Segmentation Process and Strategy

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Segmentation Process and Strategy
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Contents

• Segmentation Process3
• Darts Custom Segmentation Approach .4
• Applications for Segmentation5
• Techniques Data Used 6
• Overview of the Process with Timeline 8
• Keeping Segmentation Relevant10
• Further Analysis 11
• Segmentation Example12
• Test Case .13

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• Segmentation Process
• Darts Custom Segmentation Approach
• Applications for Segmentation
• Techniques Data Used
• Overview of the Process with Timeline
• Keeping Segmentation Relevant
• Further Analysis

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Darts Custom Segmentation Approach

• Dart builds sophisticated custom segmentation
  models.
• Purpose
• To achieve highly differentiated customer
  segments that make marketing more efficient and
  effective.
• Method
• Experienced modelers use a combination of science
  and intuition to create a custom segmentation
  scheme. A good solution requires that the
  segments be distinct, predictive of behavior,
  implementable, and reflective of the business
  needs for which they were created.
• We also perform data quality checks and report
  any problems or questions before we arrive at a
  final solution.
• Results
• An elegant cluster solution that is practical,
  makes sense and can be implemented.

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Applications for Segmentation

• There are many uses for segmentation. These are
  some examples.
• Purposes
• Needs Based Segmentation - Auto makers for
  example design vehicles to match the needs of
  buyers, ranging from economy cars to luxury cars
  and minivans to pickup trucks.
• Product Segmentation Manufacturers diversify
  products within each needs base to appeal to
  buyers with different tastes and wealth.
• Customer Segmentation Customers are segmented
  based on their needs and product preferences.
  Segments grow or shrink over time as products
  improve, become obsolete or tastes change.
• Niche Segmentation Niche segments are
  characterized by strength in one needs base and
  product within it. Restaurants are good examples,
  ranging from delis to Chinese food with décor
  appealing to McDonalds patrons to those
  preferring a three star experience.
• Global Segmentation - Insurance firms and medical
  and legal practices also use product
  segmentation, and sometimes attempt to cover all
  the product space.
• In-store Display Segmentation Drug stores,
  grocery stores, book stores, and other retail
  outlets use segmentation in order to keep like
  products close to each other within the store,
  making shopping convenient and cross selling more
  profitable.

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Techniques
Techniques to Developing Clusters Statistical
clustering techniques include neural networks,
discriminant analysis, factor analysis,
hierarchical clustering, and perhaps most
commonly, "nearest neighbor" or "k means"
algorithms. All of these approaches determine
what variables are similar and dissimilar in
statistical terms, forming segments. The analyst
picks the number of clusters through an iterative
process, looking for uniqueness between the
segments and a number of segments that are
practical and manageable from a marketing
perspective. Data Definition How variables are
defined makes a substantial difference in the
outcome. Age, for example, can be characterized
as a set of age-ranges or as a continuous
variable. These characterizations lead to
different segmentation solutions. So, selection
of the best way to characterize the variables
used for segmentation involves considerable
judgment, from both a statistical and a business
perspective.
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Data Used

• Within practical limits, the more data the
  better, in the initial stages. The data relevant
  to the segmentation scheme is revealed through
  the statistical process. But, the solution must
  make sense and the variables used must make a
  contribution.
• Customer Data
• Transaction Details Frequency, amount and
  timing of purchases, items bought, prices paid,
  use of cash or credit, and use of coupons.
• Acquisitions Details Marketing channel,
  promotion type, and address/city.
• Appended Database Data
• Life Style Profession/occupation, vehicle
  ownership, Internet use, travel, pets, and
  hobbies.
• Financial Investments, credit card usage and
  type, living expenses, and credit worthiness.
• Demographic Age, income, education, gender,
  marital status, and number of kids.
• Geographic Own/rent, urban/rural, size of city,
  region, and size of dwelling.
• Market Research Data
• Behavioral Purchase patterns, why they bought,
  what they use the product for, responsiveness to
  different marketing channels.
• Attitudinal Product preferences, willingness to
  try other brands, price sensitivity, shop for
  convenience, opinion of the company and the
  competition.

