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Mastering Omnichannel Buying Journeys With Predictive Analytics

  • Written by  Sean Kendall, TetraVX

0aaaSean Kendall TetraVXTo compete with Amazon, Walmart has invested heavily in automation. Just recently, the company recruited a fleet of autonomous floor scrubbers and more than doubled the number of conveyor belts in its shipping processes.

But automating operations is unlikely to give Walmart stores a significant leg up on Amazon. Although increasing efficiency and reducing costs have a place in retail, those strategies do little to encourage customer engagement and loyalty.

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The truth is, customers aren’t shopping exclusively in-store or online. So strategy should not focus on becoming like Amazon, especially because most brands can’t compete with Amazon’s prices. Today’s customers use up to five touch points across both digital and physical experiences before they complete a purchase. The crux of staying ahead of the competition is elevating the entire customer journey — across online, social, in-store, mobile and more.

Leading retailers have mastered the personalization of online touch points by tracking search histories, customer preferences, buying journeys and habits. But by combining both online and in-store insights and supporting the data with a predictive model, retailers can elevate the journey across all touch points.

Analytics And The Customer Experience

Data plays a key role in creating streamlined, omnichannel customer experiences. And fortunately, the retail sector doesn’t lack data. In fact, retailers often have access to so much data that it causes a sort of “data paralysis.” Point-of-sale transactions, store navigation insights, competitive intelligence, seasonal trends and loyalty programs are just some of the data points available.

Analyzing data across all customer touch points can provide new insights into who customers are, the motives behind their purchases, the channels they are on and the patterns of their shopping behavior. However, making sense of all this data to make significant changes requires retailers to first understand their KPIs. What elements of the customer journey must change to facilitate consistency across all of the possible touch points?

To identify the necessary changes, retailers must determine who their customers are and why they choose to shop in-store rather than online — and vice versa. Do shoppers see the in-store experience as a form of escapism or perhaps an opportunity to browse and touch luxury goods? Or does it involve a rite-of-passage experience, like picking out appliances with your partner for the first time? Whatever the motivation, retailers must cultivate customer experiences to take advantage of the reasons customers choose to shop for certain products at a certain touch point.

While no single brand can win at every experience, retailers can effectively compete against online giants by executing specific in-store experiences exceptionally well. Shoppers are more likely to remember — and remain loyal to — a great brand experience over a great price.

Using Data To Cultivate Experiences

After retailers pinpoint a unique experience, they can tailor and refine it by using a data-powered predictive model to forecast how potential changes will impact sales, provide suggestions and guide customer service teams.

For example, if a customer is shopping for a table on a furniture brand’s web site, they may have a question about the size. Using the customer support feature, they can chat with the support team directly and ask them any questions they may have about the sizing. When the representative chats back to the customer, the customer may realize that the table is not a good fit. With predictive analytics, the representative has access to alternative suggestions based on the items similar customers purchased.

Customers expect consistency across the entire buying journey. If customers receive an option for in-store pickup to save on shipping, they should be treated to similar in-person customer service as they experienced online. All omnichannel communication points are managed by and through a contact center. Communication happens across multiple channels, and the in-store environment must reflect the same brand experience that is cultivated online.

Predictive analytics can provide retailers with an understanding of how their shoppers make purchases and what the variations of that journey look like — from finding the product in-store and buying a different size online, to browsing online and going in-store to make the final decision. As shown above, it can also streamline the journey by making suggestions for alternative products, or products that are frequently bought together.

Predictive Analytics Require A Culture Shift

Adapting store operations to align with changing customer demands is a significant undertaking — it’s important for retailers to determine whether their culture is ready for such a commitment. For predictive analytics to flourish, leadership, employees and the business plan must be agile and capable of adapting to new information quickly.

To stay afloat in a sea filled with Amazons, the next generation of retail can’t focus exclusively on historical data. Predictive analytics position retailers ahead of the curve and enable them to offer superior experiences to customers. Alone, online retail may offer lower prices. But in combination with brick-and-mortar, it has the ability to create more memorable and meaningful experiences.


 

Sean Kendall is Director of Customer Experience at TetraVX and has over a decade of experience in project management and customer success. Kendall has an accomplished track record of delivering high-quality customer insights and results by utilizing his expertise in customer experience solutions, growth strategies, product development, big data analytics, quality assurance and information security. He is an active member of the Project Management Institute (PMI), SOCAP International and Customer Experience Management (CEM) Group and is Scrum Master Certified (CMS).

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