How Predictive Analytics Optimizes Frugal Environments
February 19th, 2009 by Tyler FrielingI am a bit discouraged today. I am surprised that we have not had more takers on our free customer scorecard application. Granted we have not unleashed the Social Media channel on the tool but I figured that after a week of availability we would have 10% more customers. The customers that have used it are overwhelmingly surprised of the results but I still wonder if the tool’s purpose is clear.
One of the leading, and might I say insightful, retail marketing professionals, Kelly Mooney, recently posted a blog titled: “Myths about online Retail Marketing”. In the blog she does a great job of providing an analysis of several marketing myths and there are a couple of takeaways that directly support how we perceive our tools to add value.
“Viewing time has shrunk 20% in the last 10 years, while time on the web has grown eight-fold in the same time period (Forrester).” Why not use that additional web activity as data to further predict what products customers are interested in and the likely hood of what they will or will not buy? Your customer’s traffic patterns are already being logged (either passively by the hosting server or explicitly using tracking or analytics software) so it is important to start supplementing your marketing decisions with web visiting and buying behavior. Currently the scorecard is focused on purchasing data but our services and algorithms can be quickly adapted to support website navigation data.
“Brands need to move the digital channel toward their internal center of gravity, where it can radiate out to integrate and improve all consumer touch points, including the in-store experience.” Fantastic observation! Mooney’s suggestion will require some operational and organizational changes but it is important that retailers re-examine the data points being used for marketing decisions. There are numerous digital channels out there and a good analytics foundation will help aggregate the data. Once the data is centralized and enveloped by an analytics tool(s) you can start to implement the tactical steps required to realize Mooney’s vision.
Another recent, topical blog article was published by Roger Dooley at Neuro Science Marketing.: Optimizing Retail For Frugal Times. Although Mr. Dooley focuses more on the brick-mortar environments he summarizes the key points of a shopper behavior research study performed by Paco Underhill. Paco wrote Why We Buy: The Science of Shopping and Call of the Mall: The Geography of Shopping. The blog notes that Paco hypothesizes “that people more often make buying decisions when they are out shopping, not before”. Interesting and probably supported by common knowledge but without a decent understanding of what your customers are going to buy next, or data suggesting complimentary or non-complimentary products how can you capitalize on a customer browsing your store or website? With predictive tools you can incorporate the results into your online product lists to create more suggestive, dynamic opportunities. Another concrete benefit of predictive data is that you can create on-the-fly discounts or pricing customized to each customer segment.
Even our free customer scorecard tool will provide useful data to help you identify what marketing strategies may be advantageous. Knowledge of recency, affinity and loyalty can enable you to allocate limited marketing funds more appropriately and tactically.
Tags: loyalty, marketing allocation, marketing analytics, marketing budget, recency, value of marketing
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