Identifying and Quantifying the Lost Opportunities Not Captured By Big Data
In recent years, the promise and reality of big data have yielded new possibilities for understanding and interpreting purchase behavior. Because of these advances, there is the natural tendency to rely on such data to guide marketing, merchandising and sales planning. While the potential benefits are attractive because of their scalability and cost-effectiveness, big data do not tell the whole story of shopping behavior, leaving an area of uncertainty where key insights can be generated to drive practical, actionable, revenue-producing solutions.
In this session, John Dranow, CEO, SmartRevenue, discusses data-driven approaches for improving retail performance through the analysis and modification of the shopping process. Drawing from case study examples, we look at how to capture the larger context of the shopper story: how to uncover motivations, purchase drivers and barriers, which are critical to understanding why shoppers select or de-select products, brands and retailers.
By studying how and why shoppers buy, marketers can:
- Identify lost opportunities — shoppers with latent product needs that can be activated at the point-of-purchase and those who express a need but fail to buy — and address them by stimulating product consideration and removing purchase barriers.
- Develop and reinforce shopper habits by closely matching shoppers’ requirements with product and service offerings.
- Optimize the performance of pre-store and in-store touch points by targeting relevant messaging to shopper segments.