Big Data and analytics are closely related. What good is Big Data if not as a better basis for analysis? Indeed it is commonly said that more data trumps better algorithms. One area that is very popular, particularly with retail concerns is marketing analytics. UCLA professor Dominique Hanssens is a recognized expert in this area. He noted that marketing analytics as an academic endeavor started in the 1960’s with a more rigorous look at what happens in markets. Some of the first work was done looking how a new product gains acceptance (or not) in the market. How fast does it gain acceptance? There have been many studies on segmentation also from the 1960’s, which is critical to develop marketing messages that are more apt to produce results that blanket marketing to an undifferentiated audience. Market response models followed in the 1970’s and consumer choice models in the 1980’s.
The bar has been raised for marketing in recent years and indicates how business is reflecting a more quantitative approach, and ROI for all parts of the business is now common. Marketing analytics isn’t just about PPC, SEO and analytics on the clickstream. Big Data analytics in marketing allows a more complete picture of not just market segments but even individuals. Together they can be used to better manage the marketing budget. One area that can have significant impact on the company’s profitability is pricing management with analytics. Some industries such as airlines and hotels have a perishable offering that benefits from a dynamic pricing strategy, and indeed consumers have found it more challenging to shop just on price, since the timing of the purchase has a great deal to do with the price paid for the airline seat or hotel room. This is much more about the vendor getting value for the offering, and not just a cost plus pricing model. The difference can significantly impact the profitability of the company.
One example of how traditional analytics combined with recent access to Big Data can change the company’s behavior is the diffusion of a new product in the market. By viewing the pre-sales behavior among prospective customers, you can predict the eventual sales in different segments. Big Data allows a more comprehensive description of market segments, as well as detailed information on pre-sales behavior. New products will typically show different rates of adoption among different market segments. By segmenting the market more completely you can more accurately predict the prospective customer, and perhaps offer additional promotional efforts to these segments that are more likely to produce a higher return on investment for the company where a blanket outreach may not be appropriate for the entire market space. The Bass diffusion model has been around since 1969, but now with Big Data capabilities it allows a more granular look at how the product will move into the market and will suggest segments appropriate for additional marketing outreach.
One way that marketing outreach might gain greater effectiveness with Big Data is in the creation of “buzz” for highly enthusiastic fans of a particular product by identifying the leads in a given market segment. Big Data allows us to analyze the connectedness of different individuals, which allows us to focus on the perceived experts in a given segment to provide extra information and motivation to promote their transmitting their support for the new product to their network, and thereby spur adoption in the market.
By combining the traditional marketing analytic models developed and refined over the decades, with relatively new looks at what used to be hard-to-analyze unstructured data from the web capturing consumer behavior, we have a new depth in our understanding of how markets work, and the basis for more focused and accountable marketing.