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Acxiom Analytics Summit: Predicting Automotive Shopper Intention from Online / Offline Data

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Presented at Acxiom Advanced Analytics Summit III (Oct 24, 2012): …

Presented at Acxiom Advanced Analytics Summit III (Oct 24, 2012):
Jie Cheng, VP of Analytics, Acxiom
Ranjan Butaney, Senior Director, Compete

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  • 1. Predicting Automotive Shopper Intention from Online / Offline Data Jie Cheng, VP of Analytics, Acxiom Ranjan Butaney, Senior Director, Compete October 24, 2012#AcxiomAS3
  • 2. Background and Objectives• Understanding consumers’ online site visits, browsing and search behavior is a key to help predict their shopping intentions• Critical to the prediction of purchasing considerations such as timing and brand preferences• Extremely difficult to tie with offline purchases. Little has been understood to predict shopping intention / purchasing decisions• Objectives of this research – Data mining to recognize and understand consumer online auto shopping patterns – Develop analytic insights for the prediction of automotive shopping intention / purchase considerations – Identify online applications such as collaborative targeting and online personalized offer / messaging decisions – Explore application to industries beyond automotive #AcxiomAS3 2
  • 3. Integrated Online / OfflineAuto Shopping / Purchasing Analytic Data Compete Acxiom JD Power Online Consumer Automotive Panel Information Transactions Integrated Shopping / Purchasing Analytic Data Set #AcxiomAS3 3
  • 4. Multiple Sources Create the Industry’s Largest Panel• Compete’s multiple data sources create the industry’s largest panel with 2 million people in the U.S. – Proprietary recruited panel – Licensed clickstream-sharing partnerships with national ISPs and application providers – Ability to survey and match from both categories of data sources• Multiple data sources mitigate bias, improve panel accuracy and allow more data sources to be added over time #AcxiomAS3 4
  • 5. Overview of the Compete Data Methodology• Compete’s approach provides marketers with deeper insights across more websites and consumer behaviors• Products and services support a broad range of marketing decisions Multiple data Harmonization Normalization Metrics that matter sources #AcxiomAS3 5
  • 6. JD Power’s Data Set• J.D. Powers Power Information Network® (PIN) provides automotive market intelligence based on the collection of daily new- and used-vehicle retail transaction data from thousands of automotive franchises.• Details from these transactions are evaluated to create products that focus on key measures, including price, cost, profit, finance, lease, and trade-in values.• This research focuses on data elements associated with new vehicle purchases (vehicle make, segment, model, and model year, purchase date, and buyer information) #AcxiomAS3 6
  • 7. A Consumer’s Online Shopping Journey (1) January, 2011Sessions: 7 Page Views: 121 Time – Minutes: 67.6Web visit Domain Page Time - timetop SearchInfo Date Views minutes urchase 1/16/2011 ford.com 1 3.9 103 1/23/2011 chevrolet.com 6 2.8 96 yahoo:chevy trailblazer 1/23/2011 ford.com 21 10.9 96 1/23/2011 ford.com 5 2.8 96 1/24/2011 ford.com 2 2.9 95 1/29/2011 edmunds.com 46 15.0 90 1/29/2011 edmunds.com 40 29.2 90 #AcxiomAS3 7
