IBM Customer Analytics Strategy

This is a draft of the cross-business unit IBM Customer Analytics strategy that I led post Tealeaf's acquisition by IBM. This strategy included all of IBM's analytics assets at the time, Tealeaf, Coremetrics, Cognos, etc.

Transcript

1. © 2014 IBM Corporation Predictive Customer Intelligence • Future Buying Insight • Anticipate and delight customers with solutions • Buying propensities and patterns • Product preferences • Related products Digital Analytics • Quantitative Insight • Web site and mobile traffic data • Customers in purchase funnel • Completed transactions • Conversion metrics Optimizing Customer Experience Requires Combining Digital, Behavioral, Sentimental & Predictive Analytics 1 Customer Behavior Analytics • Qualitative Insight • Surface Customer struggle on digital channels • Session replays to understand actual customer journey • Identify impacted customers Sentiment Analytics • Soclal Media Insight • Prevailing sentiments • Relevant relationships • Potential risks
2. © 2014 IBM Corporation Customer Analytics For The Enterprise Understanding The Context of All Customer Interactions Has Profound Business Impacts § Increased Revenue – Better Conversion/Adoption – Higher Lifetime Customer Value – Increased Share of Wallet – More Repeat Customers/Buyers § Higher Customer Satisfaction – Reduced Customer Struggle & Friction – Better Net Promoter Scores – Reduced Churn; Higher Customer Loyalty – Understanding Social Commentary & Sentiment § More Effective Customer Acquisition – Better Targeting Based on Intents/Interests § Reduced Operational Costs – Higher Call Avoidance/Deflection – Faster Site Maintenance, Problem Resolution 2 Customer WWW
3. © 2014 IBM Corporation CA Use Case 1 Customer Acquisition - Retail March 2014 INTERNAL USE ONLY – Do not Distribute
4. © 2014 IBM Corporation Step 1 - Analysis of Keywords data with Digital Analytics PPC and SEO keywords Site search keywords
5. © 2014 IBM Corporation Step 2 – Develop list of sites where the potential customers hangout with SMA using DA keywords list IBM Digital Analytics Keywords data IBM SMA Identify potential customers List of websites
6. © 2014 IBM Corporation Step 3 – Targeting the websites with targeted display ads List of websites Display ad targeting/retargeting
7. © 2014 IBM Corporation Step 4 – Measuring Attribution Digital Analytics
8. © 2014 IBM Corporation Step 4 – Measuring Attribution xxxx
9. © 2014 IBM Corporation Step 4 – PCI Optimizes Attribution 9 WWW WWW WWW PCI IBM Digital Analytics IBM SMA Attribution Marketing Center
10. © 2014 IBM Corporation Questions? 10
11. © 2014 IBM Corporation CA Use Case 2 Customer Retention - Telco March 2014 INTERNAL USE ONLY – Do not Distribute
12. © 2014 IBM Corporation Step 1 – Building complete customer profile 12 Repeat customer clicks on a link from a trusted brand tweet •User lands on a TL and DA monitored site • DA matches cookie data to session ID •TL session ID synched with DA session ID •TL captures all behavioral data • Customer opted in with social media IDs Environmental data
13. © 2014 IBM Corporation Step 2 – Understand Customer Behaviors 13 Browsing and adding products to shopping cart TL captures customer behaviors Remove an item Check out
14. © 2014 IBM Corporation Step 3 – Understand Customer Profile 14 Browsing and adding products to shopping cart DA captures customer profile Check out
15. © 2014 IBM Corporation Step 4 – Understand User Sentiments 15 SMA captures customer sentiment: brands, preferences, and challenges Love the new Android phone from Telco Especially the gold one But will have to wait for sales…L
16. © 2014 IBM Corporation Step 5 – Complete Insights 16 TL, DA, and SMA provides complete insights Behaviors Sentiments Profiles
17. © 2014 IBM Corporation Step 6 – PCI improves Meaningful Engagement 17 Behaviors PCI Sentiments Profiles Xtify 10% off on Gold Android phone from Telair 10% off on Gold Android phone from Telair
18. © 2014 IBM Corporation CA Use Case 3 Reduce Churn – Financial Services March 2014 INTERNAL USE ONLY – Do not Distribute
19. © 2014 IBM Corporation Step 1 – Capturing All Interactions 19 Customer clicks on a link from an email from her bank Bank site deploys IBM CA that captures both Behavior and Profile data Environmental data Narrative: •Customer receives an email offer for a low rate for refinancing home loan from her bank • She is interested and clicks on the link from her smartphone to find out more information •Bank site is monitored by IBM CA that includes both Behavior and Profile data • All interactions are being captured by IBM CA (behaviors and profiles) •Being an existing customer, she as opted in with contact information including social media IDs
20. © 2014 IBM Corporation Step 2 – Understand Customer Behaviors and Profiles 20 Downloaded native app IBM CA captures customer behavior and lifecycle data Started the refinancing application process Stopped using the app X Called Contact Center Narrative: •She saw the call to action to down load the newly available mobile app. She proceed with the download and installation process. •She logs in using her username and password • She proceeds to start the quick quote option in the app •At the enter the house address field she keeps receiving an error messaging: invalid address •After several tries she called the contact center. She abandoned the process
21. © 2014 IBM Corporation Step 3 – Understand User Sentiments 21 IBM CA captures customer sentiment: brands, preferences, and challenges Looking into new refi rates from Finance1 Especially the 30 fixed rate But their app sucks…L Narrative: •She tweeted her friends about her experience. •With the opted in social media IDs IBM CA can monitor for customer sentiment
22. © 2014 IBM Corporation Step 4 – Complete Insight 22 IBM CA provides complete insight Behaviors Sentiments Profiles Narrative: •Combining Behavior, Profile, and Sentiment data IBM CA provides complete insight into customer interactions.
23. © 2014 IBM Corporation Step 5 – PCI Identifies Potential Churn Segments 23 Behaviors Sentiments Profiles PCI Narrative: •PCI provides deep insight analytics leveraging Behavior, Sentiment, and Profile data •Discovers that home loan is the key difference between churned and loyal customers •Need to improve the refinancing product and processes
24. © 2014 IBM Corporation Step 6 – IBM CA Uncovers Challenges 24 Home Loan is key loyalty metrics PCI Behaviors •Mobile app address error struggles •Website struggles Profiles •Churned segment profiles •Lifecycle data Sentiment •Mobile App issues •Quick quote did not work on site •Rates are good Narrative: •Sentiment uncovers 3 themes around mortgage: •Mobile app sucks •Website quick quote did not work •Rates are competitive •With data from Sentiment, Profile provides key segments on abandoned mortgage new/refinancing processes •Leveraging segments from Profile, Behavior analyzed specific interaction and found •Mobile app has non-descriptive error •Error was caused by “-” (program logics) •Quick quote from website has the same issue •Need to improve the refinancing product and processes
25. © 2014 IBM Corporation Step 7 – Actionable insight 25 Home Loan key metrics PCI Behaviors •Mobile app address error struggles •Website struggles Profiles •Churned segment profile •Lifecycle data IT •Fixed “-” program logics Sentiment •Mobile App issues •Quick quote did not work •Rates are good Social Media •Check out our new app •Same great rates Real time interactions •Check out our new app •Same great rates WWW