WHAT S THE BIG DEAL ABOUT BIG DATA?

Transcription

1 WHAT S THE BIG DEAL ABOUT BIG DATA? PRESENTED BY JIM MAROUS PAUL LEAVELL RISHI CHHABRA

2 BIG DATA: PROFITABILITY, POTENTIAL AND PROBLEMS IN BANKING JIM MAROUS CO-PUBLISHER, THE FINANCIAL BRAND PUBLISHER, DIGITAL BANKING REPORT

3 DATA, DATA AND MORE DATA Page 3

4 NOT ALL BANKS ARE USING BIG DATA Page 4

5 PAGE TITLE Headline Level 1 HEADLINE LEVEL 2 Body text. Mus doluptat re, omnimus, a es quae cum volor rectio. Mene estis si sinvel eos dolut re nos venimus mod explanim aut voluptate quatus ute laudion sequid explique et facessi minulpa vel ellate laut aciandiam volupta plametur mod earis nuscid qui dolorepudis ut erunt landignis mo quatis mo volorepra con commolu ptatem auditio blaut in nonsequi repra quiandi dolorro venihiliqui andi aut qui consequia sere qui simet ipsunt occae nonsecati atem consequi iunt eati ut la verum fuga. >> Bullet Level 1 Bullet Level 2 Bullet Level 3 Page 5

6 MARKETERS UNPREPARED FOR BIG DATA Page 6

7 WHAT HOLDS BIG DATA INITIATIVES BACK Page 7

8 BENEFITS OF BANKING BIG DATA Page 8

9 DO CUSTOMERS BELIEVE US? Page 9

10 HOW MUCH WILL CUSTOMERS SHARE? Page 10

11 PAGE TITLE Headline Level 1 HEADLINE LEVEL 2 Body text. Mus doluptat re, omnimus, a es quae cum volor rectio. Mene estis si sinvel eos dolut re nos venimus mod explanim aut voluptate quatus ute laudion sequid explique et facessi minulpa vel ellate laut aciandiam volupta plametur mod earis nuscid qui dolorepudis ut erunt landignis mo quatis mo volorepra con commolu ptatem auditio blaut in nonsequi repra quiandi dolorro venihiliqui andi aut qui consequia sere qui simet ipsunt occae nonsecati atem consequi iunt eati ut la verum fuga. >> Bullet Level 1 Bullet Level 2 Bullet Level 3 Page 11

12 BIG DATA: START WITH THE BASICS J. PAUL LEAVELL SENIOR MARKETING ANALYST CHARLOTTE METRO FEDERAL CREDIT UNION

13 WHAT IS BIG DATA? Page 13

14 WHAT DEFINES DATA IN YOUR INSTITUTION? Cooper-Leavell Matrix Data Managed vs. Analysis Capacity Capture/Manage Today Can t Capture/Manage Today Could Analyze Today Little Data (LD) Middle Data (MD t ) Can t Analyze Today Middle Data (MD a ) Big Data (BD) Page 14

15 EXAMPLE: PAYTUNES p =.001; d = 1.1 p =.001 d = 0.59 BCa 95% CI: +/- $151 Campaigns since discovery: 2.6% response rate, ROI: Page 15

16 EXAMPLE: LIFESTAGE MARKETING Identifying signals of a life-stage change: Jewelry store purchase (2) Bridal store purchase First baby store purchase (2) Surname change New individual in HH Individual turned ages 16, 18, 62, 65 Not new Results: Response rate 2.1% - 5.7% Profit average: $5,132 Page 16

17 CAUTION!

18 OTHER ITEMS What brands do your customers patronize? Idiosyncratic fit heuristic: Ran Kivetz, Itamar Simonson (2003) The Idiosyncratic Fit Heuristic: Effort Advantage as a Determinant of Consumer Response to Loyalty Programs. Journal of Marketing Research: November 2003, Vol. 40, No. 4, pp URL: Policy decisions Decoupled debit card fee?