  • 8. A Consumer’s Online Shopping Journey (2) February, 2011Sessions: 7 Page Views: 347 Time – Minutes: 218.6 Web visit Domain Page Time - timetop SearchInfo Date Views minutes urchase 2/3/2011 edmunds.com 7 4.2 85 2/3/2011 ford.com 58 42.0 85 2/3/2011 ford.com 13 15.6 85 2/4/2011 ebay.com 1 0.7 84 2/8/2011 kbb.com 60 17.5 80 yahoo:bbb blue book vehicles 2/8/2011 ford.com 49 13.8 80 yahoo:ford vehicle phone service 2/10/2011 edmunds.com 3 4.4 78 2/13/2011 ford.com 22 19.3 75 2/13/2011 ford.com 7 12.3 75 2/20/2011 motors.shop.ebay.com 1 0.9 68 yahoo:snap on 47cp pliers 2/24/2011 ford.com 10 6.7 64 2/24/2011 ford.com 1 0.8 64 2/24/2011 edmunds.com 43 4.7 64 2/24/2011 edmunds.com 4 11.0 64 2/27/2011 ford.com 21 29.7 61 2/27/2011 ford.com 35 32.0 61 2/27/2011 ford.com 12 3.1 61 #AcxiomAS3 8
  • 9. A Consumer’s Online Shopping Journey (3) March, 2011 Sessions: 13 Page Views: 145 Time – Minutes: 69.4Web visit Domain Page Time - timetop SearchInfo Date Views minutes urchase 3/1/2011 ford.com 60 18.2 59 3/2/2011 edmunds.com 36 2.8 58 3/2/2011 edmunds.com 1 0.7 58 3/2/2011 edmunds.com 11 2.4 58 3/2/2011 autos.com 1 1.0 58 yahoo:"how to transfer" ford extended service plan 3/3/2011 ford.com 5 6.2 57 yahoo:extended services plan ford "how to transfer" 3/8/2011 ford.com 1 2.3 52 yahoo:at&t sync phones ford 2011 3/10/2011 edmunds.com 8 2.0 50 3/10/2011 edmunds.com 8 1.0 50 3/18/2011 ford.com 1 3.2 42 yahoo:ford sync phone 3/21/2011 ford.com 11 9.2 39 yahoo:2011 ford explorer manual 3/21/2011 ford.com 1 17.5 39 yahoo:2011 ford explorer manual 3/22/2011 ford.com 1 3.1 38 #AcxiomAS3 9
  • 10. A Consumer’s Online Shopping Journey (4) Shopping Journey Starting DateVehicle Purchase: Ford Midsize SUV: Explorer #AcxiomAS3 10
  • 11. Summarized On-line Signals from a Shopping Journey #AcxiomAS3 11
  • 12. Who Is That Shopper
  • 13. Detecting Shopping Signals for the Prediction of Purchase Timing#AcxiomAS3
  • 14. Pattern Recognition from the Online Shopping Footprints eventualpurchasemake1 shortdotpattern Chevrol et b.....b..bt.....................................................P Chevrol et c....c.t.c..c.c.t...c..........c.c..cS.t.S..c..P Chevrol et t.tc................................t.t.t.P Chevrol et t..t...t.t.t.t.......P Chevrol et t..P Chevrol et c.t.t.t.B.Bt.t....t.t..t.P Chevrol et s b.........................c.t...t.tc.c..P Chevrol et t.t..t...s ..P Chevrol et tb.t..s cs c..s .....s .....................t......................P Chevrol et b....b..b..............b....b..b...P Chevrol et bBt.bBt...Bb.t.P Chevrol et t.t....bt......S...s St.t......t..P Chevrol et B.t.B..............P Chevrol et t.ctc.t.t.t......................................................P Chevrol et b.............b......c....t.............................P B – Visit to site of brand purchased S – Search to site of brand purchased b – Visit to site of a different brand s – Search to site of a different brand t – Visit to a third-party site c – Search to a third-party site #AcxiomAS3 14
  • 15. Defining Shopping Metrics / MeasurementShopping Intensity• A function of # of sessions, # of seconds, # of page views)• Hypothesis: – As a shopper moves closer to a purchase decision point, the level of on-line shopping activities increases noticeably, serving as an indicator of purchasing timingShopping Focus• A function of # of brands, # of domains• Hypothesis: – As a shopper moves closer to a purchase decision point, the shopping becomes more focused – The brands reviewed and the domains visited decreases over the shopping journey, leading to the final brand selections #AcxiomAS3 15
  • 16. Defining Shopping Metrics / MeasurementMeasurement Framework• Moving window: 3 days• Window shifting unit: 1 day• Purchase timing signal definition: – Type #1: Increase in SI (Shopping Intensity) or SF (Shopping Focus) from previous time – Type #2: SI exceeds a threshold level – Type #3: Either #1 or #2• Signal occurrence: First, second and third signal• Performance metrics: Median Time to Purchase (MTTP) at the brand, brand family (corp or country of origin) and product category levels #AcxiomAS3 16
