A white paper for data analysis customerstatus

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Consequently, most organizations are on the lookout for new products and analydis, or variations on current ones, that meet different needs or wants of ofr customers or which bring new customers into the fold. Market segmentation is a set of concepts and tools that can guide management thinking and lead to new profitable product or service offerings.

When ehite company moves beyond one product or service offered to one type of customer, it has started down the path of segmentation. If the product traditionally appeals to men, perhaps a variation will appeal specifically to women, thus segmenting the customer base. If the product appeals to ehite adults, perhaps a variation will appeal wnite tweeners.

There may be a product that appeals to tweener males vs. What began as segmentation based solely on gender can be extended to segmentation based on many, many factors. The key is to find a clear-cut homogenous group of customers or potential customers whose demand for a uniquely configured product or service supports a sustainable market opportunity.

The rationale for segmenting is that customers within segments typically have more in common with each other than with customers in the remaining segments. This commonality helps focus marketing efforts toward each segment. The Basics of Market Segmentation A successful market segmentation initiative answers the following critical business questions: There are tangible costs such as development, production, distribution, marketing, pricing and sales, along with intangible costs such lost opportunities or diminished brand appeal.

A white paper for data analysis customerstatus

Segmenting customers and prospects into groups with shared characteristics is a frequently used method of limiting risk. In effect this approach allows a company to move toward a "one product for one customer" business model. Market segmentation is a broad term that covers a variety of approaches to analyzing customers.

Broadly speaking, it focuses on assigning customers to groups that can be further analyzed for a number of different issues.

  • This commonality helps focus marketing efforts toward each segment.
  • Statistical Methods There are two broad classes of statistical models 1 that are used in segmentation models, and within each of these classes there are numerous specific algorithms that yield different results.
  • Note that often by this stage it is not necessary to test alternative algorithms since the data will usually be quite clear as to the best approach to use.

There are many different ways that the general research goal of segmentation can be achieved. Typically, these revolve around two key decisions that must be made in the process of developing segments: How are the variable s used to create the segmentation determined? Once the variables have been determined, what statistical methods will be used to determine the number of segments present in the sample data. Unfortunately, there are no clear-cut correct answers to either of these questions, but there are ramifications that ripple out from the decisions made at each stage.

Segmentation methods more so than many other areas of customerstattus analysis combine art and interpretation to a great degree. Determining Variables Satisfaction Management Systems, Inc. Variables can be determined by management perception of business opportunities, because of the world view that managers have of their business world.


A white paper for data analysis customerstatus

In its simplest form, this sometimes leads to segmentation models that are based on a single criterion, such as which product a customer purchases, or the region of the country where a customer or prospect resides. Alternatively, complex multivariate models can be developed with this approach where project managers select measures based on an a priori view of the factors perceived as most relevant in differentiating customers and prospects. Determining the variables to use in a segmentation model can also be done on the basis of statistical criteria.

For example, statistical models predicting sales volume may be carried out, and the key drivers in these models used to select the measures that are used in a subsequent segmentation model. Many different statistical techniques, as well as outcome measures, may be used to determine the variables that are ultimately used in the segmentation analysis.

If this approach is used, those decisions are typically based upon the data collected and other overall project objectives. Of course, it is also possible to use hybrid models where some of cusgomerstatus measures used in the segmentation are drawn from the statistical approach, and others are based on management decisions regarding what they customerstaus to see included in the model. Is there one best approach? Simply put, there is not. The management guidance approach is cor problematic because it explicitly ignores what the market is saying, and as such can lead the analysis in a direction that may not be terribly fruitful.

On the other hand, blind reliance on statistical modeling procedures can be inherently conservative. There is a tendency to focus on past trends and to allow them to drive the analysis to a great degree. If the market is undergoing or about to undergo a revolutionary change, then the statistical modeling approach may miss opportunities that an insightful management may anticipate.


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Alternatively, complex multivariate models can be developed with this approach where project managers select measures based on an a priori view of the factors perceived as most relevant in differentiating customers and prospects. Review the key driver data with the client and suggest a set of measures for the segmentation analysis, augmenting this recommendation with other measures as desired by the client. This process keeps repeating iterating until no respondents change segments, and the centroids stop drifting. Typically, this will suggest a small range s should be tested. See also Exact Optimization methods.

Regardless, for each of these wihte the 1 st selection of variables need not necessarily be the last. In particular, subsequent analysis may well indicate in fact, it usually does that some of the measures selected for use in the segmentation model really add little value and simply confound and confuse the model.

  • There is a tendency to focus on past trends and to allow them to drive the analysis to a great degree.
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  • His research interests are in supply chain management, production and operations management, evolutionary computation, and manufacturing management.

