Sales reporting for the board: 12 KPIs, a results dashboard and forecast accuracy

In many mid-sized companies, the management board sees sales in a presentation once a quarter, in an Excel spreadsheet once a month, or when it asks. Each of these has the same flaw: a problem is visible in the data several weeks before it reaches the boardroom table.

Take a hypothetical example. In June, the sales manager notices fewer new opportunities but puts it down to a temporary dip. July is similar, and in August everyone blames the holidays. The management board finds out from the quarterly report in September, when there is little time left to save the year’s result.

The solution is not more charts, but a fixed set of metrics, up-to-date data and regular reviews. Then a worrying trend shows up after a week or two, not after a quarter.

KPIs, or key performance indicators, are the figures that together tell you whether sales are on plan. The management board does not need fifty of them. The set in the table below covers results, the state of the sales pipeline, team effectiveness and forecast accuracy.

Show each metric with a 12-month trend and a comparison against target. Do not change the definitions every quarter, or you will lose the ability to compare.

We deliberately do not give “industry norms”. Reliable, comparable data for Polish B2B companies is lacking, and sales effectiveness depends on the market, price and the length of the cycle. Set the alert threshold based on your own results, for example as the weakest quarter of the last two years.

Two of the metrics in the table raise the most questions, so let us show them with hypothetical figures. A company has a target of PLN 2 million per quarter. From its history, it knows that it wins opportunities worth 30% of the pipeline value.

The unweighted pipeline it needs is therefore PLN 2 million ÷ 0.3 ≈ PLN 6.7 million, or about 3.3 times the target. The popular “three-times rule” is exactly this calculation for companies that win one opportunity in three. With a 20% win rate you need five times the target, and with 50%, twice.

Remember not to confuse the unweighted pipeline with the weighted one. The weighted value is already expected sales, so the reference point for it is the target itself, not a multiple of it.

Sales velocity: the team has 40 open opportunities, wins 25%, the average deal is PLN 80,000 and the cycle lasts 90 days. That gives 40 × 0.25 × 80,000 ÷ 90 ≈ PLN 8,889 of sales per day. Shortening the cycle to 75 days raises velocity to about PLN 10,667 per day without hiring anyone new.

A results dashboard is a single screen with all the metrics, available to the management board at any time. It can be built in three places, and the choice depends mainly on where the data is and what the company already uses.

If the company is just starting and is not sure about the metrics, it makes sense to use the reports built into the CRM for a few months. Once the definitions have settled, it is easier to move them to Power BI without rebuilding anything.

Licences are only part of the cost. Most of the work is agreeing definitions, connecting data sources and checking that the figures match the accounts.

The dashboard alone is not enough. Without regular meetings, the management board looks at it only occasionally and draws no conclusions. Below, we suggest a rhythm that works in mid-sized companies – adjust the timing and attendees to your organisation.

Put all the meetings in the calendar for the whole year in advance. A rhythm planned “month by month” usually fades away after a few weeks.

Forecast accuracy is a metric many companies do not calculate at all. Without it, the management board does not know whether it can trust the figures in the sales pipeline. The formula is simple: forecast error = (forecast – actual) ÷ actual × 100%.

For example: at the start of the quarter, the sales department forecast PLN 2.4 million, and the actual result was PLN 2.0 million. The error is (2.4 – 2.0) ÷ 2.0 = +20%, so the forecast was too high.

Always record the forecast from the same point in time, for example the first week of the quarter. At the end of the quarter every forecast is accurate, because most contracts have already been signed.

If the error goes in the same direction for several quarters, the cause is usually the data in the CRM: out-of-date stages and probabilities entered out of habit. We describe forecasting methods, including AI-based ones, in a separate article.

The order matters. Companies that start by choosing a tool often rebuild the dashboard after a few months, because it turns out that the data in the CRM cannot support half the metrics.

The second step already brings the first results. Simply tidying up the pipeline stages and mandatory fields in the CRM often improves the quality of conversations about sales.

AI in reporting makes sense once the metrics are settled and the data in the CRM is reliable. Before that, an AI tool will confidently give you figures calculated from the wrong data.

Copilot in Power BI lets you ask questions about data in plain language, but it requires Fabric F2 or Premium P1 capacity. Microsoft also recommends preparing the model beforehand: descriptions, instructions and approved answers to typical questions. According to the documentation, some Copilot features in Power BI do not yet officially support questions in languages other than English.

The second use is forecasting. AI tools in the CRM score opportunities based on history, but they need data from several quarters. We explain why AI can get numbers wrong in our article on talking to your company’s data.