
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 786.359 3 262.120 267.532 .000b
Residual 156.763 160 .980
Total 943.122 163
a. Dependent Variable: Delta_Customer_Centricity
b. Predictors: (Constant), Mod_Variability_CMulti, Dummy_Variability,
Mod_Variability_CMono
Again, the model is pretty solid with an R Square of 83% and it has successfully passed
the ANOVA F-Test for significance.

68. 63
Coefficient Report
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) -.067 .124 -.537 .042
Dummy_Variability -4.132 .164 -.945 -27.616 .000
Mod_Variability_CMono 4.210 .273 .510 14.685 .000
Mod_Variability_CMulti .173 .180 .032 3.456 .000
a. Dependent Variable: Delta_Customer_Centricity
An important preliminary caution to bear in mind when analyzing the Coefficient table
is that this model is the only one in which the Dependent Variable is
∆CustomerCentricity (∆CC).
As in previous cases, the dependent variables are Price Varibility Dummy, broken
down by cultural traits.
Analyzing the Coefficients one by one:
• Dummy_Variability: Since the Western Culture is the Baseline of the Cultural
Trait Dummy, the Dummy Variable will shed light on the impact of Price
Variability on ∆CC for Westerners. It is a strong negative Relation, signaling that
when Price Shifting appears, There is a decrease of ≈4/10 scores in CC.
• Mod_Variability_CMulti: Given the size of the coefficient (really small), the
Impact on Multicultural Chinese people is the same as the one experienced by
Westerners.
• Mod_Variability_CMono: Customer Centricity is not impacted by Price
Variability, as the 2 coefficients cancel out.

69. 64
5.2.2.3 Regression Model 3-4 – The Effect of Customer Centricity (CC) on ∆WTB
Regression 3-4 will cover the second segment of the relation.
Model Summary and ANOVA
Model Summary
Model R
R
Square
Adjusted
R Square
Std.
Error of
the
Estimate
Change Statistics
R
Square
Change
F
Change df1 df2
Sig. F
Change
1 .718a
.516 .513 1.882 .516 172.748 1 162 .000
2 .809b
.655 .646 1.630 .126 28.054 2 160 .000
a. Predictors: (Constant), Delta_Customer_Centricity
b. Predictors: (Constant), Delta_Customer_Centricity, Mod_Multiculture_CC,
Mod_WestCulture_CC, Mod_Monoculture_CC
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 611.923 1 611.923 172.748 .000b
Residual 573.851 162 3.542
Total 1185.774 163
2 Regression 776.414 4 194.103 75.392 .000c
Residual 409.360 159 2.575
Total 1185.774 163
a. Dependent Variable: Delta_WTB
b. Predictors: (Constant), Delta_Customer_Centricity
c. Predictors: (Constant), Delta_Customer_Centricity, Mod_Multiculture_CC,
Mod_WestCulture_CC
Both of the regression are fairly solid and they both passed the ANOVA F-test for
significance. The second one have a slightly higher strength with a 63% R Square.

70. 65
Coefficient Report
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) -.088 .189 -.468 .641
Delta_Customer_Centricity .805 .061 .718 13.143 .000
2 (Constant) -.313 .164 -2.104 .023
Delta_Customer_Centricity 2.813 1.146 2.543 2.454 .015
Mod_Monoculture_CC -2.536 .439 -.272 -5.777 .000
Mod_Multiculture_CC -1.999 1.156 -.993 -1.729 .023
Mod_WestCulture_CC -2.030 1.152 -1.752 -2.162 .020
a. Dependent Variable: Delta_WTB
All of the Coefficient are statistically significant with a 95% Confidence level. Please
notice that the Dependent variable is again ∆WTB.
The first Regression simply shows that CC has an overall slight positive effect on WTB.
However, the Second Regression underlines that:
• Westerners: experience an overall positive relationship between CC and WTB,
with a unitary increase (or decrease) in CC corresponding to an almost unitary
– 2,8-2=0.8≈1 – increase (or decrease) in WTB.
• Multicultural Chinese: have almost the same score, experiencing the same
effect
• Monocultural Chinese: experience what we discovered from the scatterplot
presented in the last section. The Customer Centricity does not affect the WTB.
About Monocultural Chinese, Bear in mind that:
• In Regression Set 1, it was discovered that, differently from Westerners and
Multicultural Chinese, Monocultural Chinese had a positive impact of Price
Variability on WTB.
• In Regression Set 2 - Model1, it was discovered that Variability caused no
effect in Customer Centricity in Monocultural Chinese

71. 66
5.3 Overall Summary of Results
The main Results coming from the two Research Segments has confirmed the three
hypotheses proposed at the beginning. These results can be summarized as follows.
It has been statistically confirmed that the Price Behavior differs across e-commerce
channel (Monobrand vs. Multibrand) and geography (Italy vs. China), with Multibrand
platforms in China showing positive variability (Price Shifting).
When customers are exposed to this variability, their Willingness to Buy (WTB) the
product is affected in a different way, according to
1) Their Brand Perception (Customer Centricity) which filters the variability impact on
WTB, and
2) Their Cultural Traits
Westerners and Chinese Multicultural people experience similar reactions to Price
Variability. They both experience a decrease in Brand Perception (Customer
Centricity) which, in turn, lowered their WTB the Brand.
Chinese Monocultural people have a different reaction. Price Variability does not
affect their Brand Perception (Customer Centricity) but their WTB is positively
affected and increases.
5.4 One-to-One Interviews
In order to give an explanation to the increase in WTB experienced by Chinese
Monocultural Customers, 3 one-to-one interviews lasting 20 minutes each were
conducted with the help of an English-Mandarin Mediator. The interviews were very
simple and started with the description of the Price Shifting Phenomenon. After that,
some questions about the Brand Perception were asked and, indeed, all three
respondents declared not to have a different impression of the Brand.
When asked if they preferred to buy Luxury products under a Price Fixing or Shifting
regime, they responded that the Price Shifting Scenario was preferable because
prices could be tracked and compared to Monobrand prices, allowing them to make
the purchase at the moment of “highest convenience” – “最不贵的”－when the
prices reached an all-time low.

