Dynamic, Individualised Pricing and Customer Loyalty in the Swiss Retail Market: Chances and Risks Evangelos Xevelonakis, Professor, HWZ University of Applied Sciences in Business Administration Zurich, Switzerland.

1. 16th Annual International Conference on Marketing, 2-5 July 2018, Athens
Prof. Dr. Evangelos Xevelonakis
Dynamic Pricing and Customer Loyalty in the
Swiss Retail Market: Chances and Risks
Abstract
Individualized dynamic pricing is a flexible pricing strategy. The supplier defines flexible prices using
machine learning algorithms and information extracted from big data. Factors taken into consideration
include supply and demand, competition, time and customer profile. Pricing is individualized by grouping
customers into segments based on their purchasing behaviour, loyalty, demographic characteristics and
price demand elasticity. Loyal customers are identified and rewarded with discounts and special offers. This
is achieved via a network of in-store dynamic price tags placed at the edge of a store’s shelves. The
customer’s personal loyalty card data is stored on the mobile phone. The mobile phone interfaces with the
price tag to dynamically change the price of the item. Individualized dynamic pricing leverages on the
increasing popularity of in-store smart phone use. Customers typically use a device to scan QR codes and
compare prices. While dynamic pricing has numerous benefits, there are also risks. One is that price
differentiation may not seem fair to customers paying full price. Acceptance of dynamic pricing and whether
it leads to an increase in loyalty are largely unexplored. Furthermore, the technical requirements are
burdensome.
This study empirically examines dynamic pricing factors that potentially increase customer loyalty. A system
architecture is then proposed to realize this concept. One conclusion is that integration of dynamic pricing to
next-best offer concepts and loyalty programs can lead to higher acceptance rates. On the other hand, the
non-transparent use of customer data is problematic. Whether a customer is willing to provide data depends
on the expected value. Contradictory customer behaviour was also present in the data set, although this was
accounted for through behavioural approaches. Ultimately, dynamic pricing can be enhanced with emotional
factors to provide a more personal and unique customer experience.
2

2. Introduction
 Loyalty cards have long enabled supermarkets and other major retailers to offer
personalized offers based on the spending habits of repeat customers.
 That every person will purchase the same item at the same price as anyone else is
changing because of the applications of advanced technology and big data.
 B&Q has tested electronic price tags that display different prices to different customers
using information gathered from their phones (the company made clear that their
intention was to “reward regular customers with discounts”, not to raise the price for more
profligate shoppers.1)
 In the US, Coca-Cola and Albertsons supermarkets have experimented with targeting
shoppers in-store by sending personalized offers to their phones when they approach
the soft drinks aisle in an Albertsons store.
 “…..But any [bricks and mortar] retailer who charges different prices to different people
for the same product is an idiot. They’re only going to lose loyalty”.2
1 Paszek (2017)
2 Horgan in Walker (2017)
3
Research Purpose
The purpose of this research is
• to develop a better understanding of the relationship among dynamic pricing and dynamic
next best offering in the retail industry concepts
• to examine to what extend customers would accept dynamic pricing & offering
• to find out whether dynamic pricing & offering lead to an increased customer loyalty
• to discuss the technical requirements for an implementation
4

3. 5
high
low
Dynamic
low
high Situational
Pricing
Individualised &
Situational
Pricing
Fixed Pricing
Individualised & Non-
situational
Pricing
Individualised
From Dynamic Pricing to Dynamic Next Best Offering
 The general dynamization of prices must not be equated with individualized pricing.
 Individualised and Situational Pricing: In real time, prices are determined individually and
situationally, depending on the customer profile.
 The Next Best Offer Approach as Enhancement of the Dynamic Pricing Concept
o The Next Best Offer concept is a marketing tool that enables companies to cross-sell
and upsell based on customer profiles.
o That’s not only about changing the price of individual products, it’s more about
changing deals.
Source: besed on Krämer et.al. (2016)
.
6
 Hypothesis: Customers are willing to disclose personal data for dynamic personalised
pricing and offerings if they expect a reasonable value.
 Population: Consumers in Zurich area older than 18 years.
 Sampling: Mix between convenience, stratified and virtual snowball sampling1
 Sample n = 240 persons
 Sampling error e = ± 6.1%
 Level of Significance: 5%
1 Like any non-random sampling method, snowball sampling does not guarantee representation.
Survey Design

