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MRS Advanced Analytics Innovation Symposium Presentation

Using a combination of choice modeling and econometric techniques, the authors develop an integrated approach to more accurately forecast how changes in price could impact both market share and market volume. Choice modeling captures consumer preferences and brand switching, while econometric modeling accounts for macroeconomic factors. By combining the output of share simulations from choice models with volume projections from econometric models, the authors are able to estimate how changes in price could impact both a brand's share and the overall market size. The methodology was validated across different product categories and markets, demonstrating predictive accuracy rates of around 88% on average.

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MRS Advanced Analytics Innovation Symposium 30th April 2015 #MRSlive
Brand Share & Industry Size: Will the twain ever meet? Using a portfolio of techniques to improve accuracy of market volume evolution in price change scenarios April 2015 Sreeram Srinivasan, IMRB International Ranjana Gupta, IMRB International
30 40 Marketer’s pricing dilemma 30 Market volum e 100 bn Marke t volum e 70 bn With new prices, my share will grow… … but will my volume also grow? Market share grows….. ….but, market size shrinks
Both the consumer and the macro variables needed to answer the questions.. C o n s u m e r M a c r o Brand choice Same brand, switching etc. In context of price Category choice Frequency, consumption, substitutes etc. Industry Past volumes, substitutes, prices etc. Economy Income, affordability, Inflation etc. Relies on past trends Future may be different Future oriented. Past learnings not fully leveraged
Hence for accurate volume forecasts, no single methodology can provide complete answer Consumer research and macro-economic models should be integrated Choice Model Consumer Research • Brand shares • Switching Econometric Macro Modelling • Market size
An approach to integration of choice and econometric model However, before integration, the individual tools need to be refined, adapted…
…to account for the nature of the category How to account for occasion? Is it an impulse or considered purchase? Is it a repertoire or non- repertoire category? Do number of units matter? What about frequency of purchase? How can we ensure that the respondent reacts to only relevant offers?
Adapting the Choice Model
The questioning technique uses FORCE principle to make the consumer response more realistic… Familiar Only brands that the respondent interacts with / likely to interact with shown – customized for each respondent real time Evoked set created using respondent’s current repertoire, past usage and future disposition Occasio n Respondent’s answers using an occasion as a context in occasion led categories like CSDs, Snacks etc. There can be other household purchase categories where occasion is irrelevant Repertoir e Respondents allowed to select multiple brands, SKUs and units, as they might in real life Channel Primary channel of purchase identified and specified in the questioning Event Recurren ce Frequency of purchase
Here’s an example… around 20 such choice tasks shown Imagine you are doing your monthly grocery shopping from the supermarket and you have to buy bathing soaps. On the shelf you see the following brands with the given prices? Which brands are you likely to buy? You can choose as many brands as you like. Or you can walk out of the shop without buying any. Do state the number of packs that you would be buying. I will not buy any anything 2 Johnson’s Baby Soap 75 gms Rs. 30 Dove 75 gms Rs. 40 Santoor – pack of 4 100gms X 4 Rs. 50 Now at Rs. 40 Pears 100 gms + 25 gms extra free Rs. 25 Rexona 200 gms Rs. 25 I would buy a shower gel Lux 100 gms Rs. 10 1
Consumer choices are converted into utilities – two levels of calculations Main Effect For every level within each attribute at a respondent level Example: Utility or preference for Brands like Dove, Pears etc., for SKUs like 100 gms,, 75 gms and for various price levels Cross Effect Interaction between attributes Example: Utility for Dove by itself and for Dove at a particular price may be different The utilities are transformed into share of preference and weighted to give the shares in various scenarios For all existing brands & SKUs, the current levels of distribution are built into the model – to ensure current scenario shares are in line with actual market shares Respondent level estimation of preference helps in calculation of gains and losses from one brand to another
The output Current Scenario New price scenario (Client’s brands cut prices by 10%) Company Brand X 20.0% 21.8% Brand Y 17.0% 17.2% Brand A 10.0% 9.9% Brand B 5.0% 4.4% Brand P 8.0% 7.6% Brand Q 15.0% 14.8% Brand R 10.0% 9.7% Brand S 15.0% 14.6% 39%37% Brand X Gaining From Brand B, Brand S Net Gain/Loss 1.8%
Econometric Model: Inputs and Outputs
The input Past volumes Population Substitute categories (Real) Price Purchase frequency No. of packs(Real) Income Basket size
The statistics Options Simple Regression Volume = fn (Price) Easy but can lead to situations like increase price to increase volumes! Simple Time Series Volume = fn (Past volumes) Builds in past volume trends but assumes that history will definitely repeat itself Price Volume
The statistics Adopted method ARIMA (Auto Regressive Integrated Moving Average) Volume = fn (Real Price, Past Volume, Real Income, etc….) Moving average included – accounts for any possible prediction error in previous time periods Accounts for autocorrelation Better prediction accuracy
The statistics Adopted method ARIMA (Auto Regressive Integrated Moving Average) Deseasonalized data - predict organic change in volumes Model by major sub-groups to account for different trends – break the market Price gap between sub-groups used – inter-movement built in
The output Market volumes 125000 tonnes Current scenario 126250 tonnes New price scenario 1%
Bringing the twain together
It’s quite simple actually… Shares X Market Volumes = Company Volumes 37 Current Scenario 125000 tonnes New Scenario Share Market Volumes 46250 tonnesCompany volumes 39 126250 tonnes Share Market Volumes 49238 tonneCompany volumes Share change: 5% Volume change: 6%
Proof of the pudding
Results validated across markets 0 20 40 60 80 100 Predicted volume accuracy by brands in various markets (74 data points in this graph) Average : 88% The trick: improve accuracy of the individual models
Identifying the relative impact of touchpoints: A tailored statistical technique for real-time data Shane Baxendale, Cranfield School of Management Heval Ceylan-Gilchrist, MESH MRS ADVANCED ANALYTICS NETWORK 30th April 2015
Why are we here today?  Real-time Experience Tracking Methodology  Analysis - using linear mixed-effects regression 24
Our thinking  Consumers experience brands through multiple channels (not just advertising!)  Brand experiences influence a consumer’s attitude toward brands  The majority of existing literature focusses on the impact of one or two types of experience What impact are different encounters having on consumer attitudes toward the brand? 25 *Baxendale S., Macdonald E.K., Wilson H.N., (2015), The impact of different touchpoints on brand consideration, Journal of Retailing, 37(2), 203.
