How Using Analysis of Competing Hypotheses Shows What’s Really Happening in Your Market, Not Just What You Hope Is True

Confirmation bias means looking for evidence that supports what you already believe. Real intelligence is about searching for evidence that could prove you wrong.

The Competitor “Threat” That Didn’t Exist

In 2024, we spoke with a SaaS company that was sure its main competitor had a complex plan to move upmarket and steal its enterprise deals.

Here’s what they “knew”:

• The competitor hired three enterprise sales reps.
• Competitor launched “enterprise-grade security features”.
• The competitor won three enterprise deals in Q2.
• The competitor announced enterprise partnerships.

The story made sense. Their strategy seemed clear. The threat looked obvious.

But that wasn’t what was really happening.

Instead of just looking for patterns in surface evidence, we used a structured analysis. We listed out different possible explanations:

Hypothesis A

The competitor is executing a serious enterprise strategy to displace us.

Hypothesis B

The competitor is testing the enterprise market with limited commitment.

Hypothesis C

The competitor is chasing vanity wins and enterprise partnerships without a sustainable business model.

Hypothesis D

The competitor had one customer request and oversold it as an enterprise strategy.

Download a ACH template here.

Next, we gathered evidence for each hypothesis. Rather than looking for proof of what we already believed, we searched for evidence that could show each hypothesis was wrong.

We found that the three enterprise sales reps hadn’t closed any deals yet. Two of the three enterprise wins actually came from the existing sales team. The so-called “enterprise-grade security” was just a new name for features they already had. The partnerships were non-exclusive and weren’t bringing in customers. Most importantly, there was no hiring in customer success, implementation, or enterprise support, all of which are needed to serve enterprise customers well.

When we used structured analysis instead of relying on confirmation bias, we revised our hypothesis. The competitor wasn’t really following an enterprise strategy. They were just testing it with minimal commitment because their main business was under pressure to grow. This was the most likely explanation based on all the evidence.

Once we used structured analysis, the idea of a “sophisticated threat” faded away.

My client was preparing to defend against an enterprise threat that didn’t exist. In the process, they missed the real problem that their own growth was slowing for the same reasons the competitor was venturing into new markets.

What is ACH (And Why Most Companies Get It Wrong)

Analysis of Competing Hypotheses is a methodical process for evaluating evidence and lessening bias in your analysis.

At Octopus Intelligence, we’re a competitive intelligence agency based in the UK, Dubai and the US, founded by former British military intelligence analysts. ACH is at the core of our work because in military intelligence, being wrong about threats isn’t an option.

Here’s how ACH works:

Standard Analysis (What Most Companies Do)

1. Develop a hypothesis you believe in
2. Look for evidence supporting it.
3. Present that evidence as analysis.
4. Conclude that your hypothesis is correct.

The problem is confirmation bias. You tend to notice what you expect, see unclear evidence as supporting your own view, and often ignore other possible explanations.

Analysis of Competing Hypotheses (ACH)

1. Develop multiple hypotheses, including ones you don’t believe in
2. For each piece of evidence, ask: Does this support Hypothesis A, B, C, or D?
3. More importantly, does this evidence disconfirm any hypothesis?
4. Evaluate which hypotheses have the least disconfirming evidence.
5. Finish with the hypothesis that best explains all the evidence, not just confirming evidence.

This change matters. ACH pushes you to look for evidence that could disprove your preferred hypothesis, not just evidence that supports it.

Why ACH Reveals What’s Really Happening

Most competitive intelligence efforts fail because they rely on confirmation bias.

You see competitor hiring and assume it confirms your hypothesis about their strategy. You see them winning deals and assume it confirms your fears about their positioning. And you build a narrative and find evidence supporting it.

ACH requires a different approach: ask what evidence would disprove this hypothesis? What would I need to see to make this hypothesis less likely?

When you ask these questions honestly, your original story often doesn’t hold up.

Example of the “Aggressive Pricing” Hypothesis

Hypothesis: “Competitor is winning deals through aggressive pricing”

Confirming evidence:

• The competitor announced lower pricing.
• You lost three deals to them at a lower price point.
• The sales team reports they’re heavily discounting.

This evidence seems to confirm the hypothesis, and you may decide you need to match their pricing.

But ACH makes you ask different questions. What evidence would go against the aggressive pricing hypothesis?

• High prices in specific segments could indicate that selective pricing, rather than aggressive across-the-board pricing, is being used.
• Customers switching away despite lower pricing suggests price isn’t the winning factor.
• Competitor not discounting beyond what’s economically sustainable probably means pricing discipline, not aggression)
• A win rate that doesn’t improve despite price cuts could suggest that price wasn’t the limiting factor.

When you consider these disconfirming questions, you start to see a different picture. Perhaps the competitor is only discounting in markets where they have a cost advantage. Maybe they’re losing customers they discounted to because those customers aren’t good fits. Maybe they’re only winning when they also have other advantages besides price.

A different hypothesis is that the competitor is using selective pricing in specific segments where they have a cost advantage, rather than pursuing aggressive, across-the-board price competition. This leads you to adopt a new strategy: focus on the segments where you are strong and avoid a price war.

