Marketing Applications 

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Clustan has been used successfully in the following marketing applications:

    • Market and customer segmentation
    • Product branding and design
    • Targeting direct mail and tele-mail
    • Customer conversion and retention
    • Lower marketing costs

Clustan's approach is to obtain a representative sample of customers, products or competitors and construct a clustering model which reflect the "types" operating within a company's market.  Clustan is the only professional software which can produce a hierarchical cluster analysis efficiently on very large surveys or corporate databases.  If you currently use a decision tree approach, have a look at our comparative critique.

Using Clustan, a customer "type" can represent a tight market segment; identifying their particular needs in that market allows products to be designed with greater precision and direct appeal within the segment; targeting specific segments is cheaper and more accurate than broad-scale marketing; customers respond better to segment marketing which addresses their specific needs, gaining market share and improving retention.

Brand image analysis, or defining product "types" by customer perceptions about them, allows a company to see where its products are positioned in the market relative to those of its competitors.  This type of modelling is valuable for branding new products or identifying possible gaps in the market.

Clustering supermarket products by linked purchasing patterns can be used to plan store layouts, to maximize spontaneous purchasing opportunities.

Bear in mind that Clustan is the only professional software which can produce a hierarchical classification efficiently on very large surveys or corporate databases.  Our banking application involved producing a tree for a sample of 16000 customers of a bank, and then classifying the bank's remaining 4m customers by finding their cluster of best fit within the sample tree.

If marketing applications interest you, have a look at our retail banking application or see our critique in Clustering versus Decision Trees.