Banking and Retail Analytics: Using Data to Drive Growth

Retail & Banking Analytics
Banking is Retail:
Data Without Use Is Overhead

#analytics2012Copyright © 2012, SAS Institute Inc. All rights reserved.
The Journey Begins, Again
Déjà vu
Effective Data Usage
Where does it come from?
What can you do with It?
Our heroine continues her trip to the Pot of Gold

Last Year: Our Analyst Was Searching for Answers
 What data is worth using
 Where do I find this elusive data
 What can I do with it after I find it
Banking and Retail….. The same thing?
How can that be !.

Chain Organization Structures
Banks are Retail Outlets with “Stores” that Sell Stuff
Region A
Branches
Region B
Branches
Region A
Branches
Region B
Branches
The number of Bank Branches has increased >118% since 1981
Emmett Cox World BankEmmett Cox Retail Magnate
Pale
Fins
Goggles
Raft
CD’s
MM
Checking
CC
Would you like to Up-Size that CD today

Next Best Sale or Next Best Offer
 What does it mean:
Identifying the next likely product a customer would think of buying. Then presenting it to them through
some offer.
 How it works:
Typically Activity based behavioral modeling is used. Determine the “Basket Affinity Products” that
appear together most frequently. Compare to a “Customer Segment” of similar groups
 Data required: Big DATA
The more data you have the better. Transaction Level – Time Series is a must.
But Big data is relative: To WMT big data may mean 800 terabytes,
A large men's apparel chain considers BIG to be 5 terabytes,
Just take it a Byte at a time
Both Banks and Retailers Use Predictive analytics to
build NBO Engines.

Last Year we Covered:
CROSS PURCHASE – Nappies and Beer
Basket Data can show many cross category purchase groups
Luvs and Life Savers
Diapers and Frozen
meals
Diapers and Advil
Diapers and Beer
Historical Product affinities can help predict Likely Next Purchase
Next Best Offer & Next Best Sale
Knowing what the next likely NEED is can accelerate sales.

First Home,
Better Furniture, Better Cloths,
Silver ware, Plates
Joint Checking Account
Joint Credit Card, Home Loan
Retirement Account Setup
New Auto Loan
First Apartment, First Job
Toaster, Dress Cloths, Pots
Pans, Furniture, TV, Computer
First Checking Account
First Credit Card,
First Debit Card
First Auto Loan
Merchandise That Sells-To the Same Consumer
Daily and Life Plan: NBS & NBO!
Bigger Home,
Children's cloths, Toys
Family car, Car seats
New Mortgage
Savings account,
Bigger Credit Line
(Family Car) Auto Loan

Age is now Irrelevant: They’re Still OUR Consumer
Retirement Planning, Investments, Cash Flow
Old way of
thinking:
Fragile
New way of thinking:
Empowered

Big Data = Big Money
Big Data = Big Ideas

Attrition Modeling: Data Dependant
More Data The Better
• We all have attrition issues,
• Banks are not Immune
• Retail is hit hard (Heavy CHURN)

