One of the primary reasons why ProductSolve™ outperforms other simulators relates directly to our adaptive survey system. That system: 1) reduces irrelevant attributes for each respondent 2) reproduces the real-world purchase environment 3) requires the respondent to make the same product feature trade-offs in the survey that they must make in real life 4) adapts in real time to prior choices to zero-in on that particular consumer's most critical values and 5) discovers "how much" preference (summed importance) it takes to meet real-world thresholds (e.g. switching, purchase, etc.)

One of the truisms of decision-support systems is that it can only be as accurate as the respondent-level utilities that undergird it.  One of the central reasons why ProductSolve™ is so accurate is that we don't used a "canned method" to calculate the utilities. In fact, we search and solve for the optimal market level utility set by testing over 20,000 combinations of algorithms, weightings, exponents and "TopK"  cutoffs.  This guarantees that we empirically converge upon the utility set that best reproduces real-world outcomes (RWO's) such as sales, new customer capture, switching, etc.

Once we have 1) captured the value system (the  importance scores) of each customer and have 2) captured "how much" additional preference is needed to "get consumers  off the dime," then, in effect we have created an electronic copy of the  customer: an E-customer.  The advantage of an E-customer? E-customers respond as original customers do except they never get tired...and never need to be paid! When E-customers are placed inside market simulators they allow us to test millions of potential products per day.  The result? More comprehensive risk-mitigation while lowering marketing spend.

The correct theory is necessary, of course, but in the final analysis commercial decision support platforms must predict real world outcomes (RWO's.)  ProductSolve™ leads the industry in predicting real world outcomes.  This reason?  First, ProductSolve™ produces the most accurate respondent-level importance scores (utilities).  Second, we use a proprietary approach to finding behavioral thresholds in the survey.  Third, we exhaustively calibrate to real-world outcomes using both internal and external calibration techniques.

It has never been enough to just guess at an outcome.!  Nonetheless, many simulation-based approaches only allow manual "what-iffing." In contrast, truly thorough risk-mitigation requires goal-solving.  The goal solving built into ProductSolve™ is called Multi-Attribute Simulation Scanning or MASS.  MASS automatically builds and tests millions of products to find the subset that produces critical real world outcomes (RWO's) such as maximal new customer capture, switching, etc.

One of ProductSolve™'s most powerful capabilities is its marginal benefit model (MBM).  The MB model combines ProductSolveTM's real world outcome model (e.g. new customer capture, likelihood of purchase, switching, retention, etc) with two types of cost data, cost to implement (CTI) and cost to serve (CTS) data.  By using CTI and CTS, the Marginal Benefit Model finds strategies that maximize real world outcomes at the lowest cost. The MB model, quite literally, searches and solves for optimal changes to your product or service that will yield the maximal "bang for the buck."

Most marketers are aware that, not infrequently, the entire profit model from a product or service may arise from only one or two market segments. Accordingly ProductSolve™ has the capability to automatically set up and scan for 1) significant analytical findings (e.g. importance, performance, expectation, etc.)  and 2) optimal products across hundreds or thousands of market segments.  The latter capability can be combined with goal-solving to automatically search and solve for product configurations within segments of customers that will produce optimal real world outcomes (RWO) for your product or service.

One of the most powerful features of ProductSolve,™ is it's ability to search and solve for things we would normally never think of.  It does this by automatically scanning any type of data (performance, importance expectation) and finding statistical differences between known or derived market segments.  This remarkable capability, computer-aided significance scanning (CASS), finds natural breaks in the data that a) directly translate to new product ideas and b) provides insights into previously undiscovered segmentation insights.

Although ProductSolve™ is an advanced decision support tool, it has been designed to be extraordinarily easy to use.  To make ProductSolve™ even more immediately useful we have combined the metaphor of the standard user manual with innovative sections on theory and practice.  By including many of the actual exercises we use in our on-site training, a number of our clients have used ProductSolve™ with no additional training beyond the manual itself.