case study

Taking Price Faster Using Real-time Hyper-local Product Data

The Client

Fortune 500 Multinational Home Goods Company

The Situation

The home goods market is experiencing unprecedented market dynamics, including a dramatic increase in raw material expense, 40 year high inflationary pressures, and new pandemic-driven demand for cleaning products. The combination of spiking material costs and high demand requires manufacturers to adopt nimble and precise pricing strategies. This manufacturer owns numerous first and second positions in the market across a number of key home goods categories. They deploy multiple pricing strategies depending on market position for each of their products.

Key Challenge

This manufacturer is one of the leading brands within the categories they compete. Category leaders are typically the first to move on price, setting pace for the rest of the market. Being able to quickly identify movement in today’s market is a critical measure for success, but speed and hyper-local, banner by banner, store level data will dictate how market share is either won or lost. Current data sources used to monitor the market are traditionally composed of monthly, syndicated data. This makes it incredibly difficult to monitor and respond to marketplace price changes quickly and accurately.


Leveraging Datasembly’s real-time, hyper-local data, this manufacturer was able to immediately spot a marketplace pricing shift in a specific geographical region. This change in market pricing was quickly communicated internally and helped accelerate strategic pricing discussions.


Datasembly’s enterprise application was able to visualize this pocket of relevant price changes in the category, sending an early signal to the Revenue Growth Management Team that broader marketplace pricing changes were imminent. This manufacturer was then able to immediately adjust their pricing strategy faster than the historical process supported by just syndicated data, and with a much greater degree of accuracy.

AHA! Insight

This manufacturer was able to spot this specific marketplace price shift at just 80 stores from the thousands of stores across the US thanks to Datasembly’s hyper-local technology. Using traditional data sources, this change in the market would have been too small to detect (even though it represents sizable revenue), and would have been masked within the averaged market data. As a result of the early detection, this company was able to develop cohesive plans and execute on revenue-driving strategies 3x faster than it would have been able to with standard, syndicated data.

Datasembly app image showcasing retail data collection