Values Static Pool Customer Counts Across Months -- Standard perspective
Row Labels Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9 Month 10 Month 11 Month 12 ------>
Month
22
Degrade
Forecast
Oldest
Month 1 2,308 2,293 2,290 2,278 2,275 2,238 2,230 2,202 2,138 2,116 2,106 2,055 1,888 -18%
Month 2 2,457 2,447 2,435 2,433 2,402 2,401 2,397 2,339 2,309 2,300 2,243 2,218 1,977 -20%
Month 3 2,499 2,488 2,477 2,437 2,431 2,430 2,429 2,393 2,369 2,313 2,292 2,257 1,996 -20%
Month 4 3,166 3,157 3,115 3,108 3,105 3,105 3,098 3,035 2,977 2,925 2,861 2,817 2,468 -22%
Month 5 3,703 3,661 3,646 3,642 3,638 3,635 3,631 3,551 3,527 3,433 3,333 3,264 2,905 -22%
Month 6 2,686 2,673 2,665 2,656 2,652 2,648 2,645 2,597 2,550 2,482 2,448 2,419 2,100 -22%
Month 7 4,390 4,367 4,353 4,350 4,340 4,333 4,325 4,217 4,164 4,073 4,005 3,923 3,549 -19%
Month 8 4,371 4,355 4,340 4,330 4,325 4,313 4,312 4,261 4,237 4,157 4,056 3,983 3,582 -18%
Month 9 4,206 4,186 4,179 4,173 4,165 4,162 4,154 4,091 4,070 3,941 3,886 3,806 3,488 -17%
Month 10 3,156 3,147 3,132 3,125 3,118 3,109 3,101 3,057 3,011 2,941 2,878 2,841 2,567 -19%
Month 11 2,853 2,828 2,822 2,813 2,800 2,797 2,791 2,732 2,713 2,657 2,605 2,571 2,301 -19%
Month 12 2,901 2,879 2,874 2,872 2,870 2,864 2,831 2,792 2,756 2,687 2,650 2,623 2,353 -19%
Month 13 2,678 2,655 2,651 2,642 2,638 2,602 2,600 2,558 2,533 2,482 2,455 2,428 2,158 -19%
Month 14 2,458 2,441 2,439 2,434 2,413 2,407 2,404 2,382 2,356 2,329 2,302 2,275 2,005 -18%
Month 15 3,209 3,189 3,183 3,148 3,143 3,139 3,135 3,091 3,064 3,037 3,010 2,983 2,713 -15%
Month 16 3,391 3,376 3,341 3,334 3,329 3,327 3,325 3,298 3,271 3,244 3,217 3,190 2,920 -14%
Month 17 2,711 2,684 2,672 2,666 2,664 2,660 2,633 2,606 2,579 2,552 2,525 2,498 2,228 -18%
Month 18 2,334 2,320 2,317 2,311 2,306 2,279 2,252 2,225 2,198 2,171 2,144 2,117 1,847 -21%
Month 19 4,585 4,573 4,561 4,555 4,547 4,520 4,493 4,466 4,439 4,412 4,385 4,358 4,088 -11%
Month 20 4,136 4,106 4,084 4,027 3,970 3,913 3,856 3,799 3,742 3,685 3,628 3,571 3,001 -27%
Month 21 2,731 2,674 2,617 2,560 2,503 2,446 2,389 2,332 2,275 2,218 2,161 2,104 1,534 -44%
Recent
Month 22 4,562 4,505 4,448 4,391 4,334 4,277 4,220 4,163 4,106 4,049 3,992 3,935 3,365 -26%
Average 3,250 3,227 3,211 3,195 3,180 3,164 3,148 3,099 3,063 3,009 2,963 2,920 2,592 -20%
71,491 57,033 14,458
Vintage Curves to Visualize Defection
*Red numbers indicate projected attrition levels* Figures have been adjusted to protect the innocent
Read This Way

