In physical distribution networks, sales performance no longer relies solely on instinct or experience: it increasingly depends on the quality of available data. At a time when artificial intelligence and automation tools are being integrated into sales departments, the question is: is the data sufficiently reliable, consistent and structured to feed these tools?
Building an effective data strategy means laying the foundations for smarter, more responsive and more accurate management. Here are the key steps to achieving this, particularly for field sales forces.
Good data is not abundant data, but useful data. It must be :
Collecting information that is too vague or little used (such as non-analyzable comments or facings that are never re-read) is counter-productive. Conversely, a few well-chosen indicators can be enough to generate effective steering.
A commercial data strategy is based on three fundamental principles:
There's no point in multiplying the number of fields to be filled in if the data is neither read nor used. The challenge is to define, in consultation with field and head office teams, the indicators that have a real impact:
This data must reflect the sales team's objectives, as well as those of management (territory coverage, regional growth, operational execution, etc.).
If you distribute to supermarkets, and don't know where to start, we've got the 5 key data you need to analyze first.
Non-standardized data is worthless. If each salesperson uses his or her own terms, formats or units of measurement, analysis becomes impossible. It is therefore essential to :
As the guarantor of data, it's up to you to define the statements your sales reps will use in the field. They don't have to choose which data to collect, or how to structure it. Your role is to provide them with the right tools to ensure that the data they collect is consistent, reliable and in line with the company's needs. This controlled standardization is essential if you are to exploit the full potential of the data collected in the field.
Standardization also involves people. Sales people need to understand why this rigor is necessary. Showing them concrete examples where incorrect data has distorted an analysis or slowed down a decision can convince them more effectively than a simple theoretical reminder.
As long as data is scattered across Excel, paper documents, personal notes or unconnected business applications, it's impossible to draw reliable conclusions. Centralizing data on a single platform enables you to :
A centralized database provides real-time access to field data. A sales manager can monitor the coverage of weekly visits, detect an abnormal breakage rate in a region, or react rapidly to the under-execution of a national campaign.
To ensure long-term data reliability, it is necessary to define :
On this subject, see also this external article on data governance strategy.
Modern tools make it possible to integrate automated quality controls:
Well-structured data can be used to :
AI can do nothing without reliable data. It can speed up analysis, spot correlations, suggest forecasts... but only if it is based on a coherent foundation.
Before buying a crm powered by artificial intelligence or predictive tools, you need to make sure that :
Building an effective data strategy doesn't mean adding layers of complexity. On the contrary, it's about simplifying, structuring and making reliable the way teams collect, share and exploit information from the field.
This groundwork is the prerequisite for any more ambitious transformation. Automation, artificial intelligence, real-time dashboards... will only be effective if the raw material - your data - is robust.
For a sales department managing an indirect network, with a dispersed field sales force, this data strategy is no longer an option: it's a lever for steering, aligning and improving performance.
