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
   

   View Slide

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
   

   View Slide

3. © 2014 IBM Corporation
   CA Use Case 1
   Customer Acquisition - Retail
   March 2014
   INTERNAL USE ONLY – Do not Distribute
   

   View Slide

4. © 2014 IBM Corporation
   Step 1 - Analysis of Keywords data with Digital Analytics
   PPC and SEO keywords
   Site search keywords
   

   View Slide

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
   

   View Slide

6. © 2014 IBM Corporation
   Step 3 – Targeting the websites with targeted display ads
   List of websites
   Display ad
   targeting/retargeting
   

   View Slide

7. © 2014 IBM Corporation
   Step 4 – Measuring Attribution Digital Analytics
   

   View Slide

8. © 2014 IBM Corporation
   Step 4 – Measuring Attribution xxxx
   

   View Slide

9. © 2014 IBM Corporation
   Step 4 – PCI Optimizes Attribution
   9
   WWW
   WWW
   WWW
   PCI
   IBM Digital
   Analytics
   IBM SMA
   Attribution
   Marketing
   Center
   

   View Slide

10. © 2014 IBM Corporation
    Questions?
    10
    

    View Slide

11. © 2014 IBM Corporation
    CA Use Case 2
    Customer Retention - Telco
    March 2014
    INTERNAL USE ONLY – Do not Distribute
    

    View Slide

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
    

    View Slide

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
    

    View Slide

14. © 2014 IBM Corporation
    Step 3 – Understand Customer Profile
    14
    Browsing and adding
    products to shopping cart
    DA captures customer profile
    Check out
    

    View Slide

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
    

    View Slide

16. © 2014 IBM Corporation
    Step 5 – Complete Insights
    16
    TL, DA, and SMA provides complete insights
    Behaviors Sentiments
    Profiles
    

    View Slide

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
    

    View Slide

18. © 2014 IBM Corporation
    CA Use Case 3
    Reduce Churn – Financial Services
    March 2014
    INTERNAL USE ONLY – Do not Distribute
    

    View Slide

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
    

    View Slide

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
    

    View Slide

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
    

    View Slide

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.
    

    View Slide

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
    

    View Slide

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
    

    View Slide

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
    

    View Slide