How Online Retailers Use Predictive Analytics To Improve Your Shopping Experience

Predictive Analytics and Online Retail

It’s an understatement to say that online retail is a competitive space. As a recent Deloitte report points out, consumers are more empowered than ever.

“Today’s consumer has vastly different and more sophisticated expectations of product, service, value, and environment than five or even three years ago,” says Ian Geddes, Deloitte partner and head of UK retail.

How Online Retailers Use Predictive Analytics To Improve Your Shopping Experience image 272402 l srgb s glHow Online Retailers Use Predictive Analytics To Improve Your Shopping ExperienceShoppers want personalized experiences, on-demand information, and unprecedented customer service. Retail leaders are under immense pressure to keep up—it takes less than two minutes for unsatisfied customers to write negative reviews and flock to competitor websites.

The future of online retail depends on customer connections.

Key Analytical Shifts in Data: Intelligence vs. Curation

Retail giants rely on information about their customers to make strategic decisions.

“To begin with, companies can gather data about their existing customers and use that to dream up new products and services to offer those customers,” writes marketing professor Werner Reinartz for Harvard Business Review.

What retailers need to understand, however, is that data has value beyond information-gathering and surveillance.

“Generally speaking, analytics is about improving the decision-making process,” says David Rogers, principal at web analytics and interactive marketing agency ConvertClick. “The goal of predictive analytics is to analyze past and present behavior patterns to predict trends before they happen and build sound business strategies. That’s the next level for online retail.”

Retailers should leverage data to uncover and pursue untapped opportunities to drive growth and efficiency. The strategy is to target customers who are problem-aware, but who are not necessarily solution-aware.

Wal-Mart: Prioritizing the Point of Sale

Long-term shopping history, product preferences, and social media activity are all valuable customer data points. What matters most though is the retail point of sale. Is there a way to design a shopping experience that optimizes for that 30-second moment?

To address that question, Wal-Mart Labs has acquired Inkiru, a predictive analytics startup that specializes in analyzing big data to improve merchandising, marketing, and fraud prevention goals. “Its ‘predictive analytics’ technology is designed to pull data from multiple sources and help retailers build merchandising and marketing campaigns targeting shoppers when they are most likely to buy,” wrote Internet Retailer’s chief technology editor Paul Demery.

Sales are the shining light of predictive analytics. That’s the idea behind Inkiru’s strategy to target active shoppers when they’re most ready to buy.

CVS: Matchmaking through Algorithms

Companies have valuable data in two separate areas: (1) customers and (2) products. Predictive analytics bring these realms together to drive sales.

Thomas H. Davenport, Leandro Dalle Mulle, and John Lucker point out drugstore leader CVS as a company that exemplifies this idea. Coupons are CVS’s power tool of choice—the company provides discounts on items that customers have bought previously.

“Companies that have systematically gathered information about their customers, product attributes, and purchase contexts can make much more sophisticated and effective offers,” wrote Davenport, Dalle Mule, and Lucker for the Harvard Business Review. “Statistical analysis and predictive modeling can create a treasure trove of synthetic data from these raw information sources to, for example, gauge a customer’s likelihood of responding to a discounted cross-sell offer delivered on her mobile device.”

Predictive analytics connect consumers with information and products that they’ll need and love.

Nordstrom: Building 1:1 Relationships

Branding is mission-critical to sales. But how do companies pinpoint an exact brand persona? The answer is straightforward: address exact shopper needs. The problem is, though, that shoppers come from many walks of life.

That’s why Nordstrom, for example, employs a rigorous customer segmentation process.

“The idea is simple: figure how to promote the right products and brands to the right customers, maximizing revenue in the process,” wrote CustomerThink’s Bob Thompson in an analysis of Nordstrom’s brand relationships strategy. “That’s not a simple problem when you’ve got a busy website along with 225 stores doing about $10B in sales annually.”

Statistical tools help marketers make sense of their customer segments to deliver personalized messaging. Data helps companies become everybody’s brand.

Final Thoughts: Optimize Goals

The success of a data strategy depends on your analytical framework. Make data make sense, and leverage predictive analytics to address a specific company need.

“An area for retailers to start is supply chain management,” Rogers explains . “Think of the benefits of understanding demand and being able to more effectively manage purchasing, planning, assortment, storing and not to mention, optimization and discounts.”

Make sure that your business heads are well-aligned, recommends Rogers.

“It’s all about understanding the consumer’s lifetime value,” Rogers says . “This business-focus should guide your consumer analytics team.”

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