  • 17. Defining Shopping Metrics / Measurement Experiments Time Window 7 3 Shifting Size 7 1 Signal Definition 1 2 3 Signal Occurance 1 2 3Measurement Level Veh Brand Country of Origin Category Best Configuration Window size: 3 Window shifting unit: 1 Signal Definition: 2 Signal Occurrence: 1, 2, 3 Measurement Level: All #AcxiomAS3 17
  • 18. Sample Test Results (Shopping Intensity) Coverage: % of population with the signal strength at above the indicated level Median: Median # of days to purchase from the signal detection time#AcxiomAS3 18
  • 19. PredictingPurchasing Decisions onChoice of vehicle Brand #AcxiomAS3
  • 20. Research Findings• On-line signal/noise ratio very low on brand choices• Cross-shopping at third-party sites leads to most of the switching of interests from those brands considered early on in the shopping journey• Further shopping activities at dealers’ sites can be a more reliable indicator, but often too late and too difficult to influence the purchase decisions• On-line shopping behavior provides more reliable predictions of purchasing timing window, whereas Acxiom’s syndicated propensity model scores worked better in predicting the brand choices #AcxiomAS3 20
  • 21. Acxiom Syndicated Model Propensity Scores Credit Communications Auto Purchase Savings and Retail Product Investment In Market Timing Categories Card Issuer Card TypeManufacturer Footwear Line Vehicle Type Investor Type Apparel Line Retail Store Type Product Propensity Brand Affinity (ownership & usage) Type of Travel Type of Healthcare Airline Member Appliance Line Consumer Electronics Type of Mobile Device Email Online Phone Direct Mail InternetOutdoor Media Usage Shopping Channel Mobile Propensity Preference Print Radio Email Direct Mail Primetime TV Daytime TV Brick & Mortar Net Worth Technology Fashion Conscious Adoption Upscale Hotels Retail Spend Attitudes & Spending Index Social Home Improvement Behaviors Mobile Warehouse Club Heavy Transactors Green Economic Stability
  • 22. Brand Prediction Performance• Using the highest scored brand as the prediction of the final brand purchased, the prediction is correct 30% of the time• Using the top three highest scored brands as the prediction of the consideration set, the prediction is correct 45% of the time #AcxiomAS3 22
  • 23. Brand Prediction Examples #AcxiomAS3 23
  • 24. Research Findings• With analytically derived shopping signal measures (Shopping Intensity and Shopping Focus), automotive purchasing can be predicted as they approach at or below 30 day window• With automotive brand affinity propensity scores, automotive brand purchase choice can be predicted at 30% accuracy level, and the final brand is a part of the predicted top-three consideration set at 45% accuracy• More research will be required into price-sensitivities or willingness-to-pay for vehicles or different configurations #AcxiomAS3 24
  • 25. Potential Applications• Audience selection for on-line campaigns• Collaborative Targeting with major on-line publishers (especially those 3rd party information sites) to selectively engage the audience with best tailored display advertising• Combine on-line intelligence about shoppers with off-line data about prospects and customers for 360 degree view/insights in terms of their demographics, purchasing transactions, consumption behavior, and predicted attitudes towards brands• Enable integrated multi-channel marketing including on-line display, website dynamic personalization, SEO, email, DM, Call Center, and local agent engagements #AcxiomAS3 25
  • 26. Thank You! Jie Cheng, jie.cheng@acxiom.com Ranjan Butaney, rbutaney@compete.com#AcxiomAS3

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