Retail Sales Analytics using DSR

(Retail Velocity DSR Is Key to Success In Retail Sales Analytics, Category Management, and Retail Sales Demand Planning.  Originally titled: Keys to DSR Success)
By Albert Guffant, CGT-Consumer Goods Technology
Retail Sales Analytics using DSR
This month, CGT interviews Jennifer Beckett, vice president Sales and Marketing, Vendor Managed Technologies, Inc. (VMT), on a critical area of need for most consumer goods (CG) manufacturers: The Demand Signal Repository (DSR). Here, she shares insight for driving measurable value from downstream data based on her company’s more than 17 years of experience in providing solutions and consulting services to CG companies. 
CGT: Can you provide advice to CG companies that are working on DSR and Retail Analytics journeys?
Beckett: Involve everyone in the project planning discussions — both internally and externally with your retailer, distributor and broker customers. Our clients are always surprised on how widespread the need is to integrate consumption data into their decision-making process, mostly because they didn’t know it was possible. It goes back to the old adage of “you don’t know what you don’t know.” This is more than just a sales, customer logistics, IT or category management tool. The need transcends to marketing, finance, merchandising, demand planning, customer service, retailers, brokers and distributors. Once the concept and potential is introduced, the wheels start churning and they get excited about the potential impact on their line of business.
We recently implemented at an organization where the discussions were initially focused on IT and the demand planning team. We asked them to involve a couple people from different areas, and reluctantly, they did. After about an hour with the entire group in the room, the conversations exploded. Each team began sharing the manual reports they were creating and maintaining and how they knew they could do so much more if the information was readily available and granular. From there, the project expanded to include marketing, customer service, finance, sales and category management. We were really amazed when we discovered the immense duplication that existed and how little everyone knew about each other’s needs for retail consumption data. This project got the conversations and collaboration started.
CGT: You recommend including retailers in these projects. What has been your experience with those discussions?
Beckett: The responses from retailers vary widely, but usually they are receptive once they understand what it means to them. The value has to be put into their terms. If they know that it isn’t substantially more work on their end to do the analysis and they trust the data, then it really starts the groundwork for a more collaborative relationship that focuses on jointly improving sell-through and maximizing everyone’s profitability. In the end, everyone wins.
We’ve even seen situations where the retailer wasn’t sharing the data with any suppliers. We went to this retailer with our client, presented the value of the data and what the supplier would help them do with the data, and the retailer turned on the data feeds to them within a week. 
I think this was another case of “you don’t know what you don’t know.” Once the retailer learned what suppliers were willing to do, it was really a simple decision for them to make.
CGT: With a wide user community, have you seen any common challenges emerge?
Beckett: Yes, of course there are challenges with any project. When you involve so many areas, don’t expect this to be a two-week project. It takes time to implement the right solution that crosses over so many departments and requirements. Think of ERP implementations. We’ve seen these take five to seven years to implement. This type of project is nowhere near that complex, but it isn’t something that can be thrown together in 30 or 60 days. There is typically quite a bit of internal integration with ERP data, like item master data, forecasts, shipments, orders, costs, trade promotions, etc. and then external data, such as demographics, syndicated data, retailer data, promotions, etc. This integration has to be carefully orchestrated to ensure that end results are accurate and timely. 
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