72. 67
6. Conclusions and Recommendations
6.1 Main Conclusions on Price Shifting
In order to organize the conclusions in a structured way, all of the confirmed Research
Hypotheses should be summarized and commented.
Segment 1: “e-Pricing Strategy Investigation”
Preliminary Hypothesis: Monobrand platforms apply Price Fixing everywhere
Research Hypothesis #1: Multibrand platforms in Italy apply Price Fixing, Multibrand
platforms in China apply Price Shifting (Price Variability).
Segment 2: “Effects on Brand Perception and WTB”
Research Hypothesis #2: The impact of Price Shifting (Price Variability) on WTB
varies according to (or is moderated by) the cultural traits of the respondent.
Research Hypothesis #3: The impact on WTB is created (or moderated by) the Brand
Perception, which is directly impacted by Price Shifting (Variability),
Through the first segment, it has been confirmed that Price Shifting (Variability) is a
pricing strategy applied by Multibrand platforms in China only, while the rest of the
platforms (Multibrand in Italy and monobrand platforms around the world) applies Price
Fixing.
The second segment has clarified the influence of Price Shifting on customers. It has
emerged that Price Shifting Strategy has an indirect impact on Willingness-To- Buy
(WTB) through Brand Perception (encapsulated in the Customer Centricity index).
Both of the effects of Price Shifting (direct towards CC and indirect towards WTB) are
mediated by the impact of Cultural Traits, which filter the reactions to Price Shifting in
a different fashion.
Westerners and Chinese Multicultural have commonalities in their responses. Price
variability negatively impacts their CC index (∆CC_West = -4.5/10 scores; ∆CC_Multi
= -1.5/10 scores) which, in turn, having a positive unitary relationship with WTB, make
the overall WTB decrease by the same token.
Chinese Monocultural do not experience this mediation. Price Variability does not
affect CC and CC does not seem to affect WTB. However, Price variability positively

73. 68
affects WTB (making it increase by approximately 4 scores) in a DIRECT WAY. After
having conducted one-to-one interviews with Monocultural respondents, it has
emerged that they value Price Shifting in a positive way because of their high Price
sensitivity. When Price Shifting is applied, they are able to monitor the item’s price,
compare it with the item’s official Monobrand price and purchase it when it is at a low
price point.
6.2 Conclusions on Chinese Monocultural and Multicultural
As noted above, Chinese Monocultural and Multicultural people are impacted very
differently by Price Variability practices. It is important to understand the composition
of the two groups in order to understand how to better recommend brands.
To determine the demographics of Chinese Multicultural individuals we will use “very
high fluency” in the English language use as a proxy. The Study “The statistics of
English in China” reports that the number of respondents with “very high fluency” has
increased from 2% (approx. 28 millions) in 2006, to 3.26% (approx. 45 Millions) and
that most of those belong to the upper-middle class or higher, thus eligible to be
possible Luxury consumers. The CAGR (Compounded annual growth rate) is 8.2%
increase per year, which is pretty high.
In conclusion, even though Chinese Monocultural individuals experience a positive
effect from Price Shifting, it has been noticed that this subgroup is doomed to be
reduced as time goes by. On the other hand, the number of Chinese Multicultural
(which have a negative impact from Price Shifting) is going to increase.
6.3 Recommendations for Brands
The previous analyses have shown that Price Shifting is a pricing strategy adopted by
Multibrand Platforms in China only and that these strategies have a negative impact
on the Brand Perception and the WTB of Multicultural Chinese People, which are
forecasted to increase with respect to Chinese Monocultural People.
Price Shifting has an overall negative impact on consumers and as such, Brand should
take all the necessary actions to reduce, or better, eliminate the existence of this
phenomenon from Multibrand Platforms.

74. 69
The following is a possible multi-step roadmap strategy that Brands should take into
consideration in order to meet the aforementioned target.
During the short-run Brands should try to tighten up their existing contractual
relationship with Multibrand Platforms, highlighting the forbidden nature of Price
Shifting Practices. This step should be coupled with the set-up of a professional figure
hired by the brand that should monitor and control the behavior of Multibrand Platforms.
This first step is not excessively heavy in terms of investment, so it is feasible for both
small Luxury business and big corporations.
If the first step has not succeeded in eliminating the problem, in the medium run
brands should push a process of customer acquisition in order to divert customers’
purchases on the brand’s Monobrand platform, where Price shifting strategies are
obviously not allowed. Brands should target the customers that tend to buy their
product using Multibrand Platforms and make them migrate to their proprietary e-
commerce platform through an advertising/promotion strategy to incentivize this
migration. This second step requires a higher investment in terms of marketing and it
is only possible for those brands which already have a functioning proprietary e-
commerce platform in China (medium-large companies).
If the second step has succeeded in making customers migrate from Multibrand
Platforms to Monobrand Platforms, in the long run Brands should make sure to sustain
the higher traffic which will be present on their Monobrand Platform. The Brand should
make investments to enlarge/strengthen their operations in China (in terms of
warehouses, inventory and logistics) and accommodate the higher flux of purchase
that will go through their internal e-commerce. This can be done either through internal
growth or JV. It is advisable to use a JV for what concerns logistics and shipping, which
are very expensive and tough to realize in China, especially at the level of service
excellence required by the Luxury industry. This last step requires very high
investments in terms of operations, inventory management and logistics and it is only
possible to large Luxury players.
A complementary strategy which could support the aforementioned ones is to implement a
total and pervasive world-wide price harmonization (explained in Section 3.1.2) which will
level up the price levels around the world. The higher level of price transparency will lower the
existence of parallel market phenomena (daigou) and will, at the same time, highlight the
existence of Price shifting strategies, which will become easier to spot and fight.

75. 70
Appendix 1 – Brand selection within Clusters
The number of Clusters had been calculated in order to mirror the Luxury Pyramid. This
Appendix explains how the brands for each cluster have been selected. Every cluster is
represented by one brand only, with the Exception of cluster 5 which is a Mix of 3 Brands of
the “Accessible” family, each one covering ONLY ONE product category.
Cluster Pyramid Preliminary
Selection
Characteristics Final
Decision
Cluster 1 Absolute Valentino Very Good e-commerce, Mixed Products X
Saint Laurent Good e-commerce, mostly leather
Dior Good e-commerce, mostly Haute Couture
Cluster 2 Aspirational Stella
McCartney
Very Good e-commerce, mixed Products X
Luis Vuitton Good e-commerce, scarce presence on Multibrand
e-commerce platform
Gucci Good e-commerce, scarce presence on Multibrand
e-commerce platform
Cluster 3 Aspirational Dolce &
Gabbana
Very Good e-commerce, mixed Products X
Prada
Cluster 4 Accessible Burberry Very Good e-commerce, mixed Products X
Ralph Lauren The brand is very close to “Premium Brands” which
follow other rules with respect to Accessible
Cluster 5 Accessible Moschino Very Good e-commerce, Mainly Apparels X (Only
Apparel)
Tory Burch Very Good e-commerce, Mainly Leather X (Only
Shoes)
Michael
Kors
Very Good e-commerce, Mainly Leather X (Only
Bags)
Calvin Klein Same As Ralph Lauren

76. 71
Appendix 2 – Final Panel Selection
The aim of this appendix is to clarify the rationale behind the Final Panel Selection presented
in section 4.1.
Type Name China Italy Subtotal
Mono-brand30
Valentino Store 5 5 10
Stella McCartney Store 5 5 10
Dolce & Gabbana Store 5 5 10
Burberry Store 5 5 10
Michael Kors Store31
2 2 4
Moschino Store
31
2 2 4
Tory Burch Store
31
1 1 2
Multi-Brand32
Luisa Via Roma 25 25 50
Farfetch 25 25 50
MyTheresa 25 25 50
Net-A-Porter 25 25 50
尚品网 (Shangpin wang)33
x 25 25
天猫 (Tianmao – T-mall)
33
x 25 25
走秀网 (Zouxiu wang – Xiu)
33
x 25 25
Grand Total 325
The complete Taxonomy of single items, explaining 1) Item Code, 2) Cluster, 3) Brand, 4)
Channel type (Monobrand vs Multibrand), 5) e-Commerce Platform and 6) Category type, is
presented below:
30
Mono-brand proprietary e-commerce only sell the items of their own Brand. Thus,
5 category items * 2 Geographies = 10 single items each.
31
Cluster 5 is a Mixed Brand cluster, so in Case of:
- Moschino (2 apparels* 2 Geographies= 4 single items);
- Michael Kors (2 bags * 2 geographies = 4 single items);
- Tory Burch (1 shoes * 2 Geographies = 2 single items).
32
Multi-brand e-commerce sell all of the items in different Geographies. Thus, 5
clusters * 5 category items * 2 Geographies = 50 single items per store.
33
Chinese Multibrand e-commerce sell all of the items in ONLY ONE Geography
(China). Thus, 5 Clusters * 5 category items * 1 Geography = 25 single items per store

