By Georges Smine, Opera Solutions
Recent news about closures of brick-and-mortar stores paints a gloomy picture for the retail industry, with retailers seemingly having no hope for recovery in the face of the e-Commerce giants. Some media outlets are even predicting another big short in commercial mortgage-backed debt. In light of all this doom and gloom, should retailers give up and shut down their stores, consolidating their operations to the Web? Maybe not. At least not without a fight. We think with the help of Big Data, predictive analytics and some savvy marketing, the retail industry can bounce back.
Many people are under the impression that great marketing is an art, but the field of data science has introduced a scientific component to marketing campaigns. Clever marketers are now relying on data more than ever to assess, test, and plan their strategies. Although data and analytics will never replace the creative minds behind the best marketing campaigns, they can provide marketers with the tools to help improve performance.
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Consumers’ 24/7 access to abundant product information has revolutionized the retail sector. With digital technology becoming pervasive, shoppers can make informed decisions using online data and content to find, compare, and purchase products from anywhere and at any time.
For brands and retailers, data is also a game-changer. It provides the ability to help companies stay abreast of the shopping trends by applying predictive analytics methods to reveal, interpret, and act on meaningful data insights, including online and in-store patterns. Retailers, both offline and online, are adopting data-centric strategies that help them understand the buying behavior of their customers and then use that understanding to develop marketing campaigns that successfully map customers to products.
Today, retailers are attempting to find innovative ways to draw insights from the ever-increasing amount of structured and unstructured data available about customer behavior. Retailers can apply advanced behavioral predictions and clustering techniques to this data to determine ways to encourage each customer to change his or her spending.
Predictive Analytics Guides the Retail Process
Predictive analytics can’t solve all the problems for the retail industry, given the number of factors that determine the success of a retail operation. Retailers are challenged by a range of issues, from personalized targeting to improving the in-store customer experience, as discussed by National Retail Federation Chief Economist Jack Kleinhenz. One must recognize that some consumers will prefer an in-store experience for some categories, and predictive analytics can help them, too.
Predictive analytics is now being applied at every step of the retail process, and it’s an integral part of guiding the customer through the stages of the customer lifecycle. From predicting popular products to identifying customers who are likely to be interested in those products and what to offer next, the technology can help anticipate the demand of the shopper and produce a seamless customer experience. Beyond these basics, predictive analytics will be the engine that drives improvements in retail operations, leading retailers to recovery and possibly even surpassing online-only competitors. Consider the role of predictive analytics in these five areas:
Customer Retention and Engagement — Leverage mass personalization by looking at customers’ purchase history to keep them engaged with the brand and coming back for more. Retailers can predict what each customer is likely to purchase next by identifying their needs at a granular level and associating each customer with the appropriate need states. Using parallel computing architectures, retailers can now analyze more data, continuously segment their customers based on dynamic parameters, and properly target them with offers that drive online purchases or store visits.
Customer Acquisition – Predictive analytics helps retailers avoid the spray-and-pray spending on advertising campaigns to acquire new customers. With machine learning, they can compare attributes of existing customers with known attributes of prospective ones, with the help of widely available third-party data, to better target and win the right customers that have the desired Customer Lifetime Value (CLV). Once this framework for determining who to target is established, the same technology and methodology used to retain existing customers by properly determining the what, when, and how of targeting can be used on new prospects.
Customer Experience – Some retailers are known for their emphasis on customer experience excellence, and they extensively train employees on how to provide a very pleasant in-store experience and outstanding customer service. With more interactions shifting to digital and mobile, brokering the right information to the customer at every interaction becomes very important. Predictive analytics is central to consolidating as much data as possible yet distilling the most essential elements for the respective interaction, which may be occurring in any number of ways, from a mobile app purchase to a called-in complaint. Yet one should not forget the digital role in the in-store experience. A January 2017 Cap Gemini report “Making the Digital Connection: Why Physical Retail Stores Need a Reboot” makes the case for inserting digital technology into the in-store experience.
Demand Forecasting — One way to retain the advantage of in-store shopping — after all, many segments of the population still prefer in-store visits — is to ensure the customer experience is still very positive. This includes having the right items in stock for customers to see, try on, or touch. Predictive analytics helps retailers make informed business decisions about products and customers, allowing companies to stock each store according to demand at a granular level — even varying colors and sizes based on predicted demand. Using economic indicators, demographic data, and need states, retailers can recognize trends and better understand market demands and directions at the macro and micro levels. This goes beyond overall sentiment analysis about general product trends and extends to properly ensuring the right products are stocked in respective locations. Of course, when items are out of stock, retailers can facilitate the shipping or pick-up.
Pricing Optimization — No matter how advanced technology becomes, the laws of supply and demand are unlikely to change anytime soon. Predictive pricing analytics looks at historical product pricing, customer interest, competitor pricing (through Web scraping and social media monitoring), inventory levels and margin targets to set optimal prices in real-time that deliver maximum profits. In Amazon’s marketplace, for example, sellers who use algorithmic pricing benefit from better visibility, sales and customer feedback.
Choose the Right Direction, Solution
Retailers face a spectrum of challenges in today’s hyper-competitive environment, and choosing the right solution is paramount to success. However, as stated above, retailers haven’t been asleep at the wheel, and many have implemented some or parts of the above capabilities. Should predictive analytics truly rescue the retail industry, it will not occur by the mere adoption of some tools or the application of them to certain business functions.
It is critical to choose the direction first and let that guide your solution. By taking a comprehensive look at how predictive analytics can impact all areas of retail operations, companies can ensure their endeavors will have a successful technical deployment, turn their business around, and master operations across online and physical channels.
An analytics platform that uses machine learning science can extract meaningful insights from Big Data and address all the areas of retail operation discussed here. Businesses that use such solutions are better able to predict customer needs, preferences, and behavior with high accuracy and are able to quickly adjust factors, including offers, promotions, and inventory, that influence the customer journey. They have a newfound ability to orchestrate customer growth, loyalty, and experience and create leaner supply chain operations with optimized pricing and demand forecasting to make a lasting impact on growth, profits, and customer satisfaction.
Georges Smine, VP Product Marketing at Opera Solutions
Georges Smine leads the marketing organization at Opera Solutions, a Big Data analytics software provider. He is responsible for all marketing activities, including go-to-market execution, product strategy and positioning, market research, content marketing, demand-generation, and sales enablement.
Prior to Opera Solutions, Smine was VP/General Manager of Mailbox Provider Products at Return Path. In that capacity, he managed the data acquisitions business and product lines in email security and email optimization. Previously, he led product management and marketing at Nominum, where he launched advanced DNS solutions for IP telephony, security, and the enterprise cloud. Smine also held senior positions at Tellme Networks, Ecrio, and Netscape, pioneering over-the-top messaging for smartphones at Ecrio, and achieving many industry firsts at Netscape that were precursors to today’s Internet. Smine started his career in engineering at Oracle building ERP software.
