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Scoring Customer Churn Risk
- 1. Scoring Customer Churn Risk A Customer Use Case An overview Israel Kloss Findable Consulting @risraelkloss Israel@findable.me
- 2. *A.k.a. “The Godfather of Web Analytics” Jim Sterne* is concerned! He has a client with a big challenge.
- 3. Jim’s Task: Help Build a Strategy Measuring: ●Customer acquisition ●Customer lifecycle ● Customer churn prevention
- 4. DATA SILOS! ● Marketing Data Problems ● Engineering Data Problems ● Finance Data Problems Jim digs in and his alarm bells start going off… department by department:
- 5. Jim's client has: Let’s Dive in! Customer Churn Prevention 1. A technical problem 2. A data sharing problem
- 6. Customers come and customers go. What if you could know before they go? Customer Churn Prevention
- 7. Customers are telling you their intentions. Let’s look at some. Customer Churn Prevention
- 8. We collected a set of data on CUSTOMERS WHO LEFT the Company. Step 1: Data Clarity, Collection & Cleaning
- 9. Step 2: Using that data set of churning customer behaviors, we CALCULATE A PROBABILITY MODEL to monitor customer’s behaviors. Calculation (client references hidden)
- 10. But R is a bit beyond the scope of this webinar. Here’s a peek at how the “Flight Risk” Scores (Churn Risk) can be generated
- 11. Jim is still concerned! We aren’t done! Data Sharing is next!
- 12. Data Sharing is Data Caring! Using internal customer data and APIs, we connected multiple systems across departments. Ahhh… DASHBOARDING Happiness! Step 3: Data Sharing Go Cross-Departmental I Data Stitching
- 14. Data Sharing Continued After a customer completes some behavior related to churn, APIs are set to trigger unique alerts to each appropriate department.
- 15. We started DASHBOARDING CHURN INDICATORS across multiple departments. Starting with the CUSTOMER SERVICE TICKETING SYSTEM (client name hidden) Churn Risk Dashboard Step 4: Dashboarding!
- 16. Next, we started MONITORING the client’s AT- RISK CUSTOMERS on social media to collect more churn data. (client name hidden) Dashboarding Continued
- 17. Jim Seems Happier! (But there’s a more work to do)
- 18. What Now? Still Not Done! ●Customer acquisition ●Customer lifecycle Customer churn prevention
- 19. Customer churn prevention What’s Next? ●Customer acquisition ●Customer lifecycle Remember This?
- 20. Jim Says “Stage 1 of 3 Complete” (for now)
- 21. Happy Customer, Happy Life. Say hi to Marcos (our happy customer).
- 22. ● social Media Dashboard (Full Contact + Hootsuite) ● Customer Service Integration (Zendesk) ● Sales Integration (Salesforce, Builtwith & Evergage) ● Marketing Integration (Marketo, Adobe Analytics & Evergage) ● Finance Integration (Freshbooks or Netsuite) ● Engineering Integration (Segment.io + Evergage) More Helpful Products (some with APIs) Considered For This Project: Big ML, Zapier, CloudHQ, Postman, Good Data, Tableau & Leftronics Wrap-up: Products That Integrate Nicely
- 23. Questions? Special Thanks to Jim Sterne for the fine photography.
- 24. Feel free to contact me with any questions: israel@findable.me http://bit.ly/DAA_webinar You can download this deck at
Editor's Notes
- Marketing is buying a Marketing Automation tool. But they bought the Marketing Automation tool with the API that nobody knows how to harness. That’s a Marketing data problem. Engineering is all excited about their latest software upgrade. But Customer Service can’t see the customer’s login records, and they don’t know what the customer experience or history has been when they get a complaint. That’s an Engineering data problem. Finance just bought a new billing & renewals system but didn’t tell anybody what they were buying. And nobody verified that it will work with the Marketing Automation API or with the Customer Service Ticketing System API or Engineering’s customer system (unpaid customers need payment reminders). That’s a Finance data problem.
- I'm going to dive a bit into the technical issue and offer a customer churn solution and then take a data stitching approach to tackle the data sharing problem
- Because we connected the Marketing Automation system to the CRM, we are now ready to do full analysis of the customer acquisition. And with a customer data warehouse also connected to the CRM, there there is also the opportunity to marry the customer churn data with the customer acquisition data -- giving a view into the full customer lifecycle.