4. 7
77%
23%
Do you use a Customer Loyalty Program?
Users
Non Users
44%
56%
Are you active on Social Media?
Yes No
The Effect of the Intension-Behaviour Gap
16%
33%
20%
18%
13%
0% 5% 10% 15% 20% 25% 30% 35%
The effort is too high for me
I do not want to disclose any personal information
I do not want to receive more advertising
I do not see the real value
Others
Why not?
The "gap" refers to the difference between the intention, and the actual result of the behavior
Source: based on Müller (2018)
8
Understanding the Rules of Game
In general, when offers are transparent to consumers and they understand the ‘rules of the game’,
consumers adapt to a dynamic offering fairly seamlessly.
81%
19%
Are you interested in being alerted to
offers on your phone while in-store?
Yes No
29%
71%
How important is transparency about your
collected data for you?
No Improtant Important
27%
33%
41%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45%
no influence
moderate influence
strong influence
How much would transparency affect your willingness to
provide personal data?
Source: based on Müller.(2018)

5. 9
Millennials are more amenable to Dynamic Offerings
69%
55%
36%
0% 10% 20% 30% 40% 50% 60% 70% 80%
Millennials (Gen Y)
Generation X
Baby Boomers
Amenabilty of segments to dynamic offers
54%
49%
70%
0% 10% 20% 30% 40% 50% 60% 70% 80%
Millennials (Gen Y)
Generation X
Baby Boomers
Users of Customer Loyalty Programs
….but they are less amenable to traditional Loyalty
Programs
10
Event
Purchase
Window of
Opportunity
The customer has
not yet decided
to act
Actively Looking
The customer needs
to act and starts to
look for a solution
Interest
Trigger-based Campaigning
64% of those questioned would prefer proactive communication on specific
events like delays, upgrades etc.
Experiment: Movers— The response of customers contacted in telco
campaign who were planning on moving house was three times higher than
that of non-movers in the same campaign (Telecommunication Campaign, 2017).
Time
StatusQuo
StatusQuo
Zero Moment of Truth
Source: based on Eventricity ltd.(2018)

6. 11
ExternalBigDataExternalRealTimeDataInternalOperationalData
Market Data
Demographics
Weather Data
Product &
Services
Complains
Campaigns
Big Data
Analytics
Web
Social Media
Data Warehouse
(structured data)
Bluetooth Low Energy
Beacons
Front End System &
Streaming Analytics
Customers
Internal Position System
E
T
L
On Demand Information
Data & Front-End System Architecture
BI Analytics
DataLake
E
T
L
On the Fly
Source: Xevelonakis (2016), Müller (2018)
12
 A data lake is a storage repository that holds a vast amount of raw data in its native
format until it is needed.
 Prescriptive Data Modelling
o Schema on write
o Relational DB Modell
 Descriptive Data Modelling
o Schema on read
o Data in its native format
o ETL on the fly
 The data lake provides easier access, but lacks the optimization needed for
visualizations or modeling.
 Data marts or data warehouses better address analytic-specific requirements.
Creating Data Warehouses from Data Lakes

7. 13
 This study empirically examines dynamic pricing & offering factors that potentially
increase customer loyalty.
 A system architecture is then proposed to realize this concept. However retailers
have often huge, complicated logistics systems what hampers its implementation.
 One conclusion is that integration of dynamic pricing to next-best offer concepts
and loyalty programs can lead to higher acceptance rates.
 On the other hand, the non-transparent use of customer data is problematic.
 Whether a customer is willing to provide data depends on the expected value.
 Contradictory customer behavior was also present in the data set, although this
was accounted for through behavioral approaches.
 Ultimately, dynamic pricing can be enhanced with emotional factors to provide a
more personal and unique customer experience.
Conclusion
14
 Eventricity ltd. (2018) What is EDM & what it can do for your bank. Retrieved from
https://www.eventricity.biz/what-is-edm [Accessed: 17 June 2018]
 Lecinski J (2011) Winning Zero Moment Of Truth. Mountain View: Google Inc. Retrieved from
https://www.thinkwithgoogle.com/marketing-resources/micro-moments/2011-winning-zmot-ebook
[Accessed:15 June 2018]
 Müller M. (2018) Personalisierte Preisdifferenzeirungen im Schweizer Detailhandel: technische
Möglichkeiten Chancen und Risiken. [Personalised Price Differentiation in Swiss Retail] Bachelor
Thesis, HWZ University of Applied in Business Administration: 50-62.
 Paszek S. (2017) Weekly Roundup #72 – UK eCommerce, Luxury Digital & Pricing Changes. Retrieved
from http://www.spotstudio.net/blog/digital-marketing-news-and-resources/uk-ecommerce-luxury-
digital-pricing-changes [Accessed:15 June 2018]
 Walker T. (2017) How much …? The rise of dynamic and personalised pricing. Retrieved from
https://www.theguardian.com/global/2017/nov/20/dynamic-personalised-pricing [Accessed: 31.5.2018]
 Xevelonakis (2016) Social Influence and Customer Referral Value, Athens Journal of Business and
Economics: 7-15.
References