Sense-and-Respond Marketing. Accelerate Revenue with Customer-Centric Offers. CBA Webinar Series Presents: Emerging Trends in Banking:

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1 Page 1 CBA Webinar Series Presents: Accelerate Revenue with Customer-Centric Offers Emerging Trends in Banking: Sense-and-Respond Marketing Ron Shevlin Senior Analyst Aite Group Page 2 RON, YOU HAVE FOOD STUCK IN YOUR TEETH. AMERICAN DENTAL ASSOCIATION Page 3 Page 4 RON, YOU HAVE $1.17 IN YOUR BANK ACCOUNT. CASH N GO PAYDAY LOANS YOUR ALIMONY PAYMENT IS 3 MONTHS LATE, RON. NY ASSOC. OF DIVORCE LAWYERS Page 5 Page 6

2 Page 7 Theme Sense-and-respond marketing Marketing needs more than new technologies it needs a new competency Sensing consumer needs and intentions based on their behavior and responding with the appropriate advice, guidance, and/or offers Page 8 Agenda The three P s of marketing What s wrong with the existing competency? What is sense-and-respond marketing? Are you a sense-and-respond marketer? Predict Push Pray Page 9 Page 10 ain t working like they used to CRM has underperformed Source: CRM Guru Page 11 Page 12

3 Page 13 Onboarding under-delivers What the bank doesn t know Weeks New customer opens account Call to review accounts and offer products that may fit customer s needs Call to offer products based on determination of products that are advantageous to customer Adapted from a post on the bank s blog, written by a Relationship Associate with the bank Which products are advantageous Which channels are preferred If the customer trusts the bank yet Page 14 What changed? Demand Self-directed consumers Less reliant on firms More pull, less push More involved Supply More points of influence Web sites Search engines Social media Page Page 16 Marketing thinking hasn t changed Many data types are underutilized Marketing is the story marketers tell to consumers. Seth Godin All Marketers Are Liars Q. Which of these types of data do you use for marketing models? Demographic 88% Credit 67% Survey 61% Psychographic 54% Behavioral 43% Purchase 30% Co-operative 23% Customer interaction 17% Source: Aite Group report Marketing Analytics Trends In Retail Financial Services Page Page 18

4 Page 19 Analytical capabilities need refreshing Poll Question #1: Analytical Models Completely Mostly out-of-date out-of-date Our analytical models are 13% 33% The process for identifying and implementing new models is Our model development cycle is Undefined or broken 46% Always too long 29% 33% Mostly up-to-date 42% Defined, but Works a challenge well 17% 25% Usually too long Completely up-to-date 13% Very effective 13% Best in Acceptable industry 29% 8% Our analytical models are. A) Completely Out of Date B) Mostly Out of Date C) Mostly Up to Date D) Completely Up to Date Campaign performance reporting is Untimely, inaccurate 25% Untimely, Timely, Timely and accurate inacc. accurate 25% 17% 33% We are able to conduct quick and accurate campaign sizing scenarios. Never Infrequently Often Always 21% 38% 25% 17% Source: Aite Group report Marketing Analytics Trends In Retail Financial Services Page 20 Poll Question #2: Inbound Marketing Poll Question #3: Customer Lifetime Value Which of the following statements best describes your firm s use of analytical models for inbound marketing purposes? A) We have no inbound marketing capabilities B) We are in the process of developing analytical models to support inbound marketing C) Our analytical models that support inbound marketing purposes are mostly ineffective D) Our analytical models that support inbound marketing purposes are mostly effective Which of the following statements best describes your firm s customer lifetime value measures? A) We don t measure customer lifetime value B) We measure customer lifetime value, but don t use it effectively in our marketing efforts C) We measure customer lifetime value, and use it effectively in our marketing efforts Page 21 Page 22 Theme Sense-and-respond marketing Marketing doesn t just need new technologies it needs a new competency Sensing consumer needs and intentions based on their behavior and responding with the appropriate advice, guidance, and/or offers Page 23 Page 24