Real-time Experience Tracking (RET) ONLINE REAL-TIME ONLINE Day 9Day 2 - 8Day 1
Text us whenever you see, hear or experience anything to do with the following brands… Text framework 27 BRAND: A)Brand A B)Brand B C)Brand C D)Brand D E)Brand E F) Other OCCASION: A)TV B)Poster/Billboard C)Radio D)In store E)Cinema F)Newspaper G)Magazine H)Conversation I)Online/Mobile J)Mailing/leaflet K)Me Purchasing L)Me using M)Someone else using N)Sponsorship O)Other FEELING: 5)Very positive 4)Fairly positive 3)Neutral 2)Fairly negative 1)Very Negative CHOICE: 5)Much more likely to choose 4)Slightly more likely to choose 3)No difference 2)Slightly less likely to choose 1) Much less likely to choose
Imagine you experienced Brand A Online… …you would text: 28 a 5i 5 CHOICE: 5) Much more likely to choose 4) Slightly more likely to choose 3) No change 2) Slightly less likely to choose 1) Much less likely to choose ENGAGEMENT: 5) Very positive 4) Fairly positive 3) Neutral 2) Fairly negative 1) Very negative BRAND: a) Brand A b) Brand B c) Brand C d) Brand D e) Brand E f) Other OCCASION: a) TV b) Poster/Billboard c) Radio d) In store e) Cinema f) Newspaper g) Magazine h) Conversation i) Online/Mobile j) Mailing/Leaflet k) Me purchasing l) Me using m) Someone else using n) Sponsorship o) Other Which brand was it? Where did you experience it? How likely did it make you to choose the brand next time? How did it make you feel?
Now tell us more in an online diary… 29 This is an individual’s experience log By clicking on each entry, the experience can be expanded upon in detail Wednesday 13th February 2012,11:54 Wednesday13th February2012,11:54 Wednesday13th February2012,10:22 Tuesday512h February2012,18:46 Tuesday12th February2012,13:05 Tuesday12th February2012,08:38 Brand A,Online, Very Positive, MuchMoreLikely toChoose Brand C,Conversation, Very Negative, MuchLess Likely toChoose Brand E, TV, Fairly Positive, Slightly More Likely to Choose Other, Instore, Fairly Positive, Slightly morelikely toChoose Brand B, Mailing/Leaflet, Slightly Negative, Nochange Brand A Brand’s website Very positive Much more likely to choose I was looking on the brand website to find out more information about the product range. Looks like there are some good options. Look for product info 13/02/2012, 11:54 Please tell us exactly what yousaw? : What was the purpose ofyour online activity? : Brand’s website Ad from brand In the news Social networking site Price comparison site Other For each level of data captured in real-time we can tailor extra questions to get more granular information in near-time
Data For one individual Brand A Brand B … Brand N Consideration Wk0 Consideration Wk1 Consideration Wk0 Consideration Wk1 Consideration Wk0 Consideration Wk1 Freq. & Pos. Brand Ad Retailer Ad In Store WOM … Freq. & Pos. Brand Ad Retailer Ad In Store WOM … Freq. & Pos. Brand Ad Retailer Ad In Store WOM … 30 +ve -ve -ve
Model © Cranfield University 31 Change in Consideration = ??? Data Rationale Considerations Implications Demographics / Participant information Certain consumer groups may be more / less likely to change their opinion towards brands over time Multiple responses per participant means that we can learn more about individual tendencies Need to account for the homogeneity in repeated responses, therefore random effects modelling Frequency of experience More experiences can be a positive impact (via reinforcement of messaging) or negative (over-exposure) There could be many potential ways of including this in the model; Constant effect Diminishing returns Check the validity of the model by testing multiple approaches Positivity of experience A consumers’ perception of an experience can determine the impact it has on them How do we account for positivity over multiple encounters? Average? Check the validity of the model by testing multiple approaches
Parameter operation Frequency © Cranfield University 32 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 Exposure Impact = x if Freq.>0 Increasing Impact Impact = x*Freq. Diminishing Returns Impact = x*ln(Freq.+1) Impact = x*Freq. - y*Freq.^2
Parameter operation Positivity 1. Average positivity across experiences 2. Average positivity and variance of positivity 3. Positivity of last experience 4. Freq. of positive and Freq. of negative experiences © Cranfield University 33
Results Focal Frequency Positivity In-store communications =1 1 Brand advertising =1 =2 Retailer advertising =1 =2 Peer observation =1 =4 Traditional earned =5 =4 WOM =5 6 © Cranfield University 34 Competitor Frequency Positivity In-store communications 1 =1 WOM =2 =1 Peer observation =2 =1 Retailer advertising =4 =1 Brand advertising =4 =1 Traditional earned 6 =1
Thank You!
The contents of this document are the sole and confidential property of Lieberman Research Worldwide, and may not be reproduced or distributed without the express written permission of Lieberman Research Worldwide. Prepared for CLIENT TITLE LRW Europe BAYESIAN ANALYSIS FOR MARKETING IMPACT April 2015 LRW Europe 1, Heathcock Court, 415, Strand London WC2R 0NT Prepared by: Adele Gritten & Graham Williams for MRS Advanced Analytics Conference
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL.  Why should our industry care about BayesNets?  What is BayesNets?  What are the Advantages of BayesNets?  Illustrative outputs  Live UK Case Study  Summary TODAY’S AGENDA
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. Why should our Industry Care about BayesNets?
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. Why Should Our Industry Care About BayesNets?  BayesNets is a unique and more comprehensive driver analysis to assist Marketers  Overcomes the shortcomings of traditional [drivers analysis] methods  Allows the integration of profiling, behavioural and other metrics with attitudinal/preference ratings to create a more holistic view of what drives the dependent variable “Bayesnets has played a major role in several recent wins. It’s especially helpful with brand positioning research where the complex relationships between brand attributes demands a more nuanced and flexible approach to analysis”. LRW Account Director
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. What is BayesNets?
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. What is BayesNets? Think of BayesNets as “drivers analysis on steroids” Let’s review the logic and goals of “key drivers analysis”:  There is a market attitude or behaviour – the “target outcome” – which:  A client needs to favourably influence in the marketplace, but which…  They cannot influence directly  So we need to find the best “levers to pull” through which to indirectly influence that attitude or behaviour; e.g. customer attitudes or perceptions:  Which we can influence through product/service design or marketing, and…  Which have strong “derived importance” in driving the “target outcome”  Ultimately, the purpose of “key drivers analysis” is to empirically identify the best “levers to pull” for maximum in market impact. BayesNets:  Can be thought of generally as a more powerful key drivers analysis methodology  Offers a number of significant advantages compared to hitherto commonly used key drivers analysis approaches
Where Did BayesNets Come From? | Thomas Bayes “Bayesian” refers to Reverend Thomas Bayes’ Theorem from the 18th century that paved the way for data to be used in prediction. Bayes’ Theorem basically allows us to look at multi-directional probabilities.  18th Century English statistician, philosopher, and minister  Formulated Bayes’ Theorem: a mathematical expression of probabilities from observed data  Hotly debated and contested by Frequentists until recent years 42 © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL.
