Here at Datasembly, we’ve seen how data makes all the difference when it comes to smarter, faster business decisions for CPGs and Retailers. But so often, companies are using old, averaged, or incomplete data to make those critical decisions, risking millions. The real-time data our platform collects offers our clients the fresh, right-off-the-shelf intelligence they need to win.
Our proprietary platform collects over two billion omnichannel product observations daily, from over 38,000 store locations, providing real-time, hyper-local data that can be used to make fast, actionable decisions. Never before have CPGs and Retailers been able to access up-to-the-minute competitive pricing data in real time, at such an incredibly granular level. Our founders saw how impactful this kind of product transparency could be and created Datasembly to empower companies across the country.
In the past, businesses relied on pricing data that was past its prime, trusting it was accurate enough to guide their decisions. But real-time decisions require real-time data. Days can make a difference in effectiveness and value of price changes. Retailers could be pricing KVIs either too low or too high, risking millions on competitor prices that are yesterday’s news. CPGs may struggle to make efficient, optimized trade spend decisions because they lack access to real-time pricing intelligence. Real-time data can change the game, here’s how.
Challenge: XYZ Wholesale Clubs had an agreement with Big Fizz soft drinks stipulating the brand’s products would not appear at a lower price in competing ABC Grocery Stores, located within a 10 mile radius of its own stores, for more than a 12- week period. XYZ saw the agreement as necessary to upholding its low price commitment to its members. ABC Grocery Stores, however, subsequently conducted a price rollback sale that XYZ claimed lasted longer than the agreed 12 weeks. XYZ therefore demanded a trade spend as compensation from Big Fizz, per their agreement.
Solution: Using a proprietary price intelligence application, Big Fizz was able to get views of the daily prices being charged for its products at individual area locations. Data showed many of the stores offering the lower prices were outside of the specified 10-mile radius. Further, the ABC Grocery Store promotion lasted only two weeks beyond that allowed period. Big Fizz was able to greatly reduce the amount of trade dollars it paid to XYZ.
The Takeaway: Without accurate data on daily prices at specified locations, the CPG would have been in the dark and forced to pay up as if the violations occurred across the entire locale. Such data solutions can be very effective in combating the gamesmanship that retailers use to generate margins where they simply don’t exist.
Challenge: Store W, Store X and Store Y are grocery competitors in the Cincinnati, OH suburbs, As they compete for pricing of KVIs, such as eggs, milk and butter, each store has a different method of collecting the data they need to make winning pricing decisions. Store W uses their syndicated data with its averaged pricing in an attempt to be competitive. Store X sends auditors to sample prices on selected categories at local competitors. But Store Y takes a different approach.
Solution: By using Datasembly’s real-time, hyper-local data, Store Y doesn’t have to guess at pricing - they know the price of all KVIs at competitors in their trade area. They can choose if they want to compete with the lowest neighboring stores, or if it’s safe to raise prices as their own costs rise.
The Takeaway: By using real-time pricing in that hyper-local area, rather than syndicated or sampled data, Store Y increases its profit margin and likelihood of attracting new, existing, and local customers.
What if you could see our real-time, hyper-local, shareable data in action? You’re in luck! Request a demo today and experience the data difference for yourself. And don’t miss your next blog, where we introduce the great minds behind our Datasembly platform and our powerful team responsible for making data magic.