Profitability of Retail Execution implementation - a process-based approach
The implementation of a Retail Execution system does not start with a spreadsheet or declared savings. The starting point is always an understanding of actual operational processes - how the organization operates in the field, how it collects information from the sales network, and what really happens to that data afterward. Only on this basis is it possible to calculate the impact of an implementation in a reliable and transparent way.
Selecting a process for analysis
In practice, we do not attempt to analyze the entire organization at once. Such an approach usually leads to simplifications and imprecise conclusions. Instead, we select one specific process that:
- is important from the client’s perspective,
- is largely based on data collection from the field,
- can be clearly described, broken down into stages, and realistically measured over time.
A process selected in this way becomes a reference point for further analysis and makes it possible to quickly see how the system affects the organization’s day-to-day operations.
Example: reporting on the execution of promotional campaign objectives
A good example is reporting related to the execution of promotional campaign objectives within a sales network. Let us assume:
- a network of 130 stores,
- reporting performed on a daily basis,
- an average reporting time of 25 minutes per store (a conservative estimate).
In this scenario, the organization spends approximately:
- 54 working hours per day,
- 1,080 hours per month
solely on collecting data from the sales network.
Reducing data collection time by at least 50%
Based on experience from completed implementations, the use of a Retail Execution system such as cHow makes it possible to reduce the time required for data collection by at least 50%. This is not a maximum or declarative value, but a safe and repeatable standard observed across different organizations and process types.
It is important to emphasize that this refers exclusively to the data collection and data entry stage. This is not yet full process optimization, but rather the first, fundamental step.
In the example above, this means:
- approximately 540 fewer hours per month spent solely on data collection.
Assuming a conservative, market-based retail labor cost in Poland of PLN 35–40 per hour (approximately GBP 7–9 / EUR 8–9 per hour), this translates into:
- approximately PLN 19,000–21,000 per month (approximately GBP 3,800–4,200 / EUR 4,300–4,800) in savings for a single, clearly defined operational process.
Why this is only the first stage of optimization
Collecting data does not yet mean that an organization can effectively use it. In many companies, a significant portion of operational time is consumed later by:
- verifying data completeness,
- organizing data from different sources,
- analysis,
- and translating insights into decisions and operational actions.
These stages often prove to be more demanding than data collection itself, especially when information is fragmented, inconsistent, or delivered to the wrong roles.
For this reason, optimization in Retail Execution should be approached in stages. Reducing data collection time by at least 50% is the first measurable outcome of implementation. The next step is structuring, analyzing, and properly using data in decision-making processes – this is where the next layer of tangible benefits emerges.
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