Using Data Science & Predictive Models to Produce Foresight: The case of the presumptuous assumptions

Transcription

1 Using Data Science & Predictive Models to Produce Foresight: The case of the presumptuous assumptions Steve Cohen Partner in4mation insights Needham, MA

2 Founders are Thought Leaders Top Marketing Science Advisors Hierarchical Bayesian Statistics Proprietary Hardware & Software Big Data Analytics Consumer & Market Segmentation Customer Lifetime Value & Churn Digital Attribution Market Structure Marketing Mix Modeling New Product & Service Design Pricing & Promotion Optimization Assortment Optimization Retail Site Location Marketing Ecosystem Models 2014 by in4mation insights, LLC 2

3 Why should you care what I have to say? First to do Choice based Conjoint Analysis commercially (1983) First academic paper for Latent Class CBCA (1995) First integrated model for multiway segmentation based on Latent Class Models (1996) First academic paper for Menu based Conjoint Analysis (2000) Introduced MaxDiff scaling at ESOMAR (2000) Best paper at Sawtooth Conference: MaxDiff (2003) ESOMAR best paper of the year award for MaxDiff (2004) Best paper in Marketing Research Magazine: MaxDiff (2005) American Marketing Association Parlin Award (2011) NextGen Marketing Research LinkedIn group: Individual Disruptive Innovator award (2012) Marketing Research Council of NYC: MR Hall of Fame (2013) Over two dozen papers and presentations at industry conferences on analytics & modeling 2014 by in4mation insights, LLC 3

4 What is Data Science?

5 What is Data Science? 2014 by in4mation insights, LLC 5

6 What is Data Science? 2014 by in4mation insights, LLC Credit: Drew Conway 6

7 What is Data Science? 2014 by in4mation insights, LLC 7

8 Market Segmentation

9 Analytic steps in a typical segmentation study Tandem Clustering Collect Ratings Factor Analysis Cluster Analysis Assignment Tool Tandem clustering (i.e. factor analysis followed by cluster analysis) is an outmoded and statistically insupportable practice. Arabie& Hubert (1994) 2014 by in4mation insights, LLC 9

10 What are you doing? Principle Components Analysis Principal Components Analysis 2014 by in4mation insights, LLC 10

11 What are you doing? Discriminate Analysis Discriminant Analysis 2014 by in4mation insights, LLC 11

12 What s my beef with common segmentation practice? The short list. Guiding Principle: Segmentation is a search for differences Rating Scales Factor Analysis Cluster Analysis 2014 by in4mation insights, LLC 12

13 Hierarchical Bayesian Modeling

14 What are the effects of price and in-store display on sales of supermarket product? Lower Model Upper Model LnVolume!= a+b 1 Display+b 2 ln (Price) b 1 = c 1 +d 11 State+d 21 Trade_Area_Demos+d 31 Channel+d 41 Store_Format b 2 = c 2 +d 12 State+d 22 Trade_Area_Demos+d 32 Channel+d 42 Store_Format 2014 by in4mation insights, LLC 14

15 What could effect sales of SKUs in a store? Lower Model Base Price Discounted Price Feature Display Form Size Coupons Seasonality Holidays Weather Lower Model National TV Local TV Radio Outdoor Magazines Newspapers Social media activity Website & search Upper Model Channel Geography Ingredients Location at point of sale Store size Store age Store format Company vs. franchise Demos of trading area 2014 by in4mation insights, LLC 15

16 Bayesian analysis works best when there are many items, brands, stores or regions that need to be compared. Items can be compared to average $28,328 $16,980 TV Effectiveness: Sales/GRPs Growth opportunity $11,939 Items can be indexed against their volume $9,318 $6,828 $4,766 $4,751 $3,312 $1,518 Brand A Brand B Brand C Brand D Brand E Brand F Brand G Brand H Brand I Category Average $9,722 Effectiveness Index per $1MM in Brand Size: by in4mation insights, LLC 16

17 Choice Modeling & Trade-Up

18 What are you doing? Discreet Choice Model Discrete Choice Model 2014 by in4mation insights, LLC 18

19 True or False: Discrete Choice Models are the exact same thing as Choice-based Conjoint Analysis

20 A typology of choice models How many brands or items chosen? Only one More than one How many units of each item chosen? More than one Only one Dell 100 GB Hard Drive 4 MB RAM Basic Processor 17-inch Screen MS Office 90-day Warranty Total Price: $1,170 HP 200 GB Hard Drive 2 MB RAM Enhanced Processor 19-inch Screen MS Office 90-day Warranty Total Price: $1,480 Sony Vaio 500 GB Hard Drive 4 MB RAM Basic Processor 14-inch Screen No MS Office 1 Year Warranty Total Price: $1, by in4mation insights, LLC 20