Values Customer Spend Across Months Decay Chart Of Those Accounts That have Attrited
Row
Labels
Accounts
Closed Month 1 Month 2 Month 3 --- Month 5 Month 6 Month 7 ------ Month 12 Month 13 Month 14
------
-
Month
18
Month
19
Month
20
Month
21 Gone
Month 1 420 $55,723 $63,460 $51,290 $41,706 $37,535 $33,782 $19,948 $17,953 $16,158 $10,601 $9,541 $8,587 $7,728
Month 2 545 $57,341 $30,046 $36,386 $36,055 $32,449 $29,204 $17,245 $15,520 $13,968 $9,165 $8,248 $7,423 $6,681
Month 3 393 $72,154 $26,745 $35,537 $33,065 $29,759 $26,783 $15,815 $14,233 $12,810 $8,405 $7,564 $6,808 $6,127
Month 4 401 $61,256 $53,314 $60,023 $61,870 $55,683 $50,114 $29,592 $26,633 $23,970 $7,092 $3,475 $3,128 $2,815
Month 5 425 $27,715 $58,469 $68,352 $64,789 $58,310 $52,479 $30,988 $27,890 $25,101 $16,469 $13,010 $10,278 $8,120
Month 6 523 $28,654 $32,151 $46,393 $42,089 $37,880 $34,092 $20,131 $18,118 $16,306 $8,243 $6,512 $5,144 $4,064
Month 7 389 $69,551 $79,013 $92,529 $87,846 $79,062 $71,156 $42,017 $37,815 $34,033 $15,102 $11,930 $9,425 $7,446
Month 8 420 $49,343 $67,043 $80,394 $79,773 $71,796 $64,616 $38,155 $34,340 $30,906 $12,038 $9,510 $7,513 $5,935
Month 9 453 $19,756 $63,948 $79,837 $74,390 $66,951 $60,255 $35,580 $32,022 $25,298 $9,853 $7,784 $6,150 $4,858
Month 10 449 $37,116 $42,788 $51,772 $49,875 $44,888 $40,399 $23,855 $18,846 $14,888 $8,574 $7,717 $6,945 $6,251
Month 11 617 $67,300 $39,866 $51,349 $49,444 $44,500 $40,050 $20,759 $16,399 $12,955 $7,461 $6,715 $6,044 $5,439
Month 12 690 $13,566 $41,156 $52,127 $49,868 $44,881 $40,393 $18,378 $14,518 $11,469 $7,525 $6,773 $6,095 $5,486
5,727 46,623 49,833 58,832 55,898 50,308 45,277 26,039 22,857 19,822 10,044 8,232 6,962 5,912
-5% -56% -86%
Spend Chart Of Closed / Attrited Accounts
3 months Out9 months Out16 months Out
At 3 months till close, we have already lost 86% of spend potential.
Read This Way

You Cant Save
Everyone…………
You Really Don’t Want To

Segmentation –
Current Value – Real Case Study #1
Decile
Annual
Households
% Sales
Cum %
Sales
Annual
Visits
Annual
Value
Market
Basket
Marketing
Spend
1 1,000,000 52.88% 52.88% 220.9 $14,175 $64 $70
2 1,000,000 21.18% 74.06% 148.5 $8,444 $57 $70
3 1,000,000 11.72% 85.78% 114.6 $6,059 $53 $70
4 1,000,000 6.76% 92.54% 90.0 $4,449 $49 $70
5 1,000,000 3.83% 96.37% 69.4 $3,262 $47 $70
6 1,000,000 2.08% 98.44% 52.4 $2,345 $45 $70
7 1,000,000 1.02% 99.46% 37.4 $1,611 $43 $70
8 1,000,000 0.42% 99.88% 24.5 $1,010 $41 $70
9 1,000,000 0.11% 99.99% 13.0 $514 $39 $70
10 1,000,000 0.01% 100.00% 4.0 $133 $33 $70
Total 10,000,000 100.00% 100.00% 77.5 $4,200 $47 $70
Retail Customer Spend by Decile
Prioritize Customers Based on Current and Potential Value
Top 20%
Drive 74% of Sales
Ignore Retain
}
Activate
Cross Sell /
Upsell
Marketing Spend was
Not differentiated by
Segment

Decile
Annual
Households
% Sales
Cum %
Sales
Annual
Visits
Annual
Sales
Market
Basket
Marketing
Spend
1 12,342 48.56% 48.56% 153.0 £9,088 £3,169 £100
2 12,342 20.95% 69.51% 75.1 £3,920 £1,817 £100
3 12,342 11.75% 81.25% 42.9 £2,198 £1,083 £100
4 12,342 7.16% 88.41% 27.4 £1,341 £724 £100
5 12,342 4.64% 93.06% 18.5 £869 £501 £100
6 12,342 3.02% 96.08% 13.0 £566 £368 £100
7 12,342 1.94% 98.02% 9.1 £364 £258 £100
8 12,342 1.16% 99.18% 5.8 £216 £167 £100
9 12,342 0.60% 99.78% 3.0 £112 £94 £100
10 12,302 0.22% 100.00% 1.5 £41 £35 £100
Total 123,380 100.00% 100.00% 35.0 £1,872 £822 £100
Segmentation –
UK, Credit-Card, -- Case Study #2
Ignore Retain
}
Activate
Cross Sell /
Upsell
Top 30%
Drive 80% of Sales
Marketing Spend was
Not differentiated by
Segment

