DATA WAREHOUSING
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Why Use an Enterprise Demand Signal Repository
For POS Data?
Many people think of a Demand Signal Repository as data that is used
strictly for category management. Although it remains valuable for category
management teams, departments across the enterprise will find it useful.
“About 50% of our requests for information are now coming from the supply chain side of the business. Some also come from forecasting or marketing teams that are looking at leveraging POS data for forecasting and promotion analysis. But the majority of time, we are still seeing the category management teams and supply chain spearheading the effort for an enterprise DSR,” said Janet Dorenkott, co-founder and chief operating officer, Relational Solutions.
A Demand Signal Repository is a process that integrates point-of-sale (POS) data into a centralized database on a scheduled basis for sales reporting. Additionally, an enterprise DSR is a central, integrated, truth database with clean, valid POS data that also integrates internal data, third-party sources, loyalty data, and any other data source for enterprise reporting. The architecture is designed with modifiable business rules and the data model supports easy management, growth and change. It supports sales reporting to retailers, as well as reporting requirements across many departments.
With a true enterprise DSR, consumer goods companies are not just improving retailer relationships and providing them with the reports that they need, but supporting the needs across all the departments within the company. An enterprise DSR is not an entity, but a process of integrating, cleansing and managing the data flow. Enterprise DSRs can improve retailer relationships, provide faster access to information, increase productivity, optimize efficiencies, provide you with more information that you had in the past, maximize sales, reduce costs, and increase profits.
Dorenkott outlined nine of the key benefits of implementing an enterprise DSR:
- Identify out-of-stocks
- Improve demand forecasting
- Optimize the supply chain.
- Collaborate better with retailers
- Launch successful new items
- Maximize ROI on trade promotion activity
- Enhance consumer insight
- Gain clear visibility of product movement
- Involve the strategic executive team.
According to a recent Gartner Research study, responses to a question about how downstream data is used showed a wide array of departmental uses. Demand planning was cited by 55%; category management by 48%; channel inventory by 45%; replenishment by 45%; manage third-party inventory levels by 42%, and new product launch opportunities by 40%.
Different departments will find the answers to specific questions by using an enterprise DSR. For example:
Account management sales teams might ask where are the out of stocks. What is the potential impact on sales? What products and stores are the most profitable? How to better serve the customer and the retailer?
Category management marketing and sales teams want to know how to manage shelf space better. What is their market share? How to help retailers sell more product?
Promotion management marketing and sales executives need to know if their promotions are effective. How to minimize deductions? What demographic areas and media are promotions most effective in?
Sales and manufacturing forecasting executives ask if their shipment and order forecasts are accurate. Can sales forecasts be automated? How can sales forecasts be made more accurate to help manufacturing?
Demand insights marketing will seek information about marketing trends. Who is buying the company’s product? What and where are they buying? Are they loyal? What else are they buying?
Logistics, supply chain and inventory departments need data on what shipped vs. what actually sold. Also, where is shrink occurring? Are out-of-stock products at the distribution center or in transit, or is there product on order?
For promotion analysis, companies should be able to leverage POS data to track the effectiveness of promotions by selecting the promotions, selecting the time frame to measure, and then being able to see the effectiveness of different events.
Forecasting becomes much easier and saves salespeople a great deal of time because the enterprise DSR solution is already collecting the POS data. Forecasts can then be dynamically created for the salespeople who can then adjust and edit them as needed.
Enterprise DSRs can help companies leverage loyalty data, which is becoming more important to CPGs. For loyalty, companies can use it for:
- Analyzing the market basket
- Improving an understanding of shopper behavior
- Identifying and rewarding the best customers
- Targeting shoppers effectively with mailings, coupons and other promotions
- Building upon the share of customer
- Identifying customer share of wallet
- Developing expectations based on observed spending
- Identifying loyal versus non-loyal customers.
The enterprise DSR will also help with predictive analytics. Once there is clean POS data, data enrichment metrics can be applied to leverage the more basic data that is coming from some of the less sophisticated retailers.
CPGs are also seeking ways to leverage data derived from social media. By taking tools that collect social media chatter, and formatting that data it in such a way that that words are categorized, and then loading that formatted data into the DSR, companies can get a better understanding what sort of social chatter is going on during the promotional period. There are positive, negative or neutral words that can be captured to determine how consumers are reacting to different promotions as they are taking place. With daily-level POS data, daily social media chatter can be integrated to provide that kind of information.
“The whole concept behind the enterprise DSR is faster access to accurate information, improving profits, improving your retailer relationships, maintaining and growing your customer base, and servicing your customers better,” Dorenkott concluded. “A true enterprise solution should have the ability to integrate all of the different data sources, whether that is retailer POS, syndicated data or other internal data that you may want to access from ERP systems.”
This article was abstracted from a webinar presentation, “What Can POS Data Do for You?” by
Janet Dorenkott, co-founder and chief operating officer, Relational Solutions, Westlake, Ohio.
More information can be found at www.RelationalSolutions.com and by contacting Dorenkott at info@shoppertech.org
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