The Recommendation Conundrum - Spree Conference

Transcript

2. Hype or reality?
3. Why Recommendations? Email Response Rates Customer Acquisition Average Order Value Purchase Frequency Customer Lifetime Value Revenue Bottom Line: Boost ROI
4. Purchase Share How does it work? RELEVANCE ENGAGEMENT Recommendations It Drives Engagement
5. How they’re built? Collaborative - User Based. “People like you like…” Content - Feature Based. “Other music like this…” Contextual - Action Based. “You viewed x, you might also want to view a similar item.” Most choose one of three frameworks – or a hybrid
6. Data Collection Explicit (actively given) - Purchase history - Likes/Reviews - Preferences indicated (e.g. wish lists) Implicit (behavioral) - Clickstream - Abandoned cart - Geolocation - Frequency of visits They define the data set they wish to collect
7. Algorithms They build a model to predict behavioral outcomes Define the variables and desired outcome Assign a weight to the variables Develop a propensity score (likelihood to buy X or click on Y) Test
8. Best Case Example Customer A’s Data: - Items purchased: The Four Hour Work Week. - Items owned (user defined): null - Items rated (user defined): Cannon camera - Items liked (user defined): null - Browsing history: baby clothes (2x), flat screen tv (4x), mystery novels (8x) Customer A’s Recommendation: - The Lean Startup. - Camera case. - The Hunger Games.
9. So What’s Missing? ??? DATA ALGORITHM REC
10. Individual Relevance Purchase Share RELEVANCE ENGAGEMENT Recommendations The individual DATA ALGORITHM REC
11. Why? Garbage in, Garbage out. Imperfect Data - Linking customer behavior across channels is a challenge. - Lack of scalable data to create valid propensity scores. - Expiring cookies causing gaps in information - 3rd party cookies don’t provide all data
12. The Result Segments based on Look-Alikes Reliance on: • 3rd Party Demographic Data (Age, Gender, Location, Income level, etc) • Explicit Data • Merchandising strategy
13. Amazon’s Example
15. So How Can It Be Made Better? The Individual DATA ALGORITHM REC
16. The Interest Graph Focus on Their Behavior Psychographic data - Frequency of clicks/opens/ site visits - Time of day/year they purchase vs. look - Device used - Items viewed/clicked - Frequency of items viewed - Purchases made - Abandoned purchases
17. How? Build for the Individual Create a Unique Identifier - email address Primary Cookies - appended to email address for security Develop a Behavioral propensity model - reduce reliance on demographic variables
18. But, that only takes you so far… The Individual DATA ALGORITHM REC
19. Engagement Purchase Share RELEVANCE ENGAGEMENT Recommendations The individual DATA ALGORITHM REC
20. Leverage Data Across All Channels TREAT customers holistically by talking with them across email, website, mobile and social. GIVE them a reason to come back to you How?
21. Dynamically Adapt To Serve Up Different Content Newsletter Landing Page Recommendation
22. How? Respect Your Users LISTEN to where and when they respond to you TRANSLATE it into meaningful actions RESPOND to each individual appropriately
23. Thank You. Neil Capel neil@sailthru.com Sailthru Built for Relevance. :-) DATA ALGORITHM REC