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Replace attribution myths with optimal allocation

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Does your organization get stuck with attribution and marketing mix modeling? This presentation shows you how companies use both consumer-level and aggregate data to optimize their media allocation with double-digit performance gains. The cases include an online retailer and L'Occitane's multi-channel and multinational optimization of customer segment targeting, which got the 2018 MSI/Informs Practice Prize Award. I also offer a conceptual and modeling framework to your company

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Replace attribution myths with optimal allocation

  1. 1. Prof. Koen Pauwels Replace attribution myths with optimal media allocation
  2. 2. Agenda • The New Consumer Journey • Current State of Attribution Research • How to Optimally Allocate Media Budgets • Case 1: Individual Consumers for L’Occitane • Case 2: Aggregate Data on 11 Ad Forms for Online Retailer • Implications: Replace Common Attribution Myths
  3. 3. Reaching the right customer with the right offer at the right time …
  4. 4. Past Metaphor: The linear purchase funnel Purchase LoyaltyConsiderationAwareness
  5. 5. Initial Consideration Set Moment of Decision Loyalty Loop Post Purchase Experience Ongoing process Active Evaluation Information gathering, shopping McKinsey 2009 The Consumer Decision Journey Has the Web made the funnel fat? Purchase LoyaltyConsiderationAwareness
  6. 6. Implications for managers “Fat Loop” means that consumers 1) Dynamically go back and forth between sources 2) Hesitate to purchase, then revisit Which means that marketing actions 1) Can influence consumers on many occasions 2) Can help consumers gain confidence in their decision making
  7. 7. What do we already know? • Display ads are effective in early stages; search is effective throughout the journey (Abhishek et al. 2012) • Consumer finds choice in mid to late search, then revisits chosen product (Bronnenberg et al 2016) • Branded keywords are less effective for large sellers (eBay) than non-branded, but very effective for small providers of urgent products, e.g. office furniture (Pauwels et al. 2016)
  8. 8. What don’t we know (yet)? • How responsive are different types of consumers to online and offline ads ? • Which online advertising forms are more effective, when and where in the journey? • How to (re)allocate ad budgets accordingly?
  9. 9. We Need a Sales Response Model • How does your sales respond to marketing actions, controlling for outside influences? • Allows us to calculate ELASTICITY o Elasticity = % sales lift for a 1% increase in a media’s budget • Elasticity is the key input for optimal allocation
  10. 10. How to Allocate Budget: Ratio of Elasticities • Analysis shows doubling on comparison ads lift sales by 15% (elasticity = 0.15) while doubling ‘retargeting’ increases your sales by only 5% (elasticity = 0.05) • How should you divide your budget of $ 100,000 ? 10 • Sum up elasticities: 0.15 + .05 = .20 • Ratio of elasticity = 0.15/0.20 = 75%, 0.05/0.20 = 25% • Result: Comparison spend allocated $ 75 K and Retargeting spend allocated $ 25 K
  11. 11. • Natural ingredients cosmetics and well-being retailer • €1.3 billion revenues, €168 million profits (2017) • 8,500 employees in 90 countries with 3,037 stores • Multichannel: offline and online sales channels L’Occitane Case Study 11
  12. 12. Primary dataset 12 • 84,110 randomly selected customers from 6 countries • Germany, Spain, France, Great Britain, Italy, USA • Purchase history: offline sales, online sales, and discounts • 4 years of data, 2011-2014 • Marketing actions: direct mail and email • 2 years of data, 2013-2014
  13. 13. Current Approach Follows Conventional Wisdom 13 Marketing Channel Direct Mail Email Customer Segments High Value All Segments $$$ $$$
  14. 14. Modeling Approach Quantify customer value Create customer segments Evaluate responsiveness to marketing Assess sales variation drivers Predict sales Steps Methodology RFMC framework Cluster analysis Hierarchical linear model Cross-Random Effects model Forecasting accuracy comparison Description Quantify the values of R-F-M-C for each individual customer Segment the customer base according to customer value and country Evaluate responsiveness to marketing actions at different aggregation levels: time, customer value, and country Assess the extent to which sales variation can be explained by time, customer value, and country Compare forecasting accuracy of our model to benchmarks 1 2 3 4 5 Period Calibration Calibration Estimation Estimation Hold-out Allocate marketing resources Relative elasticities Reallocate marketing actions within each country keeping budget constant 6 Estimation Design and implement a field experiment 7 Descriptive Predictive Prescriptive
