If you study Marketing, you learn that pricing is part of the “marketing mix.” The firm combines price, product, place and promotion in the hope of finding the appropriate relationship to appeal to the target market. The degree at which these variables are manipulated is based on available data, i.e. geographic assumptions and customer qualities within the geography. If your product has features that are different from what is currently offered in the market, it may be possible to garner a higher price, if consumers can distinguish the feature differences.
But in situations where offerings are similar, differentiation must be established at the company level. Why would consumers buy from me vs. my competitors, if I offer similar products? In this situation the company must adjust the value it delivers to customers, i.e. its value proposition. The answer to the question – you should buy from me because of my knowledge, experience and customer service expertise. It may be possible to garner a higher price, if consumers can distinguish the value difference.
It only makes sense that if you improve the quality of the data used to make decisions regarding the marketing mix components and the value offered, the firm will benefit financially. Through the use of large data sets that consider consumer preferences and actions “Big Data” analytics may help you achieve this goal.
As reported in Game changers: Five opportunities for US growth and renewal a McKinsey Global Institute study (July 2013), “Amazon has taken cross-selling to a new level with sophisticated predictive algorithms that prompt customers with recommendations for related products, services, bundled promotions, and even dynamic pricing; its recommendation engine reportedly drives 30 percent of sales. But most retailers are still in the earliest stages of implementing these technologies and have achieved best-in-class performance only in narrow functions, such as merchandising or promotions.” (page 75)
Big Data analytics are typically used for the following –
-improve internal processes;
-improve products or services;
-develop new products or services; and,
-enhance targeted offerings.
Implementing a “Big Data” approach requires hardware, software and highly technical quantitative analysts that have the specific knowledge to glean results from large data sets. If you were looking to investigate the potential benefits that you may receive from a Big Data analytics program, it would make sense to outsource a test. If the test is successful and you believe that an internal resource should be developed, you will be in a better position to develop that function internally.
There are a few companies today that offer “Big Data” services – Accenture, Deloitte, Oracle, PROS Pricing, SAP, Vendavo, Vistaar, and Zillant.
Does your company use “Big Data? How?