If you manufacture or distribute machinery and you already know your after-sales revenue potential against the install base (all the machines you have already sold), the next task is turning that number into a list: which customers and machines are worth a call, and why.
In Calculate after-sales revenue potential yourself, real example we showed how a small machinery distributor got from three exports to 55 177 Eur of after-sales revenue potential still unrealized across 48 customers and 58 machines. Below we go through nine customer-level and machine-level checks on that same example. Where a check was weak on a small base, we still keep it in the list so you can see how it behaves on real data.
The checks stay the same for manufacturers and distributors. Who can act on them depends on the business model. An OEM selling direct owns the relationship and can go through these itself. A distributor usually sits closest to the customer and can act day to day. An OEM that sells only through distributors still needs the distributor in the loop, or a shared way of seeing the same machine at the same customer address.
What data is needed
You need the same starting material as the previous guide: customer list, machinery list, invoice data. Service logs make step 8 possible. If install-base percentage is still unclear as a metric, we explained the three ways to measure after-sales performance in After-Sales Performance Metrics: 3 Ways to Measure It.
Step 1: Compare customers with the same machine type
Group customers by machine model and compare after-sales spend inside each group. The ones well below the group pattern are the ones to look at first.
Here we tracked one brand only. The customer with the most machines of the same model had four. That is too small for the comparison to mean much yet. On a larger install base, with more machines per model, this check gets more useful.
Step 2: Identify customers whose average order value decreased
Split after-sales invoices into parts, consumables and service labour before you trust this number. A drop in the overall average can just mean a customer ordered cheaper consumables instead of parts, not that they are spending less overall.
If accounting does not already split it that way, categorize it after the fact. AI tools help a lot with that cleanup. Until the split exists, treat an overall average-order drop as an early insight, not a decision.
Step 3: Identify customers whose orders started to decline
Compare recent order activity to the same length of time before it.
Out of 48 customers, 16 had declined when we compared the last 6 months to the 6 months before that.
We chose a 6-month period because a full 12 months of history was not there yet. For an older company, or machines that are ordered less often, pick the period that matches how those machines are actually used.
Step 4: Flag customers who bought machinery and then stopped ordering
8 out of 48 customers had placed no after-sales order since buying the machine.
That is a churn risk: they may have gone to a third party, or the after-sales relationship never started. Either way, the overall potential number alone does not show them. This check does.
Step 5: Check discounted customers who order little and hold no agreement
Look at customers who got used to discounted pricing, order little, and hold no parts or service agreement.
On this distributor’s size, when checking, we found nobody worth to be put on a special list. A small team often already knows those accounts by memory. On a larger customer base, discount patterns are harder to see without the export, so we still recommend doing it. Same method. How useful it is depends on the scale.
Step 6: Identify warranties expiring in the next 3-6 months
Warranty end is a concrete moment to start a conversation: extend it, or negotiate another agreement, while the customer still has a reason to pick up the phone.
Pull the expiry dates from the machinery list and put the next 3-6 months on a short list for the team. This one does not need invoice history. It needs warranty dates that are actually filled in.
Step 7: Find customers overdue on recommended maintenance
Find customers who should be ordering again based on recommended maintenance intervals, but have not. This is the most important moment to reengage, and the hardest to notice without tracking it.
Out of 48 customers, only 1 operator called and organized regular maintenance on his own. Everyone else needed a reminder.
That pattern is common in the industry. One person thinking ahead is not a process. Everyone else waits until something breaks, or until you call.
Step 8: Compare warranty orders with post-warranty orders
Count how many customers order while still in warranty versus after it ends. This needs service-log information if it exists.
We worked with a head of service who documented every visit by hand. Once those notes became data, multiple post-warranty visits showed up that were never invoiced: work already done, revenue never collected. Calculating after-sales revenue potential from invoice exports alone would have missed that.
Step 9: Flag machines close to typical end-of-life
A machine close to the end of its usual working life is the natural moment to offer a replacement, a retrofit, or an upgrade, when the customer is already thinking about the next machine.
Use the age and typical life of each model from your own history, not a generic industry year count. You are choosing when to start the conversation, not inventing an exact end-of-life year.
This method’s limitations
Every one of these checks can be done in an excel, by hand, from the exports you already have. That works for 48 customers. It gets slower and less reliable as customer count and machine count grow, especially once customers hold multiple machines across different models and warranty states.
Steps 2 and 8 both depend on data your systems may not already track in the shape you need: categorized spend, and full service logs. The first real cost of this method is often getting your own data into a usable shape before the checks are trustworthy.
On a small distributor, not every check is equally useful. Step 1 and step 5 were weak on this base and still worth keeping in the sequence so the method stays complete when the install base is larger.
And it is a snapshot. Six months from now, you have to do the whole exercise again from scratch if you want the lists current.
How to turn one time effort into a live data
Getting this far, from raw exports to ranked lists of customers worth calling, is what we can do without any software. Each list needs a different message: a customer who stopped ordering is not the same conversation as a warranty that expires next quarter.
If the bottleneck is building the first list once, the same three exports from the previous guide plus service logs where you have them are enough. If you want these checks live across a larger install base, refreshed when invoices and warranty dates change, let’s have a conversation about it and we’ll show you how that might look like using software.