  15. 15. RFMC distribution 15 1 0246 %customers 2011w402012w12012w142012w272012w402013w12013w132013w26 R R 020406080 %customers 0 20 40 60 80 100 F F 01020304050 %customers 0 1000 2000 3000 4000 5000 M M 0246810 %customers 0 .2 .4 .6 .8 1 C C RFMC distribution (weekly values) Calibration period: 2011w39-2013w30. N=36,244 98 84 70 56 42 28 14 1 R - Recency F - Frequency M - Monetary Value C - Clumpiness
  16. 16. • Segmentation: customer value level within each country • K-means on standardized RFMC metrics • 4 clusters (36k customers) + dormants (28k) and prospects (19k) • Dissimilarity measure: Euclidean distance • Starting points: 20%, 40%, 60% and 80% values of standardized RFMC 16 Cluster analysis2
  17. 17. Customer segments description 17 Prospects Dormants Non-recent low value Recent low value Medium value High value Total Germany 22% 33% 8% 10% 24% 4% 10,000 Spain 16% 36% 12% 8% 25% 4% 10,000 France 26% 36% 8% 6% 22% 3% 14,111 Great Britain 10% 40% 10% 8% 26% 4% 20,000 Italy 23% 31% 12% 8% 25% 1% 10,000 USA 38% 26% 9% 6% 19% 2% 19,999 Recency (weeks ago) - - 79 9 33 12 38 Frequency (#) - - 1.22 1.34 1.99 8.76 2.15 Monetary value (€) - - 48.6 53.5 82.0 468.1 94.7 Clumpiness (#) - - 0.61 0.69 0.36 0.21 0.47 Note: weekly data. Individual customer RFMC values during calibration period. 2
  18. 18. Direct mail: Own-channel effects for prospects across all countries 18 3 Offline sales All countries: Germany, Spain, France, Great Britain, Italy, USA Elasticities -0.02 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 Prospects Dormants Non-recent low value Recent low value Medium value High value
  19. 19. Email: Affects across Channels and Segments 3 Offline sales Online sales USAElasticities 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.00 0.10 0.20 0.30 0.40 0.50 Prospects Dormants Non-recent low value Recent low value Medium value High value
  20. 20. Marketing Resource Reallocation 20 Direct Mail 6 Email 0% 20% 40% 60% 80% 100% Segment size Current allocation Reallocation 0% 10% 20% 30% 40% 50% Prospects Dormants Non-recent low value Recent low value Medium value High value
  21. 21. Marketing Resource Reallocation 21 Direct Mail 6 Email 0% 20% 40% 60% 80% 100% Segment size Current allocation Reallocation 0% 10% 20% 30% 40% 50% Prospects Dormants Non-recent low value Recent low value Medium value High value Revenue +16%
  22. 22. The different effectiveness of direct mail and email depending on the customer type was surprising to us. Rethinking about this finding, we have a deep and increasing interest in investing in direct mail activities for customer acquisition and inactive customers. In the Words of L’Occitane 22 “ “ Ms. Delphine Fournier CRM Senior Manager, L’Occitane
  23. 23. Study: Expand to dozens of ad forms • “The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework” International Journal of Research in Marketing • Compare the long-term effectiveness of a wide range of online advertising forms, over multiple product categories, and controlling for offline advertisements.
  24. 24. Purpose of our research • Which form of online advertising (e.g., email, display, search, comparison) is the most effective? o Effectiveness as measured by “revenue elasticity” • When do these effects take hold and how long do these effects last? • Where in the conversion funnel are these effects the strongest? o Bringing in new customers o Increasing the conversion rate o Higher average sale
  25. 25. Study Dimension: CIC vs. FIC Firm Initiated Contact (FIC) • Companies “pushing” messages to consumers. • Used to stimulate prospective customers. • Increasingly unwanted. • TV • Radio • Email • Display Customer-Initiated Contact (CIC) • Advertising triggered by (prospective) customer action. • Customer closer to purchase decision. • Higher response rates. • Less intrusive. • Search (organic & paid) • Comparison • Retargeting • Referrals
  26. 26. Study Dimension: Content-Integrated vs. Content- Separated CIC CIC - Content- Integrated • Advertising that are an integral part of a medium’s editorial content. • Search (organic) • Price comparison • Hobby sites • Referral sites CIC – Content- Separated • Advertising separated from a medium’s content. • Search (paid) • Retargeting
  27. 27. Content-Integrated CIC Example
  28. 28. Content-Separated CIC Example
  29. 29. Online Retailer’s Conversion visits conversion revenue
  30. 30. • Daily data across 5 product categories • Spend data for 11 ad activities per category • Number of visits to different website parts • Revenue per product category • Enabled the calculation of revenue elasticity by activity by category • Revenue elasticity = % performance lift for a 1% increase in the media’s budget 11/13/18 | 30 Subject: Major European Online Retailer
  31. 31. Current Marketing Allocation Portals 7% Compare 6% TV 18% Radio 14% Email 1% SEA-brand 4% SEA-product 23% Retargeting 10% Affiliates 17% Concentration in Search Engine Advertising (SEA), only 6% to Comparison sites