Statistical Methods There are two broad classes of statistical models 1 that are used in segmentation models, and within each of these classes there are numerous specific algorithms that yield different results. The two primary statistical approaches to developing segmentation models include: Satisfaction Management Systems, Inc. A matrix is calculated that determines how similar each respondent in the data file is to every other respondent. The most similar respondents are joined together, the similarity matrix is re-calculated, and the next-most-similar respondents are joined.

This process continues until all respondents are joined together.

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At each stage, diagnostic whige are presented that help to determine if this stage constitutes a good stopping point in the segmentation process. While this sounds quite simple in theory, there are over 20 different statistical algorithms 11 of which are quite commonly available that are used in the hierarchical segmentation models to achieve this general goal.

One of the key benefits of the hierarchical models is the fact that assorted diagnostic data are provided to help inform decisions regarding the proper number of segments to retain for the segmentation model, but even with these diagnostics there are no clear-cut unambiguous rules. A key limitation of all of the hierarchical segmentation models more true of some of the methods than others is that they can produce segments which in multidimensional space resemble elongated chains as opposed to the typically desired tight, spheroidal clusters.

How are the variable s used to create the segmentation determined? This powerful feature allows you to put context around the numbers that you are tracking. A key limitation of all of the hierarchical segmentation models more true of some of the methods than others is that they datq produce segments which in multidimensional space resemble elongated chains as opposed to the typically desired tight, spheroidal clusters. If the product traditionally appeals to men, perhaps a variation will appeal specifically to women, thus segmenting the customer base. There is a tendency to focus on past trends and to allow them to drive the analysis to a great degree. If the market is undergoing or about to undergo a revolutionary change, then the statistical modeling approach may miss opportunities that an insightful management may anticipate. Why your district needs a data analysis system What type of data system works best for your district A series of questions to ask vendors in order to make a good selection decision A guideline of how to setup the data analysis procurement process Negotiating tips to deal with vendors A sample Data System RFP that you can adjust for your own district's needs This white paper will help guide your district through a complex process helping to make sure proper due diligence was done and a data informed selection was made. Variables can be determined by management perception of business opportunities, because of the world view that managers papeer of their business world.

Iterative Centroid Methods develop in a more deductive fashion. Given a set of variables and a specification of the number of segments to retain, a set of initial starting points or centroids equal to the number of desired segments is defined for each measure. Respondents are then added to analydis segment that they are most similar to, the centroids are re-calculated, and respondents are re-assigned to the new aalysis that best characterizes their pattern of scores on the measures.

This whtie keeps repeating iterating until no respondents change segments, and the centroids stop drifting. There are 7 major variations in the iterative centroid methods three of which control the vast majority of uses available for the analysis. The strength of this approach is that it tends to produce the tight, spheroidal segments that are typically preferred for subsequent analysis.

The primary limitation is that it requires a priori determination of the number of segments, and provides few diagnostics that help to determine the number of segments that should be used in the analysis. What Do We Recommend? Our usual approach to developing segment models includes the following stages: Work with the client to select a key outcome criterion or criteria, and develop multivariate models that identify the key drivers that characterize the data. Review the key driver data with the client and suggest a set of measures for the segmentation analysis, augmenting this recommendation with other measures as desired by the client.

Review the data on the proposed measures, and select different hierarchical clustering algorithms that appear best suited for the project and data at hand. Typically, this will suggest a small range that should be tested. If necessary, select of the different iterative centroid methods and test click the following article models representing each of the different solutions for the number of clusters, as determined by the previously completed Monte Carlo probability study.

Note that often by this stage it is not necessary to test alternative algorithms since the data will usually be quite clear as to the best approach to use. Review the findings of the iterative centroid models and, if necessary refine by dropping measures that are clearly random and do not contribute to the segmentation structure. Client involvement at each stage of the process is a welcome addition that can help to ensure management buy-in for the final segmentation model. A segmentation model that relaxes the rigid requirement that a respondent be assigned to a single cluster and allows estimation of the probability of segment membership.

A segmentation model that uses both the similarity among the measures in the data set but also the sequential positioning of the records within the data set, i. Often used in files where records are sorted by time and sequence "makes sense. The beta parameter allows you to check for different cluster shapes in multidimensional space.

Weighted pair-group method which minimizes the average linkages among members in the segment. A biological model that focuses on minimizing the mutations required when moving from a simpler to more complex model. A weighted pair-group method using centroids defined by medians as opposed to averages.

All, there customerstatus a paper data for analysis white revision

Customeerstatus form of segmentation where the nearest neighbor ana,ysis define a join. Tends to produce cluster chains. A variation on the complete linkage model see above that extends the usual pairwise best way to papers raw roll of distance into looking at triads.

A variation of density linkage model where customerstatu individual records must be assigned to a modal cluster before modal clusters are allowed to join together. Another segmentation model that uses both the similarity among the measures in the data set but also the sequential positioning of the records within the data set. See also Exact Optimization methods.

Minimize the within-group error sum-of-squares. Tends to produce equal sized clusters.

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