Working across multiple brands, customer interaction data was reviewed at scale to better understand demand. The findings highlighted patterns that were not always visible through existing reporting.
Customer demand is often understood through assumptions, internal knowledge, or limited data points. Looking at it at scale provides a clearer understanding of what customers are trying to achieve.
When interaction data is reviewed across multiple brands and channels, recurring signals begin to appear, much like the patterns seen in How to get more from contact centre data. These highlight common queries, gaps in coverage, and areas where expectations and existing answers do not align.
This shifts the focus from volume to intent, which also shapes how a How to tell if knowledge is working performs. Understanding what customers are trying to do, how they phrase it, and where they encounter friction makes it easier to prioritise improvements across content, AI, and customer journeys.
The value comes from structuring this information so it can be used, including improvements to What improves Help Centre performance. When applied effectively, it informs decisions, shapes content strategy, and supports ongoing optimisation across the operating model.
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