With price competitiveness increasing, center-store challenges continuing and shopper demands and preferences constantly shifting, predicative analytics is essential to retailers for establishing a sustainable competitive advantage. In order to anticipate and respond to these marketplace challenges (and others), retailers must integrate this data-driven function into their Marketing and Sales operations or risk losing precious, hard-earned share.
Today, most retailers are not currently using predictive analytics. The prevailing situation in the industry is that most of the data being employed is forensic, that is — backwards looking. What happened last month? Last week? Therefore, the analytics are oriented in that same direction.
For so long, we’ve been focused on “comps” – asking “How did we do compared to last year?” Instead, we need to be asking “What is changing that allows us to grow compared to the market?”
Shoppers are “speaking” to retailers through their online behavior, as well as through the register. Each time they download content or clip a coupon, they are telling us their future intent. By matching engagement metrics with actual purchases, advanced prediction tools get smarter faster – helping retailers anticipate needs, build smarter promotions and drive true loyalty.
Retailers, to be successful, must move from a “sku backwards” mentality to a “shopper forward” mentality. Store Operations and Marketing must be guided by shoppers’ needs rather than logistics. Marketers can use predictive analytics to proactively identify trends and anticipate shopper behavior – informing more targeted promotion, more effective shopper engagement and a better product mix.
By employing a collaborative filter (that is, analyzing collective purchase behavior), retailers can shape marketing messages to include product suggestions for shoppers who have a genuine propensity for purchase. With this capability, retailers can execute targeted marketing in cadence with shopper behavior and aligned with demonstrated shopper wants and preferences.
As shoppers are demanding more personalized engagement and are more inclined to do business with those who meet this demand, retailers must have predictive analytics in place in order to engage at this level, particularly with members of their loyalty programs. Therefore, predictive analytics must become a core competency for retailers – right beside logistics, inventory and labor scheduling.
A major challenge to retailers in establishing this capability is finding and recruiting the analytics talent they need. Fortunately for our clients, Inmar has that talent in place. We take a network approach with our clients, bringing huge amounts of data, industry norms and other cross-market metrics to those we serve so they can learn what best practices exist and what best-in-class behaviors to emulate. This speeds up retailer learning – and accelerates revenue growth.
At Inmar, we see the use of predictive analytics as determining who will be the “winners” going forward. Agree or disagree? I invite you to share your thoughts in the comments section below.