© 2014 Northern Trust Corporation

The Power of the Cross-Tab

Andy Curtis, Senior Vice President, Northern Trust

Big Data & Analytics for Banking Summit

Topics to be Covered

Evolution of Analytics

Discussion of Cross-Tabs

Intro to Markov Chains

“What If” Models

Pulling It All Together

Evolution of Analytics

Business Intelligence• What Happened?

Business Analytics• Why Did it

Happen?

Predictive Analytics• What Could

Happen?

Business Intelligence—What Happened?

Leverage one-way frequencies* to answer:

Status # (K) %

New 400 20

Active 600 30

Lapsed

1,000 50

Source %

Web 50

Print 20

Walk In 20

Referral 10

12 Mo Rev %

$5 K+ 5

$2 to $5 K 15

$1 to $2 K 20

< $1 K 60

Average $1,500

What does our client base look like?

What is the source of new clients?

What is the annual revenue per client?

* All numbers are fictional

Business Intelligence—Best Practices

Understand every field in the database

Eliminate fields that are too new, poorly filled, or

unreliable

Look at distributions of values for each field

Know what every field means

Work with the finance team to define the business rules for

properly attributing revenue, cost, and other key business

drivers

Support tools (i.e. Tableau, Microsoft BI, etc.) that allow

users to “safely” do their own data discovery

Business Analytics—Why Did it Happen?

Leverage two-way frequencies* to answer:

Bank-

ingLoan

Broke-rage

Trusts

Profit

Index

Y Y Y Y 400

Y Y Y 280

Y Y 175

Y 80

Y 75

Y 50

Y 30

Client Size Web Print

Walk-Ins

Refer-ral

5 M+ 5% 20% 30% 40%

2-5 M 30% 17% 23% 30%

1- 2 M

50% 14% 16% 20%

< 1 M 70% 11% 9% 10%

Year 2

Year 1 Active

Lapse

New 50% 50%

Active 70% 30%

Lapsed

10% 90%

What product bundles are most profitable?

What sources are best for acquiring large clients?

What is the client retention rate?

* All numbers are fictional

Business Analytics—Best Practices

This step begins the process of converting data into insights

Divide key variables into meaningful groups large enough to be meaningful, and not too small to be spurious

Leverage cross-tabs to identify key interactions

Understand client profitability across product bundles

Understand source of new customers Understand retention by client group

Use multivariate techniques to build predictive models, segments, etc.

Andrey Markov, Russian mathematician (1856-1922)

Studied random process where future based solely on present state (i.e., Coin Flips and Population Migrations)

Example of migration of 100 new clients across 3 years

Year 2Year 1 Active Lapsed

New 50% 50%

Active 70% 30%

Lapsed 10% 90%

YearStatus 0 1 2 3

New 100

Active 50 40 34

Lapse 50 60 66

50

5035 28

45 54

15 5 12 6

Predictive Analytics—Markov Chains

* All numbers are fictional

Predictive Analytics—What Could Happen?

Build 3 year forecast leveraging Starting population Revenue per active

YearStatus 0 1 2 3New 400 400 400 400 Active 600 720 832 939

Lapse 1,000 1,280 1,568 1,861 Rev ($M) 1,080 1,248 1,409 3,737

Year 2

Year 1 Active

Lapse

New 50% 50%

Active 70% 40%

Lapsed

10% 90%

Status # (K) %

New 400 20

Active 600 30

Lapsed

1,000 50

12 Mo Rev %

$5 K+ 5

$2 to $5 K 15

$1 to $2 K 20

< $1 K 60

Average $1,500

200

100420

720 K * $1.5 K =

* All numbers are fictional

10% Acquisition Improvement: $3.9 B

10% Cross-Sell Improvement: $4.1 B

10% Retention Improvement: $4.5 B

Prescriptive Analytics—What Could Happen?

Year 2 YearYear 1 Active Lapsed Status 0 1 2 3New 60% 40% New 400 400 400 400 Active 80% 20% Active 600 820 1,014 1,190 Lapsed 10% 90% Lapse 1,000 1,180 1,386 1,610 Rev / Year $1,500 Rev ($M) 1,230 1,521 1,785 4,536

Year 2 YearYear 1 Active Lapsed Status 0 1 2 3New 50% 50% New 440 440 440 440 Active 70% 30% Active 600 740 868 989 Lapsed 10% 90% Lapse 1,000 1,300 1,612 1,931 Rev / Year $1,500 Rev ($M) 1,110 1,302 1,483 3,895

Year 2 YearYear 1 Active Lapsed Status 0 1 2 3New 50% 50% New 400 400 400 400 Active 70% 30% Active 600 720 832 940 Lapsed 10% 90% Lapse 1,000 1,280 1,568 1,861 Rev / Year $1,650 Rev ($M) 1,188 1,373 1,551 4,111

* All numbers are fictional

10% Improvement: Acquisition + Retention + Cross-Sell

$5. 2 Billion!

Prescriptive Analytics—What Could Happen?

Year 2 YearYear 1 Active Lapsed Status 0 1 2 3New 60% 40% New 440 440 440 440 Active 80% 20% Active 600 844 1,059 1,253 Lapsed 10% 90% Lapse 1,000 1,196 1,421 1,667 Rev / Year $1,650 Rev ($M) 1,393 1,747 2,068 5,207

* All numbers are fictional

12

Concluding Thoughts

Driver Business Intelligence Business Analytics Predictive Analytics

Acquisition • Marketing Effectiveness

• SEO / Web Reporting

• Acquisition Models• Channel Optimization• Lifetime Value• Web Analytics

• Behavioral Triggers

Cross-Sell • Client Profitability• Client Migration

• Segmentation• Cross-Sell Models• Product Bundles

• Recommend-ation Engines

Retention • Market Research• Quality KPIs

• Process Improvement• Retention Models• Satisfaction Analysis

• Sentiment Analysis

To achieve goals, organizations must leverage BI, BA, and Predictive Analytics

13

Questions

© 2014 Northern Trust Corporation

Big Data & Analytics for Banking SummitThank You