The Analytics “Gold Rush”: Mountains of Data, Hidden Profits

March 2012 Page 2

AgendaPresenter: Steven Callahan, Robert E Nolan Company§ Analytics Update

§ Techdecisions / Nolan Survey Results

§ Analytics Application Examples

Presenter: Alan Rault, American Family§ From Lead to Gold: A Practical Approach to Get the Best

ROI from Data Analytics

Shared by both presenters§ Question and Answer Session

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ANALYTICS UPDATE

March 2012 Page 4

Enough Already!

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May 2011 Bloomberg Research Study Key Findings§ Business analytics is still in the “emerging stage”

§ Organizations are proceeding cautiously in their adoption of analytics

§ Intuition based on business experience is still the driving factor in decision making

§ Companies look to analytics to solve big issues, with primary focus on improving the bottom line

§ Data quality, acquisition, and integration is the #1 challenge in the adoption and use of business analytics

§ Many organizations lack the proper analytical talent and know how to effectively apply the results – to move from insights to action

§ Culture plays a critical role in or barrier to the effective use of business analytics

March 2012 Page 6

Why Bother?

Leveraging the Foundations of Wisdom:: The Financial Impact of Business Analytics., Copyright © 2002, IDC

Both predictive and nonpredictive projects yielded high median ROI, 145% and 89%, respectively per IDC.According to the Aberdeen Group, predictive analytics carriers achieved a 1% improvement in profit margin and improved by 6% in year on year customer retention. Those who had not yet adopted predictive analytics dropped 2% in profit margins and decreased 1% in year on year customer retention.

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Top Line Benefits Touted for AnalyticsBusiness analytics enable organizations to be able to:§ Gain deeper, more relevant business insights to inform decision making§ Bring predictive analysis and regression modeling to entire organization§ Use analytics to identify and determine options for industry challenges

– Be prepared to respond to significant business challenges as they emerge§ Strengthen data governance at each level of the organization§ Reduce costs through more accurate, data-driven decision-making§ Use analytic capabilities and outcomes for change management efforts§ Create a culture that thrives on fact-based decisions versus anecdotal

– Achieve more consistent, objective and prospective business decisions§ Effectively and proactively manage risks

March 2012 Page 8

The Analytics Capability Maturity Evolution

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March 2012 Page 10

Extract, Verify, Clean

Agents Apps UW Info Services Claims

ENTERPRISE DATA WAREHOUSEExternalDataSources

InternalTextMining

Select, Scrub, Transform

Distributed Data Stores

CRMMarketing

Business Rules

ReportsDashboardsModelsCorrelationsSimulations

See next page*

March 2012 Page 11

Available Third Party Data is Extensive

Survey Data:• Self-reported information • Contains many lifestyle elementsBasic demographics• Age, sex, number & ages of kids, marital status• Occupation categories, education levelFinancial information• Income level, net worth, savings, investments• Home value, mortgage value, credit card infoLifestyle data• Activity: running, golf, tennis, biking, hiking, etc. • Inactivity: TV, computers, video games, casinos • Diet, weight-loss, gardening, health foods, pets

Third party marketing datasets are often used to develop the predictive models, they include over 3,000 fields of data, contain no PHI, are not subject to FCRA requirements, and do not require signature authority. The match rate with insured’s is typically around 95% based only on name and address. Third party marketing data includes:

**

Rewards programsMagazine subscriptionsEmail listsWebsitesGrocery store cardsBook store cardsPublic records

March 2012 Page 12

SURVEY REVIEW

March 2012 Page 13

Composition of Survey Participants

43%

36%

21%

Executive TeamMiddle ManagementIndividual Contributor

22%

16%62%

Large (over $1000M)Medium ($500M to $1000M)Small (under $500M)

57%

5%7%

27%

4%

P&C L&AHealth MultilineOther

March 2012 Page 14

Source of Increasing Interest in Analytics

18%

4%

7%

16%

2%

27%

5%

15%

25%

7%

5%

11%

35%

38%

31%

18%

23%

31%

19%

20%

49%

45%

34%

55%

51%

59%

4%

2%

7%

15%

11%

11%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Nothing New/Just press

Quantitative Importance Grown

Information Quality Improved

Too Much Info for Old Ways

New Tools Make Analyzing Easier

Complex Data Arrays Add Value

Predictive analytics/GLM BringOpportunities

Not at all Some Average Amount Consistently Significantly

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Leadership Decisions Moving To Data Driven

5%

2%

7%

2%

2%

31%

7%

34%

28%

25%

41%

38%

32%

39%

43%

36%

24%

25%

55%

24%

22%

36%

33%

5%

1%

0% 20% 40% 60% 80% 100%

Intuition

Experience

Group Dynamics

Collaborative Consensus

Historical Data

Future Projections (Predictive)

Not at all Some Typical/Common Almost Always Exclusively

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Location Of Analytics Expertise Varies Widely

11%

11%

16%

4%

19%

31%

25%

33%

28%

38%

22%

45%

30%

33%

26%

34%

19%

19%

33%

13%

2%

2%

2%

4%

0% 20% 40% 60% 80% 100%

Key Resources By Dept

Centralized IT

Teams by LOB

Centralized Finance or Actuarial

Dedicated Shared Team

Not at all Some Typical/Common Almost Always Exclusively

?