77. 72
Item
Code
Research
Cluster
Product
Category
Retailer
Retail
Type
Country
Brand
FF_1 Cluster1
Apparel
Farfetch
Multibrand
China
Valentino
FF_10 Cluster1
Shoes
Farfetch
Multibrand
Italy
Valentino
FF_2 Cluster1
Apparel
Farfetch
Multibrand
China
Valentino
FF_3 Cluster1
Bag
Farfetch
Multibrand
China
Valentino
FF_4 Cluster1
Bag
Farfetch
Multibrand
China
Valentino
FF_5 Cluster1
Shoes
Farfetch
Multibrand
China
Valentino
FF_6 Cluster1
Apparel
Farfetch
Multibrand
Italy
Valentino
FF_7 Cluster1
Apparel
Farfetch
Multibrand
Italy
Valentino
FF_8 Cluster1
Bag
Farfetch
Multibrand
Italy
Valentino
FF_9 Cluster1
Bag
Farfetch
Multibrand
Italy
Valentino
LVR_1
Cluster1
Apparel
Luisa_Via_Roma
Multibrand
China
Valentino
LVR_10
Cluster1
Shoes
Luisa_Via_Roma
Multibrand
Italy
Valentino
LVR_2
Cluster1
Apparel
Luisa_Via_Roma
Multibrand
China
Valentino
LVR_3
Cluster1
Bag
Luisa_Via_Roma
Multibrand
China
Valentino
LVR_4
Cluster1
Bag
Luisa_Via_Roma
Multibrand
China
Valentino
LVR_5
Cluster1
Shoes
Luisa_Via_Roma
Multibrand
China
Valentino
LVR_6
Cluster1
Apparel
Luisa_Via_Roma
Multibrand
Italy
Valentino
LVR_7
Cluster1
Apparel
Luisa_Via_Roma
Multibrand
Italy
Valentino
LVR_8
Cluster1
Bag
Luisa_Via_Roma
Multibrand
Italy
Valentino
LVR_9
Cluster1
Bag
Luisa_Via_Roma
Multibrand
Italy
Valentino
MT_1 Cluster1
Apparel
Mytheresa
Multibrand
China
Valentino
MT_10 Cluster1
Shoes
Mytheresa
Multibrand
Italy
Valentino
MT_2 Cluster1
Apparel
Mytheresa
Multibrand
China
Valentino
MT_3 Cluster1
Bag
Mytheresa
Multibrand
China
Valentino
MT_4 Cluster1
Bag
Mytheresa
Multibrand
China
Valentino
MT_5 Cluster1
Shoes
Mytheresa
Multibrand
China
Valentino
MT_6 Cluster1
Apparel
Mytheresa
Multibrand
Italy
Valentino
MT_7 Cluster1
Apparel
Mytheresa
Multibrand
Italy
Valentino
MT_8 Cluster1
Bag
Mytheresa
Multibrand
Italy
Valentino
MT_9 Cluster1
Bag
Mytheresa
Multibrand
Italy
Valentino
NAP_1
Cluster1
Apparel
Net-­‐a-­‐Porter
Multibrand
China
Valentino
NAP_10
Cluster1
Shoes
Net-­‐a-­‐Porter
Multibrand
Italy
Valentino
NAP_2
Cluster1
Apparel
Net-­‐a-­‐Porter
Multibrand
China
Valentino
NAP_3
Cluster1
Bag
Net-­‐a-­‐Porter
Multibrand
China
Valentino
NAP_4
Cluster1
Bag
Net-­‐a-­‐Porter
Multibrand
China
Valentino
NAP_5
Cluster1
Shoes
Net-­‐a-­‐Porter
Multibrand
China
Valentino
NAP_6
Cluster1
Apparel
Net-­‐a-­‐Porter
Multibrand
Italy
Valentino
NAP_7
Cluster1
Apparel
Net-­‐a-­‐Porter
Multibrand
Italy
Valentino
NAP_8
Cluster1
Bag
Net-­‐a-­‐Porter
Multibrand
Italy
Valentino
NAP_9
Cluster1
Bag
Net-­‐a-­‐Porter
Multibrand
Italy
Valentino
SP_1
Cluster1
Apparel
Shangpin
Multibrand
China
Valentino
SP_2
Cluster1
Apparel
Shangpin
Multibrand
China
Valentino
SP_3
Cluster1
Bag
Shangpin
Multibrand
China
Valentino
SP_4
Cluster1
Bag
Shangpin
Multibrand
China
Valentino
SP_5
Cluster1
Shoes
Shangpin
Multibrand
China
Valentino
TM_1
Cluster1
Apparel
TianMao
Multibrand
China
Valentino
TM_2
Cluster1
Apparel
TianMao
Multibrand
China
Valentino
TM_3
Cluster1
Bag
TianMao
Multibrand
China
Valentino
TM_4
Cluster1
Bag
TianMao
Multibrand
China
Valentino
TM_5
Cluster1
Shoes
TianMao
Multibrand
China
Valentino
V_1
Cluster1
Apparel
Valentino_Store
Monobrand
China
Valentino
V_10
Cluster1
Shoes
Valentino_Store
Monobrand
Italy
Valentino
V_2
Cluster1
Apparel
Valentino_Store
Monobrand
China
Valentino
V_3
Cluster1
Bag
Valentino_Store
Monobrand
China
Valentino
V_4
Cluster1
Bag
Valentino_Store
Monobrand
China
Valentino
V_5
Cluster1
Shoes
Valentino_Store
Monobrand
China
Valentino
V_6
Cluster1
Apparel
Valentino_Store
Monobrand
Italy
Valentino
V_7
Cluster1
Apparel
Valentino_Store
Monobrand
Italy
Valentino
V_8
Cluster1
Bag
Valentino_Store
Monobrand
Italy
Valentino
V_9
Cluster1
Bag
Valentino_Store
Monobrand
Italy
Valentino
X_1
Cluster1
Apparel
Xiu
Multibrand
China
Valentino
X_2
Cluster1
Apparel
Xiu
Multibrand
China
Valentino
X_3
Cluster1
Bag
Xiu
Multibrand
China
Valentino
X_4
Cluster1
Bag
Xiu
Multibrand
China
Valentino
X_5
Cluster1
Shoes
Xiu
Multibrand
China
Valentino
FF_11 Cluster2
Apparel
Farfetch
Multibrand
China
Stella_McCartney
FF_12 Cluster2
Apparel
Farfetch
Multibrand
China
Stella_McCartney
FF_13 Cluster2
Bag
Farfetch
Multibrand
China
Stella_McCartney
FF_14 Cluster2
Bag
Farfetch
Multibrand
China
Stella_McCartney
FF_15 Cluster2
Shoes
Farfetch
Multibrand
China
Stella_McCartney
FF_16 Cluster2
Apparel
Farfetch
Multibrand
Italy
Stella_McCartney
