LEARN HOW LENOVO & Tesco MAKE ADOBE & ENTERPRISE DATA ACTIONABLE IN HADOOP

Transcription

1 LEARN HOW LENOVO & Tesco MAKE ADOBE & ENTERPRISE DATA ACTIONABLE IN HADOOP David Searle General Manager, Ashish Braganza Global Business Intelligence Simon Ricketts Data, Analytics and Optimisation

2 Log in to your account using the details sent to you when you registered Enable social and select the room for your session e.g. Room CS8 Ask questions and answer polls Don t forget to tweet using

3 LEARN HOW LENOVO & Tesco MAKE ADOBE & ENTERPRISE DATA ACTIONABLE IN HADOOP David Searle General Manager, Ashish Braganza Global Business Intelligence Simon Ricketts Data, Analytics and Optimisation

4 Predictive Behavioural Analytics 4

5 17% Product / Service Innovation 17% Customer Service Across All Touchpoints 29% Source : E-Consultancy : Digital Intelligence Brief 2017 Digital Trends Gartner

6 17% Product / Service Innovation 89% 17% Customer Service Across All Touchpoints Source : E-Consultancy : Digital Intelligence Brief 2017 Digital Trends Source - E-Consultancy Gartner : Digital - Intelligence Brief 2017 Digital Trends Gartner Customer Experience

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11 ACHIEVING the single digital customer view Simon Ricketts Data, Analytics and Optimisation Strategist

12 Who We Are 470,000 colleagues 3,500 stores in the UK 11 countries 310,000+ colleagues in the UK 78 million shopping trips weekly

13 Our Purpose Serving Britain s shoppers a little better every day

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16 Building the single Digital Customer View Clickstream Data Clickstream Data BigQuery BigQuery Tesco Analytics Platform

17 Clickstream Data Datafeed BigQuery BigQuery + Tesco Analytics Platform

18 Integrate enterprise data Consolidate disparate RSID s Map Adobe Schema to Tesco Behavioral Schema Consolidate multiple clickstreams Overlay Adobe classifications

19 WHERE WE ARE NOW

20 LET THE Algorithms DO THE TALKING Ashish Braganza Director, Global Business Intelligence

21 Lenovo 2017

22 Business Question: Can we identify today s visitors that will account for 80% of future purchases? Lenovo 2017

23 Use Case: Display Retargeting Optimization Lenovo 2017

24 So Why Algorithmic Retargeting? Lenovo 2017

25 Algorithmic vs Rule-Based Rule-Based Segments are heuristic Building rules are manual and can get complicated very quickly Managing rules requires ongoing attention Hard to control rule-based audience size o Some rules create small audience sizes e.g. Cart Abandoners Algorithmic Segments are data driven Algorithms manage the complexity without user intervention Continues to relearn as data changes Knowing purchase likelihood scores enables value-based bidding strategies Easy to adjust audience sizes o Other rules pick up everyone e.g. Homepage or Product pages Lenovo 2017

26 So we first did some fancy math S... Lenovo 2017

27 Future Purchasers Business Question: Can we identify today s visitors that will account for 80% of future purchases? Without any rules or algorithms we need to randomly pick 80% of the visitors 100% 90% 80% 70% 60% 50% 40% Random 30% 20% 10% 0% 0% 20% 40% 60% 80% 100% Today s Visitors * Test results validated for a 30 day period with +1M visitors with over 95% confidence level Lenovo 2017

28 Future Purchasers Business Question: Can we identify today s visitors that will account for 80% of future purchases? Without any rules or algorithms we need to randomly pick 80% of the visitors What s the theoretical best case? 100% 90% 80% 70% 60% 50% 40% Random 30% 20% 10% 0% 0% 20% 40% 60% 80% 100% Today s Visitors * Test results validated for a 30 day period with +1M visitors with over 95% confidence level Lenovo 2017

29 Future Purchasers Business Question: Can we identify today s visitors that will account for 80% of future purchases? Without any rules or algorithms we need to randomly pick 80% of the visitors What s the theoretical best case? 100% 90% 80% 70% 60% 50% 40% 30% Random Best Case 20% 10% 0% 0% 20% 40% 60% 80% 100% Today s Visitors * Test results validated for a 30 day period with +1M visitors with over 95% confidence level Lenovo 2017