21 Price elasticity is about substitutability $13.99 $10.99 $229 $249 $12.99 $234 $199 $179 $ by in4mation insights, LLC 21

22 Trade-up happens when shoppers are willing to spend more. Quality Count Size Coca-Cola Classic 12-pk, 12oz cans Coca-Cola Classic 12-pk, 12oz cans Coca-Cola Classic 2 liter bottle $5.49 $5.49 $1.89 Private Label Cola 12-pk, 12oz cans Coca-Cola Classic 6-pk, 12oz cans Coca-Cola Classic 20 oz bottle $2.99 $3.99 $ by in4mation insights, LLC 22

23 Trade-Up Model assumptions Products are not substitutes Trade up/down is asymmetric Consumers will purchase the most quantity that they can Subject to their budget limit Subject to diminishing returns Having money left over after making the purchase is good 2014 by in4mation insights, LLC 23

24 Market share simulations: Trade-Up vs. HB CBCA Logit Model 18% Predicted Market Share 16% 14% 12% 10% 8% 6% 4% 2% BRAND A (Tradeup) BRAND A (HB Logit) BRAND B (Tradeup) BRAND B (HB Logit) BRAND C (Tradeup) BRAND C (HB Logit) Market share is predicted to be higher for Brand A in the Trade-up model. 0% $299 $399 $499 $699 $899 Price of Brand A 2014 by in4mation insights, LLC 24

25 Market share simulations: Trade-Up vs. HB CBCA Logit Model Which price elasticity makes more sense? 18% Predicted Market Share 16% 14% 12% 10% 8% 6% 4% 2% 0% $299 $399 $499 $699 $899 Price of Brand A BRAND A (HB Logit) BRAND A (Tradeup) 2014 by in4mation insights, LLC 25

26 Modeling the Marketing Ecosystem

27 The Business Intelligence Landscape is changing. More holistic view of business needed Increasing role of social & digital media Fusing data sources into new databases Mine existing data Existing analytic tools assume static rather than dynamic view Integrate consumer-based metrics into modeling and planning models Need to accurately measure both short- and long-term marketing effects Need reliable measurement of effects of traditional marketing vs. social/digital media 2014 by in4mation insights, LLC 27

28 Any time series can be modeled as a simple process, where next month is a function of previous months. Offline & Online Marketing Tactics BHMs & Social Metrics Sales 2014 by in4mation insights, LLC 28

29 Some marketing tactics may have animmediate effect on sales, while others may take time to change opinions. Offline & Online Marketing Tactics BHMs & Social Metrics Immediate Sales 2014 by in4mation insights, LLC 29

30 Once opinions have changed, time passes before the impact on sales may be seen. Offline & Online Marketing Tactics BHMs & Social Metrics Immediate Sales 2014 by in4mation insights, LLC 30

31 Feedback occurs as higher sales affect consumer perceptions, leading tochanges in consumer sentiment and more online buzz and activity. Offline & Online Marketing Tactics BHMs & Social Metrics Immediate Sales 2014 by in4mation insights, LLC 31

32 Recent application 200 MillwardBrown attributes & funnel metrics 20 marketing spend tactics Number of channels = 11 Number of SKUs = 15 Number of time periods = 39 each SKU 2014 by in4mation insights, LLC 32

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34 Hierarchical Bayesian statistics Complex systems of linear or nonlinear equations Often no analytic solution Uses Monte Carlo simulation Predict quantitative or qualitative phenomena Incorporate sensible prior beliefs or knowledge Different coefficient for each unit of analysis at the lower level Upper level = Context = why behind the what 2014 by in4mation insights, LLC 34

35 Big Data in, Big Data out Total coefficients = N_Units * (Lower + Lower * Upper) at every iteration of the Bayesian estimation So, if Lower = 50 and Upper = 100, for 5,000 iterations Confectionery ~ 3,000 SKUs 15,000,000 coefficients Laundry products ~ 5,000 SKUs 25,000,000 coefficients Auto Parts ~ 75,000 SKUs ~ 400 Million coefficients 2014 by in4mation insights, LLC 35

36 Not all SKUs and retailers are created equal. Fragrances Makeup Skin Care Retailers A B C D Low High Low High Low High Lower Price Elasticity Higher 2014 by in4mation insights, LLC 36

37 Trade-up has become a familiar part of the global consumer landscape. Perceptually superior and higher price 2014 by in4mation insights, LLC 37

38 Behavioral Model Analytic Framework What Marketers Do What Consumers ` Think & Feel What Consumers Do Brand Tracking Lack comprehensive view Advertising Testing Market Response Models Proposed model Marketing Tactics Brand Health & Social Metrics Sales Performance 2014 by in4mation insights, LLC 38