FF_17 Cluster2
Apparel
Farfetch
Multibrand
Italy
Stella_McCartney
FF_18 Cluster2
Bag
Farfetch
Multibrand
Italy
Stella_McCartney
FF_19 Cluster2
Bag
Farfetch
Multibrand
Italy
Stella_McCartney
FF_20 Cluster2
Shoes
Farfetch
Multibrand
Italy
Stella_McCartney
LVR_11
Cluster2
Apparel
Luisa_Via_Roma
Multibrand
China
Stella_McCartney
LVR_12
Cluster2
Apparel
Luisa_Via_Roma
Multibrand
China
Stella_McCartney
LVR_13
Cluster2
Bag
Luisa_Via_Roma
Multibrand
China
Stella_McCartney
LVR_14
Cluster2
Bag
Luisa_Via_Roma
Multibrand
China
Stella_McCartney
LVR_15
Cluster2
Shoes
Luisa_Via_Roma
Multibrand
China
Stella_McCartney
LVR_16
Cluster2
Apparel
Luisa_Via_Roma
Multibrand
Italy
Stella_McCartney
LVR_17
Cluster2
Apparel
Luisa_Via_Roma
Multibrand
Italy
Stella_McCartney
LVR_18
Cluster2
Bag
Luisa_Via_Roma
Multibrand
Italy
Stella_McCartney
LVR_19
Cluster2
Bag
Luisa_Via_Roma
Multibrand
Italy
Stella_McCartney
LVR_20
Cluster2
Shoes
Luisa_Via_Roma
Multibrand
Italy
Stella_McCartney
MT_11 Cluster2
Apparel
Mytheresa
Multibrand
China
Stella_McCartney
MT_12 Cluster2
Apparel
Mytheresa
Multibrand
China
Stella_McCartney
MT_13 Cluster2
Bag
Mytheresa
Multibrand
China
Stella_McCartney
MT_14 Cluster2
Bag
Mytheresa
Multibrand
China
Stella_McCartney
MT_15 Cluster2
Shoes
Mytheresa
Multibrand
China
Stella_McCartney
MT_16 Cluster2
Apparel
Mytheresa
Multibrand
Italy
Stella_McCartney
MT_17 Cluster2
Apparel
Mytheresa
Multibrand
Italy
Stella_McCartney
MT_18 Cluster2
Bag
Mytheresa
Multibrand
Italy
Stella_McCartney
MT_19 Cluster2
Bag
Mytheresa
Multibrand
Italy
Stella_McCartney
MT_20 Cluster2
Shoes
Mytheresa
Multibrand
Italy
Stella_McCartney
NAP_11
Cluster2
Apparel
Net-­‐a-­‐Porter
Multibrand
China
Stella_McCartney
NAP_12
Cluster2
Apparel
Net-­‐a-­‐Porter
Multibrand
China
Stella_McCartney
NAP_13
Cluster2
Bag
Net-­‐a-­‐Porter
Multibrand
China
Stella_McCartney
NAP_14
Cluster2
Bag
Net-­‐a-­‐Porter
Multibrand
China
Stella_McCartney
NAP_15
Cluster2
Shoes
Net-­‐a-­‐Porter
Multibrand
China
Stella_McCartney
Item
Code
Research
Cluster
Product
Category
Retailer
Retail
Type
Country
Brand
NAP_16
Cluster2
Apparel
Net-­‐a-­‐Porter
Multibrand
Italy
Stella_McCartney
NAP_17
Cluster2
Apparel
Net-­‐a-­‐Porter
Multibrand
Italy
Stella_McCartney
NAP_18
Cluster2
Bag
Net-­‐a-­‐Porter
Multibrand
Italy
Stella_McCartney
NAP_19
Cluster2
Bag
Net-­‐a-­‐Porter
Multibrand
Italy
Stella_McCartney
NAP_20
Cluster2
Shoes
Net-­‐a-­‐Porter
Multibrand
Italy
Stella_McCartney
SMC_1
Cluster2
Apparel
Stella_Mccartney_Store
Monobrand
China
Stella_McCartney
SMC_10
Cluster2
Shoes
Stella_Mccartney_Store
Monobrand
Italy
Stella_McCartney
SMC_2
Cluster2
Apparel
Stella_Mccartney_Store
Monobrand
China
Stella_McCartney
SMC_3
Cluster2
Bag
Stella_Mccartney_Store
Monobrand
China
Stella_McCartney
SMC_4
Cluster2
Bag
Stella_Mccartney_Store
Monobrand
China
Stella_McCartney
SMC_5
Cluster2
Shoes
Stella_Mccartney_Store
Monobrand
China
Stella_McCartney
SMC_6
Cluster2
Apparel
Stella_Mccartney_Store
Monobrand
Italy
Stella_McCartney
SMC_7
Cluster2
Apparel
Stella_Mccartney_Store
Monobrand
Italy
Stella_McCartney
SMC_8
Cluster2
Bag
Stella_Mccartney_Store
Monobrand
Italy
Stella_McCartney
SMC_9
Cluster2
Bag
Stella_Mccartney_Store
Monobrand
Italy
Stella_McCartney
SP_10
Cluster2
Shoes
Shangpin
Multibrand
China
Stella_McCartney
SP_6
Cluster2
Apparel
Shangpin
Multibrand
China
Stella_McCartney
SP_7
Cluster2
Apparel
Shangpin
Multibrand
China
Stella_McCartney
SP_8
Cluster2
Bag
Shangpin
Multibrand
China
Stella_McCartney
SP_9
Cluster2
Bag
Shangpin
Multibrand
China
Stella_McCartney
TM_10
Cluster2
Shoes
TianMao
Multibrand
China
Stella_McCartney
TM_6
Cluster2
Apparel
TianMao
Multibrand
China
Stella_McCartney
TM_7
Cluster2
Apparel
TianMao
Multibrand
China
Stella_McCartney
TM_8
Cluster2
Bag
TianMao
Multibrand
China
Stella_McCartney
TM_9
Cluster2
Bag
TianMao
Multibrand
China
Stella_McCartney
X_10
Cluster2
Shoes
Xiu
Multibrand
China
Stella_McCartney
X_6
Cluster2
Apparel
Xiu
Multibrand
China
Stella_McCartney
X_7
Cluster2
Apparel
Xiu
Multibrand
China
Stella_McCartney
X_8
Cluster2
Bag
Xiu
Multibrand
China
Stella_McCartney
X_9
Cluster2
Bag
Xiu
Multibrand
China
Stella_McCartney
DG_1
Cluster3
Apparel
Dolce&Gabbana_Store
Monobrand
China
Dolce&Gabbana
DG_10
Cluster3
Shoes
Dolce&Gabbana_Store
Monobrand
Italy
Dolce&Gabbana
DG_2
Cluster3
Apparel
Dolce&Gabbana_Store
Monobrand
