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Product Search Optimization

We are pleased to announce the availability of the Advanced Simulation Module (ASM) for product optimization searches. Marketers often turn the market simulation question around: rather than ask "How good would this product be?" they often ask "What product would be best?"

If product optimization is the goal, you can save a great deal of time and find potentially better solutions using automated searches rather than the manual process of modifying products and processing simulations one at a time.

You can optimize products vis-a-vis a set of fixed competitors, or without regard to a competitive set. Criteria for optimization include utility, share, purchase likelihood, revenue, profit and cost minimization. For profit or cost minimization searches, a new area is available for specifying costs associated with levels in the study.

The Advanced Simulation Module leverages the current simulator's share estimation models and point-and-click interface--no "programming" is involved to set up and run sophisticated optimization scenarios. The optimization routines include hill-climbing, Genetic Algorithms, and exhaustive search techniques.

The Advanced Simulation Module can analyze conjoint data from any of Sawtooth Software's conjoint systems (ACA, CBC, or CVA), including full-profile, partial-profile, and alternative-specific designs. You can even use conjoint or preference data provided by the researcher that was not necessarily generated by Sawtooth Software's systems.

Conjoint simulators provide an exceptional tool for product optimization. They can take into account the characteristics of currently-available products as well as the desires of a heterogeneous population of potential buyers. Subject to reasonable caveats about the quality of respondent sampling and questionnaire design, conjoint simulations can accurately assess likely product success long before a product is ready for test market.

Specifying Products for Optimization

When using the market simulator in typical simulation mode, you specify multiple products using a spreadsheet-like grid. For example, you might specify three products as following:

However, when searching for optimal products, you enter ranges of levels rather than fixed levels for the product to be searched. For example, in the grid below we specify that "Our Product" must be brand level 1 (our brand), but all the other values can vary.

Notice that we've used three kinds of syntax for specifying the levels that can vary:

          "The levels of Screen Size can vary from 25 to 33"

          "The Style levels can be either 1 or 3"

          "Price must be greater than or equal to 250"

You can search for multiple products simultaneously, such as finding the best 2 products to offer. In the example below, the product search will seek to maximize the sum of the shares, revenues, profits, or utilities of products 1 and 2.

When conducting product searches, you can also specify that certain static (fixed) products should be included in the objective function (the value to be maximized, whether share, revenue, profit or utility). For example, you may have a current product on the market that will not change, but you want to search for another product to offer, such that the profit for the existing product and the new product are maximized.

Many researchers and firms do not have access to cost data, and thus will not be able to conduct profitability searches. However, many firms have pricing information, linking features to specific price deltas (e.g. sunroof adds $500 to base price). Attribute-based pricing can be specified in the Advanced Simulation Module. This eliminates the trivial outcome of an optimal product simply reflecting the best features at the lowest price. When attribute-based prices are considered, the marginal value of the feature must exceed its marginal price to be included in an optimal product.

For more information about the Advanced Simulation Module, please see the technical paper at