Statistical Analysis

Our company has unique experience in our ability to analyze the information, the data, collected on behalf of its clients. In many cases, a straightforward presentation of data in tabular format will serve our research objectives. For example, if 30% of the sample prefers “configuration A” and 70% prefers “configuration B,” then we are safe to assume that our client should offer the latter configuration.

In many instances, however, a proper analysis involves much more than the simple “value based” decision described above. For example…

Demand estimation may involve an analysis of responses to range of price levels (to determine price sensitivity). Further, demand estimation may involve various weighting schemes to factor in the effects of environmental, social and other factors affecting customers' likelihood to purchase a product or service.



Functional analytical techniques (modeling) may be required when it is necessary to demonstrate “cause and effect” situations. For example, it may be desirable to determine the community's attitudes on a wide range of characteristics toward a proposed development in the community. Using functional techniques, it may be easy to determine those characteristics most in need of changing in order to improve chances of project acceptance within the community (i.e., answer the question: “what most causes dissatisfaction with our project?”). Or conversely, these techniques will help determine the factors which enhance project acceptance in the community.



Structural analytical techniques (mapping) may be very helpful in discovering customer segments (in terms of demographic, sociographic or psychographic characteristics) that may respond favorably to a product or service offering. These techniques may also be very helpful in developing a “product” mix that closely matches demand.

Again, CDS will determine, at project design time, the analytical technique most appropriate for satisfying research objectives.