As the in-store and digital customer experiences rapidly converge, retail sales are being attacked from all directions. Powerhouses like Amazon, Walmart, and other online retailers have developed aggressive pricing algorithms with the power to change a price multiple times in a single day. Sophisticated players are even changing the prices on 10-20% of their online assortment daily (and prices on their top items as frequently as 3-4x per day). Other threats to retailers’ price image include the continued growth of hard-discounters and dollar stores and the proliferation of private label products.
Hi All, my name is Kevin Johnson and I work in Product Marketing for Engage3. I’ve spent the better part of four years in the competitive intelligence industry and I’ve spoken with hundreds of different retailers in the space about pain points and inefficiencies as they persist today in so many organizations. This article is meant to be a two-part culmination of what I’ve learned about the evolution of retail technology and comp shops. I’ll spend time reviewing some of the key challenges facing those operating in the “Old World” of competitive intelligence and why it is absolutely critical to have a dynamic solution to combat these challenges. Please don’t hesitate to reach out here if you think there’s something I may have missed – my “door” is always open.
Typical regional comp shop programs today leak up to 65 basis points* of margin largely due to inaccurate data and pronounced gaps in ability to respond to these dynamic price changes quickly. However, new technologies and data-driven processes are emerging to help traditional retailers adjust in this new world of price transparency, hyper-competitiveness, and shifting consumer behaviors. At Engage3, we see more often than not that retailers don’t always have the visibility they need to make informed pricing decisions such that both profit is maximized and price image is maintained. It’s become apparent that traditional, static approaches are not suitable anymore. The problems that we’re constantly seeing are as follows:
Poor Visibility, High Error Rates, and Low Find Rates
Due to the combination of slow competitive product and price check cycles, expensive and error-prone data collection processes, product linking complexities, departmental resource constraints, and the ever-shifting competitive landscape, it is increasingly difficult to achieve the right level of competitive visibility. The “Old World” of competitive intelligence does not allow for intelligent ways to match products across retail chains, which results in low find rates and leaves the door wide open for natural human error.
Retailers working within this “Old World” of competitive intelligence are exceedingly at the mercy of the labor collecting the data. When we begin working with a new client, we on average see error rates between 20%-40%, find rates between 50-80% and low overall visibility. Whether you use a price optimization system for execution or have a more manual approach to pricing, these challenges contribute significantly to the aforementioned 65 basis point leakage.
Comparing Apples to Oranges
Retailers are flying blind in an age where private label products are becoming increasingly commonplace. According to Nielsen, private label products share of retail sales grew from 16.2% to 17.4% between 2009 and 2011, with year-over-year sales growth outpacing national brands by a significant margin. The trend highlights the importance of quality product linking methods as store brands continue to consume market share. Without accuracy in like-for-like products, retailers are getting little or no value for the money they spend on competitive intelligence. How can they, when their auditors are unknowingly collecting data for apples instead of oranges?
More to follow…
