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SEGMENTATION

PRODUCT LINE REDESIGN

PREDICTIVE ANALYTICS

CUSTOMER SATISFACTION

VIDEO

CENTER

CONSULTING

PLATFORMS

SOFTWARE

SOLUTIONS BY INDUSTRY

SERVICE LINE: SEGMENTATION

EXAMPLE:

CLIENT:

National Retail Banking Segmentation Study (NRBSS)

1993-1995

One of the World's Largest Consulting Firms / 90 Retail Banks

 SUMMARY

OBJECTIVES

RESULTS

DSE TECHNOLOGIES USED

DIFFERENTIATION

DSS partnered with a world-class consulting firm in launching the National Retail Segmentation Study (NRBSS) in 1993-1995. The NRBSS is still the largest segmentation and simulation modeling study ever deployed in the financial services industry.

 

The success of that study launched that consulting firm's flagship segmentation business line in the mid 90's. (This resulted in client banks increasing revenues by hundreds of millions of dollars.) The team that implemented the NRBSS won one of the industry's most coveted thought leadership awards in its inaugural year. It also yielded the book, Vision 2000: the Transformation of Banking authored by Dr. Waino Pihl, Jim Burson and BAI.  DSS provided the conjoint analysis, simulation modeling and  segmentation analytics for the NRBSS.

  • Perform the largest value-based segmentation on retail banking customers (90 participating banks.)
  • Identify the key buyer value segments in the market (buyer value based segmentation.)
  • Identify and prioritize the key attribute (service and sales characteristics) that will that will maximize sales, deepen existing customer growth, and reduce customer attrition.  Do so for every segment within every major business line (deposit, loan, investments, card products.)
  • Re-engineer the go-to-market strategy of the industry.

 

  • Meeting the actual "buyer values" of consumers grows targeted customer capture (acquisition) by 20%, deepens the dollar volume relationship an average of 10% per business line, and decreases attrition 30% bankwide.
  • TRADITIONAL FOCUS GROUPS  FAIL TO ARRIVE AT CORE BUYER VALUES. instead one needs to reproduce the real-world TRADE-OFFS faced by the consumer.
  • Main Segments: Price,/Fee, Speed, Service/No Errors, Relationship.

 

  • Adaptive Pairwise Trade-off Surveying: reproduces the real-world trade-offs and learns from prior answers to customize the survey to each consumer.
  • Advanced Algorithms Find What Is Truly Important: Hundreds of combinations algorithms, sub-algorithms, iterations, weighting of survey sections uncovers the "utilities" that result in the simulator making the same choices as the consumer.
  • SegmentSolveTM: our segmentation platform which uses 13 separate algorithms to converge upon the most highly separated consumer segments.
  • ProductSolveTM: is our goal-solving market simulator.  By capturing values (what's important to the consumer) we no longer need to create endless surveys.  Instead we create new products  in the simulator (by what-iffing or goal solving)  to discover which products  consumers will buy and which will maximize customer retention.
  • Gets Down to the Bedrock - the Real "Key Drivers": adaptive trade-off whittles down the factors (attributes) that move the needle for real-world outcomes.
  • Industry-Leading Accuracy in Quantifying Those Key Drivers : uncovers the "utilities" that result in the simulator making the same choices as the consumer. (Via hundreds of combinations of algorithms, weighting of survey sections, etc.)
  • Produces the Strategies That Maximize Profit: via our goal-solving simulator.
  • You Get the Fishing Pole Not the Fish: By capturing values (what's important to the consumer) we no longer need to create endless surveys.  Instead we create new products  in the simulator to discover which products  consumers will buy / keep.
  • Generated the Most Operational Value-Based Segments.: We use value based segments because it emerges "up from the data."  It is the most operational segmentation because it is defined by things you have control over - pricing, speed, front office changes, etc.

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SERVICE LINE: PRODUCT REDESIGN

EXAMPLE:

CLIENTS:

REBOOT: Retail Product Line Re-Design

Six of the Top 20 Banks in the United States

1995-Present

 SUMMARY

OBJECTIVES

RESULTS

DSE TECHNOLOGIES USED

DIFFERENTIATION

REBOOT is based on a simple idea.  If you build what consumers really want...they will come.

 

Since 1993 we have performed more advanced, large-scale (super regional or national), multi-business line buyer value-based research projects for top 20 U.S. banks than any other strategy firm worldwide. Buyer value research realigns the banks' product lines to consumers' preferences via the technologies listed above.

