Analytics across the Customer Lifecycle
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This document discusses how analytics can be used across different stages of the customer lifecycle, including acquisition, usage, cross-sell/upsell, retention, and advocacy. It provides examples of how analytics have helped increase response rates, perform behavioral segmentation, implement cross-selling, predict churn, and analyze sentiment. The document emphasizes that analytics can help solve business problems by gaining insights from customer data at each stage of the lifecycle.
Analytics across the Customer Lifecycle
- 1. Analytics across the customer lifecycle Nirmal Palaparthi 21 July 2015
- 2. Why is Analytics important? • Latest buzzword? • Provides jobs? • Solves business problems
- 3. Customer lifecycle • Acquisition • Usage management • X sell and Upsell • Retention • Advocacy
- 4. Acquisition Analytics • Start from prospects • Goal: better ROI • Same budget more conversions or • Lower budget same conversions • Solutions • Response models, Conversion models, Funnel analytics • Leading bank increases mailer response rate from 4% to 19%
- 5. Well…. data is kinda important!
- 6. Usage Analytics • Different strokes for different folks • Behavioral Segmentation • Parameters for segmentation that reflect useful behaviour • Watchout: Demographic segmentation is useful in product design, rarely in usage
- 7. Behavioral Segmentation Credit Card portfolio segmentation construct
- 9. X Sell and Upsell Analytics • right product to the right customer at the right time using the right channel • Selling to existing customers is far easier than selling to new customers • Incoming channels make for better campaigns • Examples: Bancassurance, beer and diapers • Solutions: Market Basket Analysis, X-Sell modeling
- 10. Xsell and Upsell in action Product Details Product DetailsXSell Upsell
- 11. Retention Analytics • Leaky funnel • Different definitions of attrition • NUNP, Dormancy, Inactive • Early warning signals • Example: Attrition models: Photo card
- 12. Churn Model in action
- 13. Advocacy Analytics • Member get Member • Reviews are powerful, even with strangers • Solutions: Sentiment analysis, Social Listening
- 14. Sentiment Analysis has multiple benefits Source: Slideshare: How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews (RE2014 Paper)
- 15. uh… and one more thing…