Retail marketers have made it a high priority to anticipate and cater to customer needs even before those needs appear. Shoppers may not know they need a specific product yet, but an analytically astute retailer may be able to guide them to that realization based on data-driven intelligence they’ve gathered.
This anticipatory capability, when set in motion, not only leads to higher levels of customer satisfaction but also has the economically therapeutic effect of increasing revenues. It is about predicting a customer’s latent desires accurately and making relevant targeted offers which, in fact, save time, reduce their search costs, and serve their interests. It is also about sensing their interests without ensnaring them in pointless rounds of deviant search behavior — which does everything to distract them from their quest for that one product or service and nothing to make shopping convenient.
Companies are taking many measures to ensure customer satisfaction; the range of marketing activities spans the sublime to the ridiculous. From sophisticated models to the more mundane gathering of anecdotal information that may be representative of true underlying trends, the activities continue with mixed results.
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What has been absent is the implementation of a holistic analytic solution that is able to gather intelligence methodically and deliver insights from relevant detailed data in a consistent manner. A holistic algorithmic exercise incorporates multiple analytics techniques rather than just relying on one, so that richer datasets can be mined for richer foresight.
Let’s take an example of something called on-site search. How many times have you visited a retailer’s page and typed in a product name that you have in mind only to be shown pages and pages of results that have only remote, tangential connections to the object of your search?
Imagine if this were now a holistically designed process. More precisely, imagine a search process immediately associated with a finite product listing that hews to not only the letter of the search but also the spirit of it.
This is the brilliance of a search motion driven by multi-genre analytics; a procedure in which multiple analytic approaches are integrated and executed in sync aimed at providing a richer, truly relevant, and more refined result.
The key question is how this is all done. To start, data on previous searches and outcomes is needed, as is a technology solution that is able to ingest all of these data elements at scale, and of various types. Next, there must be a user environment that can run an application intuitive enough for virtually anyone to click on and run at any time. That app should contain pre-programmed logic that is transparent to the user. Last, that logic should include analytic elements that span multiple analytic approaches and sequences.
Doing this transparently in an easy-to-run app is the Holy Grail of retail. The result is a model that then advises the retailer to add to their search engine mappings all the relevant terms for products that a customer purchases. The benefits are many. For the customer, there is a much quicker attainment of a satisfactory purchase and improved search relevance in the context of other customer purchases. For the retailer, the targeted offerings ensure greater revenue, and improved brand equity.
The very best retail marketers know they are in the business of using advanced data-tracking technological tools to thoroughly understand a wide spectrum of customers. They examine detailed data to track real customer search behaviors — to learn what shoppers actually do, step by step as they search online. From these behaviors, marketers can better engage and serve shoppers. This process requires multiple analytic views — integrated to create a holistic picture. Marketers should also understand why shoppers are attracted to their total value propositions and brands as well as the specific product.
The answer is a solution that enables retailers to quickly address customer behaviors and preferences in a self-service manner. This is the purpose and promise of multi-genre analytics. For major retailers today that may get as many as 5,000,000 queries per month on 100,000 products, the more optimized search process can improve by 100 queries per person per day — and that’s a reasonable starting point.
The journey to on-site search optimization driven by multi-genre analytics is best begun with help from an experienced team that has proven success. That team can help marketers avoid data land mines as well as technological challenges — and accelerate the very best marketing outcomes.
Sri Raghavan is Senior Global Product Marketing Manager for Teradata Aster with more than 20 years of experience developing products, marketing and sales initiatives that drive the performance and profitability of organizations across Big Data Applications.