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Increase in Analytic Methods Being Used

4%

2%

2%

4%

5%

25%

4%

20%

7%

30%

13%

34%

26%

35%

38%

35%

33%

27%

38%

35%

32%

31%

27%

37%

53%

39%

36%

25%

32%

9%

29%

4%

7%

2%

11%

2%

5%

2%

0% 20% 40% 60% 80% 100%

Unit Measures/Ratios

Trending/Comparisons

Benchmarks

Dashboards/Scorecards

Segmentation/Clustering

Data Mining

Text/Content Analysis

Predictive Models/Simulations

Not at all Some Typical/Common Almost Always Exclusively

March 2012 Page 18

Across A Wider Variety of Areas

7%

2%

2%

2%

4%

2%

2%

30%

20%

18%

16%

15%

18%

26%

34%

27%

30%

30%

28%

23%

41%

25%

46%

48%

43%

49%

46%

26%

4%

5%

2%

9%

4%

11%

5%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Channel Management

Revenue Growth

Operational Efficiency

Retention Analysis

Risk Management

Loss Control and Fraud

Workforce Management

Not at all Some Typical/Common Almost Always Exclusively

March 2012 Page 19

Common Barriers to Using Analytics

9%

4%

19%

20%

12%

23%

34%

35%

37%

20%

34%

26%

31%

39%

35%

34%

24%

32%

21%

20%

9%

26%

28%

17%

5%

2%

2%

2%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%

Fragmented Data

Inadequate Tech Resources

Lack of Business Expertise

Lack of Exec Sponsorship

Perceived Costs > Expected Benefits

Cultural Barriers to Data Sharing

Not at all Some Typical/Common Almost Always Exclusively

March 2012 Page 20

Survey Comments on Using AnalyticsAreas of Increasing Use of Analytics§ “Fastest growth will be in claims and in fraud detection, SIU”§ “Product development / pricing, marketing, and underwriting”§ “Property / territory grouping and experience rating”§ “Social media analytics and text mining as sources of data for products and claims”§ “Profitability modeling on specific segments of market or agents”§ “Risk profiling by discrete market segments and then different levels of service”§ “Combining internal/external data, unstructured data, geospatial & self-service analytics.”

Barriers to Growth in Use of Analytics§ “Resistance comes from most experienced, those requiring 100% accuracy”§ “Access to critical data that is not captured in the system but is on paper”§ “Getting away from tribalism, managing by anecdote, and subjective decisions”§ “Availability of resources and the money necessary to do it right”§ “Data is spread all over and difficult to integrate or consolidate”§ “Privacy will become a major issue as external data sources start to drive decisions”

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Analytics: From Reactive to Predictive

* Insurance Customer Retention and Growth, © Copyright IBM Corporation 2010

March 2012 Page 22

ANALYTICS APPLICATION EXAMPLES

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Agency Compensation§ Background:

– Large P&C Carrier– Direct sales through captive agencies

§ Problem:– Single measure agency bonus program failed to motivate the desired behavior– Desire to strongly link growth behavior change to compensation

§ Approach:– Establish comprehensive database of agency data – Built modeling tool and complete extensive baseline modeling process using historical data

• Established performance ranges, eligibility requirements, and targeted levels of participation• Modeled top agents to verify soundness

– Determine impact on agencies more and less focused on growth

§ Solution:– Balanced scorecard approach that incorporated differences in state and product strategies– Modeling of impact on higher performing agencies– Product and measure weight factors based on individual state priorities

§ Impact:– Significant shift in bonus dollars paid to agents best supporting company goals– Improved alignment with state and market strategies– More balanced distribution of bonus dollars– Increased rewards for top half of agency force

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Customer Retention§ Background:

– Lincoln Financial Distributors• Marketing and retail distribution arm of Lincoln Financial Group• Annuities, life insurance, long-term care insurance, and investment products

§ Problem:– Desire to better understand customer base and optimize the acquisition,

development, and retention of its customers

§ Approach:– Selection of analytics software (SPSS) to segment and understand customer base

and to implement actions to increase retention

§ Solution:– Identification of opportunities to reach out to customers to strengthen relationships

and create long-term loyalty– Longer-term use for acquisitions and to identify up-sell and cross-sell opportunities

§ Impact– Significant change in the way data is viewed and used– Improved strategic decision making directly tied to business goals– Maximize customer value by transforming data into important insight

