Leveraging Past Failures
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Leveraging Past Failures into Future Opportunities Jan Ekonomy Manager-- Business Market Segmentation October 3 2000. Jan Ekonomy Manager - Market Segmentation. Past segmentation efforts have not fulfilled our expectations.

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Leveraging past failures into future opportunities jan ekonomy manager

Leveraging Past Failures

into

Future Opportunities

Jan Ekonomy

Manager--

Business Market Segmentation

October 3 2000

Jan Ekonomy

Manager - Market Segmentation


Past segmentation efforts have not fulfilled our expectations

Past segmentation efforts have not fulfilled our expectations

  • Prior efforts were not sufficiently actionable

    • Primarily behavior based

    • Did not provide insight into future needs and behaviors

  • Others were actionable but relatively ineffective

  • None focused on what was valuable to Sprint and to prospects/customers

  • None sufficiently modeled the complexity of the decision-making process within large businesses

  • None focused on a holistic view of the business market

  • Resources, time, and leadership were significantly underestimated in past efforts


A snapshot of past failed segmentation approaches

A snapshot of past failed segmentation approaches


Segmentation problem statement

Segmentation problemstatement

Find an actionable segmentation approach that will enable Sprint Business to support efficient, customer focused revenue acquisition, retention and growth.

The methodology chosen must be able to be implemented successfully and supported across the organization.


Leveraging past failures into future opportunities jan ekonomy manager

Best practices assessment: A step by step approach

Step I: Documented what we know about past failed and traditional segmentation approachesStep II: Examined refinements on the traditional approaches, and drew hypotheses about the utilities of each Step III: Conducted in-depth discussions with thought and opinion leaders on the topic of business market segmentationStep IV: Established criteria for developing the best segmentation approach for Sprint Business Step V: Development of the methodology


Step i documented what we knew about traditional segmentation approaches

Step I: Documented what we knew about traditional segmentation approaches

Traditional segmentation approaches can be categorized into four general classes

  • Firmographics

    • Segments based on enterprise or location size by number of employees or revenues, industry classification: SIC

  • Needs

    • Segments based on categories of needs such as strategy, operational, or functional.

  • Usage Behaviors

    • Segments based on amount of product usage

  • Product Purchase Behaviors

    • Segments based on combinations of products purchased/used


Step ii examined refinements on the traditional approaches

Step II: Examined refinements on the traditional approaches

Segmenting on easily identified characteristics (e.g., Verticals/SIC)

  • Benefits: organizing and aligning resources (e.g., sales territories and channels)

  • Overall utility hypothesis: 

    Segmenting by combining geography, size with current Sprint revenue

  • Additional benefits:

    • Prioritization of current accounts for growth and retention purposes

    • Insight into purchase behavior by location and size

  • Overall utility hypothesis :  

    Sub-segmenting on purchase behavior (Voice, FR, IP, etc.)

  • Additional benefits: creates understanding of previous behaviors of customer/prospects

  • Overall utility hypothesis :   

    Segmenting on customer value as well as customer needs and behaviors

  • Additional benefits:

    • Basis for prioritizing efforts/investments and allocating resources

    • Ability to develop offerings that meet needs and have greater success probabilities

    • More precise tactical targeting and messaging based on an understanding of needs and behaviors

  • Overall utility hypothesis :    


Step iii spoke to thought and opinion leaders on segmentation

Step III: Spoke to thought and opinion leaders on segmentation

What the experts told us

  • Few success stories

  • Value is KEY

  • Common ground among experts in that each recommended a form

    of “macro/micro” methodology

  • Iterative process

  • Build upon what you have


Step iv established segmentation criteria

Step IV: Established Segmentation criteria

  • Must yield an understanding of the market

    • Who to target?

    • What to offer?

    • Why do customers behave as they do?

    • Where to advertise and market?

  • Must be actionable

  • Must support prioritization

  • Must be durable

  • Must be believable

  • Predictive of future behavior


  • Leveraging past failures into future opportunities jan ekonomy manager

    Step V: Methodology developmentThe combination of two tiers ensures that all of our important marketing requirements can be met

    Key Purposes

    Key Benefits

    Tier 1: Customer Value

    • Channel assignments

    • Easy to understand

    • and use

    • Stable over time

    • Valuation

    • Prioritization

    • Resource

    • allocation

    Value

    Group A

    Value

    Group B

    Value

    Group C

    • Granular view of

    • target markets

    • & motivators

    • Basis for offer

    • development,

    • targeting and

    • positioning

    • Market & product

    • planning

    • Campaign planning

    • & execution

    • Marketing

    • communications

    • planning

    1

    2

    1

    2

    3

    1

    2

    4

    5

    3

    4

    5

    3

    4

    Tier 2: Customer Specifics


    Where we are today

    Where we are today

    • Research Phase is completed and took 1 year to complete

    • We have a segmentation framework that encompasses the entire

      business market.

      • Tier 1 and Tier 2 segments have been defined and have executive approval

      • Segments incorporate what is important to Sprint and to the customer

      • Segments deal with complexity of decision making environment in large businesses

  • Predictive models to assign all businesses in D&B to macro and

    micro segments are in development

  • Pilots are in development to demonstrate the value of the work that

    has been done

  • Internal focus groups were conducted to inform program

    socialization and implementation plan


  • Internal focus group insights

    Internal Focus Group Insights

    There is a cautious optimism regarding the segmentation initiative

    • “Once burned, twice shy” - perceived failure of segmentation (and other) initiatives leads to hesitancy to invest time and resources.

    • Understanding and agreement of segmentation approach was wide spread

    • Early involvement of cross functional and level of detail were encouraging

      Executive ownership and follow through are essential

    • Buy-in at front end and back end

    • Culture change identified as critical success factor

    • Keep stakeholders informed

      Effectiveness of communication plan is key

    • “Bite size” pieces

    • Customized messages to different audiences, at different times

    • Limit to those whose behavior needs to change

    • Communication of project needs to continue past launch - close the loop

      Infrastructure plan must be articulated

    • Great deal of concern regarding systems and ability to support


    Lessons learned

    Lessons Learned

    • Traditional research methodologies are getting less reliable

    • Make sure your respective technical teams understand one another

      • Survey-based modeling vs. Database modeling

  • Take time to get agreement on definitions and terminology

    • Simple terms like profile or model mean different things to different people

    • Minimizes errors of expectation

  • Communicate segmentation program cautiously

    • Model performance may result in changes to segments

    • Predictive models may not be possible on all segments with the desired level of accuracy

  • Periodic reviews of objectives and budgets essential

  • Front end efficiencies in research design can lead to high complexity and

    greater analytical costs on the back end.


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