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Quri Launches Performance Driven Merchandising Platform

Quri’s Performance Driven Merchandising platform reduces costs and increases profits via in-store merchandising, execution and efficiency improvements through three solutions.

Lindsey Wojcik

January 1, 2018

3 Min Read
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Quri has unveiled its Performance Driven Merchandising platform. The offering provides CPG manufacturers and brands routine, granular store-level data, software, and services to drive sales through better merchandising results both in and out of cycle. Quri developed the platform over the past nine months in partnership with dozens of leading manufacturers in the beverage, health and beauty, and home care categories, among others, to help improve overall merchandising performance and capture the estimated $33.5 billion growth opportunity through improved in-store merchandising execution.

“With roughly 30 percent or more of a brand’s profit and loss dedicated to in-store merchandising and field labor execution resources, it’s vital that CPG manufacturers drive better returns on these assets than they are at present,” says Justin Behar, CEO and co-founder of Quri. “Our new Performance Driven Merchandising platform provides the data, software, and services necessary to better plan, measure, and collaborate with field labor teams and retail customers for improved merchandising results.”

Quri’s Performance Driven Merchandising platform reduces costs and increases profits via in-store merchandising, execution and efficiency improvements through three solutions: 

Transforms field labor assets from a cost center to be minimized into a strategic growth engine for the company. Case in point, manufacturers in the beer and wine category saw that they were able to identify and close compliance gaps across all classes of trade in just 6 months. Insights from Quri’s platform allowed for an increase of 5 percentage points in display execution performance which equates to $45 million in incremental revenue and the protection of $200 million in base sales annually.

Increases the profitability of trade spend by helping eliminate unprofitable spend and increasing compliance on profitable programs simultaneously. For a major manufacturer in the household goods category, Quri was able to identity an opportunity to save more than $13 million in incremental sales over the summer season by identifying and consolidating around a “best in class” display configuration to replace unprofitable display vehicles early in the season.   

Uses unbiased, scaled data and analytics that quantify the shopper view of the shelf. By measuring on-shelf availability (OSA) as seen by these shoppers at the shelf across dozens of power SKUs for a major personal care brand, Quri identified an OSA gap unseen by the retailer or manufacturer when using existing, outdated techniques. Through routine efforts in monthly retail customer meetings, average OSA has increased from 92 percent to 95 percent for dozens of major SKUs netting sales gains of over $200 million annually.

Dale Hagemeyer, a renowned industry analyst and partner at the Promotion Optimization Institute (POI), recently released a report that highlights the industry need to focus on and improve merchandising execution and included coverage of Quri. "The key to return on asset for field labor is not just to minimize the costs, but to adequately support it with the data, tools, and training to convert it into a strategic weapon," Hagemeyer says. "When we do so in the face of competitors that are just limping along, sales and share growth is inevitable.”

“In store merchandising remains the vital last step in converting 360 advertising into sales,” says Deepak Masand, CEO of Caterpillar Ventures and former marketing vice president at PepsiCo and CVS Health. “By eliminating unprofitable trade programs and improving execution on the winners, brands can increase profitability and secure long term sales and share gains with data and analytics previously unavailable. It’s the biggest leap forward for in-store merchandising we’ve seen since the emergence of scanner data in the 1980’s and early adopters will reap the rewards.” 

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