NextBank USA - Eric Connors, Yodlee, presentation
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NextBank USA - Eric Connors, Yodlee, presentation
- 1. © 2014 YODLEE. CONFIDENTIAL What Would You Do… A Big Data ThinkTank Eric Connors SVP, Products
- 2. © 2014 YODLEE. CONFIDENTIAL 2 The New Normal in Financial Services
- 3. © 2014 YODLEE. CONFIDENTIAL 3 The New Normal in Financial Services Government Regulation
- 4. © 2014 YODLEE. CONFIDENTIAL 4 What IS Big Data?
- 5. © 2014 YODLEE. CONFIDENTIAL 55 Bank Transactions What Data Do YOU (Banks) Have? Bank Balance Bank Account Bills Card Transactions Card Statement Card Account Insurance Transactions Insurance Coverage Limit Insurance AccountInvestment Transactions Investment Holdings Investment Account Loan Statement Loan Transactions Loan Account Real EstateRewards Activity Rewards Balance Rewards Program Bank Account Types • Checking • Savings • Money Market • CD Data Elements • A/C Holder name • Secondary name • Account number* • Routing number* • Current balance • Available balance • Account classification (personal, SMB etc.) • Account status • Historical balance Transaction Information • Transaction ID • Date • Post date • Description • Dollar amount • Status • Currency • Debit/credit indicator • Category • NSFs • Payments
- 6. © 2014 YODLEE. CONFIDENTIAL 66 Imagine the Mashup Potential of New Data Connections FIN DAT OTHER DATA Financial accounts Transactions APRs Merchants Categories Geo targeting Social media Google data Photos, video, recordings
- 7. © 2014 YODLEE. CONFIDENTIAL 7 To Answer Questions Like…What Don’t You Know About Your Customers? (Aggregation Example) High Revolver Avg. Balance of $3,457 Frequent Traveler $100k+ Income Home Owner $424,889 Loan Balance on Home Mortgage A/C held jointly with wife Son in Private School $25,650 in school expenses Remodeling Home Daughter Ready for University Saving Plan with a $51,450 Balance Affluent Customer with Investments worth $1,348,560 Favorite Merchants Avg. spend by category Total Loan Balances & External Debt
- 8. © 2014 YODLEE. CONFIDENTIAL 88 Plus External Data to Build Better Customer Experiences and Offers Today CHECK CRD PURCHASE 05/30 SAUCE SAN FRANCISCO CA XXXXXX6809 304150833216042 ?MCC=5947 Sauce Restaurant San Francisco, CA Sauce, San Francisco, CA WITH Merchant/Geo Categorization tap Arco, Mill Valley, CA Get 3% back on your gas purchases! Find out how. coupon > tap coupon v
- 9. © 2014 YODLEE. CONFIDENTIAL 99 Active SMB Checking Daily Deposits and Frequent Payments Low Balance Transfers Funds Regularly Merchant Services Daily batch settlements with Square Building Owner $224,889 Loan Balance on Real Estate Loan Charlotte Resident $150K+ Income Successful Business Draws the most spend in the Neighborhood Shared Customers Customers also shop at… Declining Share To the following 5 other shops Gaining Interesting Small Business Insight
- 10. © 2014 YODLEE. CONFIDENTIAL 1010 Example: Happening Today Market opportunity of $250M in management fees. Identified $2.5B in held- away investment assets for 10%of population. Converted 5% assets, generating $1.25M in management fees
- 11. © 2014 YODLEE. CONFIDENTIAL 1111 Duration: 5 weeks (2013) 65,658 Total Impressions 1.2% Click Through Rates (CTR) 44% Product Acceptance Rate 14 Total Campaigns (7 in December, 7 in January) * Results based on limited targeting analytics. Result: 12X CTRs vs. banner ads 3X CTRs vs. Google search ads Example: Happening Today >160% Lift in Click Thru Rate >125% Lift in Take Rate >28% Lift in Avg Balance Transfer
- 12. © 2014 YODLEE. CONFIDENTIAL 1212 Data MashUps…Endless Opportunities Fin. data Click stream data Call center data ATM interact -ion data CUSTOMIZED OFFERS AND PRODUCTS Cons- umer spend Geo locale Weath- er data Fuel data SPECIALIZED TIMING AND TYPE OF MKTG CAMPAIGNS TV viewing data POS txns Auto owner- ship data Fuel data TARGETED ADS BASED ON NETWORKS AND AUDIENCE
- 13. © 2014 YODLEE. CONFIDENTIAL 1313 Let’s Brainstorm! JANET JACKSON CMO “What have you done for me lately?” JAMES T. KIRK CRO “Protecting the enterprise” JERRY MAGUIRE Head of Wealth Management “Show me the money!”
