Field KPIs: every sales rep measured by the same rules

Each rep has four targets a month:

• Ideal Store. Named stores where a product range must be displayed to the company's standard.
• Second placements. Extra displays, such as stands and the ends of aisles.
• Visit coverage. Visiting each store as often as agreed.
• Store coverage. The share of stores visited at least once in the month.

Every visit produced rows of data, several thousand a month for Ideal Store alone. Each area manager filtered and counted them for their own team in Excel. The rules were easy to get wrong. A stand seen on two visits is still one stand, and a stand carrying three product ranges counts once for some targets and three times for others.

So two managers could count the same month and get different numbers, and every rep's bonus rested on those numbers.

Vasilis mapped the month as it really ran, wrote down each counting rule with the sales team, and built one application around those rules.

• One procedure a month. The administrator opens the month, uploads the store list and starts from last month's targets. Managers change only what is different.
• Targets with the arithmetic done. The application suggests each rep's targets from the working days, their absences and their stores. It also warns when a store was already targeted in the last three months, and says whether that target was met.
• Targets locked before results. On a fixed day the month is locked, so no target can change once results start to come in.
• One calculation for everyone. The agreed rules run on the month's data in the same order every time. The results go into one Excel workbook, with a sheet for each rep showing target, result and achievement.
• Field data from the reps. When the old field system was retired in 2023, Vasilis added a screen where reps record each visit. Once a visit is closed, it cannot be changed.
• The right view for each role. Area managers see their own reps, directors see their managers' teams, and reps see only the screen where they record visits.

71 automated tests check the calculations, reports and uploads.

Market questions, answered in minutes

The application told each manager how their reps were doing. The sales team also wanted to know about the market: which chains carry the most extra displays, which ranges meet the Ideal Store standard, and what competitors are doing, over any period. A question covering six months meant combining six exports, up to 39,000 rows, and cleaning them by hand.

Vasilis added a reporting tool to the same application. Users choose a period, filter by chain, rep, product range or type of display, and group the results as they like. Each counting rule has a name and is chosen from a list, never improvised. Extra displays, for example, can be counted once per product range or once per display. The results show as a table, a chart or an Excel export.

To size the gain, the time of each step was estimated by hand and in the application, using the company's real volumes: about 15 reps, 2,500 stores and several thousand rows of field data a month.

• The monthly KPI work fell from about 42 hours to about 4, a drop of about 90%.
• Each rep's results take about 17 minutes a month, instead of nearly 3 hours.
• An analysis covering several months takes about 10 minutes, instead of about 2 hours.
• Together, the KPI work and the analyses save about 960 hours a year.
• Every rep is measured by the same written rules, in the same order, every month. Any figure a bonus is based on can be reproduced.
• Targets are fixed before results are known, and closed visits cannot be edited.
• The same rule always gives the same count, whoever runs it. A figure shown in a meeting can be traced back to its rows.

The time figures are estimates based on the company's real volumes, not measurements.