There are two types of unprofitable customers for investees:

1. Those you know will be unprofitable, and
2. Those you don’t know will be unprofitable

The first group is often the result of a “loss leader” approach where you expect to create future value from sacrificing some current value. The potential future value may be in the form of increased volume, new synergies, higher prices, better reputation, etc.

Something as simple as sample paint (that you take home to compare to other colours in your home) is a loss leader; the paint company loses money on supplying free paint, but it gains from brand loyalty/awareness, should you like the sample colour and decide to purchase it.

However, the risk with loss leaders is that they continue to lose and don’t start to lead. Imagine if someone took enough sample paint home to paint their entire house. Or, imagine if potential customers used your sample paint, but bought the identical colour from a competitor at a lower price. So, you really need to keep abreast of  loss leaders and ensure they stop losing and start leading.

The second group of unprofitable customers is generally the result of mispricing. Either your investee didn’t estimate costs accurately or they didn’t charge the customer enough at the time of purchase. This is inevitable in most businesses, but it shouldn’t be accepted without action. The loss can be more than just lost profit; it could set a precedent, it could convey a message of inferior quality, or it could create a price war.

To prevent customers becoming unprofitable, it is important to monitor investee profitability by customer (in addition to by product/service, by location and by salesperson). This granular approach will ensure the sources of losses are found quickly and rectified effectively. If you’re thinking it sounds overly bureaucratic to take such granular measurements, then you must simplify the process. Get the investee comfortable with a standard reporting template and ensure the process of measuring, calculating and reporting data is consistent.