The organization of today faces a number of challenges in a tough competitive environment; a significant challenge among these is managing change. Long gone are the days when organizations relied on traditional right brained quantitative approaches (MERKLE, 2004). With out a progressive quantitative approach, it is impossible to succeed in this dynamic environment. The marketing of today revolves around information; this can range from information of media and channels to events and customers. Thus to be successful as a marketer, it is imperative that all this information should be arranged in efficient formats, for analysis and interpretations.
There a number of applications of quantitative analysis in marketing. A very important factor in any marketing strategy is the profit driver; these are actually the only thing that can change market performance. Usual profit drivers from any marketer perspective can be Audience, offer, Contact, media etc. To measure and use all these profit drivers in order to maximize your revenues an organization must use quantitative statistical techniques to measure and analyze them. For example changing demand trends are measured using surveys and interviews.
Marketers than apply statistical tools on them to understand correlation with different variables that could have caused these changes. Comparative analysis helps marketers analyze customer switches and identify weak spots for their own organization. Demand forecasts are basis of any marketing strategy; quantitative marketing techniques are used to determine demands for different products and services. There is thus a growing need to move towards structural models which predict behaviors with market data (Chintagunta, 2006). In the marketing terminology, qualitative assumptions are taken as truths, but facts can only be drawn based on quantitative research.
For the purpose of this discussion we will follow the consensus theory of truth because it is the most relevant to our topic (Nicholas, 1995). Truth according to the consensus theory is what is usually agreed upon. Fact on the other hand is a ‘pragmatic truth’, this usually refers to a statement that can be checked, proved and confirmed. This pragmatic statement is a contrast to opinions and beliefs. In management science terminology we can take fact to be a verifiable objective terminology that has been proven by empirical evidence. In market qualitative statements are usually taken as truth, a valid example is expecting girls to always like pink.
These are usually common flaw that in an era of customization are not valid and pertain again to qualitative marketing. How can we differentiate a truth from a fact? This is a question all marketers consciously or subconsciously; face regularly. The only solution is to apply quantitative techniques and use empirical evidence when the line between truth and fact is thin. A valid example will be conducting a survey to calculate the number of girls who actually like pink.
Turning truth into facts can some times be costly and requires a lot of work and cost but can save organizations huge amounts of money in form of sales and cost of sales. A number of other analysis tools such as Regression, correlation, covariance etc can help establish facts about relationships and observations, thus differentiating fact from truth. References Barry, G.W. (1997). Scientific Method: A Historical and Philosophical Introduction. Routledge. ISBN 0415122821 Nicholas, R.N. (1995) Pluralism: Against the Demand for Consensus. MERKLE.
(2004). Mastering Quantitative Marketing: CREATING AND LEVERAGING INFORMATION TO IMPROVE MARKETING PERFORMANCE. Chintagunta, P.C. , Erdem, T.E. , Rossi, P.R. , Wedel, M.W. , (2006). Structural Model in Marketing: REVIEW AND ASSESSMENT. Market Science, 604-616.