Leveraging Big Data to Drive Bank Customer Engagement and Loyalty
1. Leveraging Big Data to
Drive Customer
Engagement and Loyalty
The Future of Retail Banking 2012
28th November, 2012
Jim Marous
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2. Surrounded by ‘Big Data’?
THE
FINANCIALBRAND.COM
London Heathrow
Yesterday
November 22
Monday
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3. What is Big Data?
TERABYTES BATCH STRUCTURED
RECORDS NEAR TIME UNSTRUCTURED
TRANSACTIONS REAL TIME BOTH
STREAM
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4. Big Data Already at Work
• Amazon recommends products customers are likely to
buy
• Netflix recommends the next movie their customer
would like to see with repeated success
• Pandora describes its service as “personalized radio
that plays music you’ll love.”
• LinkedIn identifies, “people you may know”, with
extremely high accuracy
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5. Big Data Opportunity For Banks
• Banks have customer demographic, financial, and
transaction insight that provides a wealth of insight
• Big data is already being used with risk and fraud
monitoring successfully
• Banks can reach sophistication in targeting that no other
industry can achieve
• Better targeting ensures a better response as well as
optimization of the marketing investment
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6. Big Data Challenge For Banks
• Consumers have transient relationships with multiple
banks
• Banks no longer monopolize a consumer’s financial
transactions thanks for PayPal, Amazon, Google, etc.
• Social profile becoming as important for view of
preferences, buying patterns and networks
• Not all data is stored and far less is analyzed
• Lack of unified customer view and sharing across
enterprise
• Privacy regulations
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11. Big Data For Engagement & Loyalty
• Acquisition: Propensity models based on customer and
prospect insight
• Engagement: Behavior and transaction driven
promotions with geo-locational opportunities
• Social Graph Analysis: Customers with most social
‘clout’
• Sentiment Analysis: Brand and product sentiment
• Cross-Selling: Recommendation engine for next best
product, product propensity, etc.
• Customer Experience Analysis: Across channels
• Customer LTV: Current and potential value analysis
• Churn Analysis: Event triggers and attrition patterns
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23. Big Data Crowdsourcing Engagement
Fidor Bank
First Direct Bank
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24. Big Data Maturity Curve
Russell Ackoff, "From Data to Wisdom"
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25. New Data Paradigm
• Benefits. Most consumers are willing to share their personal data,
given sufficient awareness on the benefits of digital identity
applications and the implications for data collection and usage.
• Responsibility. Personal data needs to be recognized as a highly
valuable asset, requiring an adequately high level of security, and a
limitation to the least sensitive data for the targeted use.
• Transparency. By being open and clear about their data processes,
organizations effectively mitigate confidence risks and attract more
informed customers to their platforms and applications.
• Control. Individuals' preferences regarding data sharing differ
widely. Easy-to-use options and controls allow individuals to adapt
their sharing to their specific needs.
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26. MAKE SURE YOU NEED A BIG
DATA SOLUTION
Review volume, velocity and variety
needs
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27. USE MORE DIVERSE DATA
NOT JUST MORE DATA
Combining multiple data sources can
yield the most interesting insights
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28. DATA DOESN’T HAVE AN
EXPIRATION DATE
Keep data longer than you would think
you would need to
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29. EXPECT DATA TO GROW
EXPONENTIALLY
Adding mobile and geo-locational data
expands the amount of insight
significantly
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30. DON’T MISTAKE REPORTS
FOR INSIGHT
Ask if the data is yielding meaningful
insights that affect the success of the
business
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31. DATA BECOMES MORE
VALUABLE THE MORE PEOPLE
WHO USE IT
Broaden access across the organization
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32. SOLVE SMALL DATA ISSUES
FIRST
Eliminate internal data silos before
tackling the larger issue of big data
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33. Big Data Staged Execution
Novantas, 2012
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34. Jim Marous
SVP, Corporate Development
New Control
Cleveland, Ohio
1-216-218-4257
@jimmarous
BANK MARKETING STRATEGY
WWW.JIMMAROUS.BLOGSPOT.COM
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