  32. 32. • System of equations: explain each variable by its own past and the present and past of all other variables • (eg TV à search à sale) • Captures dynamic effects from ads to web funnel stages and revenue • wear-in/wear-out • We allow customer to ‘skip stages’ • e.g. go straight to product page or checkout page 11/13/18 | 32 Method: Vector Autogression (Chris Sims) Christopher Sims receiving his Nobel prize in December 2011
  33. 33. 11/13/18 | 33 Structural Vector Autoregression (SVAR) • Block 1: no current back funnel Block 2: no current skip stages • Block 3: no dynamic back funnel Block 4: no dynamic skip stages
  34. 34. Results: Integrated > Separated > FIC 0.003 0.027 0.133 0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 FIC CICs (Content Separated) CICs (Content Integrated)
  35. 35. • Comparison sites work best for Electronics • WHY? Consumers want to compare prices for utilitarian products • Retargeting and Portals work best for Fashion • WHY? Hedonic product: fall in love but hesitate to buy à tempt again • Referral sites work best for Sports & Leisure, Home & Garden • WHY? Consumers look to authorities’ recommendations 11/13/18 | 35 Matching ad forms with categories
  36. 36. Which Ad Forms Were Effective ? • Only 53% of Firm-Initiated forms: ‘Half of my advertising is wasted’ still true today! • 60% of Customer-Initiated Content-Separated • 80% of Customer-Initiated Content-Integrated
  37. 37. 0.051 0.155 0.148 0.151 0.133 0.034 0.022 0.035 0.042 0.027 0 0.004 0.003 0.003 0.003 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 MeanAdvertisingElasticity Content Integrated Content Separated Firm Initiated Homepage Product Page Basket Checkout Revenue Where? Integrated Best in Later Stages
  38. 38. When is the largest effect ? • Same day for ALL Content-Integrated CIC forms • Same day for 80% of Content-Separated CIC forms • After 1 or 2 days for most Firm-Initiated firms
  39. 39. Why? • Content-Separated CICs interrupt: an email at work, retargeting when you are on a different purpose website, ads when organic search key • Content-Integrated CICs help with the purpose of visiting site: price comparison, portals on category with links to other websites • Firm-initiated campaigns do not catch customer at time of interest in purchase
  40. 40. How to Allocate: Ratio of Elasticities • Analysis showed doubling on comparison ads lift sales by 15% (elasticity = 0.15), while doubling ‘retargeting’ increases sales by only 5% (elasticity = 0.05) • How should you divide your budget of $ 100,000 ? • Ratio of elasticity = 0.15/0.05 = 3 • Spend 3x as much on Comparison = $ 75,000
  41. 41. Budget reallocation lifts revenues 9.6% 41 41 Revenue Impact 100.0 90.2 109.6 0 20 40 60 80 100 120 Current Allocation Last Click / Same Session SVAR 10.8% 30.8% 5.5% 6.0% 11.7% 0.7% 5.6% 10.1% 4.8% 18.7% 9.3% 44.3% 8.2% 10.7% 15.2% 6.4% 34.0% 1.2% Comparison Portals Retargeting SEA Branded SEA Product E-mail Current Allocation Last Click / Same Session SVAR
  42. 42. But, How About Synergy? • Synergy means that actions together have a stronger effect than by themselves • Sales = 2*OFFline + 5*ONline + 1 *OFFline*ONLine • In other words, lower-elasticity action helps higher- elasticity action and should therefore get a higher allocation • Example: Old Spice TV + social media campaign 42
  43. 43. Does your guy smell like the Old Spice Guy ? • Focus on key benefit: smell • Funny, creative, consistent: 1) TV ad gets reach 2) You Tube and fast response to fan tweets get engagement Doubles sales within 1 year
  44. 44. What works best for your customers? • Among Content-integrated ads: • Affiliate sites if high involvement (e.g., shoes, fashion, cars, alcohol) • Paid search, price comparison site if high need NOW (e.g., refrigerator, travel) • Among Content-Separated (e.g. retargeting) • When mood of website matches your offer 44
  45. 45. 3 attribution myths • ‘Bottom funnel’ (retargeting, comparison) ads are overvalued with last-click attribution • Switching from Last-Click to other static models fixes your marketing attribution problems • Buying & implementing an attribution system is all you need to do to improve decisions
  46. 46. Instead • Customers switch back and forth between channels & can buy way after taking ‘bottom funnel’ action • Model-based attribution quantifies your customized journey with your own data • But, it needs your goals and insights to better assist you with communicating and deciding
  47. 47. • Contact me at koen.h.pauwels@gmail.com • LinkedIn/Twitter handle: koenhpauwels • My blog: https://analyticdashboards.wordpress.com • Professional Facebook page: https://www.facebook.com/pages/Smarter-Marketing- with-Analytics-Dashboards/586717581359393 • And check out my practical book: It’s not the Size of the Data, it is How You Use it: Smarter Marketing with Analytics & Dashboards Want to learn more?

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