China
Dolce&Gabbana
DG_3
Cluster3
Bag
Dolce&Gabbana_Store
Monobrand
China
Dolce&Gabbana
DG_4
Cluster3
Bag
Dolce&Gabbana_Store
Monobrand
China
Dolce&Gabbana
DG_5
Cluster3
Shoes
Dolce&Gabbana_Store
Monobrand
China
Dolce&Gabbana
DG_6
Cluster3
Apparel
Dolce&Gabbana_Store
Monobrand
Italy
Dolce&Gabbana
DG_7
Cluster3
Apparel
Dolce&Gabbana_Store
Monobrand
Italy
Dolce&Gabbana
DG_8
Cluster3
Bag
Dolce&Gabbana_Store
Monobrand
Italy
Dolce&Gabbana
DG_9
Cluster3
Bag
Dolce&Gabbana_Store
Monobrand
Italy
Dolce&Gabbana
FF_21 Cluster3
Apparel
Farfetch
Multibrand
China
Dolce&Gabbana
FF_22 Cluster3
Apparel
Farfetch
Multibrand
China
Dolce&Gabbana
FF_23 Cluster3
Bag
Farfetch
Multibrand
China
Dolce&Gabbana
FF_24 Cluster3
Bag
Farfetch
Multibrand
China
Dolce&Gabbana
FF_25 Cluster3
Shoes
Farfetch
Multibrand
China
Dolce&Gabbana
FF_26 Cluster3
Apparel
Farfetch
Multibrand
Italy
Dolce&Gabbana
FF_27 Cluster3
Apparel
Farfetch
Multibrand
Italy
Dolce&Gabbana
FF_28 Cluster3
Bag
Farfetch
Multibrand
Italy
Dolce&Gabbana
FF_29 Cluster3
Bag
Farfetch
Multibrand
Italy
Dolce&Gabbana
FF_30 Cluster3
Shoes
Farfetch
Multibrand
Italy
Dolce&Gabbana
LVR_21
Cluster3
Apparel
Luisa_Via_Roma
Multibrand
China
Dolce&Gabbana
LVR_22
Cluster3
Apparel
Luisa_Via_Roma
Multibrand
China
Dolce&Gabbana
LVR_23
Cluster3
Bag
Luisa_Via_Roma
Multibrand
China
Dolce&Gabbana
LVR_24
Cluster3
Bag
Luisa_Via_Roma
Multibrand
China
Dolce&Gabbana
LVR_25
Cluster3
Shoes
Luisa_Via_Roma
Multibrand
China
Dolce&Gabbana
LVR_26
Cluster3
Apparel
Luisa_Via_Roma
Multibrand
Italy
Dolce&Gabbana
LVR_27
Cluster3
Apparel
Luisa_Via_Roma
Multibrand
Italy
Dolce&Gabbana
LVR_28
Cluster3
Bag
Luisa_Via_Roma
Multibrand
Italy
Dolce&Gabbana
LVR_29
Cluster3
Bag
Luisa_Via_Roma
Multibrand
Italy
Dolce&Gabbana
LVR_30
Cluster3
Shoes
Luisa_Via_Roma
Multibrand
Italy
Dolce&Gabbana
MT_21 Cluster3
Apparel
Mytheresa
Multibrand
China
Dolce&Gabbana
MT_22 Cluster3
Apparel
Mytheresa
Multibrand
China
Dolce&Gabbana
MT_23 Cluster3
Bag
Mytheresa
Multibrand
China
Dolce&Gabbana
MT_24 Cluster3
Bag
Mytheresa
Multibrand
China
Dolce&Gabbana
MT_25 Cluster3
Shoes
Mytheresa
Multibrand
China
Dolce&Gabbana
MT_26 Cluster3
Apparel
Mytheresa
Multibrand
Italy
Dolce&Gabbana
MT_27 Cluster3
Apparel
Mytheresa
Multibrand
Italy
Dolce&Gabbana
MT_28 Cluster3
Bag
Mytheresa
Multibrand
Italy
Dolce&Gabbana
MT_29 Cluster3
Bag
Mytheresa
Multibrand
Italy
Dolce&Gabbana
MT_30 Cluster3
Shoes
Mytheresa
Multibrand
Italy
Dolce&Gabbana
NAP_21
Cluster3
Apparel
Net-­‐a-­‐Porter
Multibrand
China
Dolce&Gabbana
NAP_22
Cluster3
Apparel
Net-­‐a-­‐Porter
Multibrand
China
Dolce&Gabbana
NAP_23
Cluster3
Bag
Net-­‐a-­‐Porter
Multibrand
China
Dolce&Gabbana
NAP_24
Cluster3
Bag
Net-­‐a-­‐Porter
Multibrand
China
Dolce&Gabbana
NAP_25
Cluster3
Shoes
Net-­‐a-­‐Porter
Multibrand
China
Dolce&Gabbana
NAP_26
Cluster3
Apparel
Net-­‐a-­‐Porter
Multibrand
Italy
Dolce&Gabbana
NAP_27
Cluster3
Apparel
Net-­‐a-­‐Porter
Multibrand
Italy
Dolce&Gabbana
NAP_28
Cluster3
Bag
Net-­‐a-­‐Porter
Multibrand
Italy
Dolce&Gabbana
NAP_29
Cluster3
Bag
Net-­‐a-­‐Porter
Multibrand
Italy
Dolce&Gabbana
NAP_30
Cluster3
Shoes
Net-­‐a-­‐Porter
Multibrand
Italy
Dolce&Gabbana
SP_11
Cluster3
Apparel
Shangpin
Multibrand
China
Dolce&Gabbana
SP_12
Cluster3
Apparel
Shangpin
Multibrand
China
Dolce&Gabbana
SP_13
Cluster3
Bag
Shangpin
Multibrand
China
Dolce&Gabbana
SP_14
Cluster3
Bag
Shangpin
Multibrand
China
Dolce&Gabbana
SP_15
Cluster3
Shoes
Shangpin
Multibrand
China
Dolce&Gabbana
TM_11
Cluster3
Apparel
TianMao
Multibrand
China
Dolce&Gabbana
TM_12
Cluster3
Apparel
TianMao
Multibrand
China
Dolce&Gabbana
TM_13
Cluster3
Bag
TianMao
Multibrand
China
Dolce&Gabbana
TM_14
Cluster3
Bag
TianMao
Multibrand
China
Dolce&Gabbana
TM_15
Cluster3
Shoes
TianMao
Multibrand
China
Dolce&Gabbana
X_11
Cluster3
Apparel
Xiu
Multibrand
China
Dolce&Gabbana
X_12
Cluster3
Apparel
Xiu
Multibrand
China
Dolce&Gabbana
X_13
Cluster3
Bag
Xiu
Multibrand
China
Dolce&Gabbana
X_14
Cluster3
Bag
Xiu
Multibrand
China
Dolce&Gabbana
X_15
Cluster3
Shoes
Xiu
Multibrand
China
Dolce&Gabbana
BB_1
Cluster4
Apparel
Burberry_Store
Monobrand
China
Burberry
BB_10
Cluster4
Shoes
Burberry_Store
Monobrand
Italy
Burberry
BB_2
Cluster4
Apparel
Burberry_Store
Monobrand
China
Burberry
BB_3
Cluster4
Bag
Burberry_Store
Monobrand
China
Burberry
BB_4
Cluster4
Bag
Burberry_Store
Monobrand
China
Burberry