How to Apply ACH to Competitive Intelligence

Step 1: Identify the Core Question

Not: “What are competitors doing?” But, “What is actually happening in our market and why is it happening?”

Specific questions ACH works best for:

• Why is this competitor growing faster than others?
• What’s driving the market shift we’re seeing?
• Will this competitor sustain their advantage, or is it temporary?
• What’s the real reason we’re losing these specific deals?
• Is this market trend structural or cyclical?

Step 2: Develop Competing Hypotheses

Generate 3-5 hypotheses that could explain the evidence you’re observing. Include:

• Your preferred hypothesis (what you believe)
• Opposite hypothesis (what would contradict your belief)
• Alternative hypotheses (other explanations that fit some evidence)
• Null hypothesis (nothing significant is happening, it’s noise)

Why is Competitor X winning enterprise deals?

It could be hypothesis A

They’ve built genuinely superior enterprise capabilities.

Hypothesis B

They’re chasing vanity wins without sustainable economics.

Hypothesis C

Enterprise buyers are just testing alternatives; they’re not switching in the long term.

Hypothesis D

They have one enterprise customer with an outsized influence, driving referrals.

Step 3: Gather Evidence for Each Hypothesis

For each piece of evidence, determine which hypothesis it supports or disconfirms.

Don’t just ask, “Does this support my preferred hypothesis?” Try to ask which hypothesis does this best explain?

Example evidence:

• Competitor hired three enterprise sales reps → Supports A and B (both need sales team)
• Competitor’s enterprise customers not renewing → Disconfirms A, supports B
• Competitor’s enterprise deals all came from one customer referral → Supports D.
• Competitor’s enterprise team is remote-based without on-site support capacity → Disconfirms A (suggests lack of commitment)

Step 4: Identify Disconfirming Evidence

This is the key step that most analyses overlook.

For each hypothesis, ask what evidence would prove this hypothesis wrong?

Then follow up by asking if we have that evidence to back this up?

If you have disconfirming evidence, the hypothesis is less likely to be correct.

Step 5: Evaluate Hypotheses Against Disconfirming Evidence

Don’t judge hypotheses by how much supporting evidence they have.

Instead, judge them by how little disconfirming evidence there is.

The hypothesis with the least disconfirming evidence is probably the right one.

Example final assessment:

Hypothesis A: 3 pieces of disconfirming evidence = unlikely
Hypothesis B: 1 piece of disconfirming evidence = more likely
Hypothesis C: 0 pieces of disconfirming evidence but weak confirming evidence = possible
Hypothesis D: 2 pieces of disconfirming evidence = unlikely

So, hypothesis B is most likely because it has the least disconfirming evidence and enough support.

Real Examples of ACH Revealing Truth

A fintech company was convinced that a well-funded competitor was ready to become the market leader.

Hypothesis A (preferred)

“Competitor is building genuine competitive advantage and will become market leader”

Hypothesis B:

“Competitor is burning cash unsustainably and will hit financial problems”

Hypothesis C:

“Competitor and we will coexist in different segments”

Evidence analysis

Confirming A

Large funding round, impressive brand, good press coverage, winning some enterprise deals

Disconfirming A

Customer acquisition cost is 3x higher than ours; unit economics are worsening; customer churn exceeds new customer acquisition; burning $50M annually with no path to profitability.

When you weigh disconfirming evidence, Hypothesis B becomes more likely.

Follow-up question

How long can this continue?

Evidence: 18 months of remaining runway at current burn rate.

Conclusion

The competitor will likely face financial pressure in 12-18 months.

Result and actions

Don’t panic about their growth. Instead, prepare for when their growth slows and they need to change strategy. In the end, the competitor faced a cash crisis in month 14, laid off 40% of its team, and shifted its focus to profitability. The company that used ACH was ready and grew its deals by 200%.

Common Mistakes in Applying ACH

Straw Man Hypotheses

You set up alternative hypotheses that are obviously wrong, so your preferred hypothesis looks good.

The wrong hypothesis is that the competitor has no strategy and is just doing things randomly.

A better hypothesis is that the competitor has a focused strategy. An alternative hypothesis should be realistic, not far-fetched. Hypotheses need to be plausible, not ridiculous.

Confirmation Bias in Evidence Gathering

You look for evidence supporting your preferred hypothesis, but skip seeking disconfirming evidence.

Fix this by actively searching for evidence that would disconfirm each hypothesis. Asking what I would have to see that would prove this hypothesis wrong? Then conduct that search intentionally.

Weighting All Evidence Equally

Some evidence is stronger than others. Weak evidence shouldn’t count as much as strong evidence.

For example, weak evidence is that the competitor announces a strategy (they might be bluffing)
There is strong evidence that the competitor is actually hiring and product development corresponds to the announced strategy (they’re committing resources)

Give more importance to evidence from strong, reliable sources.

Ignoring Absence of Evidence

What’s not happening can be as important as what is happening.

Example:

A hypothesis that the competitor is serious about the enterprise market.