19 BIG DATA: APPLICABILITY OF PAYMENTS DATA RISHI CHHABRA VICE PRESIDENT, INFORMATION & ANALYTICS FIRST DATA

20 FIRST DATA 2 M ATM and retail locations with STAR Network PIN-secured debit transaction acceptance 778M worldwide card accounts on file 2 B online transactions 2% Other 12 B US debit issuer transactions Worldwide transaction volume 58 billion 25% International POS check writers 40+ years of merchant experience Leading electronic payments processor 53% $10.7 Billion Retail and Alliance Services 20% Financial Services Settles trillions across the financial system 6.2 M worldwide merchant locations US PIN debit transactions Card issuing & consumer finance solutions Merchant acquiring & processing services ATM management & processing Page 20 Debit POS network management Electronic check solutions and warranty Prepaid & loyalty programs Internet commerce & electronic payment solutions Mobile Commerce Data Analytics and fraud solutions

21 POWER OF A COMMERCE NETWORK MODEL Every FI & Merchant benefit whenever a new Merchant, Bank or Customer joins the network My new customer acquisition rate dropped 3% in May, but grew 2% for other Merchants in my category/area. What should I do? What kinds of loyalty campaigns best perform for restaurants? Can I leverage the template? Should I design a joint campaign with other related/neighboring businesses where our joint customers shop? Is my AOV of $32 good or bad? How am I performing against best in class? Shops at Banks with Served by network of commerce relationships between people, merchants and FIs Page 21

22 DATA AVAILABLE TO FIS & BUSINESSES Page 22

23 WHAT MAKES PAYMENT DATA INTERESTING More Signal than Noise Understand Consumer Behavior Better Engagement & Targeting Micro vs Macro Closing the loop Page 23

24 USE OF DATA IN BANKING With the power of transactional data (at both, card holder & merchant level), FIs can: BENCHMARK A FIS CUSTOMER BASE AND QUANTIFYING THE IMPACT OF POTENTIAL STRATEGIES (LAUNCHING A NEW CARD PROGRAM, CROSS-SELL) SCORING AND GENERATING ACTIONABLE, TARGETED MARKETING CAMPAIGNS OPTIMIZE THE NEXT BEST OFFER GENERATE & COMPARE PERFORMANCE REPORTS ACROSS DIFFERENT METRICS SUCH AS PENETRATION, ACTIVATION, USAGE OF CARD PORTFOLIOS PROMOTE SPEND BY MCC & AT SPECIFIC MERCHANTS STUDY THE PERFORMANCE TRENDS OF MERCHANTS IN PORTFOLIO TO EXTEND OTHER PRODUCTS (CREDIT, LENDING ETC.) Page 24

25 Product Strategies BRINGING IT TOGETHER Aligned Objectives Integrated Approaches Actionable Insights Existing Customers New Customers Revenue Enhancement First Data Transactions Integrated Data First Data Sources SpendTrend Merchant related data Value-Based Customer Segmentation Customer Value? High Value 5 6 Product Migration FI data Third Party Data Medium Value Low Value Increase Credit Usage? Offer Credit Card? Optimize Debit? No Change Offer Charge Card? Offer GPR? Third Party Data Demographics Bureau Multi-dimensional segmentation, e.g.; Products held Inflow/outflow amount Transaction behavior Customer age etc Product Propensity Scores Cross sell products Marketing campaigns Page 25

26 UNCOVER MERCHANT INSIGHTS Page 26

27 TO ENABLE INTELLIGENT ENGAGEMENT Imagine if a restaurant could specifically target customers living in the neighborhood, who have not visited for past 3 months, but had spent >$100 eating out at ethnic restaurants in the last 30 days Shops in your area Frequent shopper High ticket size Page 27

28 UNDERSTAND THE IMPACT OF LOCAL customer habits/ preferences, demo/ psychographics competitive moves, weather, economy, events,... Page 28

29 CLOSING THE ONLINE-OFFLINE LOOP What is the value of an on-line click or impression to a local deli? How many new people walked in? Did somebody buy? Making online advertising work for offline merchants: cost per offline action (CPOA) Page 29

30 PREDICTIONS, TAKE-AWAYS AND CONTACTS

31 PREDICTIONS FOR 2015 Apple Pay continues to grow as the public gets more comfortable with mobile wallets. High-tech receipt technology gains ground. Financial institutions continue to struggle with leveraging payment data. Page 31

32 KEY TAKE AWAYS Don t boil and ocean: Worry about Little and Middle Data first. Using payment data, identify idiosyncrasies in your customer base. Combine payment data with demographic data for segmentation. Build initial use cases and gain buy-in (budget) Just do it. Page 32