30 Future Purchasers Business Question: Can we identify today s visitors that will account for 80% of future purchases? Without any rules or algorithms we need to randomly pick 80% of the visitors What s the theoretical best case? Test 1: How good are rules? 100% 90% 80% 70% 60% 50% 40% 30% Random Best Case 20% 10% 0% 0% 20% 40% 60% 80% 100% Today s Visitors * Test results validated for a 30 day period with +1M visitors with over 95% confidence level Lenovo 2017

31 Future Purchasers Business Question: Can we identify today s visitors that will account for 80% of future purchases? Without any rules or algorithms we need to randomly pick 80% of the visitors What s the theoretical best case? Test 1: How good are rules? Test 1*: Rule-based Retargeting Measurements Segment Visitors (Cumm.) Future Purchasers (Cumm.) Conversion Rate (Cumm.) Cart Abandoners 8% 21% 7.0% Product Viewers 44% 42% 2.4% Home 57% 43% 1.9% Other 100% 100% 2.5% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0% 20% 40% 60% 80% 100% Today s Visitors Rule Based Random Best Case * Test results validated for a 30 day period with +1M visitors with over 95% confidence level Lenovo 2017

32 Future Purchasers Business Question: Can we identify today s visitors that will account for 80% of future purchases? Without any rules or algorithms we need to randomly pick 80% of the visitors What s the theoretical best case? Test 2: How good are algorithmic segments? 100% 90% 80% 70% 60% 50% 40% 30% Rule Based Random Best Case 20% 10% 0% 0% 20% 40% 60% 80% 100% Today s Visitors * Test results validated for a 30 day period with +1M visitors with over 95% confidence level Lenovo 2017

33 Future Purchasers Business Question: Can we identify today s visitors that will account for 80% of future purchases? Without any rules or algorithms we need to randomly pick 80% of the visitors What s the theoretical best case? Test 2: How good are algorithmic segments? Test 2: Algorithmic Retargeting Measurements Segment Visitors (Cumm.) Future Purchasers (Cumm.) Conversion Rate (Cumm.) High (>0.9) 3% 55% 41.3% Medium ( ) 9% 80% 21.7% Low ( ) 22% 90% 10.5% Very Low (<0.3) 100% 100% 2.5% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 0% 20% 40% 60% 80% 100% Today s Visitors Algorithmic Rule Based Random Best Case * Test results validated for a 30 day period with +1M visitors with over 95% confidence level Lenovo 2017

34 The Constructs of the Algorithm The model learns from visitor s past online behavior (last 7 days) Every hour the algorithm estimates the visitors likelihood to make a future purchase Adobe Analytics data used All page views All events The model relearns every week Testing 30 days with +1M visitors Lenovo 2017

35 And the results were Lenovo 2017

36 Bigly Huuuge!! Lenovo 2017

37 It s the Dogs Lenovo 2017

38 A/B Test Design and Methodology Rule-based Algorithmic - Anyone who added a product to the shopping cart - Anyone who viewed a Product Page (Yoga, X, Y, etc.) - Anyone who visited the Homepage - High: 15X over average conversion rate - Medium: 3X over average conversion rate - Low: 0.5X over average conversion rate Splits 50% Splits 50% Population 6.1M Population 6.1M Duration 6 Weeks Duration 6 Weeks Metrics Impressions, Conversion, Revenue & Expenses Metrics Impressions, Conversions, Revenue & Expenses Geo United States Geo United States Lenovo 2017

39 Results and opportunities for efficiencies identified Reduced display ad costs by over 97% maintaining the same conversion rate. Impression counts for algorithmic segments significantly smaller yielding a nearly equal number of purchasers. Duration: 6 weeks Geo: United States Segments Visitors Purchasers Conversion Rate Net Spend Impressions Algorithmic 6.1M 39.5K % $8.3K 2M Rule-Based 6.1M 39.4K % $356K 86M Total 12.2M 79K % $364K 88M Lenovo 2017

40 How Algorithmic Retargeting Works Lenovo Hadoop Cluster Behavioral Traits Adobe Marketing Cloud Audience Manager DSPs DSPs Ads Clickstream Analytics Impressions Publishers - Behavioral Schema - Machine Learning - Audience Manager API Tags

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43 Session Prize 9