You can do a lot of things right in a decision-making process and still end up solving the wrong problem.
You can gather the numbers, pull together the options, build a decision matrix, score the trade-offs, and bring the right people into the room. All of that helps once you know what decision you are actually trying to make.
The harder part comes earlier: defining the problem you are trying to solve.
Something has changed. Sales are down. Deals are taking longer to close. Customer retention has slipped. Marketing activity is up, but revenue is not moving with it.
Those things tell you something is wrong. They do not tell you why.
A drop in sales might be framed as a lead-generation problem, a conversion problem, a pricing problem, a positioning problem, a market problem, or some combination of them. The organisation has to make some judgement about where to look first.
The difficulty is that an early theory about what is happening can quickly harden into “the problem”. People need a working explanation in order to act, but once that explanation starts being treated as fact, the organisation can move straight into solving it before it has really tested whether it is true.
“Maybe we are attracting the wrong leads” becomes “Marketing is sending Sales bad leads.”
“We should check whether the pricing change affected conversion” becomes “The new pricing is the problem.”
“We are hearing more objections about value” becomes “Our positioning is broken.”
At that point, the organisation has moved beyond describing what is happening. It has started to decide what caused it.
And that shapes what happens next.
A hypothesis is simply your current best explanation for what might be happening. It is not a conclusion. It is something you can investigate, test against what you know, and change when the evidence points somewhere else.
In a commercial context, that might mean starting with: “We think the pricing change is contributing to the drop in conversion.” That gives you something specific to investigate without assuming you already know the answer.
Getting to that hypothesis usually means working through three different levels.
If pricing changed in June and conversion fell soon afterwards, it is reasonable to suspect that the pricing change may be affecting sales. You can look at what happened before and after the change, where deals are stalling, what customers are saying, and whether the same pattern appears across different markets or customer groups.
But the investigation also needs to be capable of challenging that theory.
Perhaps conversion had already been falling steadily since April. Perhaps the decline is concentrated in one market where pricing did not change. Perhaps customers are not mentioning price at all, but Sales has started taking longer to follow up.
Now the pricing theory looks less convincing.
You may end up with a different explanation, or with two or three plausible explanations that need more investigation before you narrow them down.
This might sound like a lot of work, but the first pass often does not need to be. Looking through your CRM, pipeline data, conversion rates, customer segments, and the timing of any recent changes may be enough to eliminate some possibilities and strengthen others.
Perhaps the pricing theory falls apart immediately because the decline started two months earlier. Perhaps the problem is concentrated in one segment. Perhaps the leads are coming in at the same rate, but sitting untouched for longer.
That is what makes a hypothesis useful. It narrows a messy situation enough to investigate without pretending you already know what is wrong.
You are not trying to prove one explanation right. You are trying to narrow the field enough to see where it is worth looking more closely.
Commercial problems are often layered and complex. That is where the language of “root cause” can start to become less useful.
Techniques such as the Five Whys can work well when you are tracing a relatively clear chain of cause and effect. But a revenue problem may involve several things changing at once: lead quality, sales follow-up, pricing, customer behaviour, internal processes, or the wider market.
Some of those things may be connected. Others may simply be contributing to the same outcome.
You might find that pricing is affecting one segment while slower follow-up is hurting another. Or that Marketing is generating broadly the same volume and quality of leads, but buyers across the market are taking longer to make decisions.
You are not always trying to keep digging until you uncover one thing sitting underneath everything else. You are trying to build an accurate enough picture of what is contributing to the problem.
That picture may be more complicated than the explanation you started with.
It may also be more useful.
The obvious risk is going too far in the other direction.
If every commercial problem has several possible causes, and every hypothesis needs testing, it is easy to convince yourself that you need a complete diagnosis before making a move.
You usually don’t.
The question is whether you understand the situation well enough to make the decision in front of you.
That depends partly on the decision. Some changes are easy to reverse. Others are not. Tweaking a follow-up process is very different from hiring a new team, replacing an agency, changing pricing, or repositioning the company.
The harder a decision is to undo, the more confidence you want in the problem definition behind it.
For smaller decisions, analysis and action do not need to be completely separate. Action can be part of the investigation.
If you suspect slow follow-up is hurting conversion, you probably don't need another month of analysis. Change the process, make the expectations clear, and see what happens.
If you think one customer segment is responding differently to pricing, test that assumption in a limited way before changing the whole commercial model.
You don't need every uncertainty to disappear. You need to work out which uncertainties matter enough to resolve before you commit more time, money, or people.
You are unlikely to arrive at a perfectly objective definition of the problem. Commercial decisions are made with incomplete information, competing explanations, internal pressure, and a market that keeps moving while you are trying to understand it.
You are trying to get to a definition that is good enough to make the next decision.
If you are trying to work through a problem like this, a useful place to start is to make your current thinking explicit:
Those questions do not give you the answer. They make it easier to see where you are working from evidence and where you are still working from assumption.
They also give different teams something more useful to contribute than a defence of their own function. Sales can bring what they are hearing from customers. Marketing can bring acquisition and conversion data. Product or Customer Success may have noticed something neither team can see from its own numbers. Something outside the business may turn out to explain part of the picture.
You may still end up deciding that Marketing needs to change something. Sales may need to change something too. Pricing, positioning, product, or something outside the business may turn out to be part of the problem.
The important difference is that those actions follow from a better understanding of what is happening, rather than becoming the way the organisation tries to find out.
You do not need to find the one true cause. You need to understand the problem well enough to make the next decision without pretending that your first explanation was a fact.
That is a much less satisfying answer than a neat box on a decision matrix.
It is also much closer to how difficult commercial decisions actually work.
The first step isn't always more marketing activity. Sometimes it's understanding what's really happening. Book a free Alignment Call and let's diagnose the problem before you invest in the solution.