Although respondents can accurately report how likely they are to use new products in relative terms, their absolute estimates are frequently off by a large margin. But, until now, no one has really known how far off those estimates are. Each company uses its own combination of rules-of-thumb or rough-analogues to reduce respondent overstatement of likely use. But these adjustments involve a lot of guesswork, and different assumptions can lead to huge shifts in projected market share and revenue.
Demand Calibration is Ziment's approach to taking the guesswork out of adjusting for respondent overstatement in forecasting uptake of new products. This breakthrough technique, which is based upon nearly 30 years of experience in pharmaceutical marketing research, dramatically improves the accuracy of research-based uptake forecasts. This means that you can move forward from your TRIALZ or EVENTZ study with a higher degree of confidence than ever before.
The Demand Calibration algorithm was developed over the course of many years using Ziment’s primary research study database dating back to 1979. The data includes more than 17,000 respondents across more than 25 disease states and 200 projects. The algorithm was developed by comparing primary research preference shares to the actual market performance of the products that were researched from secondary data purchased by Ziment. The algorithm was developed while accounting for a multitude of model variables that drove the prediction, including:
The findings from this exercise were both instructive and unexpected. Rather than historic guesses that often assumed a linear relationship between preference and market share, Demand Calibration arrived upon a much more complex, nuanced relationship. We learned that generally speaking, historic guesses of “dividing by 2” or “dividing by 3” were often too optimistic. We learned that both higher and lower preference shares need smaller calibration, but that those in the middle range need more. We learned that the calibration required varies with the type of task used to collect preference share. And this was just the start of our learning…
For the first time ever, you can conduct primary research on new product concepts and have confidence that you can feed forecasts accurately. When marketing asks, “what’s the overstatement? ”, you finally have guidance to the right answer. People are always talking about doing analogy work and modeling the relationship between preference and market share. Ziment is the first (and only) to have actually done the work.
The Right Data. Only a company like Ziment, with enough volume of work, a long enough history of conducting strategic work in the healthcare arena, consistent management and consistent data storage systems, could ever take on a project like this. Today there is no one else in the world that meets all of these necessary criteria.
The Right Science. Only the talent to properly scale the data and build the Demand Calibration model, using state-of-the art spline regressions with cross-validation to ensure validity, could produce this sort of algorithm.
The Right Approach. Demand Calibration finally takes the guesswork out of preference share adjustment, setting you up for more accurate, defensible forecasts. Why guess when you can use science to help you narrow the range upon which to decide how to incorporate customer feedback?