March 2012 Page 25

Customer Lifetime Value§ Background:

– Farmers Insurance, third largest U.S personal lines insurer

§ Problem:– Utilize analytics tool (SAS) to establish ability to support business units by answering

questions with high level of analytical rigor for more informed decision making– Desire to understand how to adapt branding, direct mail, agency location and

behavior, and pricing to attract higher lifetime value customers

§ Approach:– Explore unchartered territory to enable strategy through the use of analytic insight– Analyze customer base data and determine that the top 20% provided nearly 80% of

revenues

§ Solution:– Analyze and model customer lifetime loyalty and profitability, determine distribution

and marketing strategies, and identify customer experience investments

§ Impact– Changes to marketing campaign success measure from number of respondents and

number of individual sales completed, as opposed to a holistic customer view – Agent performance now tied to the powerful measure of lifetime value – 14% increase in ROI related to direct marketing efforts

March 2012 Page 26

IT Efficiency & Effectiveness§ Background:

– Regional Financial Services Organization– 500 EE IT organization, Build and Run functions maintained in-house, 50% resources

§ Problem:– IT expense % of revenue high relative to peer group, complex admin processes– < 10% of programmer time spent in coding, testing, and validation– Significant time logging and tracking time – Negative perception of IT efficiency and effectiveness by business line customers

§ Approach:– Activity based costing used on functions performed and baseline resource allocation– Gather business unit results and align with IT resource and function allocations

§ Solution:– Develop related baseline customer focused IT performance ratios and metrics – Identify and reduce or eliminate non-value added work functions– Develop a business case for program / project management application that would

§ Impact– Selection of a new PPM package, capacity creation, and ROI within two years – Gradual elimination of non-value added work and reduced IT expense– Improved business unit alignment and improved IT accountability

March 2012 Page 27

From Lead to Gold: A Practical Approach to Get the Best

ROI from Data Analytics

Alan RaultStrategic Business Process Management

March 28, 2012

March 2012 Page 28

Background

Problem Definition

Measure Plan

Analytics Plan

Implementation Plan

Take Aways

Agenda

March 2012 Page 29

§ What is the business performance problem/gap that you are trying to understand/solve (outputs)?

§ Is it measureable? Is the measure(s) valid to business and considered reliable? Subjective vs. objective?

§ What is in or out of scope?

§ What are the potential factors that could influence your current performance?

If you can’t figure out the factors, STEEP is at your call

Problem Definition

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What is STEEP?

Social• Customer Wants/

Needs

Technology• Support• Systems

Economic• Incentives• Products

Political• Company Culture• Regulatory

Environment• Market• Competitors• Demographics

March 2012 Page 31

1. Determine the best means to collect the output data– Survey, Data Mining, Internal/External Research

2. From the input factors identified, determine the extreme range for each

– From that range, determine low, mid, and high level

3. Determine the number of samples to collect– Ideally want at least three replicates per factor/level

Balance is key

SalesDemographics Competetion Y1 Y2 Y3

-1 -1-1 0-1 10 -10 00 11 -11 01 1

Measurement Plan

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Unavoidable…§ Define go/no-go thresholds up front§ Build in validation checks

– Use MSA Rules: look for bias

§ Screen outliers

May have to eliminate 50% of the data – account for this in your sample size

What About Data Contamination?

March 2012 Page 33

§ High Level: – Aggregate the performance data by factor to determine any

correlation– How does it compare to industry benchmarks?– How does it compare from historical data (any trends)?

§ Identify Key Factors and Interactions (Competitive Advantage):– Moderating factors– Mediating factors

“All Models are Wrong, but some are Useful” – G.E. Box

Analytics Plan

March 2012 Page 34

§ Test the Model:– Go into the field – confirm the results– Is the model useful?

§ Pilot Some of the Changes:– Compare the control (current state) to the pilot– Compare pilot to predicted results

§ If Results are Positive, Roll Out the Change

Trust, but Verify!

Implementation Plan

March 2012 Page 35

1. Have you done your homework? Potential to eliminate inconsequential factors by leveraging results from previous internal/external studies. Less factors = faster data collection and less expense.

2. Measurement Plan – Pilots of the planned collection instrument can identify errors, or confusion areas. Usability professionals are great to test validity.

3. Smell Test – If your results, or conclusions from the model seem odd, there may be something off in the calculations or data itself.

Look before you leap

Takeaways

March 2012 Page 36

Questions?

Problem Definition

Measure Plan

Analytics Plan

Implementation Plan

Take Aways

March 2012 Page 37

Contact Information

Robert E. Nolan CompanyManagement Consultants

(800) 248-3742www.renolan.com

Steve CallahanPractice Director(206) 619-7740

steve_callahan@renolan.com

Access Nolan’s analytics information page at www.renolan.com/analytics