With more technological ability than ever to track information and results across multiple channels, retailers have begun to crunch the reams of data available to them to "scientifically" predict and plan for the future shopping habits of customers. Call it predictive analytics... data mining... retail analytics... digital customization... new metrics... big data... personalization... socialytics... No matter what you call it, the retail analytics train is pulling out of the station and gaining momentum exponentially. What's propelling the train is the newfound ability to gather, sift, and winnow data related to customers' past, present, and future buying behavior. This, in turn, will help retailers with regard to competitive intelligence, pricing, assortment management, plus promotions planning and execution -- and many of these actions will be done in real time, to boot.
In addition to the available data, retailers are also using their own purchase history data to know and predict future customer behavior. By understanding the profile of the consumer, retailers can fine tune the offer on getting people to buy a particular product or quantity. This information, in turn, provides valuable pricing and product placement strategies, allowing retailers to operate much more efficiently and effectively, better manage their assortment and merchandising, ultimately boosting sales and profit.
RSR principal Paula Rosenblum noted in a recent summary that 50% of respondents to the most recent RSR survey said they see incorporating customer data into their merchandising processes as a top-three opportunity. So it's game-on for retail strategy.
A good retailer examplar is venerable pharmacy, CVS, and see what it does with its own purchase history data "to know customers and predict future customer behavior,"(described in the article, "Know What Your Customers Want Before They Do," a webinar summary in the Harvard Business Review: The Future of Retail).
CVS, for example:
* Relies heavily on analytical tools and technologies… (and) In using these tools, CVS lets its decisions be guided by the data…
* Looks at the patterns surrounding a customer's purchase history to determine what types of offer to make on which products.
* Focuses on identifying those attributes of its products and offers that are most relevant and critical to customers.
* Updates its data in real time, and may use today's purchase history to effect tomorrow's sales…
CVS also recognizes that consumer behaviors and attitudes are constantly changing. Therefore, an offer that worked well at one point may not necessarily work well at another point in time, stressing the need for ongoing testing and learning.
Another large retailer identified a segment of customers who like to purchase "adventurous" products, so when a shopper chooses an exotic spice, they'll receive suggestions or coupons for similar "adventure" products. The different groups and affinities that can be identified and pursued are limitless.
Here are some of the benefits of using retail analytics to make informed, valuable strategic decisions:
-- Retailers can figure out the people who have the "highest lifetime value" to them -- and tailor offerings accordingly. Catalina Research firm studied purchase data from 54 million Americans over a year, and found that a significant portion of the sales of individual products came from a small percentage of shoppers. It makes sense to address the customers that are going to provide you with the highest sales.
-- Gives retailers a target. Knowing and using this information makes retailer efficient, aiming at a target, not just using instincts.
-- Makes a roadmap. Retailers can plot and apply attributes associated with each purchase and each offer, and map those attributes for future offers.
-- Can reward customer loyalty, with specific targeting.
-- Provides a process of testing and learning which can be studied longitudinally.
-- Can consider how much a customer is willing to pay based on their behavior: If customers say they "like" something on a social site, could you charge more because they already like it, or should you charge less to get them to buy more?
-- Retailers can use predictive models to create "if/then" scenarios that will serve them in a variety of ways.
-- Help retailers identify money-making products.
-- Help retailers identify products which are necessary to keep customer. Even if they're not profitable, they may be the reason the consumer comes to you in the first place, and the reason they stay and buy at your site.
-- Much, much more.
Some caveats about retail analytics:
* Customers today are inundated with numerous offers across multiple channels. If outreaches are run too often, they lose their impact and retailers risk alienating customers.
* There are significant and justifiable concerns among many consumers about the overreaching capabilities of technology tracking their personal information, so retailers should proceed with caution.
* Consumer behaviors and attitudes are constantly changing, so your offer is not static. In fact, an offer that worked well at one point may not necessarily work well at another point in time, stressing the need for ongoing testing and learning.
The future of retail is the effective application of retail analytics to understand and plan for customer behaviors. Analytical technologies and intelligence are a must. Knowing what customers are going to buy helps you plan your assortment and pricing better. When done right, retailers can approach their customers with relevant and compelling offers that will dramatically impact sales and profits.
Those who harness insights from analyzing what their customers and other consumers (and competitors) are doing in the digital universe are in a better position to make important strategic decisions, and make those important decisions more and more in real time.