Deepening Member Relationships With Big Data And Analytics

Transcription

1 Deepening Member Relationships With Big Data And Analytics

2 Rich Weissman President and CEO

3 Agenda Look at ways in which the industry traditionally goes about deepening member relationships often it produces poor results Present framework for understanding how credit unions can utilize cutting-edge big data analytic systems and methods for deepening member relationships Review what those new methods are and how to implement them, even for very small credit unions Present examples Q & A

4 Our Mantra only the most informed and profitable will survive and prosper

5 Subjectivity vs. Data Science Subjectivity based on Intuition Personal experience Anecdotes Mythologies Acting solely on hunches and instinct Making sense of situations on the basis of own knowledge, and personal experiences only Not based on scientific understanding of cause and effect through data analysis

6 3 Cultures Phase 1: Banking Hours Culture (1930 s 1980) Wait for growth to come in the door Member Service and Member Events Phase 2: Sales Culture (post 1980) Drive growth by selling and cross-selling anything to everyone Balance Sheet and Volumes Phase 3: Profitability Analytic Culture (now) Maximize profitable growth by selling and up-selling profitable products to profitable members in profitable ways Analytics and Modeling

7 3 Cultures Banking Hours Culture (Phase 1) Managing based on government regulations - Just do what the regulators say - Member Service and Member Events Sales Culture (Phase 2) NO DATA Managing based on external environment - We re victims of the market and hope for the best - Balance Sheet and Volumes BOTTOM LINE DATA Profitability Culture (Phase 3) Managing based on profitability and sustainability analytics - We have to be smarter in all ways - Analytics and Modeling BIG DATA

8 Welcome To The World Of Big Data

9 Big Data What Is It? Place that stores data? Large data repository system that warehouses data? Volumes of reports? Reporting system that provides data reports? What the heck is this thing everyone is calling Big Data

10 Big Data What Is It? All those things, but much more Detailed and un-summarized data points Lots of different data points across multiple and different units Lots of different data points across time periods Massive calculations and statistical analyses Connecting those points and creating new intelligence through the connections Analyzing (not just collecting) Predicting (not just reporting) Proactively using (not just having)

11 Big Data And Artificial Intelligence Big data utilizes disparate data and allows patterns to emerge that otherwise would not be noticeable by naked eye Series of quantitative statistical methodologies and modeling that define and simulate behaviors Results in mathematical equations that project and predict future behavior to form foundation for executing programs and targeting results to improve performance GOAL: use big data models to increase probabilities of success by understanding the patterns

12 Connecting The Dots

13 Using Big Data For Credit Unions

14 Member Relationships Two identical Interest Checking accounts Balances both at $2,000 Number of checks per month 40 vs. 10 Number of deposits per month 12 vs. 2 Number of teller visits per month 12 vs. 0 Number of ATM transactions per month 20 on-us vs. 10 foreign Number of POS transactions per month none vs. 20 Monthly fees none vs. $5 Interest rate.8% vs..2%

15 Member Relationships Percent Number of Relationships Total Profitability Concentration Average Relationship Profitability Cross Sell Services Ratio 10% 2,811 $10,571, % $3, % 2,811 $594, % $ % 2,811 ($42,372) -1.92% ($15) % 2,811 ($273,572) % ($97) % 2,811 ($464,361) % ($165) % 2,811 ($578,142) % ($206) % 2,811 ($760,032) % ($270) % 2,811 ($1,032,057) % ($367) % 2,811 ($1,587,718) % ($565) % 2,811 ($4,216,901) % ($1,496) 3.44 Total 28,110 $2,211, % $

16 Cross Sell And Profitability Cross Sell Services Ratio % 20% 30% 40% 50% 60% 70% 80% 90% 100% Profitability Decile

17 Layering Analytics Relationships Products Markets Income Statement Concentrations (where profitability is in the black) Branches/ Channels Sales Officers

18 Big Data (IDM ) System Systems Of Record General Ledger External Data Account Information Deposits/Outstandings Risk Ratings Rates Fees Transactions Officers/Branches Deposits/Loans Off Balance Sheet Non-Interest Expense Non-Interest Income Net Interest Margin Accounts Payables Demographics Businessgraphics Agrigraphics Geographics Economics Markets Data Integration Member Relationships Artificial Intelligence Models Analytics and Implementation

19 Deepening Member Relationships Profitability models Full explosion of income statement (NIE, NII, FTP) Profit risk models Concentration of income statements Cash flow models New vs. churned vs. lost money analytics Price sensitivity models Price elasticity/inelasticity analytics Share of wallet models Profitability potential analytics

20 Deepening Member Relationships Onboarding models New member up-selling analytics Propensity to buy/next profitable product purchase models Probability for next purchase analytics At-risk retention models Survival analytics Matrix marketing models Micro-marketing targeting analytics

21 Profitability Profitability isn t just the bottom line It s complex It s multi-dimensional it s a series of layers, based on huge numbers of variables including every account balance, rate, fee and transaction

22 Yes, Every Transaction Hundreds and hundreds of transaction types Every fee item paid Every account activity Every piece of information for all transactions every day Millions and millions of records each month Modeling each transaction detail and its impact on revenues and costs

23 Non-Interest Income Detailed G-L line item explosions Fully exploded to each account that created fee Roll-up to income statement Examples Checking fees NSF fees Loan servicing fees Mutual fund fees Origination fees

24 Non-Interest Expense Detailed G-L line item explosions Fully exploded to each account that created transaction/ accounts payable item Roll-up to income statement Examples Teller visits ATM transactions Marketing campaigns Statements and postage Loan provision

