Define the decline before explaining it.
Start by establishing what the measure means. Has quote-to-order conversion fallen, or are fewer customers reaching the quote stage? Are you comparing equivalent periods and customer groups? Have the way enquiries are captured, the product mix or the definition of a qualified opportunity changed?
Look at the underlying counts alongside the percentage. A larger number of speculative enquiries can reduce the conversion rate without reducing orders. A stable overall rate can conceal a serious decline in an important customer group. Check revenue, margin and retention alongside the journey measure that first raised the concern.
The first useful output is a shared description of what changed, where, when and by how much. It should include the gaps in the data. It gives the team a common problem to investigate.
Put the competing explanations on the same page.
Turn each explanation into something that can be examined. “The market is difficult” is hard to test. “Customers in this segment are choosing a competitor with shorter lead times” points to specific evidence.
For each hypothesis, record:
- what would be observable if it were true;
- what evidence would challenge it;
- which customer groups or buying stages it affects;
- what has already been checked;
- what remains unknown.
Include alternatives involving the proposition, price, product fit, availability, channels, response times and sales coverage. This keeps the investigation open long enough to find a useful explanation. It also helps colleagues see that their experience is being examined fairly.
Connect the records to the customer experience.
Commercial records can show where an outcome changed. Customer conversations can help explain the choice behind it. Operational observation shows what actually happens between the two.
Select current, declining and lost customers deliberately. Ask about a recent purchase or abandoned enquiry: what they needed, the alternatives considered, where the experience became difficult and what they did next. Review the corresponding enquiry, quote, order and fulfilment records where available.
A quoted response-time target is different from the time the customer experienced. A loss reason entered after a deal closes may describe only part of the decision. Treat both as evidence to examine in context.
Choose the smallest test that could change the answer.
If slow quoting appears consequential, examine whether faster responses improve outcomes for a defined customer group. Keep price, product availability and the competing offer visible in the comparison. Agree the baseline and the outcome that would justify continuing.
If the evidence instead points to a weaker proposition, accelerating the existing process may have little effect. The next test should follow that finding. An assessment earns its value when it changes what the business does, including which investments it avoids.
In Matt’s turnaround work, customer evidence changed the explanation and informed connected changes to quoting, pricing and the way the business was managed. The case shows why diagnosis and practical delivery need to remain connected.
Leave the team with an answer it can act on.
A useful recommendation separates supported findings, assumptions and evidence gaps. It identifies the priority intervention, the people needed to deliver it, the expected commercial effect and the conditions that could invalidate the case.
Make clear what happens now, what needs a further test and what should be deferred or stopped. A shared understanding of the cause is valuable because it enables coordinated action across the teams that influence the customer’s choice.