Disconfirming evidence is that they haven’t hired an enterprise customer success team, an enterprise support infrastructure, or implementation specialists.

If the expected support infrastructure is missing, that’s strong evidence against the hypothesis.

Changing Hypotheses Mid-Analysis

You start with one set of hypotheses, then when analysis doesn’t support your preferred hypothesis, you create new hypotheses.

So decide on your hypotheses before you start gathering evidence. Only change them if the evidence clearly doesn’t fit any of your original ideas.

The ACH Framework for Competitive Intelligence

Here’s the structured ACH Framework process

Question Definition (Week 1)

Define the specific question you’re trying to answer:

• Why is Competitor X winning in this segment?
• What’s driving the market shift we’re observing?
• Will this competitive threat sustain or fade?
• What’s going to happen in our market in the next 18 months?

Hypothesis Development (Week 1)

Generate 3-5 competing hypotheses. Include your preferred hypothesis and opposing hypotheses.

Write down what each hypothesis would predict if it were true.

Evidence Gathering (Weeks 2-3)

For each hypothesis, what evidence would support it and what evidence would disconfirm it?

• Collect 15-20 customer interviews.
• Do some Competitor product analysis.
• Read market research and analyst reports.
• Look at financial and operational indicators.
• Analyse hiring and partnership patterns
• Maybe do some win-loss analysis.

Evidence Analysis (Week 4)

For each piece of evidence, isolate and understand which hypothesis it supports. And which hypothesis does it disconfirm and determine how strong this evidence is? Then create a matrix detailing the hypotheses, evidence, and disconfirming power.

Hypothesis Evaluation (Week 4)

Score each hypothesis on disconfirming evidence:

• How many pieces of disconfirming evidence does each hypothesis have?
• How strong is the disconfirming evidence?

The hypothesis with the least disconfirming evidence is most likely true.

Conclusions and Actions (Week 5)

• What is most likely happening based on evidence analysis?
• What should we do differently based on this conclusion?
• What evidence would we need to see that would change this conclusion?

Set up a system to track that evidence going forward.

Why ACH Is Essential for MENA Markets

Octopus Intelligence expanded into MENA through our Dubai office because regional markets are more prone to unexamined hypotheses than Western markets.

MENA companies commonly operate with implicit hypotheses about:

• How will Western competition enter regional markets?
• What regulatory changes will happen
• How local customers will respond to new products
• What pricing approaches will work

These hypotheses are often wrong because they rely on Western patterns that don’t correspond to local contexts.

ACH forces examination of regional assumptions:

MENA-Specific Hypothesis Testing:

Question: “Will Western SaaS companies dominate MENA markets?”

Hypothesis A (assumed true): “Western companies will replicate Western success in MENA”

Hypothesis B: “MENA customers have different needs that require local solutions”

And hypothesis C: “Regional regulatory and data sovereignty requirements create barriers Western companies can’t easily overcome”

When you gather disconfirming evidence for Hypothesis A:

• Data localisation requirements: Western companies don’t prioritise
• Customer choices for regional companies with local teams
• Pricing sensitivity that makes Western pricing models uncompetitive
• Regulatory complexity that Western companies underestimate

Hypothesis A seems less likely, while Hypothesis C becomes more likely.

Strategic response: Focus on building a regional advantage that Western companies can’t easily copy, instead of trying to imitate their business models.

How to Start Using ACH Tomorrow

1: Pick One Competitive Question

Don’t try to use ACH for everything at once. Choose one question you’re truly unsure about:

• Why is Competitor X growing faster than we are?
• Will this market shift be permanent or temporary?
• Should we be worried about this threat from an adjacent category?

2: Develop Three Hypotheses

Your preferred hypothesis + one opposite hypothesis + one alternative hypothesis

Write them down specifically.

3: List Disconfirming Evidence

For each hypothesis, what would you need to see that would prove it wrong?

Write those down.

4: Search for Disconfirming Evidence

Actively look for evidence that would disconfirm each hypothesis, not just evidence supporting it.

5: Weight Disconfirming Evidence

Which hypothesis has the least disconfirming evidence?

That’s the conclusion that is most likely true.

6: Act on the Conclusion

What does this most likely hypothesis suggest you should do?

Use that insight to shape your strategy.

The Power of Intellectual Humility

Here’s what ACH teaches that most competitive intelligence misses:

Being remarkably certain is risky. Once you’re sure about your competitive hypothesis, you stop looking for evidence that could prove you wrong. You stop asking tough questions and start seeing unclear evidence as support for your own story.

ACH encourages intellectual humility. You keep your hypotheses flexible, look for evidence that could prove you wrong, and update your conclusions when new evidence comes in.

This kind of humility gives you a real competitive advantage. Companies that are too confident in their strategy often miss market changes. Those who stay humble and open to new evidence modify their strategy when needed.

Once you become certain, you stop thinking critically. ACH keeps you thinking, even when it’s uncomfortable.

This kind of discipline is important in markets where understanding what’s really happening matters more than mere confidence.

https://www.octopusintelligence.com/how-using-analysis-of-competing-hypotheses-shows-whats-really-happening-in-your-market/