78. 73
Item
Code
Research
Cluster
Product
Category
Retailer
Retail
Type
Country
Brand
BB_5
Cluster4
Shoes
Burberry_Store
Monobrand
China
Burberry
BB_6
Cluster4
Apparel
Burberry_Store
Monobrand
Italy
Burberry
BB_7
Cluster4
Apparel
Burberry_Store
Monobrand
Italy
Burberry
BB_8
Cluster4
Bag
Burberry_Store
Monobrand
Italy
Burberry
BB_9
Cluster4
Bag
Burberry_Store
Monobrand
Italy
Burberry
FF_31 Cluster4
Apparel
Farfetch
Multibrand
China
Burberry
FF_32 Cluster4
Apparel
Farfetch
Multibrand
China
Burberry
FF_33 Cluster4
Bag
Farfetch
Multibrand
China
Burberry
FF_34 Cluster4
Bag
Farfetch
Multibrand
China
Burberry
FF_35 Cluster4
Shoes
Farfetch
Multibrand
China
Burberry
FF_36 Cluster4
Apparel
Farfetch
Multibrand
Italy
Burberry
FF_37 Cluster4
Apparel
Farfetch
Multibrand
Italy
Burberry
FF_38 Cluster4
Bag
Farfetch
Multibrand
Italy
Burberry
FF_39 Cluster4
Bag
Farfetch
Multibrand
Italy
Burberry
FF_40 Cluster4
Shoes
Farfetch
Multibrand
Italy
Burberry
LVR_31
Cluster4
Apparel
Luisa_Via_Roma
Multibrand
China
Burberry
LVR_32
Cluster4
Apparel
Luisa_Via_Roma
Multibrand
China
Burberry
LVR_33
Cluster4
Bag
Luisa_Via_Roma
Multibrand
China
Burberry
LVR_34
Cluster4
Bag
Luisa_Via_Roma
Multibrand
China
Burberry
LVR_35
Cluster4
Shoes
Luisa_Via_Roma
Multibrand
China
Burberry
LVR_36
Cluster4
Apparel
Luisa_Via_Roma
Multibrand
Italy
Burberry
LVR_37
Cluster4
Apparel
Luisa_Via_Roma
Multibrand
Italy
Burberry
LVR_38
Cluster4
Bag
Luisa_Via_Roma
Multibrand
Italy
Burberry
LVR_39
Cluster4
Bag
Luisa_Via_Roma
Multibrand
Italy
Burberry
LVR_40
Cluster4
Shoes
Luisa_Via_Roma
Multibrand
Italy
Burberry
MT_31 Cluster4
Apparel
Mytheresa
Multibrand
China
Burberry
MT_32 Cluster4
Apparel
Mytheresa
Multibrand
China
Burberry
MT_33 Cluster4
Bag
Mytheresa
Multibrand
China
Burberry
MT_34 Cluster4
Bag
Mytheresa
Multibrand
China
Burberry
MT_35 Cluster4
Shoes
Mytheresa
Multibrand
China
Burberry
MT_36 Cluster4
Apparel
Mytheresa
Multibrand
Italy
Burberry
MT_37 Cluster4
Apparel
Mytheresa
Multibrand
Italy
Burberry
MT_38 Cluster4
Bag
Mytheresa
Multibrand
Italy
Burberry
MT_39 Cluster4
Bag
Mytheresa
Multibrand
Italy
Burberry
MT_40 Cluster4
Shoes
Mytheresa
Multibrand
Italy
Burberry
NAP_31
Cluster4
Apparel
Net-­‐a-­‐Porter
Multibrand
China
Burberry
NAP_32
Cluster4
Apparel
Net-­‐a-­‐Porter
Multibrand
China
Burberry
NAP_33
Cluster4
Bag
Net-­‐a-­‐Porter
Multibrand
China
Burberry
NAP_34
Cluster4
Bag
Net-­‐a-­‐Porter
Multibrand
China
Burberry
NAP_35
Cluster4
Shoes
Net-­‐a-­‐Porter
Multibrand
China
Burberry
NAP_36
Cluster4
Apparel
Net-­‐a-­‐Porter
Multibrand
Italy
Burberry
NAP_37
Cluster4
Apparel
Net-­‐a-­‐Porter
Multibrand
Italy
Burberry
NAP_38
Cluster4
Bag
Net-­‐a-­‐Porter
Multibrand
Italy
Burberry
NAP_39
Cluster4
Bag
Net-­‐a-­‐Porter
Multibrand
Italy
Burberry
NAP_40
Cluster4
Shoes
Net-­‐a-­‐Porter
Multibrand
Italy
Burberry
SP_16
Cluster4
Apparel
Shangpin
Multibrand
China
Burberry
SP_17
Cluster4
Apparel
Shangpin
Multibrand
China
Burberry
SP_18
Cluster4
Bag
Shangpin
Multibrand
China
Burberry
SP_19
Cluster4
Bag
Shangpin
Multibrand
China
Burberry
SP_20
Cluster4
Shoes
Shangpin
Multibrand
China
Burberry
TM_16
Cluster4
Apparel
TianMao
Multibrand
China
Burberry
TM_17
Cluster4
Apparel
TianMao
Multibrand
China
Burberry
TM_18
Cluster4
Bag
TianMao
Multibrand
China
Burberry
TM_19
Cluster4
Bag
TianMao
Multibrand
China
Burberry
TM_20
Cluster4
Shoes
TianMao
Multibrand
China
Burberry
X_16
Cluster4
Apparel
Xiu
Multibrand
China
Burberry
X_17
Cluster4
Apparel
Xiu
Multibrand
China
Burberry
X_18
Cluster4
Bag
Xiu
Multibrand
China
Burberry
X_19
Cluster4
Bag
Xiu
Multibrand
China
Burberry
X_20
Cluster4
Shoes
Xiu
Multibrand
China
Burberry
FF_41 Cluster5
Bag
Farfetch
Multibrand
China
Micheal_Kors
FF_42 Cluster5
Bag
Farfetch
Multibrand
China
Micheal_Kors
FF_43 Cluster5
Apparel
Farfetch
Multibrand
China
Moschino
Item
Code
Research
Cluster
Product
Category
Retailer
Retail
Type
Country
Brand
FF_44 Cluster5
Apparel
Farfetch
Multibrand
China
Moschino
FF_45 Cluster5
Shoes
Farfetch
Multibrand
China
Tory_Burch
FF_46 Cluster5
Bag
Farfetch
Multibrand
Italy
Micheal_Kors
FF_47 Cluster5
Bag
Farfetch
Multibrand
Italy
Micheal_Kors
FF_48 Cluster5
Apparel
Farfetch
Multibrand
Italy
Moschino
FF_49 Cluster5
Apparel
Farfetch
Multibrand
Italy
Moschino
FF_50 Cluster5
Shoes
Farfetch
Multibrand
Italy
Tory_Burch
LVR_41
Cluster5
Bag
Luisa_Via_Roma
Multibrand
China
Micheal_Kors
LVR_42
Cluster5
Bag
Luisa_Via_Roma
Multibrand
China
Micheal_Kors
LVR_43
Cluster5
Apparel
Luisa_Via_Roma
Multibrand
China
Moschino
LVR_44
Cluster5
Apparel
Luisa_Via_Roma
Multibrand
China
Moschino
LVR_45
Cluster5
Shoes
Luisa_Via_Roma
Multibrand
China
Tory_Burch
LVR_46
Cluster5
Bag
Luisa_Via_Roma
Multibrand
Italy
Micheal_Kors
LVR_47
Cluster5
Bag
Luisa_Via_Roma
Multibrand
Italy
Micheal_Kors
LVR_48
Cluster5
Apparel
Luisa_Via_Roma
Multibrand
Italy
Moschino
LVR_49
Cluster5
Apparel
Luisa_Via_Roma
Multibrand
Italy
Moschino
LVR_50
Cluster5
Shoes
Luisa_Via_Roma
Multibrand
Italy
Tory_Burch
M_1
Cluster5
Apparel
Moschino_Store
Monobrand
China
Moschino
M_2
Cluster5
Apparel
Moschino_Store
Monobrand
China
Moschino
M_3
Cluster5
Apparel
Moschino_Store
Monobrand
Italy
Moschino
M_4
Cluster5
Apparel
Moschino_Store
Monobrand
Italy
Moschino
MK_1
Cluster5
Bag
Michael_Kors_Store
Monobrand
China
Micheal_Kors
MK_2
Cluster5
Bag
Michael_Kors_Store
Monobrand
China
Micheal_Kors
MK_3
Cluster5
Bag
Michael_Kors_Store
Monobrand
Italy
Micheal_Kors
MK_4
Cluster5
Bag
Michael_Kors_Store
Monobrand
Italy
Micheal_Kors
MT_41 Cluster5
Bag
Mytheresa
Multibrand
China
Micheal_Kors
MT_42 Cluster5
Bag
Mytheresa
Multibrand
China
Micheal_Kors
MT_43 Cluster5
Apparel
Mytheresa
Multibrand
China
Moschino
MT_44 Cluster5
Apparel
Mytheresa
Multibrand
China
Moschino
MT_45 Cluster5
Shoes
Mytheresa
Multibrand
China
Tory_Burch
MT_46 Cluster5
Bag
Mytheresa
Multibrand
Italy
Micheal_Kors
MT_47 Cluster5
Bag
Mytheresa
Multibrand
Italy
Micheal_Kors
MT_48 Cluster5
Apparel
Mytheresa
Multibrand
Italy
Moschino
MT_49 Cluster5
Apparel
Mytheresa
Multibrand
Italy
Moschino
MT_50 Cluster5
Shoes
Mytheresa
Multibrand
Italy
Tory_Burch
NAP_41
Cluster5
Bag
Net-­‐a-­‐Porter
Multibrand
China
Micheal_Kors
NAP_42
Cluster5
Bag
Net-­‐a-­‐Porter
Multibrand
China
Micheal_Kors
NAP_43
Cluster5
Apparel
Net-­‐a-­‐Porter
Multibrand
China
Moschino
NAP_44
Cluster5
Apparel
Net-­‐a-­‐Porter
Multibrand
China
Moschino
NAP_45
Cluster5
Shoes
Net-­‐a-­‐Porter
Multibrand
China
Tory_Burch
NAP_46
Cluster5
Bag
Net-­‐a-­‐Porter
Multibrand
Italy
Micheal_Kors
NAP_47
Cluster5
Bag
Net-­‐a-­‐Porter
Multibrand
Italy
Micheal_Kors
NAP_48
Cluster5
Apparel
Net-­‐a-­‐Porter
Multibrand
Italy
Moschino
NAP_49
Cluster5
Apparel
Net-­‐a-­‐Porter
Multibrand
Italy
Moschino
NAP_50
Cluster5
Shoes
Net-­‐a-­‐Porter
Multibrand
Italy
Tory_Burch
SP_21
Cluster5
Bag
Shangpin
Multibrand
China
Micheal_Kors
SP_22
Cluster5
Bag
Shangpin
Multibrand
China
Micheal_Kors
SP_23
Cluster5
Apparel
Shangpin
Multibrand
China
Moschino
SP_24
Cluster5
Apparel
Shangpin
Multibrand
China
Moschino
SP_25
Cluster5
Shoes
Shangpin
Multibrand
China
Tory_Burch
TB_1
Cluster5
Shoes
Tory_Burch_Store
Monobrand
China
Tory_Burch
TB_2
Cluster5
Shoes
Tory_Burch_Store
Monobrand
Italy
Tory_Burch
TM_21
Cluster5
Bag
TianMao
Multibrand
China
Micheal_Kors
TM_22
Cluster5
Bag
TianMao
Multibrand
China
Micheal_Kors
TM_23
Cluster5
Apparel
TianMao
Multibrand
China
Moschino
TM_24
Cluster5
Apparel
TianMao
Multibrand
China
Moschino
TM_25
Cluster5
Shoes
TianMao
Multibrand
China
Tory_Burch
X_21
Cluster5
Bag
Xiu
Multibrand
China
Micheal_Kors
X_22
Cluster5
Bag
Xiu
Multibrand
China
Micheal_Kors
X_23
Cluster5
Apparel
Xiu
Multibrand
China
Moschino
X_24
Cluster5
Apparel
Xiu
Multibrand
China
Moschino
X_25
Cluster5
Shoes
Xiu
Multibrand
China
Tory_Burch