- 14. © 2014 YODLEE. CONFIDENTIAL
- 15. © 2014 YODLEE. CONFIDENTIAL 1515 Data Types & Sources FINANCIAL DATA Account balances Transactions Spending Merchants Behaviors Credit Debt … OTHER “ABOUT ME” DATA Google Cell phone TV viewing Weather, Geo Call Center MORE DATA POS transactions Federal DBs (e.g., consumer complaints) ATM interactions … SOCIAL DATA Facebook Twitter YouTube Pinterest Yelp … ACCOUNT TYPES Investments Credit cards Loans Bills Savings …
- 16. © 2014 YODLEE. CONFIDENTIAL 16 Janet Jackson: CMO “What Have You Done for Me Lately?”
- 17. © 2014 YODLEE. CONFIDENTIAL 17 James T. Kirk: COO “Running the Enterprise”
- 18. © 2014 YODLEE. CONFIDENTIAL 18 Jerry Maguire: CFO “Show Me the Money!”
Notes de l'éditeur
- A few popular examples in addition to the Target example: 1. Uber – supply chain optimization platform using predictive algorithms that optimizes idle time 2. AirBnb – disruption in hotel industry 3. FaceDeals – connects online and offline sales. Check-in to the store, uses facial recognition and get deals. 4. Personal analytics with sensors on devices – Nike+, Fitbit, Jawbone, MyFitnessPal, etc. 5. Zillow – disrupting real-estate marketplace via data 6. Flipboard – discover, view and share content. Personalize. 7. Metlife “The Wall” project using MongoDB integrated data from >70 systems to create a 360 degree view of customers for their Support organization http://www.informationweek.com/software/information-management/metlife-uses-nosql-for-customer-service-breakthrough/d/d-id/1109919? 8. British Airways “Know Me” system leveraged online behavior and buying habits from 20M users to provide customized offers for continuing loyalty or upgrade offers to offset service lapses.
- The Yodlee Platform consolidates data from a variety of sources. Each source has unique information about the consumer that provides insight into that consumer’s financial profile Within each account type Yodlee is taking appropriate action to normalize the data specific to each account type so that it can be used effectively by Yodlee and it’s clients Example: For bank and card data, transactions activity is automatically categorized to apply to expense analysis and budget information Vs. balance information which is unique for each account type – Bank account is normalized to Current Balance, Available balance, Interest rate etc. where Card balance information is centered on current balance vs. available balance vs. amount due and interest rate etc. Those examples are very different from Investment account Types that have different elements like holdings, different transaction sets etc. Challenge for Yodlee is to ensure we are effectively retrieving the information in the right context to help populate this single source repository for consumer data Data Coverage & Volumes Account types supported Banks, Credit Cards, Investment, Insurance, Loans, Mortgages, Rewards, Tax, Real Estate 13,700+ data sources in US, Canada, UK, India, Australia, S Africa, France Daily refreshes across network for over 40M users, ~75M accounts >50% volume is from data feeds Mechanisms in our Toolbox to Aggregate Data Data Feeds OFX/Web Services integrations with top FIs High performance & reliability, low latency & high success rates Non-MFA Aggregation White labeled sites that bypass MFA HTML scraping with high success rates Scraping with MFA Dynamic MFA, Static MFA Support Q&A, Screen Captcha & Tokens Batch Feeds – utilizes our Payments Engine – High Volume Process & Scalable Framework to process Issue Handling Runs Independently Initial Loads Incremental
- FINDAT Financial accounts Transactions APRs Merchants Categories ATM interactions OTHER DATA Geo targeting Social media (Yelp reviews, FB, Twitter, YouTube, Pinterest…) Google data Photos, video, recordings Cell phone data Click stream data Call center data Credit bureau data TV viewing data POS transactions Weather data Etc!!!
- This is the wealth management division. This division increased the population of aggregators from 2000-24000 over a 4 month period. When doing that, they were able to do what the statistics show above and convert 1.25 of new management fees to the FI. This was only over a 4 month period and the total market opportunity is much bigger. None of this would be possible without the data aggregation available from PFM. Major Brokerage: With 24K Users Leveraged Client Data to Generate $1.25M in 3 Months
- Example [ANZ] Example 2: In a marketing relevancy pilot, the FI used Balance Transfer Offers to very targeted users, and with very specific data. For example, FI could see all of the aggregated accounts of user…could see that one credit card had an APR of 18%, then offered a balance transfer with a APR of 12%. This targeted approach resulted in increased click throughs, increased take rate, increase in avg balance transfer amount, A big win!
- Data Mashup ideas: 1. Financial data + click stream data + call center data + ATM interaction data => customized offers and products 2. Financial data (consumer spending at stores, that includes GEO data) + weather data + fuel data + holiday data => helps retailers on timing and type of marketing campaigns 3. Financial data (transactional data) + bureau data => credit decisions, cross sell/up-sell offers 4. Set-top television viewing data + supermarket and drug-store POS transaction information + auto ownership => provides advertisers deep insights into buying behavior crosses with television viewing habits, to place ads on the right networks targeting the right audience 5. Financial data + review data (Yelp) + check-in data (FourSquare) to correlate spending and reviews