Sales predictions with German precision using innovative market simulation
Despite increasing attention to new product development and forecasting, the new product success rate hasn’t improved over the last 50 years. Only 6 per cent of all new offers launched become a greater success. Even worse, 73 per cent of all new product introductions end as flops (basis: >30.000 new product launches). – The cost of these failures in the U.S. market alone is estimated to be well over $100 billion annually. The same is likely to be true across Europe. Flop costs in Germany alone exceed 10 billion Euro annually. –One of the major challenges in marketing research is the forecasting of success or failure of new products or relaunches (including Marketing Return on Investment).
Improving new product ideas according to test results is often the key to success
Given the high flop rate the main problem is not really predicting future flops which are relatively easy to forecast, but instead identifying those new rational and / or emotional product ideas which have greater market potential and can be further optimised and developed into great successes. Experience has proven again and again that great ideas often initially do not live up to expectations because some details have been solved sub-optimally.
The diagnostic power of the $ales Effect-Market Simulation test system is high. Its result tell you why the brand potential is not higher and indicate concrete actions showing what has to be done in order to improve sales effects (e.g. which image dimension has to be improved). Using these indicated actions products and services have been developed into successes which initially were or would have been failures due to convinced target group sizes that were too small. – It is not widely known that major successes such as Beck’s Gold, Dymo LabelWriter, Iglo del Mar, Nivea Soft or Sheba would have been failures with their originally planned marketing initiatives and executions. – Many clients see this diagnostic capability – in addition to its proven forecast accuracy – as a unique and relevant selling proposition or competitive advantage versus other pretests or market simulations.
A tough and reliable brand choice criterion is used build on a broad scientific base
The $ales Effect brand choice criterion introduced is built on a broad scientific basis and incorporates recent insights of neuroscience, behavioural economics, psychology, marketing and other disciplines. (seefigure above – for a detailed discussion use the download link: R. Mayer de Groot: Using New Product Chances to Full Advantage, p&a international 1 2013 pp. 22-25 – extended version)
Our brand choice criterion is non-linear, straightforward, tough and argues: Customers switch their current main brand (= individual best problem solution) only on a long term basis if the alternative is perceived better in at least one purchase relevant criterion and free of negatives on all other criteria relevant to brand choice. It is built on a broad scientific base.
The prediction, if a respondent will purchase or not, is made for each individual separately – so called segment-of-one approach – because most markets are fragmented, if not pulverised. Therefore it is no longer good or precise enough to predict brand choice for an average person in a segment.
Validations and Forecast Reliability of the$ales Effect Market Simulation
Our brand choice criterion allows the reliable identification of successes and flops and has been validated in more than 1.000 applications in numerous categories and international markets. In those cases in which products have been (re-)launched with more or less the same marketing-mix executions as tested predictions have usually been within half a market share point of actual market figures. Its result tell you why the brand potential is not higher and indicate concrete actions showing what has to be done in order to improve sales effects. Published case studies and validations illustrate the abilities of the $ales Effect market simulation. Deviations of forecasts to real market values were only:
Similar reliable forecasts have been reported in other published case studies without showing validations in detail: Hasseröder (Lenatz 2005/ 2006; Leitz (Lübbe, Mayer deGroot et al. 2003/2004), Niederegger (Strait/Arndt/Mayer de Groot 2006), PerfectDraft (Lenatz 2005/2006), Vorwerk (Weber/Mayer de Groot/Fritzen 2006), WD 40(Gill/Mayer de Groot 2007), Wrigley Extra (Mayer de Groot 2011).