Personalizing Retail Customer Experiences

The Science of PersonalizingRetail Customer Experiences

The biggest upgrades in retail analytics are invisible to consumers. Shoppers are acquainted with creative displays, evolving store formats, and a better in-store experience. However, they might miss the latest retail predictive analytics innovations: an application with predictions on fashion preferences, a preferred payment method of a particular customer, and interaction preferences—whether the customer chooses to communicate with a sales associate or selects the check-out option.

These predictive tools or customer journey analytics tools pivot a shopper’s experience and have finally advanced to the point where marketers can use real-time data that is both evocative to customers and lucrative for companies. Personalization enhances the customer experience and it begins with knowing the customer—or remembering the customer. This is the key to the future of retail.

Personalized Marketing: How it Works

Predictive analytics in e-commerce and retail has come a long way from “Your recently viewed items and featured recommendations inspired by your browsing history”.

Let’s consider the experience of a hypothetical customer called Rachel. Rachel is an affluent married mom and homemaker who shops at a popular clothing retailer online, in the store, and occasionally through the mobile app. When visiting the retailer’s website in search of athletic clothing, she finds choices based on her shopping history, customer purchases similar to hers, and most frequently purchased items. She adds one of the track pants to her shopping cart and checks out.

Most interactions with the customer stop after a follow-up email. But here’s what actually happens when Rachel’s data gets activated: after her purchase, the retailer sends her a health-themed emailer, which links her to a video on healthy eating habits for children.

She also receives notifications from the retailer’s mobile app to unlock a 20 percent one-day discount on workout equipment. Rachel has never shopped for such items at the retailer, but she avails the discount and purchases a pair of dumbbells. It started as a simple task of purchasing track pants, but ended up being a much more engaging experience.

Such data-driven marketing based on real-time insights, customer purchasing behavior, search histories, and shopping preferences that help retailers predict the customer’s next move is an important part of growth. According to a recent study, customer behaviour analytics for retail can amp up total sales by 15 to 20 percent, while significantly improving the ROI on marketing spend across websites and mobile apps.

Getting Personal: What do Customers Expect?

To attract customers in today’s Digital era, retailers need to go beyond a welcoming storefront and catchy deals—the ideal customer behavior model entails experiences that make an emotional connection with customers. An emotional connection that drives long-term loyalty requires real-time and cross-channel data—collected in one place and connected to the customer.

According to a study by Retail Week, customers expect retailers to ‘know’ their digital journey when they enter a physical store. About one-third (32 percent) of shoppers in the survey said they expect the stores they visit to know what research they have done on the retailers’ websites and apps. This also includes their wishlists, abandoned carts, and related social media activity to receive better service. In comparison to highly satisfied customers, those with an emotional connect to the brand have twice the lifetime value.

A study published by Harvard Business Review states that they not only purchase more products and services, make more visits, and showcase less price sensitivity; but they also respond more to the brand’s communications and recommend the brand more often.

Online Retail Shopping Customer Experience

The Challenges of Data and Decisions

Leading retailers are embracing data science to connect with their customers and deliver compelling experiences. However, owing to the complexity in customer journey analytics, retailers find it tough to consistently deliver an engaging experience across channels, including in-store and online.

Moving back to Rachel, do data analytics specialists target her as a mom, a sports enthusiast, or a homemaker? What happens when data derived from tests are displayed for all three segments? Does that group her into a separate microsegment that fuses attributes and signals across all three segments?

Data derived from retail predictive analytics can undoubtedly help retailers and marketers consolidate and streamline data. But with automated lists, basic customer segmentation, and campaigns, they can fall behind on informed decision making, adaptive modelling, and data optimization to leverage personalized interactions.

Also, incorrect data can lead to poor conversion attribution and incomplete customer segmentation groups. This further leads to inconsistent or customer experiences.

Embrace Data-Driven Experiences

The best retail experiences begin with knowing your customer and identifying the types of data needed to assess their key emotional triggers. This data should not just provide real-time insights, but information on what kind of experiences their customers will best respond to. By bringing this additional data together, retailers can then better segment audiences, understand retail industry trends and predictions, discover new audiences through look-a-like modeling, and enhance attribution.

To become a growth engine, retailers need to embrace smart analytics right away. While gathering enormous amounts of data is tough enough, being able to use it effectively is critical. The power of relevant data, coupled seamlessly with an integrated landscape of connected services, helps retailers invigorate their brands and retain customer loyalty.

Identify New Opportunities with DecisionMinesTM

DecisionMinesTM for Retail helps enterprises create an effective strategy by modelling existing data, with an eye on the ROI and brand acceptance. Organizations can thus use data science to make better pricing decisions, understand customer purchasing behavior, predict the customer’s next move, and thereby take the next best action.

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