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Successfully Interpreting Big Data

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2014 saw countless ways for retailers to collect and store data. Some have made good use of it, segmenting and targeting customers and rewarding loyal behavior with discounts and offers, but there’s still a lot of untapped potential. Retailers need to discover ways to make the data more useful and apply in ways that will make their marketing efforts more successful, pricing more effective and product assortment more appealing.

Brands are now leveraging field intelligence as well as in-store data to fashion their shopper campaigns. Jim Rose, president of marketing services at sales/marketing services provider Crossmark, explained the example of a home appliance client wishing to focus its outreach efforts on loyal customers, but the collected data showed it didn’t need to return to that well of existing customers. Rose noted, “Data insights showed that it didn’t need to overinvest in those existing customers; funds were instead re-directed to targeting Hispanic women, identified as an untapped and lucrative segment.”

Shoppers are pampered with personalized recommendations and offers every time they head online to shop, since online sites have access to a proliferation of tools for online analytics and customer data. Yet most of the purchasing is done at the physical store and the under-utilization of data there is troublesome, especially since over 90% of retail purchases made are in store.

Case in point: Whenever you buy something on Amazon, there’s a treasure trove of comparable products recommended because they’ve identified who you are and what your buying patterns are, so you get personalized recommendations. Many brick-and-mortar stores have sales and traffic data, but the application of that information stalls.

Amazon responds automatically to predicted customer demand patterns. This shows up in price changes on a daily, if not hourly, basis. For example, if more people are shopping during their lunch hours, it may make sense to raise prices just for that time frame.

Applying science to pricing strategies, or price optimization, is something that physical stores can readily do, armed with the appropriate data.

Lifecycle pricing, which takes a pre-planned approach to products with limited lifecycles, such as fashion apparel, is more valuable than adopting pricing strategy on a piece-by-piece basis, and it removes any emotional component from the equation.

John Bible, senior director of Retail Data Science and Insight at Oracle, explains that if a buyer has a gut feeling about the potential success of a line of apparel, and that line doesn’t do well, often because of the buyers’ initial positive “feeling” about those pieces, price adjustments will not be made, as they really should be, based on life-cycle pricing.

So, by offering necessary science-based solutions, there’s a counterbalance.

And because these solutions’ algorithms are based on data-mining multiple years of the retailer’s own sales and customer history, they can confidently recommend that a 50% price cut will be needed, given the current inventory position and projected demand derived from sophisticated causal models.

Bible thinks that by embedding optimization technology in retail, in five years, the collected data and science-driven solutions will benefit retailers in a number of ways:

* There will be a 50% increase in inventory turn-arounds through better predictive models and inventory flow, optimization across all stores, warehouses and channels.

* Retailers will see a 25% increase in average item forecast accuracy by incorporating new data sources including web, social and mobile.

* A 10% increase will be achieved in revenue with smarter pricing, promotions, localized assortments and placements, based on advances in machine learning and intelligent cross-decision optimization platforms.

By applying science to the data the stores access, retailers can leverage this data to successfully make necessary strategies and forecasts throughout the year.

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