  • Re-engineer all business lines (loans, deposit products, home equity, investment products etc.) to better meet consumer preferences while lowering cost-to-serve
  • For each business vertical:
    • Identify "trade-offs" attributes, drivers we that are NOT differentially important.  De-service those attributes in order to lower the overall cost-to-serve.
    • Identify new delivery configurations that will grab preference share for non-customers.
    • Identify blunting strategies; strategies that blunt competitive encroachment.
    • Identify optimal service configuration that will retain consumers.
    • Identify the most distinctly different segments in each vertical and the best ways to deliver service to each segment at the lowest cost.
    • Re-engineer the go-to-market strategy for each vertical.
  • Meeting the actual "buyer values" of consumers grows targeted customer capture (acquisition) by 20%, deepens the dollar volume relationship an average of 10% per business line, and decreases attrition 30% bankwide.
  • Identified "trade-offs" attributes, drivers we that are NOT differentially important.
  • Tested hundreds of thousands of new configurations in the simulators and identified:
    • New delivery configurations that will grab preference share for non-customers.
    • Optimal service configuration that will retain consumers.
    • Competitive blunting strategies that seize maximal share from new "encroachers".
  • Identified the most distinctly different consumer segments in each vertical and the best ways to deliver service to each segment at the lowest cost.
  • Re-engineered the go-to-market strategy for each business line.
  • Main Segments: Price,/Fee, Speed, Service/No Errors, Relationship.
  • Adaptive Pairwise Trade-off Surveying: reproduces the real-world trade-offs and learns from prior answers to customize the survey to each consumer.
  • Advanced Algorithms Find What Is Truly Important: Hundreds of combinations algorithms, sub-algorithms, iterations, weighting of survey sections uncovers the "utilities" that result in the simulator making the same choices as the consumer.
  • SegmentSolveTM : our segmentation platform which uses 13 separate algorithms to converge upon the most highly separated consumer segments.
  • ProductSolveTM : is our goal-solving market simulator.  By capturing values (what's important to the consumer) we no longer need to create endless surveys.  Instead we create new products  in the simulator (by what-iffing or goal solving)  to discover which products  consumers will buy and which will maximize customer retention.
  • PositionSolveTM: Visualizes the positioning of each competitor and discover how to move into innovative but currently unpopulated areas of demand.
  • Gets Down to the Bedrock - the Real "Key Drivers": adaptive pairwise trade-off whittles down the drivers that yield real-world outcomes (sales,  etc.).
  • Industry-Leading Accuracy in Quantifying Those Key Drivers: Thousands of  combinations algorithms, sub-algorithms, iterations, etc , are tested to converge on the optimal "utilities." These utilities, in turn, enable the simulator to make  the same choices as the consumer.
  • Produces Strategies That Maximize Bang for the Buck: via our goal-solving simulator.
  • You Get the Fishing Pole Not the Fish: By capturing values (what's important to the consumer) we no longer need to create endless surveys.  Instead we create new products  in the simulator to discover which products  consumers will buy and keep.
  • Generates the Most THOROUGH Recommendations: Because our goal-solving simulator tests hundreds of millions of strategies to discover the ones that meet YOUR criteria (e.g. the feature changes that maximize revenue at lowest cost to implement).

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SERVICE LINE: PREDICTIVE ANALYTICS

EXAMPLE PROJECT:

CLIENTS:

Ultra-High Performance Data Mining (UHP-DM)

1995-Present

Four of the Top 20 Leading US Financial Firms

 SUMMARY

OBJECTIVES

RESULTS

DSE TECHNOLOGIES USED

DIFFERENTIATION

The average data mining analyst builds about a dozen predictive models per week.  What if data mining could be automated to produce millions of models per week?  The UHP-DM framework is a fundamental rethinking of how to transform data mining from the ground up.  UHP-DM sees data mining as a solution space of potential models with six primary dimensions: data transforms, model form, algorithms, sub-algorithms, iterations, and criteria.  By building a data mining assembly line that penetrates this solution space, we can zero in on high performance volumes within the solution space. For the technologies that achieve this, see the DSE TECH section above.

  • Perform initial-sell, up-sell-cross sell (etc., etc) far better (higher lift.)
  • Perform initial-sell, up-sell-cross sell (etc., etc) far less expensively.

 

  • 10% higher lift on average than "best practice" software or consulting .  (See why by inspecting the technologies described in the next tab.)
  • Deployed cost: 25% of the cost of traditional consulting approaches without the need to rearchitect the datamart.

 

UHP-DM is an R&D project fifteen years in the making.  It has yielded two linked applications; VISTAR and BigDataSolve:

  • VISTAR: is our automated sub-datamart reduction engine that identifies the best predictor combinations to maximize the desired outcome (objective function).
  • BigDataSolveTM: takes those variables and runs millions of models within that solution space.  It does so by creating a solution space that automatically rotates algorithms, sub-algorithms, iterations, criteria and transforms against the problem,  This typically results in millions of model combinations that probabilistically yields higher lift.
  • Distributed Computing: UHP-DM uses distributed computing by turning regular laptops in a network into a virtual supercomputer.
  • DELTA: a specialized form of validation (Differential Enhancement of Lift Through Arbitration) yields higher lift by finding subgroups with higher predictive performance.

