How a Leading CPG Company Discovered eCommerce Insights Hidden in Plain Sight

How a Leading CPG Company Discovered eCommerce Insights Hidden in Plain Sight


As online shopping soared with no signs of stopping, this packaged goods giant was struggling with questions about how to optimize sales. They needed insight into which SKUs, what prices and which platforms on which to sell its products in order to maximize revenues. At the same time the company was drowning in sales data which was unorganized and unusable to generate the insights needed. However, cleaning and categorizing this data would take the majority of the team’s bandwidth leaving them with limited time for interpretation and insights development. The company needed help organizing and sorting all the available data in order to apply the appropriate analytical lenses for insights.


Cleaning, preparing and analysing this fragmented (and often unstructured) data is no small task. Grail helped streamline this process by building a robust framework to automate over 60% of the work. The process offered efficiencies by automating the cleaning, categorization and calculations of data as per set rules. Grail then went on to create digestible reports and relevant product breakdowns that helped provide actionable insights to answer questions on brand performance, white space opportunities, pack prices and competitor threats.

Grail Insights Approach "Grail saved us so much time, but more importantly uncovered the key insights that were hidden right in front of us. We were drowning in ecommerce data, but couldn’t get our hands around how to organize and draw value from it. The process they put in place let us run much faster in our market by seeing trends in near-real time.” - VP of Insights and Planning"


The client was able to reduce the time it spent on data mining and manipulation by over 60%. They were also able to uncover insights that helped drive increased sales. Specific insights were gathered on popularity of pack sizes, pricing tipping points, trends in new pack types, and ecommerce shopping patterns. The new data mining model then allowed for near real time analysis and refinement of strategy going forward. Prior to the project, the team spent up to 70% of their time reconciling, mining and manipulating the critical data. After the new process was put in place, this time was shifted toward insights-generation and strategy decisions, giving the company a higher value return on their employees.

Reduced time spent on data manipulation by over 60%
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