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. What are BayesNets? | A Model of Relationships Bayesian networks (BayesNets) are a type of “path analysis” model that simultaneously describes the relationships between variables in a network system based on joint probabilities between the variables. BayesNets improves upon models that use advanced correlation and regression techniques (such as most regression analyses and Structural Equation Modeling). Basically, it’s a better way of understanding interactions between independent variables as they drive dependent variables. WET PAVEMENT SPRINKLER RAIN SLIPPERY Probabilistic Relationship Probabilistic Relationship Variables or Factors Variables or Factors High-Dimensional Probability Hypercube
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. What are the Advantages of BayesNets?
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. What are Advantages of BayesNets? BayesNets’ advantages over more commonly used techniques are “technical” but nonetheless significant. BayesNets’ advantages include:  Does fully interactive, “multivariate” modeling  Not “confused” by multicollinearity  No implicit assumption of “linear” relationships  Siloed Regression Models don’t capture indirect or interaction effects BayesNets helps us find the best model:  We don’t have to “hypothesise” the structure of the multivariate network  Rather, BayesNets’ “machine learning” algorithms seek out the best network structures quickly & cost effectively  From there we bring in the “art” that mixes with the “science” to yield a highly actionable understanding of what drives the target outcome – the dependent variable – in the marketplace. Traditional Approach Approach with BayesNets Variables or Factors Independent Variables or Factors
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. Siloed Regression Models Don’t Capture Indirect or Interaction Effects Key Satisfaction/Loyalty Metric ProductsAtmosphere Store Experience Minutes in line Seconds Ordering Speed of Ordering Ordering Process Volume Customer Service Greeted by Employee Friendly Employees EXPERIENCE DOMAINS PERCEPTIONS OF BEHAVIORS QUANTIFIABLE MICRO- BEHAVIORS SUPPORTING IMPRESSIONS Metrics can impact other domains, not just those up the ladder in our hierarchy
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. The Former Best Approach: SEM Structural Equation Modeling  Tests Complex Structures  Interactive & Indirect Effects  Explanatory & Prescriptive  Multicollinearity may still be a problem  Can only test the “fit” of specifically hypothesized networks Siloed Regression Tree  Forces Simple Structure  Direct Effects Only  Diagnostic & Descriptive
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. Illustrative Outputs
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. Variables or Factors Colours identify “nodes” that belong to different factors Probabilistic Relationship “Arcs” connect the various “nodes” in the network What does it look like? | It Starts With Networks
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. BayesNets Satisfaction Key Drivers Analysis: A Case Study  Customer satisfaction surveys with >650,000 retail customers  Surveys conducted throughout 2013  Dependent variable: Overall quality of in-store experience rating  Independent variables:  Is a place for someone like me  Clothing was neatly displayed and well organized  The wait time in the checkout line was acceptable  Service you received in the fitting room met your needs  The cashier worked quickly and efficiently to check out all customers in line  Your experience in the store was more fun and engaging than other stores you typically shop  The signs clearly indicated what was on sale  Employees were easily accessible  Employees were willing to find style, color, size  Employees acknowledged and made you feel welcome  Employees seemed genuinely glad you were there  Overall clothing quality
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. BayesNets Customer Satisfaction Drivers Analysis - Retail Example Wait time at checkout Feel welcomed For someone like me Overall clothing quality Neat displays /organised Sale signs clear More fun and engaging than other stores Accessible employees Employees glad you were there Cashier worked quickly and efficiently Employees willing to find style, colour, size Service received in fitting room met needs Overall experience
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. BayesNets “total effects” analysis looks & feels like standard drivers analysis All Brands Retailer 1 Retailer 2 Retailer 3 Retailer 4 Retailer 5 Total Effects on Target Promoter_Rev Indexed Indexed Indexed Indexed Indexed Indexed Standardized Standardized Standardized Standardized Standardized Standardized Total Effects Total Effects Total Effects Total Effects Total Effects Total Effects F1: MERCHANDISE FOR ME 144.3 134.3 159.5 137.4 150.9 133.1 F0: EMOTIONALLY ENGAGED 131.9 114.6 127.8 110.5 115.4 111.7 F17: GREAT VALUE 125.8 121.6 126.8 112.7 129.7 118.6 F4: EXCITING AND STYLISH MERCHANDISE 125.0 111.6 147.3 115.9 102.7 116.7 F5: QUALITY BRANDS 120.1 111.1 132.7 152.7 111.7 115.6 F3: GREAT FIT & SIZES 112.8 114.9 112.0 132.3 119.1 115.3 F7: GREAT PRICES AND SAVINGS 110.0 120.6 141.9 117.6 95.1 114.7 F14: MERCHANDISE FOR MY HOME AND FAMILY 109.8 107.1 146.2 75.6 128.3 105.7 F11: GREAT SALES 105.1 111.6 102.2 95.8 117.5 106.3 F2: ENJOYABLE SHOPPING 103.9 94.0 56.8 101.3 105.5 100.5 I can always count on STORE to have what I want on sale 102.9 103.2 104.2 69.5 109.1 104.4 F12: BETTER DEALS 101.0 108.3 126.9 99.9 97.2 102.0 F13: EASY RETURN POLICY 93.4 93.4 104.9 107.1 91.4 101.5 F9: PRICES I TRUST 80.6 78.5 98.0 61.9 90.8 74.7 F6: LOYALTY PROGRAM 79.0 78.7 56.3 59.3 52.2 75.2 F8: COUPONS 72.2 93.5 39.0 85.7 75.1 89.1 F16: INSPIRING DISPLAYS 70.0 78.7 26.6 120.5 74.3 95.7 F18: SUPPORTS MY COMMUNITY 61.9 61.5 68.8 71.7 72.7 73.0 F10: EASY PROMOTION 50.5 62.7 22.1 72.5 61.4 46.3 Illustrative
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. The output here gives specific advice on which factors to affect first, and when it is optimal to focus on the next factor. Bayesian Analysis can also provide clear recommendations on where businesses should focus Initial Mean Rating Mean Rating After Improving Preceding Factors Target Mean Overall Opinion Mean Initial Value 4.45 HEALTHY 9.07 9.27 4.79 SELECTION/VARIETY 8.64 8.84 9.05 4.88 QUALITY 8.25 8.40 8.59 4.91 EASY/SIMPLE 8.05 8.56 8.86 4.92 First, the goal is to move the mean on the Healthy factor from 9.07 to 9.27 This would increase the Overall Opinion 0.34 points, from 4.45 to 4.79 Moving the Healthy mean from 9.07 to 9.27 also affects Selection/Variety, moving it from 8.64 to 8.84. Illustrative Moving the Selection mean from 8.84 to 9.05 similarly impacts both the Overall Opinion mean (up to 4.88) and the Quality mean (moving it to 8.40) and so on for each successive factor
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. UK Media Owner Client: Live Case Study Current data sources include: • Brand Tracking with image metrics • Industry audience measurement • Content audit v competitors • Content appreciation • Social Media tracking They tend to do a rough & ready comparison and they are doing some KDA in the tracking data, but so far no joined up stuff With so much data they worry about how much stakeholders trust or care about the data ‘I have an overload of data and metrics and I want to see what combination of factors drive audience growth (or decline).... At the moment I can’t see the wood for the trees – I’m hoping Bayesnet will help’ LRW are working with the client to initially conduct a Bayesnet analysis on the monthly tracker (which goes back over 2 years) to identify relationship between behaviour and the metrics and which ones are the ones that they really need to look at Ideal solution would be to stream line the cumbersome tracker – strip out metrics that don’t add value and then look to widen the Bayesnet analysis to other data sources and conduct a wider analysis
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. Summary
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. In Summary | Why BayesNet Modeling?  BayesNets modeling is often more effective than more traditional advanced modeling of derived importance analysis  BayesNet measures both direct impact on the dependent variables and indirect impacts through other independent variables in the model  BayesNets overcomes multicollinearity and makes no assumptions of either normal distributions of data or linear correlations between variables.  BayesNets mathematics and software allow for quicker creation of the model, optimizations and “what if” scenarios.  BayesNets offers more effective optimization modeling with target means to guide activation and appropriate levels of effort and investment.