79. 74
Appendix 3 – PEM Coding
This appendix has the aim to clarify in details the Control Panel of the PEM and the exact
coding of its 3 subcomponents.
PEM Control Panel
This is the main interface of the PEM, which contains the Starting buttons of its 3
subcomponents.
Due to the complexity of the 3 processes and due to the lengthiness of the entire PEM, Errors
and small defects are a possibility. The Panel on the right has been designed to spot errors
and indicate exactly which was the area of error, in order to make possible to correct it quickly.
The Daily error rate fluctuated between 0.6-6.1% (between 2 and 20 items out of 325).
“Download HTML” Coding
The “Download HTML” Macro has been written using the Chrome Browser add-in “iMacros”,
which allows to connect VBA-generated34
to the Browser.
34
VBA (Visual Basic for Applications) is the programming language used within
Microsoft programs (especially Excel) to develop Macros.

80. 75
This is the Macro code:
- “SET…” and “WAIT…” are inserted to minimize the probability of defects in the
running of the Macro,
- “URL GOTO=” informs the browser which link should be opened
- “SAVEAS TYPE=HTM” instructs the browser to save the link as HTML code
- “FOLDER=” instructs the browser the folder in which it should save the HTML code
- “FILE=” instructs the browser to rename the file with a given name (which corresponds
to the Unique Item Code, in this case “NAP_48”, Net-a-Porter 48)
“Extract Prices” Coding
The “Extract Prices” Macro has been developed with Excel functions only. You can see an
example here below (MT_2; MyTheresa2):
- “Data String Content”: Scans the HTML code previously downloaded and looks for a
string containing an hotword (in this case it is “price”).
- “Match Result”: encapsulates the price data which, in this case, follows the hotword,
“1,425”.