5 Page 25 Sense-and-respond vs. push-and-pray Hallmarks of high performers Push-and-pray Attitudes Creativity Persuasion Competency Focus Skill Philosophy Sense-and-respond Behaviors Analysis Engagement Differentiators: Speed Model diversity Channel integration Measurement Customer lifetime value, channel profitability Page 26 Are you a sense-and-respond marketer? Sense Do you know which triggers/ events require a response? How well do you evaluate data types and sources? Respond How timely are responses to triggers/events? Do you gather data from the right online and offline sources? Do you collect and utilize the right data? How well do you integrate different types of data? What kind of analytical tools/capabilities do you have? Are responses consistent across channels? Are responses appropriate to where customers are in lifecycle? Accelerate Revenue with Customer Centric Offers Chandresh Modi Senior Director Equifax Page 27 Page 28 Break the status quo, make offers that are relevant to the customer Degrees of sophistication in offer management REGULATION Regulatory impact of Dodd-Frank and Durbin are making banks change the way they are generating revenue and banks must fill the revenue gap SHARE-OF-WALLET SHARE-OF-WALLET US Consumers generally have 8 financial products, but only two or three products are held with the same financial institution (huge upside still exists for banks) New offer management solutions move away from simple one size fits all product offers by leveraging analytics to determine the next best action The majority of retail banks are not satisfied with their cross-sell or offer management solutions today Offer management solutions must support these functionality components: Predictive analytics Event triggering Campaign optimization Real-time recommendations INDUSTRY Offer Management Capability Offer Management Maturity Model (OM 3 Customer ) Lifetime Value Ad-Fatigue Attrition Level 4 Personalized Offer Management Optimization Segmentation Current relationship Past response rates Customer needs Level 3 Targeted Offer Management Segment driven Risk profile Wealth profile Level 2 Qualified Offer Management Risk profile driven Level 1 All are welcome Offer Management Same offer for all consumers Competitive advantage so how do you move to the next level? Page 29 Page 30

6 Page 31 Case study #1 Purchase propensity model for more profitable product offers Performance results showed an increase in account activations by 150bps and a profit increase of 60% Case Study A Top 20 bank evaluates new-to-bank and existing customers for competing offers during real-time customer interactions Methodology Analyzed existing DDA w/ Credit Card Cross-sell interactions Understand consumers likelihood of acceptance by scoring consumers for Credit Card and Home Equity Purchase Propensity Home Equity Offer Results No. of offers made: ~25k No. of accounts activated: ~5k (20% activation rate) Potential profit: ~$6M Opportunity Scenario Analyzed Evaluate the various products the customer qualifies for, and rank-order the profitability associated with multiple offers Benchmarked the arbitration between credit card and home equity offers, to determine the likelihood of acceptance for the most profitable outcome Offer Home Equity if; Consumer falls in highest decile as scored by the Purchase Propensity Model for Home Equity Equity in Home >= 25% Does not have a Home Equity w/ Bank Lift to the bank No. of offers made: 240k No. of activations: 60k ( Potential Profit: $14MM ( 60%) Impact on Credit Card Offers No. of offers made: 215k (~6% ) No. of accounts activated: 55k (~7% ) Potential profit: ~$8M (~7% ) 150 bps) Page 32 Case study #2 Score fusion for higher activation rates Patent pending score fusion empowers you to rapidly achieve your business goals Case Study A Top 20 bank evaluates multiple consumer risk dimensions to improve activation rates for bankcard Credit Risk Bankruptcy Affordability Utility Risk Acceptance New Activation Utilization Expected life Cross sell opp Rate Surfer Patent pending score fusion Opportunity Score fusion combines multiple consumer risk dimensions into a single fused model to better predict consumer risk Risk Volume Net Revenue Scenario Analyzed Personalized score fusion provided 158% lift over the baseline scenario, allowing the bank to open more accounts Profit equation Business Goal = Expected profit Strategy Page 33 Page 34 Score Fusion combined multiple consumer models to better segment risk for bankcard activations Equifax s patent pending Score Fusion techniques Bankcard New Acquisition Models case study Problem: One model alone could not address all relevant scenarios. Can multiple risk dimensions be brought together in a way that maximizes the benefit of each model and better segments risk? Solution: Equifax combined three risk models to better identify high risk accounts, allowing the bank to confidently open more cards across various risk segments. Fused score combined credit risk, capacity (debt-to-income), and bankruptcy risk models, resulting in better risk decisions around bankcard acquisition. Offer management software leverages score fusion to meet business objective of driving higher activation rates Provides significant lift over scores built using traditional statistical techniques Overcomes the linearity assumptions of traditional techniques, yielding stronger performance Combines your internal behavioral data and models with a mix of external data assets spanning credit, wealth, employment and demographic A unique differentiator that that offers our customers a significant improvement in evaluating customer lifetime value Eliminates the complexity of managing multiple scores or models Can be developed in as little as four weeks Page 35 Page 36

7 Page 37 InterConnect Action Advisor Equifax s offer management platform Summary and next steps Software, analytics and differentiated data effectively deliver the next best action in support of your business objectives. 9 of the Top 20 banks already use Equifax solutions for real-time cross-sell. We can help you implement analytics throughout your customer marketing lifecycle. We analyze your offer data to provide deep behavioral insight on your customer population A pilot services project can estimate the uplift from implementing sophisticated offer management analytics Page 38 Who to contact Ron Shevlin Senior Analyst rshevlin@aitegroup.com Chandresh Modi Senior Director chandresh.modi@equifax.com (770) Page 39