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. If you’d like more info: We can set up a time for you to talk to one of our genuine experts! Mick McWilliams PhD, Sr. VP, Marketing Science Marketing scientist specializing in segmentation, brand engagement, database scoring, SEM, KDA and BayesNets 25+ years of MR experience with specialties in neuroscience studies & evolutionary psychology PhD, Sociology, Virginia Polytechnic Institute & State University Thank you!
‹#› © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL. Graham Williams Research Director, Europe gwilliams@LRWonline.com Lieberman Research Worldwide 1, Heathcock Court, 415 Strand London WC2R ONT www.lrwonline.com Direct Line: 0203 551 7075 Contact Information
Copyright © Nepa All Rights Reserved Space Optimisation MRS Advanced Analytics 30 April 2015
Copyright © Nepa All Rights Reserved 60 Space Optimisation is the process of maximising profit by allocating the appropriate amount of store shelf space to each product category
Copyright © Nepa All Rights Reserved Typical client – a retail chain with a wealth of sales and loyalty club data 61 ...of different locations and demographic profiles Many hundred stores of different sizes Nearly 100 product categories
Copyright © Nepa All Rights Reserved Many aspects determine the profit that a product category will yield. First, the most important ones are identified Significant factors, in selection : • Affluence in neighbourhood • Gender profile • Age profile • Proximity to low-cost competition • ... • ... • ... 62 Demography Location / competition Sales details Customer Satisfaction
Copyright © Nepa All Rights Reserved Linear regression is used to isolate the relationship between space and profit, per product category 63 Space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... β1 + β2 + β3 + β4 + β5 + βs
Copyright © Nepa All Rights Reserved ”Space elasticity” – not the same for all product types, illustrated by an intuitive example from a pharmacy 64 1 shelf Margin: £500 per day  £500 /shelf 4 shelves Margin: £1000 per day  £250 /shelf +? Margin Space Margin Space Due to its higher space elasticity, it is likely more profitable to add another beauty shelf than one for pain killers. This despite the fact that painkillers presntly give more profit per shelf unit.
Copyright © Nepa All Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 65 Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... βs
Copyright © Nepa All Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 66 0 20 40 60 80 100 120 0 20 40 60 Margin(£) Space (Shelf sections) Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... βs
Copyright © Nepa All Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 67 0 20 40 60 80 100 120 0 50 100 150 200 Margin(£) Space (Shelf sections) Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... βs
Copyright © Nepa All Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 68 0 10 20 30 40 50 60 70 80 90 0 50 100 150 200 Margin(£) Space (shelf sections) Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... βs
Copyright © Nepa All Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 69 0 10 20 30 40 50 60 70 80 90 0 50 100 150 200 Margin(£) Space (shelf sections) Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... βs
Copyright © Nepa All Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 70 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 Margin(£) Space (shelf sections) Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ...
Copyright © Nepa All Rights Reserved We will never start adding delicassy cheeses, since the start of the curve is so flat. Combining curves for the optimal space allocation – stepwise incremental assignment doesn’t always find the best solution available 71 0 10 20 30 40 50 60 70 80 90 100 0 1 2 3 4 5 6 7 8 9 10 Margin(£) ABBBC C D D E F GG/HIJK Store plan, 15 shelves 8 7 Stepwise adding products to shelves using the highest incremental value at each step will result in assigning 8 to vegetables and 7 to sauces
Copyright © Nepa All Rights Reserved The optimal distribution includes many shelves of delicassy cheeses, giving a large profit at substantial space assignment 72 0 10 20 30 40 50 60 70 80 90 100 0 1 2 3 4 5 6 7 8 9 10 Margin(£) ABBBC C D D E F GG/HIJK Store plan, 15 shelves 96
Copyright © Nepa All Rights Reserved We search through all possible combinations to find the best one – an enormous optimisation problem which we use logic to reduce 100 shelf units to allocate 73 30 categories ... ... ... ... ... ... ... ... 6 x 1028 combinations! ... ... Even this rather moderate number of shelf units and categories presents an enormous number of potential combinations. We need to use logic to reduce the computational complexity, and find the best solution available.
Copyright © Nepa All Rights Reserved An online tool is used for space allocation, bespoke to each individual store 74
Copyright © Nepa All Rights Reserved Thank you! kalle.backlund@nepa.com 0785-19 49 379 75

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MRS Advanced Analytics Innovation Symposium Presentation

  • 1.
    MRS Advanced Analytics InnovationSymposium 30th April 2015 #MRSlive
  • 2.
    Brand Share &Industry Size: Will the twain ever meet? Using a portfolio of techniques to improve accuracy of market volume evolution in price change scenarios April 2015 Sreeram Srinivasan, IMRB International Ranjana Gupta, IMRB International
  • 3.
    30 40 Marketer’s pricing dilemma 30Market volum e 100 bn Marke t volum e 70 bn With new prices, my share will grow… … but will my volume also grow? Market share grows….. ….but, market size shrinks
  • 4.
    Both the consumerand the macro variables needed to answer the questions.. C o n s u m e r M a c r o Brand choice Same brand, switching etc. In context of price Category choice Frequency, consumption, substitutes etc. Industry Past volumes, substitutes, prices etc. Economy Income, affordability, Inflation etc. Relies on past trends Future may be different Future oriented. Past learnings not fully leveraged
  • 5.
    Hence for accuratevolume forecasts, no single methodology can provide complete answer Consumer research and macro-economic models should be integrated Choice Model Consumer Research • Brand shares • Switching Econometric Macro Modelling • Market size
  • 6.
    An approach tointegration of choice and econometric model However, before integration, the individual tools need to be refined, adapted…
  • 7.
