DSR Momentum Building In Retail Channel

Use of downstream data in the retail channel has become significant to food and beverage manufacturers as they look for new ways to gain consumer insights and respond to market demand.


Use of downstream data in the retail channel has become much more significant to food and beverage manufacturers in recent years as they look for new ways to gain consumer insights and be responsive to market demand. A recent research study conducted by AMR Research found that more than 50 percent of consumer products (CP) companies have a Demand Signal Repository (DSR) or are building one.

These companies are seeing benefits in three key areas: better demand forecast accuracy, lower inventory levels and improved on-shelf availability in the retail stores that make up the highest percentage of their revenue. The results have been so good that an impressive 70 percent of companies with an active DSR plan to expand it in the coming year to reap even greater benefits.

Most food and beverage manufacturers have been working with downstream data for years, but the DSR has some advantages over the traditional approach. First, point of sale data flowing into a DSR is typically more granular (in terms of level of detail) and more comprehensive (in terms of the number of retailer stores covered). Thus, it can be more useful than the shipment data or even the syndicated data that CP companies have relied on for so many years.

Second, the DSR provides data that is more current. Manufacturers can receive daily data on a daily basis from some retailers or at a minimum, daily data on a weekly or monthly basis from most of the largest retailers. This enables companies to analyze and understand sales activity almost as it happens: across key retailer channels; with core products as well as new product launches; with specific trade promotions and events; and even how key competitor activity is impacting sales. The depth and richness of insights derived from a DSR are significant.

Downstream Data Challenges

Building a DSR is not without its challenges, however, and many companies struggle to realize the full value of their investment. Traditionally, the process of cleaning and harmonizing downstream data has been slow and tedious. Additionally, the current approaches foster an environment of silos in an organization—multiple teams working in parallel, duplicating activities and addressing similar issues, but each within its own area.

While companies can often get a quick win looking at downstream data for a particular account team, these results aren’t repeatable. For example, if your company is going to make the investment to build an out-of-stock reporting solution or a mobile application for store performance measurement, why not do it in a way that can be scaled across all account teams?

Another factor to consider with regard to DSR is the way account teams typically attribute retailer POS data to make it more useful to the end user. Account teams take POS data and add attributes such as organizational and product hierarchy data that will enable them to slice and dice the data along different dimensions. When data is managed in silos and teams are left to attribute data on their own, the result is a set of fragmented environments that are difficult to coordinate at an enterprise level.

Making The DSR An Enterprise Discipline

To combat these challenges, manufacturers are now focusing on a common, enterprise approach for managing downstream data. If companies integrate, manage and attribute all of the data in the same way, then global reporting becomes possible. Data analysts can lay reporting templates across different sets of retailer data uniformly, yielding new insights and patterns that enhance decision making. This helps companies scale analytical solutions based on that data more rapidly.

A unified DSR approach also fosters collaboration and sharing of “best practices” among teams. The sales teams focused on key retailers have many nuances that set them apart from each other, but overall there are more similarities than differences.  For example, one account team might add 100 different attributes to the data from their retailer in order to analyze it.

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