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Overview of the Segmentation Process
Project Timeline 15 days to several months
depending on the size of the project

• Data Prep/Hygiene
• Data is read into an analytic file. Data records
  and variable values are examined for accuracy.
  Records with duplicate match code ids are
  compared prior to de-duping those records.
  Variable values are examined to make sure they
  are within acceptable ranges.
• Initial Exploratory Analysis
• The heart of the work - Data description and
  looking for explanatory patterns in the data,
  which lead to a picture of your business,
  customers, products, environment, and financials.

Segmentation Analysis Selection of the
clustering technique and the variables that will
be used. Implementation First, the sample file
is scored with the segmentation scheme. Then, all
other records that contain the data used to make
the segments are scored. The remaining records
that do not contain the necessary data (such as
those not included in a survey that was used)
must be assigned to the segments using other
means. There are several methods to accomplish
this, including regression and neural networks.
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Completing the Segmentation Process

• While the segments have been defined by this
  stage, a face still needs to be put on them for
  them to make sense.
• Name Assignments
• Typically, descriptive names are given to
  segments, instead of referring to them as
  Segments A, B, and C. These names generally
  reflect the key components that describe them.
• Descriptive Profiles
• Profiles describe the attributes of each segment.
  For example, Customers in Segment A are 36
  more likely to buy frequently than customers in
  Segment B.
• Some variables not used in the clustering process
  are retained for describing the segments. For
  example, while segments may be based primarily on
  their behavioral characteristics, it is still
  worthwhile to note their demographics.
• Financial Analysis
• Determine the expected financial performance of
  each segment. Response indexes and residual
  income from likelihood of repeat business is
  often part of the analysis.

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Keeping Segmentation Relevant

• It's important to monitor the performance of a
  segmentation scheme over time and recalibrate as
  necessary.
• Shifts in Market Conditions
• Work with client to track performance measures
  for each segment. A monthly performance scorecard
  is a good mechanism for tracking changes in
  performance and the companys position in the
  market place.
• Fixed Intervals
• A simple alternative to this tracking process is
  to recalibrate the segmentation scheme at fixed
  intervals, such as once a year.

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Further Analysis

• Get the most out of your segmentation strategy.
• Optimize Profitability through Financial
  Modeling
• Expand the initial financial analysis into an
  interactive model. This allows what-if scenario
  testing to maximize the segmentation mix,
  marketing mix, mail strategy and product pricing.
  
• Increase Prices without Losing Sales
• Scientific price/incentive test to quantify the
  price elasticity of demand. This analysis drives
  the price component of the financial model.
• Improving Segmentation through Appended Database
  Data
• Database enhancement research with cost/benefit
  analysis reveals which additional data provides
  the most predictive power for the investment.
• Using Market Research in Combination with
  Segmentation
• Validate segments in the real world,
• Collect data to fine tune the segments,
• Better understand purchase motivation, behavior,
  and desirable product attributes, leading to more
  effective offers, and
• Better target creative, resulting in better
  response to solicitations.

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• Segmentation Example
• Test Case

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Descriptive Profiles

• The chart to the right shows the distribution of
  automotive credit card accounts by segment. Low
  Spenders, Game Players and Credit Needy were
  the biggest segments.
• The charts below describe the Game Players
  segment
• Segment Highlights
• They are high spenders, accumulating as much
  rebate as possible through the program.
• They have the highest likelihood to redeem their
  points
• They are more likely to own a new car made by
  the mfg sponsoring the program
• They have normal age and income distributions

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Financial Analysis

• Description This example is based on a credit
  card with an automotive rewards program, where
  people accumulate a percentage of their purchases
  towards a new automobile. Revenue is based on
  credit income and profits from auto sales.
  Expenses come from redemptions and
  marketing/operating costs.
• Key Findings Game Players were very costly to
  the program. Credit Challenged were expensive due
  to bad debt. Low Spenders were profitable as were
  Conquest Credit (due to high incremental sales
  rate).

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Contact Info

• Craig Tomarkin
• DART Marketing, LLC
• 2333 Congress St.
• Fairfield, CT 06824
• CTomarkin_at_dartm.net
• 203-259-0676
• Fax [phone]