    …to account forthe nature of the category How to account for occasion? Is it an impulse or considered purchase? Is it a repertoire or non- repertoire category? Do number of units matter? What about frequency of purchase? How can we ensure that the respondent reacts to only relevant offers?
  • 8.
  • 9.
    The questioning techniqueuses FORCE principle to make the consumer response more realistic… Familiar Only brands that the respondent interacts with / likely to interact with shown – customized for each respondent real time Evoked set created using respondent’s current repertoire, past usage and future disposition Occasio n Respondent’s answers using an occasion as a context in occasion led categories like CSDs, Snacks etc. There can be other household purchase categories where occasion is irrelevant Repertoir e Respondents allowed to select multiple brands, SKUs and units, as they might in real life Channel Primary channel of purchase identified and specified in the questioning Event Recurren ce Frequency of purchase
  • 10.
    Here’s an example… around20 such choice tasks shown Imagine you are doing your monthly grocery shopping from the supermarket and you have to buy bathing soaps. On the shelf you see the following brands with the given prices? Which brands are you likely to buy? You can choose as many brands as you like. Or you can walk out of the shop without buying any. Do state the number of packs that you would be buying. I will not buy any anything 2 Johnson’s Baby Soap 75 gms Rs. 30 Dove 75 gms Rs. 40 Santoor – pack of 4 100gms X 4 Rs. 50 Now at Rs. 40 Pears 100 gms + 25 gms extra free Rs. 25 Rexona 200 gms Rs. 25 I would buy a shower gel Lux 100 gms Rs. 10 1
  • 11.
    Consumer choices areconverted into utilities – two levels of calculations Main Effect For every level within each attribute at a respondent level Example: Utility or preference for Brands like Dove, Pears etc., for SKUs like 100 gms,, 75 gms and for various price levels Cross Effect Interaction between attributes Example: Utility for Dove by itself and for Dove at a particular price may be different The utilities are transformed into share of preference and weighted to give the shares in various scenarios For all existing brands & SKUs, the current levels of distribution are built into the model – to ensure current scenario shares are in line with actual market shares Respondent level estimation of preference helps in calculation of gains and losses from one brand to another
  • 12.
    The output Current Scenario New price scenario (Client’s brandscut prices by 10%) Company Brand X 20.0% 21.8% Brand Y 17.0% 17.2% Brand A 10.0% 9.9% Brand B 5.0% 4.4% Brand P 8.0% 7.6% Brand Q 15.0% 14.8% Brand R 10.0% 9.7% Brand S 15.0% 14.6% 39%37% Brand X Gaining From Brand B, Brand S Net Gain/Loss 1.8%
  • 13.
  • 14.
    The input Past volumesPopulation Substitute categories (Real) Price Purchase frequency No. of packs(Real) Income Basket size
  • 15.
    The statistics Options Simple Regression Volume= fn (Price) Easy but can lead to situations like increase price to increase volumes! Simple Time Series Volume = fn (Past volumes) Builds in past volume trends but assumes that history will definitely repeat itself Price Volume
  • 16.
    The statistics Adopted method ARIMA (AutoRegressive Integrated Moving Average) Volume = fn (Real Price, Past Volume, Real Income, etc….) Moving average included – accounts for any possible prediction error in previous time periods Accounts for autocorrelation Better prediction accuracy
  • 17.
    The statistics Adopted method ARIMA (AutoRegressive Integrated Moving Average) Deseasonalized data - predict organic change in volumes Model by major sub-groups to account for different trends – break the market Price gap between sub-groups used – inter-movement built in
  • 18.
    The output Market volumes 125000tonnes Current scenario 126250 tonnes New price scenario 1%
  • 19.
  • 20.
    It’s quite simpleactually… Shares X Market Volumes = Company Volumes 37 Current Scenario 125000 tonnes New Scenario Share Market Volumes 46250 tonnesCompany volumes 39 126250 tonnes Share Market Volumes 49238 tonneCompany volumes Share change: 5% Volume change: 6%
  • 21.
    Proof of thepudding
  • 22.
    Results validated acrossmarkets 0 20 40 60 80 100 Predicted volume accuracy by brands in various markets (74 data points in this graph) Average : 88% The trick: improve accuracy of the individual models
  • 23.
    Identifying the relativeimpact of touchpoints: A tailored statistical technique for real-time data Shane Baxendale, Cranfield School of Management Heval Ceylan-Gilchrist, MESH MRS ADVANCED ANALYTICS NETWORK 30th April 2015
  • 24.
    Why are wehere today?  Real-time Experience Tracking Methodology  Analysis - using linear mixed-effects regression 24
  • 25.
    Our thinking  Consumersexperience brands through multiple channels (not just advertising!)  Brand experiences influence a consumer’s attitude toward brands  The majority of existing literature focusses on the impact of one or two types of experience What impact are different encounters having on consumer attitudes toward the brand? 25 *Baxendale S., Macdonald E.K., Wilson H.N., (2015), The impact of different touchpoints on brand consideration, Journal of Retailing, 37(2), 203.
  • 26.
    Real-time Experience Tracking (RET) ONLINEREAL-TIME ONLINE Day 9Day 2 - 8Day 1
  • 27.
    Text us wheneveryou see, hear or experience anything to do with the following brands… Text framework 27 BRAND: A)Brand A B)Brand B C)Brand C D)Brand D E)Brand E F) Other OCCASION: A)TV B)Poster/Billboard C)Radio D)In store E)Cinema F)Newspaper G)Magazine H)Conversation I)Online/Mobile J)Mailing/leaflet K)Me Purchasing L)Me using M)Someone else using N)Sponsorship O)Other FEELING: 5)Very positive 4)Fairly positive 3)Neutral 2)Fairly negative 1)Very Negative CHOICE: 5)Much more likely to choose 4)Slightly more likely to choose 3)No difference 2)Slightly less likely to choose 1) Much less likely to choose
  • 28.
    Imagine you experiencedBrand A Online… …you would text: 28 a 5i 5 CHOICE: 5) Much more likely to choose 4) Slightly more likely to choose 3) No change 2) Slightly less likely to choose 1) Much less likely to choose ENGAGEMENT: 5) Very positive 4) Fairly positive 3) Neutral 2) Fairly negative 1) Very negative BRAND: a) Brand A b) Brand B c) Brand C d) Brand D e) Brand E f) Other OCCASION: a) TV b) Poster/Billboard c) Radio d) In store e) Cinema f) Newspaper g) Magazine h) Conversation i) Online/Mobile j) Mailing/Leaflet k) Me purchasing l) Me using m) Someone else using n) Sponsorship o) Other Which brand was it? Where did you experience it? How likely did it make you to choose the brand next time? How did it make you feel?
  • 29.