25 Funds Transfer Pricing Important component Creating economic value of each deposit and each loan Understanding margin Including term and external yield curves for matched funding Assessing FTP for each account Five models Model 1 traditional internal sources and uses Model 2 traditional internal matched funding sources and uses Model 3 internal yield curves sources and uses Model 4 external yield curve matched funding Model 5 external multiple yield curves matched funding

26 Example (Non-Interest Income) FEE TRANCODE FEE TRANCODE DESCRIPTOR WIRE TRANSFER FEE NSF & OD FEES NSF & OD FEE REFUND CONTINUOUS OD FEE ATM-FOREIGN SURCHARGE FEE MASTERMONEY CARD FEE SAV PAPER STMT FEE SAV DORMANT FEE SERVICE CHARGES TIME DEPOSIT FEE SERVICE CHARGES DORMANT CHECK FEE DEPOSIT CHECK RETURN FEE PAPER STMT CHARGE FEE S/C STOP PAYMENTS FEE EZ DEPOSIT FEE SAFE DEPOSIT RENTAL FEE ADVANTAGE PACKAGE FEE EXCHANGE REVENUE CHECKBOOK ORDER FEE CHECKBOOK ORDER FEE REFUND SAFE DEPOSIT BOX RENT CURRENCY EXCHANGE FEE CANADIAN CHECK DEP FEE INTERNET BANKING FEE

27 Example (Non-Interest Expense) ACTIVITY TRANCODE ACTIVITY TRANCODE DESCRIPTOR CHECK DEPOSIT BRANCH CASH DEPOSIT BRANCH CASH WITHDRAWAL BRANCH CHECK DEPOSIT ATM CASH DEPOSIT ATM CASH WITHDRAWAL ATM TRANSFER BRANCH TRANSFER ATM TRANSFER ONLINE BANKING CHECK WRITTEN ONLINE BILL PAY PAYMENT ELEC ONLINE BILL PAYMENT MAIL MAILED STATEMENT ONLINE STATEMENT POS CASH BACK POS PURCHASE CHECKBOOK ORDER TELLER VISIT CALL CENTER CALL TEXT MESSAGE ALERT INTEREST RATE ADJUSTMENT CASH ADVANCE MONEY ORDER

28 Income Statements

29 Income Statements

30 Cash Flow Models New/churned/lost money assessment Evaluating each member relationship month-to-month Creating member relationship balance sheet on cash flow basis for each deposit account within member relationship

31 Cash Flow Models New money assessment levels Member relationship level Existing relationship vs. new relationship Product level Market level Branch level

32 Price Sensitivity Models 3 key drivers of elasticity/inelasticity Rate sensitivity as function of rates paid on deposits or charged on loans and a function of margin allocation correlated with external rates Rate sensitivity as relative score as function of all other relationships in the base Rate sensitivity as function of size of balances in each member relationship

33 Price Sensitivity Models RELATIONSHIP DEPOSITS FTP PER DECILE RANKED BY AVERAGE FTP Min FTP Max FTP Avg FTP

34 Share Of Wallet Models Projecting what is across the street Modeling profitability for incremental sales by bringing accounts held at other institutions Assessing total value of member relationship from profitability perspective for wallet value Measuring impact to income statement

35 Onboarding Models Assessing each new member relationship at the start and quickly targeting them for up-sell for profitability Modeling profitability for selling track to maximize profitability potential from new relationship Determining optimal product exposure sequencing to accelerate pace based on unique qualities and profiles of each new relationship

36 Propensity To Buy/Next Profitable Product Purchase Assessing who is likely to purchase which products and which products are most profitable for targeting for up-sell for profitability enhancement Modeling profitability and propensity to buy for profitably deepening relationships Determining optimal next product selling opportunity to accelerate profitability base on unique qualities and profiles of each member relationship

37 At-Risk Retention Models Assessing each member relationship according to its likelihood to close accounts and/or close its entire relationship Determining the optimal timing for retention intervention Based on survival analytic modeling Determining at-risk at product-specific level

38 Matrix Marketing Models Systematic segmentation of database to define unique cells that are ripe for targeting (current base and prospects) for improved profitability Defining cells in varieties of ways cost reduction, pricing, product usage, new products, alternate channel usage Highly structured, repetitive/ongoing mechanism for continual micro-targeting Objective to deepen member relationships by moving the base up

39 Matrix Marketing Models Percent Number of Relationships Total Profitability Concentration Average Relationship Profitability Cross Sell Services Ratio 10% 2,811 $10,571, % $3, % 2,811 $594, % $ % 2,811 ($42,372) -1.92% ($15) % 2,811 ($273,572) % ($97) % 2,811 ($464,361) % ($165) % 2,811 ($578,142) % ($206) % 2,811 ($760,032) % ($270) % 2,811 ($1,032,057) % ($367) % 2,811 ($1,587,718) % ($565) % 2,811 ($4,216,901) % ($1,496) 3.44 Total 28,110 $2,211, % $

40 New Paradigm Each Unique Member Account/Relationship Each Unique Product, Market, Branch, Officer, Etc. Credit Union Micro-Level Mid-Level Macro-Level

41 New Starting Point Get your integrated database up and running Begin to segment database and apply modeling technologies Get deep into statistical methodologies Analyze results of statistical assessments Appreciate it is detailed and requires serious competencies in artificial intelligence capabilities get help from experts who can design and run modeling technologies Deepen member relationships through big data and analytics

42 Our Mantra only the most informed and profitable will survive and prosper

43 Questions???

44 Copyright Notice All contents of this presentation and its materials, information, and data are the exclusive property of DMA and are protected by copyright laws. No part of this presentation and its materials, information, or data may be copied or reproduced (either electronically, photocopy, or otherwise) or transmitted or recorded in any way without the prior written permission of DMA and the presenter. All contents copyright 2014 DMA. All rights reserved.