Segmentation Process…3 Dart’s “Custom Segmentation” Approach ….4 Applications for Segmentation…5 Techniques & Data Used …6 Overview of the Process with Timeline …8 Keeping Segmentation Relevant…10 Further Analysis …11 Segmentation Example…12 Test Case ….13 Contents
Segmentation Process Dart’s “Custom Segmentation” Approach Applications for Segmentation Techniques & Data Used Overview of the Process with Timeline Keeping Segmentation Relevant Further Analysis
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. Dart’s “Custom Segmentation” Approach
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 McDonald’s 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. Applications for Segmentation
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.
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. Data Used
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. Overview of the Segmentation Process 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. Project Timeline: 15 days to several months depending on the size of the project
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. Completing the Segmentation Process
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 company’s 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. Keeping Segmentation Relevant
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.
Segmentation Example Test Case
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:
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
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). Financial Analysis
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