    Now tell usmore in an online diary… 29 This is an individual’s experience log By clicking on each entry, the experience can be expanded upon in detail Wednesday 13th February 2012,11:54 Wednesday13th February2012,11:54 Wednesday13th February2012,10:22 Tuesday512h February2012,18:46 Tuesday12th February2012,13:05 Tuesday12th February2012,08:38 Brand A,Online, Very Positive, MuchMoreLikely toChoose Brand C,Conversation, Very Negative, MuchLess Likely toChoose Brand E, TV, Fairly Positive, Slightly More Likely to Choose Other, Instore, Fairly Positive, Slightly morelikely toChoose Brand B, Mailing/Leaflet, Slightly Negative, Nochange Brand A Brand’s website Very positive Much more likely to choose I was looking on the brand website to find out more information about the product range. Looks like there are some good options. Look for product info 13/02/2012, 11:54 Please tell us exactly what yousaw? : What was the purpose ofyour online activity? : Brand’s website Ad from brand In the news Social networking site Price comparison site Other For each level of data captured in real-time we can tailor extra questions to get more granular information in near-time
  • 30.
    Data For one individual BrandA Brand B … Brand N Consideration Wk0 Consideration Wk1 Consideration Wk0 Consideration Wk1 Consideration Wk0 Consideration Wk1 Freq. & Pos. Brand Ad Retailer Ad In Store WOM … Freq. & Pos. Brand Ad Retailer Ad In Store WOM … Freq. & Pos. Brand Ad Retailer Ad In Store WOM … 30 +ve -ve -ve
  • 31.
    Model © Cranfield University31 Change in Consideration = ??? Data Rationale Considerations Implications Demographics / Participant information Certain consumer groups may be more / less likely to change their opinion towards brands over time Multiple responses per participant means that we can learn more about individual tendencies Need to account for the homogeneity in repeated responses, therefore random effects modelling Frequency of experience More experiences can be a positive impact (via reinforcement of messaging) or negative (over-exposure) There could be many potential ways of including this in the model; Constant effect Diminishing returns Check the validity of the model by testing multiple approaches Positivity of experience A consumers’ perception of an experience can determine the impact it has on them How do we account for positivity over multiple encounters? Average? Check the validity of the model by testing multiple approaches
  • 32.
    Parameter operation Frequency © CranfieldUniversity 32 0 1 2 3 4 5 0 1 2 3 4 5 0 1 2 3 4 5 Exposure Impact = x if Freq.>0 Increasing Impact Impact = x*Freq. Diminishing Returns Impact = x*ln(Freq.+1) Impact = x*Freq. - y*Freq.^2
  • 33.
    Parameter operation Positivity 1. Averagepositivity across experiences 2. Average positivity and variance of positivity 3. Positivity of last experience 4. Freq. of positive and Freq. of negative experiences © Cranfield University 33
  • 34.
    Results Focal Frequency Positivity In-store communications =11 Brand advertising =1 =2 Retailer advertising =1 =2 Peer observation =1 =4 Traditional earned =5 =4 WOM =5 6 © Cranfield University 34 Competitor Frequency Positivity In-store communications 1 =1 WOM =2 =1 Peer observation =2 =1 Retailer advertising =4 =1 Brand advertising =4 =1 Traditional earned 6 =1
  • 35.
  • 36.
    The contents ofthis document are the sole and confidential property of Lieberman Research Worldwide, and may not be reproduced or distributed without the express written permission of Lieberman Research Worldwide. Prepared for CLIENT TITLE LRW Europe BAYESIAN ANALYSIS FOR MARKETING IMPACT April 2015 LRW Europe 1, Heathcock Court, 415, Strand London WC2R 0NT Prepared by: Adele Gritten & Graham Williams for MRS Advanced Analytics Conference
  • 37.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL.  Why should our industry care about BayesNets?  What is BayesNets?  What are the Advantages of BayesNets?  Illustrative outputs  Live UK Case Study  Summary TODAY’S AGENDA
  • 38.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. Why should our Industry Care about BayesNets?
  • 39.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. Why Should Our Industry Care About BayesNets?  BayesNets is a unique and more comprehensive driver analysis to assist Marketers  Overcomes the shortcomings of traditional [drivers analysis] methods  Allows the integration of profiling, behavioural and other metrics with attitudinal/preference ratings to create a more holistic view of what drives the dependent variable “Bayesnets has played a major role in several recent wins. It’s especially helpful with brand positioning research where the complex relationships between brand attributes demands a more nuanced and flexible approach to analysis”. LRW Account Director
  • 40.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. What is BayesNets?
  • 41.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. What is BayesNets? Think of BayesNets as “drivers analysis on steroids” Let’s review the logic and goals of “key drivers analysis”:  There is a market attitude or behaviour – the “target outcome” – which:  A client needs to favourably influence in the marketplace, but which…  They cannot influence directly  So we need to find the best “levers to pull” through which to indirectly influence that attitude or behaviour; e.g. customer attitudes or perceptions:  Which we can influence through product/service design or marketing, and…  Which have strong “derived importance” in driving the “target outcome”  Ultimately, the purpose of “key drivers analysis” is to empirically identify the best “levers to pull” for maximum in market impact. BayesNets:  Can be thought of generally as a more powerful key drivers analysis methodology  Offers a number of significant advantages compared to hitherto commonly used key drivers analysis approaches
  • 42.
    Where Did BayesNetsCome From? | Thomas Bayes “Bayesian” refers to Reverend Thomas Bayes’ Theorem from the 18th century that paved the way for data to be used in prediction. Bayes’ Theorem basically allows us to look at multi-directional probabilities.  18th Century English statistician, philosopher, and minister  Formulated Bayes’ Theorem: a mathematical expression of probabilities from observed data  Hotly debated and contested by Frequentists until recent years 42 © 2015 Lieberman Research Worldwide. All rights reserved. CONFIDENTIAL.
  • 43.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. What are BayesNets? | A Model of Relationships Bayesian networks (BayesNets) are a type of “path analysis” model that simultaneously describes the relationships between variables in a network system based on joint probabilities between the variables. BayesNets improves upon models that use advanced correlation and regression techniques (such as most regression analyses and Structural Equation Modeling). Basically, it’s a better way of understanding interactions between independent variables as they drive dependent variables. WET PAVEMENT SPRINKLER RAIN SLIPPERY Probabilistic Relationship Probabilistic Relationship Variables or Factors Variables or Factors High-Dimensional Probability Hypercube
  • 44.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. What are the Advantages of BayesNets?
