Leveraging Shopper Insights From Chaos to Action
Updated: Jul 13, 2019
Multidimensional shopper data is the new primordial ooze giving rise to powerful insights leading to increased retail performance. Novel technologies are providing new data on shopper behavior every day, inside and outside the store. So how do retailers, already overwhelmed by innovation, bring order to this chaos of data and develop actionable learning? There are three steps to successfully gaining and leveraging shopper insights.
The journey begins with simply gaining an awareness of all the sources of data, both old and new, that are available today. As e-commerce and digital engagement grow in importance, an understanding of shoppers’ digital behavior has become a cost of entry. The physical store is fast becoming digitized as video and other sensors provide views to true shopper behavior in the store in the form of shopping path, time in store, dwell time, department, aisle, category conversion rates and more. In addition, anonymous facial recognition technology provides age, gender, ethnicity and even sentiment (happy, sad, angry, frustrated, etc.) of shoppers throughout the store visit.
Third-party data from companies such as Experian or Acxiom can be used to supplement traditional customer purchase data, providing share-of-wallet measures and hundreds, if not thousands, of additional attributes that can be appended to shoppers’ profiles. Lastly, expanded product attributes provided by companies such as Label Insight power insights to lifestyles and preferences gleaned from the products shoppers purchase.
Next is bringing order to the chaos. While the most powerful shopper insights are driven by cross-pollinating data from different sources, all too often data is siloed or held in different departments, thereby creating barriers. To address this challenge, retailers should appoint a dedicated “shopper insights czar,” for which one executive would have responsibility for bringing together in one place all related shopper data available and thus create one source of truth.
Increasingly, data from a growing number of sources can be attributed to the individual shopper. Retailers often think of shopper attributes as being driven by purchases—things like spending or brand loyalty scores or discount propensity. But department, aisle and category conversion rates can also be stored as attributes for the shopper. Digital behavior provides yet more attributes: digital channels used, website pages viewed, mobile app behavior, social media use and more.
These attributes—and we’re talking about dozens, hundreds and even thousands of attributes updated and appended to each shopper’s profile—are the materiel in today’s stealth battle for shopper share of wallet waged in the digital world.
The third step is taking action. Stakeholders across the retail organization can work with the shopper insights czar—who is also charged with sifting through the data to uncover new insights that can provide competitive advantage—to bring disparate data to bear on specific initiatives for improved performance.
Here are several examples of shopper insights as discovered and used by actual retailers:
One retailer noticed a strong correlation between increased aisle conversion and the promotion of specific cereal brands; whenever one of three cereal brands was promoted (irrespective of price) on the front page of the weekly flyer aisle, traffic increased significantly. And when aisle traffic increased, sales of adjacent products increased more than 7%. Imagine doing ad planning by factoring in the impact of promoted items on aisle and category conversion rates.
Another retailer looked at sales of rotisserie chicken and prepared foods by hour, day and customer segment. Jumping out of the data was a new understanding of when the retailer’s most valuable customers were shopping and purchasing prepared foods. Using those new insights, the retailer was better able to schedule resources and plan production, ensuring a high level of service and shopping experience for the retailer’s best shoppers.
A leading Canadian retailer leveraged insights from the customer data gathered through its loyalty program to improve the effectiveness of advertising. Through customer surveys and focus group research, the company discovered clear correlations between its most valuable customers and specific media publications and radio stations and redirected its ad spend to those vehicles as it sought to attract more "best customers."
In the human world, shopper insights remain as much art as science. Yes, retailers can follow an insight “blueprint” developed by other companies and gain some benefit, but bigger gains can come from retail execs who are able to blend a knowledge of retail, psychology of shoppers and comfort with data into discovering new insights.
As data continues to grow exponentially, and with it the accompanying growth of attributes tied to the specific shopper, the ability of a human to sort through the complexity to take meaningful action becomes increasingly difficult. New AI-powered solutions though are tailor-made for this world, automatically surfacing correlations and insights not apparent to a human being. Coborn’s provides its shoppers weekly emails recommending sale items and digital coupons specific to each shopper driven by several hundred data attributes derived from the customer’s purchase data, product search online, the shopping list on the app, and far more. This level of personalization is only possible using latest technology AI engines.
The good news: There are a growing number of new AI-powered capabilities being brought to market designed to help retailers sift through and understand new insights from data. The challenge: Too many retailers, including multibillion-dollar-a-year companies, aren’t ready, lacking the clean, curated and accurate data needed to power these systems.