Segmentation –
Puerto Rico, Credit Card -- Case Study #3
Decile
Annual
Households
% Sales
Cum %
Sales
Annual
Visits
Annual
Sales
In-Store
Spend
Marketing
Spend
1 1,857 36% 36% 25 $2,596 $103 $154
2 1,856 19% 55% 15 $1,343 $91 $154
3 1,857 13% 68% 10 $943 $90 $154
4 1,856 10% 78% 8 $703 $84 $154
5 1,857 7% 86% 7 $525 $76 $154
6 1,856 5% 91% 5 $387 $75 $154
7 1,857 4% 95% 4 $284 $71 $154
8 1,856 3% 98% 3 $194 $58 $154
9 1,857 2% 99% 2 $119 $52 $154
10 1,856 1% 100% 1 $48 $32 $154
Total 18,565 100% 100% 8 $714 $73 $154
Top 30%
Drive 68% of Sales
Bottom 50%
Drive 5% of Sales Ignore Retain
}
Activate
Cross Sell /
Upsell
Marketing Spend was Not
Differentiated by Segment

Banks are just Retailers selling products
• SKU’s are Financial products: Checking, Credit cards, Retirement planning
• Banks have “Stores” called Branches
• They share the same customer
• They both plan merchandise by Life Stage
Next Best Offer: Same Customer - Different Offer
• The new retirement is empowered
• Less Impulse more Planned Purchases
Understand Your Customers – Through Data
• Data is the key to Success. Affinity, Time Series, Broad range of products
• Retailer vs Banking Regulations – There are Rules of Engagement
Data without use is Overhead
Concept Re-Group

BBVA Compass A Little About Us
American Banker/
Reputation Institute
2012 Survey of Bank
Reputations
BBVA Ranking Position

Competing with Big Banks

BBVA Small Business Segment
Awards & Recognition
BBVA Compass won 7
awards for Small
Business Banking.
• Overall Satisfaction
• Relationship Manager
Performance
• Branch Satisfaction
• Treasury Mgt– Overall
Satisfaction
• Treasury Mgt– Customer
Service
• Western Region – Overall
Satisfaction
• Treasury Mgt, Western
Region – Overall
Satisfaction
BBVA Compass ranked
6th
in leading banks Q2
CAMEL scores.
• Received high marks in the
following categories :
• Channel Satisfaction
• Attitude Toward Bank
• Error Avoidance
• Selling Performance
BBVA Compass saw an
increase from 2011 to
2012 across all attributes
included in the survey.
• Biggest increases were
seen across the following
attributes:
• Customer Service: Easy to
Do Business With
• Would Miss if Went Away
• Different: Forward
Thinking
• Momentum: Growing in
Popularity attributes

Banking at BBVA Compass
Integration of Retail methodologies into banking
• Market basket (Affinity) analytics is NOT retail specific. Cross Sell
• These methods are transferrable to other industries.
• At BBVA we are bridging the gap between traditional banking analytics
methods and advanced retail processes
• Attrition, Retention, Cross Sell, Next Best Offer, Trade Area Modeling,
Segmentation, Response Modeling and more.
These are just some of the areas where we are integrating hybrid solutions.

Big Data:
Better Eaten One Byte at a Time.
I am Often asked this question
So from the “For What Its Worth Department”…….
Not so much SIZE of the Data, but the components of the Data.
Social Media Data (Twitter, LinkedIn, Face book, Blogs,) +
Sentiment/Sentient analysis (Intelligence transformed from the raw data) +
Digital Data (Text Data Digitized) +
Traditional Data (POS Transactions, Online Transactions, Replenishment….) +
Qualitative Data (Surveys …..)
All combined = Big Data View
And there will always be more Data Sources uncovered !

Money Does Grow On Trees!
Secret Ingredient ….Lots Of Data Daily!

Thank You

Banking and Retail Analytics: Using Data to Drive Growth

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