The Differentiation of our UHP-DM (Ultra-High Performance Data Mining) practice is industry-leading.  In two simple ways.  It is:

  • Way Less Expensive: Consulting firms still rely on a standard formula 1) Scaring their clients to death with what is (mostly) the truth 2) Convincing them to 2) rearchitecting their datastore and b) buy expensive closed-architecture analytics.
  • In Contrast to Standard Consulting, UHP-DM Gives You What You Need Far Less Expensively...But With BETTER Results There are only four keys to ultra-high performance data mining. Our UHP-DM approach addresses them all:
    • Getting the Right Predictors / Cleaning Them Thoroughly (See prior VISTAR tab.)
    • Finding the Algorithm & Transforms That Predict Best (See BigDataSolve.)
    • Doing it Fast w/ Inexpensive Distributed Computing: (See BigDataSolve / DP.)
    • Validating Exhaustively...in a Way That Increase Predictive Fit: (See DELTA.)

SERVICE LINE: CUSTOMER SATISFACTION

SERVICE:

CLIENTS:

Customer Value Maximization

1996-Present

First Union (Wachovia), Chase, Bank of America, Others

SUMMARY

OBJECTIVES

RESULTS

DSE TECHNOLOGIES USED

DIFFERENTIATION

Many firms "get it" now.  But back in 1995 the need for customer value maximization to replace customer satisfaction was still new.  It began with our discovery at First Union that standard, old-school customer satisfaction was only associated with ONE claimed market-driven characteristic: likelihood to recommend.

 

This drove us to re-engineer customer satisfaction to link it causally to profitability, acquisition, growth and retention.

 

For more details, see the results and DSS technologies tabs above!

  • Use new generation metrics that link to:
    • Recommendations
    • Acquisition
    • Growth
    • Retention
  • Re-engineer customer satisfaction to produce revenue for the bank, rather than track metrics with no demonstrated value to either the consumer or the bank.

 

  • Demonstrated the MYTHS of Customer Satisfaction:
    • Demonstrated that 1-7 scale Likert Measurement of "Key Drivers" regressed against customer satisfaction is essentially vacuous.
    • Demonstrated that standard studies measure only 2 of 8 metrics (see next tab).
    • Demonstrated that 95% of prior CS studies have lost money or yielded NO link to customer value (deepening the relationship.)
    • Demonstrated that only  research firms benefit from endless tracking studies.
    • Demonstrated that "Delight" (exceeding expectation) doesn't produce loyalty....one must MEET expectations on as many drivers as possible to create it.
  • Discovered gaps between performance and product and service set that produce acquisition, growth and retention for each business line.
  • Identified change strategies that prioritize closing gaps that move the needle most on acquisition, growth and retention for each business line.
  • Measured RELEVANT METRICS:
    • Real-world (not scaled) performance for every key driver for every business line.
    • Real world importance for every key driver for every business line (via trade-offs).
    • Real-world apportioned expectation. - (b/c stated expectation is overstated.)
    • Customer intention, service history,  demo/corpographics, likely profitability.
    • Customer change thresholds (switching, acquisition, growth, attrition)
  • Adaptive Pairwise Trade-off Surveying: reproduces the real-world trade-offs and learns from prior answers to customize the survey to each consumer.
  • Simulator Calibrated to Make Same Choices as the Consumer
  • SegmentSolveTM: uses 13 separate algorithms to discover the most distinct segments.
  • SatisfactionSolveTM: a goal-solving market simulator that closes the gaps between REAL WORLD PERFORMANCE and EXPECTATION for each key driver and business line in order to project real world outcomes (e.g. sales, profitability, marginal benefit)
  • Gets Down to the Bedrock - the Real "Key Drivers": adaptive pairwise trade-off whittles down the drivers  that really move the needle for real-world outcomes (sales, etc.).
  • Industry-Leading Accuracy in Quantifying Those Key Drivers : Hundreds of combinations algorithms, sub-algorithms, iterations, weighting of survey sections uncovers the "utilities" that result in the simulator making the same choices as the consumer.
  • Finds Driver Changes That Maximizes Bang for the Buck: via our goal-solving simulator.
  • You Get the Fishing Pole Not the Fish: By capturing values we no longer need to create endless surveys.  Instead we risk-mitigate with simulators that yield real-world outcomes.
  • Next-Gen Customer Satisfaction: closes the gaps between REAL WORLD PERFORMANCE and EXPECTATION for each key driver and for each business line such that:
    • Each high profitability customer will maximally deepen the relationship
    • Each high profitability customer will become more loyal/retain
    • The aggregate "gap closures" at the business line level to achieve these outcomes.