CRM · Data
Decision support in an existing CRM
How quality data and CRM recommendations can support a sales team's work.
Challenge
A team has years of customer history in its CRM, but the data does not support clear sales priorities. Introducing decision support requires an understanding of the team's needs and a review of data quality.
Approach
We start from the decision the team makes regularly. We get the data in order, choose a simple useful signal, keep the final decision with a person, and compare the result with the current way of working.
Expected benefits
The sales team receives recommendations with clear reasons that it can check and use. Reviewing data quality before implementation reduces the risk of unsuitable recommendations.
This is a method note: it describes how we approach this kind of problem, not a write-up of a named client.
The real starting point
We first identify which decisions the sales team needs help with, such as selecting customers for follow-up or prioritising sales activities. We then review the available data and assess whether AI is suitable for generating recommendations.
How we approach it
- The decision first. We map who decides, when, and on what basis — then ask what could actually help them.
- Data quality. We identify and address duplicate records, empty fields and inconsistent entries before introducing decision support.
- Understandable indicators. We choose indicators with a clear calculation and a clear meaning for sales activities.
- A human stays in the loop. The system suggests, a person decides. Every suggestion carries its reason.
What changes
We integrate recommendations into the existing workflow. The team can check the reasons behind them and use them to plan sales activities. We assess data quality before implementation and compare the usefulness of recommendations with the current way of working.
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