$ales Effect Forecasts


How to use your Market Chances to full Advantage

Given the fact that 73% of all launches each year end as flops and only 6% become larger successes, it is vital to predict the sales potential of your marketing initiatives as soon and reliably as possible. We have developed several tools to increase your speed to market at different stages throughout the development process. This is based on the experience that great ideas often initially do not live up to expectations because some details have been solved sub-optimally.


The diagnostic power of these tools is high. These methods tell you why the brand potential is not higher and indicate concrete actions showing what has to be done in order to improve the sales effect (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 too small convinced target group sizes. – It is not widely known that major successes such as Beck’s Gold, Dymo, Iglo del Mar, Nivea Soft or Sheba were or would have been failures with their originally planned marketing initiatives and executions. – Many clients see in this superior diagnostic capability – in addition to its proven forecast accuracy – a unique and relevant selling proposition or competitive advantage versus other pre-tests.

Validations and Forecast Reliability of the $ales Effect Market Simulation

Quality of a particular product or service offer is whatever the individual customer perceives it to be rationally or feels it to be emotionally. However, humans do not evaluate offers in absolute terms, but focus on relative advantage of one offer over another in order to reduce complexity allowing more mental rational capacity for other (vital) issues: Humans rarely choose things in absolute terms. We don’t have an internal value meter which tells us how much things are worth. Rather, we focus on the relative advantage of one thing over another and estimate or perceive value accordingly. We humans have several quirks such as focusing more on what we may loose rather than what we might gain.

For brand choice individual customers use a simple bench mark: the actual brand most often purchased, thus reducing complexity. The current main brand offers individually, “the best problem solution” of all known alternatives. It determines which perceived combination of emotional benefits and factual features at what specific level satisfies optimally the individual need structure of a respondent.

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. (see figure 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)

The onus is on the “new” (or relaunch) brand to prove that it is better than current main brand. If it fails to establish this superiority in the perception of recipients, individual consumers will feel no motivation and see no reason to switch from their main brands on a long term basis which have served them well in the past. It is a common marketing experience that long term successful products are only those which are perceived by a sufficient number of customers as being superior and free of negatives.

If innovations are tested comparisons to current main brands may be not appropriate. Therefore in these cases we use a similar tough tool: a multivariate consistency check analysis.

The prediction of if a respondent will purchase or not is made for each individual separately – so called segment of one approach. Because most markets are becoming more and more fragmented if not pulverised. Therefore it is no longer good or “precise” enough to predict brand choice for an average person in a segment.

Our brand choice criteria have been used successfully in numerous product categories and international markets – ranging from food and beverages to services and business equipment.  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.


Deviations of forecasts to real market values were only:

  • 0,1 % Beck’s Gold. (Shaw/Schipke/Mayer de Groot 2004)
  • 0,1 % Lefax (Mayer de Groot, Fritzen 2008)
  • 0,1 % Milka Tender (Mayer de Groot 2011)
  • 0,1 % Iglo del Mar (Schäffken 2008, Mayer de Groot/Plüm 2012)
  • 0,2 % Iglo Spinat  (Schäffken 2008, Mayer de Groot/Plüm 2012)
  • 0,2 % NIVEA Soft in Italy (von Dassel,  Wecker, Mayer de Groot 2001)
  • 0,2 % Sheba (Mayer de Groot 2010)
  • 0,2 % Bärensnack (Mayer de Groot 2010)
  • 0,3 % Youce (Braunberger/Mayer de Groot 2013)
  • 0,3 % Efasit (Mayer de Groot 2010)
  • 0,3 % EnzymLefax (Mayer de Groot/Fritzen 2008)
  • 0,3 % Ibutop (Reese/Fritzen/Mayer de Groot 2004)
  • 0,4 % NIVEA Soft in Germany (von Dassel et al. 2001)
  • 0,5 % Dymo LabelWriter. (Lübbe/Mayer de Groot u.a. 2005)

Similar reliable forecasts have been reported in other published case studies without showing validations in detail: Hasseröder (Lenatz 2005/ 2006); Leitz (Lübbe, Mayer de Groot et al. 2003/2004), Niederegger (Strait/Arndt/Mayer de Groot 2006), Perfect  Draft (Lenatz 2005/2006), Vorwerk (Weber/Mayer de Groot/Fritzen 2006), WD 40 (Gill/Mayer de Groot 2007), Wrigley Extra (Mayer de Groot 2011).

The following case studies indicate the opportunities:
Beck's Gold Case Study Beck's International Case Study Birds Eye Case Study Coca Cola Zero Case Study DYMO Case Study Efasit Case Study Heinz Green Ketchup Case Study Iglo 4 Sterne Menue Case Study Lefax Case Study Leitz Case Study LEKI Case Study MC Iglo Case Study Milka Case Study Moet Case Study Niederegger Case Study Nivea Case Study PerfectDraft Case Study Sheba Case Study Vorwerk Case Study WD-40 Case Study WD-40 Smart Straw Case Study WeightWatchers Case Study Wrigley´s Extra Case Study