# Statistics Help-Modelling-Marketing United States | Europe | London

Concept Optimization is BS stats service based on conjoint analysis. In conjoint analysis, a product, concept or claim is broken down into sets of discrete elements. For instance , a new shaving cream may consists of features like color, fragrance, foam quality and positioning. These features consist of many levels, e.g.
fragrance levels = {regular, mint, aloe }. By selecting special combinations of levels (one from each feature), we create a design consisting of cards. Each card is a complete product, e.g. card # 1 = { dark green, mint, gel, “for sensitive skin” } Using fractional factorial designs, we are able to represent all possible combinations of levels in a small number of cards. The task of the respondent is then to rate or rank the cards. By using regression, we are able to evaluate utilities or part worths for each level of each feature. By looking at the span of the utilities for each feature, we can tell how important each feature of the concept is to the whole.

If each espondent rates all cards, then we can evaluate percentage preference for any product over any other single product or set of products. This is the only point in conjoint analysis where statistical inference is possible. If each respondent sees only a subset of cards, conjoint can still be done, but statistical inference are not ossible. You can access our services like Statistics help, Statistics Modelling, Marketing Mix, Statistics Dissertation,Statistical Consulting, Predictive Modelling in the whole world of BSSTATS GROUP.

Which of these concepts (product, service, name, flavor etc.) is most likely to succeed in the market place?

Which combination of flavors if launched can give me the best coverage of my target audience?

We use statistics techniques such as Q Sort, TURF and Shapley value analysis to screen, rank and optimize concept/product lines. We select the tool appropriate for the issue at hand in a way that maximizes discrimination between the options, and aids in decision-making.

For multiple product lines, we use tools to assess the strength of each individual item in the line to maximize reach and minimize overlap. A group of items are also identified in way in which the largest combined audience can be targeted.