sas analytics presentation 2012 retail ecox final1

Copyright © 2012, SAS Institute Inc. All rights reserved. #analytics2012

Retail & Banking AnalyticsBanking 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 StructuresBanks are Retail Outlets with “Stores” that Sell Stuff

Region A Branches

Region B Branches

Region ABranches

Region B Branches

The number of Bank Branches has increased >118% since 1981

Emmett Cox World BankEmmett Cox Retail Magnate

PaleFinsGogglesRaft

CD’sMM

CheckingCC

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 DATAThe 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 BeerBasket 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 SaleKnowing 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 MoneyBig 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

22Degrade 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 %

SalesAnnual Visits

Annual Value

Market Basket

Marketing Spend

1 1,000,000 52.88% 52.88% 220.9 $14,175 $64 $702 1,000,000 21.18% 74.06% 148.5 $8,444 $57 $703 1,000,000 11.72% 85.78% 114.6 $6,059 $53 $704 1,000,000 6.76% 92.54% 90.0 $4,449 $49 $705 1,000,000 3.83% 96.37% 69.4 $3,262 $47 $706 1,000,000 2.08% 98.44% 52.4 $2,345 $45 $707 1,000,000 1.02% 99.46% 37.4 $1,611 $43 $708 1,000,000 0.42% 99.88% 24.5 $1,010 $41 $709 1,000,000 0.11% 99.99% 13.0 $514 $39 $7010 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



SalesAnnual Visits

Annual Sales

Market Basket

Marketing Spend

1 12,342 48.56% 48.56% 153.0 £9,088 £3,169 £1002 12,342 20.95% 69.51% 75.1 £3,920 £1,817 £1003 12,342 11.75% 81.25% 42.9 £2,198 £1,083 £1004 12,342 7.16% 88.41% 27.4 £1,341 £724 £1005 12,342 4.64% 93.06% 18.5 £869 £501 £1006 12,342 3.02% 96.08% 13.0 £566 £368 £1007 12,342 1.94% 98.02% 9.1 £364 £258 £1008 12,342 1.16% 99.18% 5.8 £216 £167 £1009 12,342 0.60% 99.78% 3.0 £112 £94 £100

10 12,302 0.22% 100.00% 1.5 £41 £35 £100Total 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

Retail Customer Spend by DecileTop 30%

Drive 80% of Sales

Marketing Spend was Not differentiated by

Segment


Segmentation – Puerto Rico, Credit Card -- Case Study #3


SalesAnnualVisits

Annual Sales

In-Store Spend

Marketing Spend

1 1,857 36% 36% 25 $2,596 $103 $1542 1,856 19% 55% 15 $1,343 $91 $1543 1,857 13% 68% 10 $943 $90 $1544 1,856 10% 78% 8 $703 $84 $1545 1,857 7% 86% 7 $525 $76 $1546 1,856 5% 91% 5 $387 $75 $1547 1,857 4% 95% 4 $284 $71 $1548 1,856 3% 98% 3 $194 $58 $1549 1,857 2% 99% 2 $119 $52 $15410 1,856 1% 100% 1 $48 $32 $154

Total 18,565 100% 100% 8 $714 $73 $154

Retail Customer Spend by DecileTop 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 OverheadConcept Re-Group


BBVA Compass A Little About Us

American Banker/ Reputation Institute 2012 Survey of Bank ReputationsBBVA 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 CompassIntegration 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 questionSo 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!

sas analytics presentation 2012 retail ecox final1

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