  • 45.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. What are Advantages of BayesNets? BayesNets’ advantages over more commonly used techniques are “technical” but nonetheless significant. BayesNets’ advantages include:  Does fully interactive, “multivariate” modeling  Not “confused” by multicollinearity  No implicit assumption of “linear” relationships  Siloed Regression Models don’t capture indirect or interaction effects BayesNets helps us find the best model:  We don’t have to “hypothesise” the structure of the multivariate network  Rather, BayesNets’ “machine learning” algorithms seek out the best network structures quickly & cost effectively  From there we bring in the “art” that mixes with the “science” to yield a highly actionable understanding of what drives the target outcome – the dependent variable – in the marketplace. Traditional Approach Approach with BayesNets Variables or Factors Independent Variables or Factors
  • 46.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. Siloed Regression Models Don’t Capture Indirect or Interaction Effects Key Satisfaction/Loyalty Metric ProductsAtmosphere Store Experience Minutes in line Seconds Ordering Speed of Ordering Ordering Process Volume Customer Service Greeted by Employee Friendly Employees EXPERIENCE DOMAINS PERCEPTIONS OF BEHAVIORS QUANTIFIABLE MICRO- BEHAVIORS SUPPORTING IMPRESSIONS Metrics can impact other domains, not just those up the ladder in our hierarchy
  • 47.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. The Former Best Approach: SEM Structural Equation Modeling  Tests Complex Structures  Interactive & Indirect Effects  Explanatory & Prescriptive  Multicollinearity may still be a problem  Can only test the “fit” of specifically hypothesized networks Siloed Regression Tree  Forces Simple Structure  Direct Effects Only  Diagnostic & Descriptive
  • 48.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. Illustrative Outputs
  • 49.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. Variables or Factors Colours identify “nodes” that belong to different factors Probabilistic Relationship “Arcs” connect the various “nodes” in the network What does it look like? | It Starts With Networks
  • 50.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. BayesNets Satisfaction Key Drivers Analysis: A Case Study  Customer satisfaction surveys with >650,000 retail customers  Surveys conducted throughout 2013  Dependent variable: Overall quality of in-store experience rating  Independent variables:  Is a place for someone like me  Clothing was neatly displayed and well organized  The wait time in the checkout line was acceptable  Service you received in the fitting room met your needs  The cashier worked quickly and efficiently to check out all customers in line  Your experience in the store was more fun and engaging than other stores you typically shop  The signs clearly indicated what was on sale  Employees were easily accessible  Employees were willing to find style, color, size  Employees acknowledged and made you feel welcome  Employees seemed genuinely glad you were there  Overall clothing quality
  • 51.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. BayesNets Customer Satisfaction Drivers Analysis - Retail Example Wait time at checkout Feel welcomed For someone like me Overall clothing quality Neat displays /organised Sale signs clear More fun and engaging than other stores Accessible employees Employees glad you were there Cashier worked quickly and efficiently Employees willing to find style, colour, size Service received in fitting room met needs Overall experience
  • 52.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. BayesNets “total effects” analysis looks & feels like standard drivers analysis All Brands Retailer 1 Retailer 2 Retailer 3 Retailer 4 Retailer 5 Total Effects on Target Promoter_Rev Indexed Indexed Indexed Indexed Indexed Indexed Standardized Standardized Standardized Standardized Standardized Standardized Total Effects Total Effects Total Effects Total Effects Total Effects Total Effects F1: MERCHANDISE FOR ME 144.3 134.3 159.5 137.4 150.9 133.1 F0: EMOTIONALLY ENGAGED 131.9 114.6 127.8 110.5 115.4 111.7 F17: GREAT VALUE 125.8 121.6 126.8 112.7 129.7 118.6 F4: EXCITING AND STYLISH MERCHANDISE 125.0 111.6 147.3 115.9 102.7 116.7 F5: QUALITY BRANDS 120.1 111.1 132.7 152.7 111.7 115.6 F3: GREAT FIT & SIZES 112.8 114.9 112.0 132.3 119.1 115.3 F7: GREAT PRICES AND SAVINGS 110.0 120.6 141.9 117.6 95.1 114.7 F14: MERCHANDISE FOR MY HOME AND FAMILY 109.8 107.1 146.2 75.6 128.3 105.7 F11: GREAT SALES 105.1 111.6 102.2 95.8 117.5 106.3 F2: ENJOYABLE SHOPPING 103.9 94.0 56.8 101.3 105.5 100.5 I can always count on STORE to have what I want on sale 102.9 103.2 104.2 69.5 109.1 104.4 F12: BETTER DEALS 101.0 108.3 126.9 99.9 97.2 102.0 F13: EASY RETURN POLICY 93.4 93.4 104.9 107.1 91.4 101.5 F9: PRICES I TRUST 80.6 78.5 98.0 61.9 90.8 74.7 F6: LOYALTY PROGRAM 79.0 78.7 56.3 59.3 52.2 75.2 F8: COUPONS 72.2 93.5 39.0 85.7 75.1 89.1 F16: INSPIRING DISPLAYS 70.0 78.7 26.6 120.5 74.3 95.7 F18: SUPPORTS MY COMMUNITY 61.9 61.5 68.8 71.7 72.7 73.0 F10: EASY PROMOTION 50.5 62.7 22.1 72.5 61.4 46.3 Illustrative
  • 53.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. The output here gives specific advice on which factors to affect first, and when it is optimal to focus on the next factor. Bayesian Analysis can also provide clear recommendations on where businesses should focus Initial Mean Rating Mean Rating After Improving Preceding Factors Target Mean Overall Opinion Mean Initial Value 4.45 HEALTHY 9.07 9.27 4.79 SELECTION/VARIETY 8.64 8.84 9.05 4.88 QUALITY 8.25 8.40 8.59 4.91 EASY/SIMPLE 8.05 8.56 8.86 4.92 First, the goal is to move the mean on the Healthy factor from 9.07 to 9.27 This would increase the Overall Opinion 0.34 points, from 4.45 to 4.79 Moving the Healthy mean from 9.07 to 9.27 also affects Selection/Variety, moving it from 8.64 to 8.84. Illustrative Moving the Selection mean from 8.84 to 9.05 similarly impacts both the Overall Opinion mean (up to 4.88) and the Quality mean (moving it to 8.40) and so on for each successive factor
  • 54.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. UK Media Owner Client: Live Case Study Current data sources include: • Brand Tracking with image metrics • Industry audience measurement • Content audit v competitors • Content appreciation • Social Media tracking They tend to do a rough & ready comparison and they are doing some KDA in the tracking data, but so far no joined up stuff With so much data they worry about how much stakeholders trust or care about the data ‘I have an overload of data and metrics and I want to see what combination of factors drive audience growth (or decline).... At the moment I can’t see the wood for the trees – I’m hoping Bayesnet will help’ LRW are working with the client to initially conduct a Bayesnet analysis on the monthly tracker (which goes back over 2 years) to identify relationship between behaviour and the metrics and which ones are the ones that they really need to look at Ideal solution would be to stream line the cumbersome tracker – strip out metrics that don’t add value and then look to widen the Bayesnet analysis to other data sources and conduct a wider analysis
  • 55.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. Summary
  • 56.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. In Summary | Why BayesNet Modeling?  BayesNets modeling is often more effective than more traditional advanced modeling of derived importance analysis  BayesNet measures both direct impact on the dependent variables and indirect impacts through other independent variables in the model  BayesNets overcomes multicollinearity and makes no assumptions of either normal distributions of data or linear correlations between variables.  BayesNets mathematics and software allow for quicker creation of the model, optimizations and “what if” scenarios.  BayesNets offers more effective optimization modeling with target means to guide activation and appropriate levels of effort and investment.