81. 76
- “Comma Validator”: It corrects for commas and decimal digits, and pastes the final
price onto the next cell, applying the corresponding currency format (visible in the green
selection).
“Populate Database” Coding
The “Populate Database” Macro simply copy and pastes the outcome of the previous sub-
macro into an organized database, matching both:
• The Right “Item Code” and
• The Right Calendar Date.
The code is the following:
Sub Populate_Data()
ActiveCell.Offset(137, 0).Range("A1").Select
ActiveWindow.SmallScroll Down:=-783
ActiveCell.Offset(-137, 0).Range("A1").Select
Range(Selection, Selection.End(xlDown)).Select
Range(Selection, Selection.End(xlDown)).Select
Range(Selection, Selection.End(xlDown)).Select
Range(Selection, Selection.End(xlDown)).Select
Range(Selection, Selection.End(xlDown)).Select
Range(Selection, Selection.End(xlDown)).Select
Selection.Copy
ActiveCell.Offset(0, 1).Range("A1").Select
Selection.PasteSpecial Paste:=xlPasteValues, Operation:=xlNone,
SkipBlanks _
:=True, Transpose:=False
ActiveWindow.SmallScroll Down:=18
End Sub

82. 77
Appendix 4 – Currency Conversion
The aim of this appendix is to clarify the rationale behind the overall conversion to Euro applied
after the Data collection and before the ANOVA and Pivot Analyses.
After the collection, Prices extracted from different platforms in different countries sold items
with different currencies. The following table will explain in detail which currencies
corresponded to which platforms:
China Italy
Luisa Via Roma Y €
Farfetch USD €
Mytheresa € €
NetaPorter USD €
Xiu Y
Tianmao Y
Shangpin Y
Valentino Y €
StellaMcCartney Y €
D&G Y €
Burberry Y €
Michael Kors USD USD
Tory Burch USD €
Moschino USD €
In order to harmonize the results, all of the prices were converted to Euro, using Exchange
Rate data from OANDA converter.

83. 78
The following table contains the Exchange Rates used for conversion:
Date
€/Y
€/USD
14/04/16
0,137
0,896
15/04/16
0,137
0,897
16/04/16
0,137
0,897
17/04/16
0,137
0,895
18/04/16
0,137
0,910
19/04/16
0,136
0,909
20/04/16
0,137
0,908
21/04/16
0,137
0,910
22/04/16
0,137
0,909
23/04/16
0,137
0,909
24/04/16
0,137
0,914
25/04/16
0,137
0,920
26/04/16
0,136
0,919
27/04/16
0,136
0,919
28/04/16
0,136
0,915
29/04/16
0,135
0,915
30/04/16
0,135
0,915
01/05/16
0,135
0,907
02/05/16
0,134
0,908
03/05/16
0,134
0,907
04/05/16
0,134
0,907
05/05/16
0,135
0,899
06/05/16
0,135
0,898
07/05/16
0,135
0,898
08/05/16
0,135
0,900
09/05/16
0,135
0,899
10/05/16
0,135
0,897
11/05/16
0,135
0,895
12/05/16
0,135
0,891
13/05/16
0,135
0,889
14/05/16
0,135
0,889
15/05/16
0,135
0,884
16/05/16
0,136
0,886
17/05/16
0,136
0,886
18/05/16
0,136
0,894
19/05/16
0,136
0,897
20/05/16
0,136
0,896
21/05/16
0,136
0,896
22/05/16
0,136
0,893
23/05/16
0,136
0,901
24/05/16
0,137
0,916
25/05/16
0,137
0,918
26/05/16
0,136
0,924
27/05/16
0,137
0,923
28/05/16
0,137
0,923
29/05/16
0,137
0,914
30/05/16
0,136
0,917
31/05/16
0,137
0,921
01/06/16
0,136
0,921
02/06/16
0,136
0,926
03/06/16
0,134
0,926
04/06/16
0,134
0,926
05/06/16
0,134
0,921
06/06/16
0,134
0,920
07/06/16
0,134
0,917

84. 79
Appendix 5 – Survey Design
The aim of this Appendix is to clarify the design of the Survey conducted on Customers to
assess the impact of Price Variability on Brand and WTB.
Brand Selection
As noted in Section 4.2.1.2, the Survey only investigated the effects of Price Variability on one
brand – Valentino.
This decision come from the “Interest Over Time” index extracted from Google Trends, whose
results are graphically shown hereafter:
0
20
40
60
80
100
120
2004-­‐01
2004-­‐06
2004-­‐11
2005-­‐04
2005-­‐09
2006-­‐02
2006-­‐07
2006-­‐12
2007-­‐05
2007-­‐10
2008-­‐03
2008-­‐08
2009-­‐01
2009-­‐06
2009-­‐11
2010-­‐04
2010-­‐09
2011-­‐02
2011-­‐07
2011-­‐12
2012-­‐05
2012-­‐10
2013-­‐03
2013-­‐08
2014-­‐01
2014-­‐06
2014-­‐11
2015-­‐04
2015-­‐09
2016-­‐02
2016-­‐07
Interest
Over
Time
(Google
Trends
2004-­‐2016)
Valentino:
(Worldwide) Stella
McCartney:
(Worldwide) Dolce
&
Gabbana:
(Worldwide)
Burberry:
(Worldwide) Michael
Kors:
(Worldwide) Tory
Burch:
(Worldwide)
Moschino:
(Worldwide)
0
5
10
15
20
25
30
Average
Valentino:
(Worldwide)
Average
Dolce
&
Gabbana:
(Worldwide)
Average
Burberry:
(Worldwide)
Average
Michael
Kors:
(Worldwide)
Average
Tory
Burch:
(Worldwide)
Average
Moschino:
(Worldwide)
Average
Stella
McCartney:
(Worldwide)
Average
Interest
Over
time
(Google
Trends
2004-­‐2016)

85. 80
As explained on Google Trends, [the index] “represents search interest relative to the highest
point on the chart for the given region and time. A value of 100 is the peak popularity for the
term”, so the index is done in a comparative way.
Survey Questions
Hereafter are presented the Texts of the surveys, which are totally identical in their “Control”
and “Experiment” versions. The links to the different videos are contained in this endnote35
35
English Experiment: https://www.youtube.com/watch?v=YHzydfN0pqg
English Control: https://www.youtube.com/watch?v=N8hl14bBtVM
Chinese Experiment: https://www.youtube.com/watch?v=RK1Bbo7VyVc
Chinese Control:

86. 81

87. 82

88. 83
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