  • 57.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. If you’d like more info: We can set up a time for you to talk to one of our genuine experts! Mick McWilliams PhD, Sr. VP, Marketing Science Marketing scientist specializing in segmentation, brand engagement, database scoring, SEM, KDA and BayesNets 25+ years of MR experience with specialties in neuroscience studies & evolutionary psychology PhD, Sociology, Virginia Polytechnic Institute & State University Thank you!
  • 58.
    ‹#› © 2015 LiebermanResearch Worldwide. All rights reserved. CONFIDENTIAL. Graham Williams Research Director, Europe gwilliams@LRWonline.com Lieberman Research Worldwide 1, Heathcock Court, 415 Strand London WC2R ONT www.lrwonline.com Direct Line: 0203 551 7075 Contact Information
  • 59.
    Copyright © NepaAll Rights Reserved Space Optimisation MRS Advanced Analytics 30 April 2015
  • 60.
    Copyright © NepaAll Rights Reserved 60 Space Optimisation is the process of maximising profit by allocating the appropriate amount of store shelf space to each product category
  • 61.
    Copyright © NepaAll Rights Reserved Typical client – a retail chain with a wealth of sales and loyalty club data 61 ...of different locations and demographic profiles Many hundred stores of different sizes Nearly 100 product categories
  • 62.
    Copyright © NepaAll Rights Reserved Many aspects determine the profit that a product category will yield. First, the most important ones are identified Significant factors, in selection : • Affluence in neighbourhood • Gender profile • Age profile • Proximity to low-cost competition • ... • ... • ... 62 Demography Location / competition Sales details Customer Satisfaction
  • 63.
    Copyright © NepaAll Rights Reserved Linear regression is used to isolate the relationship between space and profit, per product category 63 Space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... β1 + β2 + β3 + β4 + β5 + βs
  • 64.
    Copyright © NepaAll Rights Reserved ”Space elasticity” – not the same for all product types, illustrated by an intuitive example from a pharmacy 64 1 shelf Margin: £500 per day  £500 /shelf 4 shelves Margin: £1000 per day  £250 /shelf +? Margin Space Margin Space Due to its higher space elasticity, it is likely more profitable to add another beauty shelf than one for pain killers. This despite the fact that painkillers presntly give more profit per shelf unit.
  • 65.
    Copyright © NepaAll Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 65 Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... βs
  • 66.
    Copyright © NepaAll Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 66 0 20 40 60 80 100 120 0 20 40 60 Margin(£) Space (Shelf sections) Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... βs
  • 67.
    Copyright © NepaAll Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 67 0 20 40 60 80 100 120 0 50 100 150 200 Margin(£) Space (Shelf sections) Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... βs
  • 68.
    Copyright © NepaAll Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 68 0 10 20 30 40 50 60 70 80 90 0 50 100 150 200 Margin(£) Space (shelf sections) Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... βs
  • 69.
    Copyright © NepaAll Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 69 0 10 20 30 40 50 60 70 80 90 0 50 100 150 200 Margin(£) Space (shelf sections) Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ... βs
  • 70.
    Copyright © NepaAll Rights Reserved Store-specific linear regression gives accurate space elasticity curves in steps, for each category 70 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 Margin(£) Space (shelf sections) Shelf space allocated ... ... Affluence in neighbourhood Gender profile Age profile Proximity to low-cost competition ... ...
  • 71.
    Copyright © NepaAll Rights Reserved We will never start adding delicassy cheeses, since the start of the curve is so flat. Combining curves for the optimal space allocation – stepwise incremental assignment doesn’t always find the best solution available 71 0 10 20 30 40 50 60 70 80 90 100 0 1 2 3 4 5 6 7 8 9 10 Margin(£) ABBBC C D D E F GG/HIJK Store plan, 15 shelves 8 7 Stepwise adding products to shelves using the highest incremental value at each step will result in assigning 8 to vegetables and 7 to sauces
  • 72.
    Copyright © NepaAll Rights Reserved The optimal distribution includes many shelves of delicassy cheeses, giving a large profit at substantial space assignment 72 0 10 20 30 40 50 60 70 80 90 100 0 1 2 3 4 5 6 7 8 9 10 Margin(£) ABBBC C D D E F GG/HIJK Store plan, 15 shelves 96
  • 73.
    Copyright © NepaAll Rights Reserved We search through all possible combinations to find the best one – an enormous optimisation problem which we use logic to reduce 100 shelf units to allocate 73 30 categories ... ... ... ... ... ... ... ... 6 x 1028 combinations! ... ... Even this rather moderate number of shelf units and categories presents an enormous number of potential combinations. We need to use logic to reduce the computational complexity, and find the best solution available.
  • 74.
    Copyright © NepaAll Rights Reserved An online tool is used for space allocation, bespoke to each individual store 74
  • 75.
    Copyright © NepaAll Rights Reserved Thank you! kalle.backlund@nepa.com 0785-19 49 379 75

Editor's Notes

  • #2 When I talk to companies about this most people tell me they expected me to be older
  • #17 Volume = α + β. price + ¥. Last year Volume (Autoregressive component)+ Ɵet (Moving Average) Where ¥. Last year Volume is the Autoregressive component – accounts for the fact that Yt is dependent on Yt-1 I = Yt – Yt-1 (The difference in the volumes between two consecutive periods) Ɵet is the MA term which assumes et is dependent on et-1 Simple regression will not work due to autocorrelation AR is used to account for autocorrelation whereas MA is used to account for any errors in the previous time period We do not use the integration factor into our model and only use ARMA due to the fact that we already deseasonalize the input variables. Then there would be an issue of double correction.
  • #26 Rationale for the analysis
  • #31 But what data do we have? And what links do we hypothesise?
  • #32 So what do we want to include in the model, why, and what do we have to consider as a result of this?
  • #33 How should we include frequency? Which is best from the model? Does this make sense / are we comfortable with choosing this over other options?
  • #34 How should we include positivity? Which is best from the model? Does this make sense / are we comfortable with choosing this over other options?
  • #35 What are the results?
  • #61 Done to a large extent on gut feel. We will see that there are large money to make by relatively simple steps.
  • #62 EXAMPLES of categories Grocery store, like Co-op. Second in Sweden. Clarify that we are looking at allocating space for each of those product categories. Done to a large extent on gut feel. We will see that there are large money to make
  • #63 Go through the matrix quickly! Two criteria: Drive margin in categories (+ or -), and be relatively un-correlated to each other
  • #64 R^2, around 0.6 (for the best categories)