Every investment thesis includes a view on competitive defensibility. The target company has some advantage — a technology edge, deep customer relationships, proprietary data, brand recognition, high switching costs — that protects its revenue from competitive erosion. Management presents this advantage as durable. The deal model assumes it persists.
The problem is that competitive moats are frequently weaker than management believes. Not because management is dishonest, but because the signals they rely on — customer retention, win rates, market share stability — are trailing indicators that can persist even as the underlying advantage erodes.
Customer interviews during commercial due diligence provide a direct test of moat strength. They answer the question that financial metrics cannot: do customers stay because the product is genuinely superior, or because they have not yet found a reason compelling enough to leave?
The Five Moat Types and How to Test Each
Switching Cost Moats
Switching costs are the most commonly cited moat in B2B software diligence. The argument is straightforward: customers have invested time, resources, and organizational capital in deploying the product. Migrating to an alternative would require re-implementation, data migration, user retraining, and workflow disruption. Therefore, customers are locked in.
This argument is often correct in its premise but wrong in its conclusion. Customers do face switching costs. But the presence of switching costs does not mean the moat is durable — it means the moat exists until a competitor reduces those costs below the threshold of customer dissatisfaction.
Interview questions that test switching cost moats:
-
“If a competitor offered to handle the entire migration for you at no cost, would you consider switching?” This question isolates the switching cost from the product preference. If customers say yes, the moat is the switching cost itself, not the product. If customers say they would still prefer to stay, the moat is genuine product value.
-
“What would your organization lose if you switched to an alternative?” Customers who describe deep workflow integration, proprietary customizations, and years of accumulated data have high switching costs. Customers who describe “it would be a hassle to retrain people” have moderate switching costs that a determined competitor could overcome.
-
“Have you evaluated the effort required to switch?” Customers who have already scoped out a migration — even informally — are further along the path to leaving than management realizes. The switching cost moat only works if customers believe switching is not worth the effort. Once they start quantifying the effort, the calculation has begun.
What the findings mean for the deal:
When interviews reveal that customers stay primarily because of switching costs rather than product preference, the investment carries a specific risk profile. The company has pricing power today but limited ability to raise prices without pushing customers past the switching threshold. Post-acquisition, any competitor that invests in migration tooling or offers subsidized switching becomes an existential threat.
Network Effect Moats
Network effects exist when the product becomes more valuable as more people use it. Marketplaces, collaboration platforms, and data networks are common examples. The moat thesis is that once a critical mass of users is on the platform, the value differential versus alternatives becomes self-reinforcing.
The challenge is that many products claim network effects that are actually just scale advantages. True network effects mean that users derive value from other users’ presence. Scale advantages mean that the company benefits from having more users through better unit economics, more data, or broader distribution — but individual users do not care how many other users exist.
Interview questions that test network effect moats:
-
“How important is it to you that other companies in your industry also use this platform?” If customers care deeply about the network — because they exchange data, benchmark against peers, or collaborate through the platform — the network effect is real. If they are indifferent to other users’ presence, the supposed network effect is actually a scale advantage.
-
“If this product had all the same features but no other companies used it, would it be less valuable to you?” This isolates the network component. Products with genuine network effects lose significant value in isolation. Products that are merely popular lose nothing.
-
“Do you interact with or benefit from other customers on the platform?” Direct interaction between customers — data sharing, benchmarking, marketplace transactions — indicates real network effects. Indirect benefits like “more users means better product development” are scale advantages, not network effects.
What the findings mean for the deal:
True network effects create compounding defensibility and justify premium valuations. Pseudo-network effects — scale advantages dressed up as network effects — provide meaningful but erodable advantages. The distinction matters because true network effects make the company increasingly difficult to displace over time, while scale advantages can be replicated by a well-funded competitor.
Brand Preference Moats
Brand moats exist when customers choose a product partly or primarily because of who makes it rather than what it does. In consumer markets, brand preference is well understood. In B2B, it manifests as trust, reputation, and credibility — the “nobody ever got fired for buying IBM” effect.
Brand moats in B2B are real but difficult to value because they interact with other factors. A customer might choose a product because of brand trust, product capability, sales relationship, and switching costs simultaneously. Isolating the brand component requires careful interview design.
Interview questions that test brand preference moats:
-
“If a startup offered identical functionality at a lower price, would you consider switching?” The willingness to pay a premium — or the unwillingness to take a risk on an unknown vendor — indicates brand value. Customers who say they would immediately evaluate the cheaper option have weak brand attachment.
-
“How did you first learn about this product, and what made you choose it over alternatives?” The decision narrative reveals whether brand played a role. Customers who say “we had heard of them” or “they are the market leader” made a brand-influenced decision. Customers who say “they had the best feature for our specific need” made a capability decision.
-
“If this company were acquired and the brand changed, would that affect your perception of the product?” This tests whether brand attachment is to the company identity or to the product experience. If customers would be unbothered by a rebrand, the moat is in the product, not the brand.
What the findings mean for the deal:
Brand moats that survive the acquisition test — where customers would stay regardless of ownership changes — are valuable because they transfer to the acquirer. Brand moats that depend on the current company’s identity, founder reputation, or market positioning may erode post-acquisition, especially if the acquirer plans operational changes that affect how the brand is perceived.
Proprietary Data Moats
Data moats exist when a company possesses data assets that competitors cannot easily replicate and that create measurable value for customers. The thesis is that the company’s data — accumulated through years of customer usage, proprietary collection methods, or exclusive partnerships — provides insights or capabilities that alternatives cannot match.
Data moats are frequently overstated because management conflates “having data” with “having a data moat.” A true data moat requires three conditions: the data must be proprietary (competitors cannot access it), valuable (customers derive measurable benefit from it), and compounding (more data makes the product better in ways customers can observe).
Interview questions that test proprietary data moats:
-
“Are there specific insights or capabilities this product provides that you believe you could not get elsewhere?” If customers can articulate unique value derived from the data — benchmarks, predictions, recommendations that competitors cannot replicate — the data moat has substance. If they describe generic capabilities that any product with sufficient data could provide, the moat is thin.
-
“How important is the historical data you have accumulated in this platform to your ongoing usage?” Data accumulation moats depend on customers valuing their own historical data within the platform. If customers would willingly start fresh with a new vendor, the accumulated data is not a meaningful lock-in.
-
“If a competitor had access to the same data, would their product be equivalent?” This tests whether the moat is in the data itself or in the product built on top of it. If the answer is yes, the moat is the data. If the answer is no, the moat is the product — and the data is an input, not the advantage.
What the findings mean for the deal:
Validated data moats are among the most durable competitive advantages because they compound over time and are genuinely difficult to replicate. But the validation must come from customers, not from management assertions. The question is not whether the company has proprietary data — it is whether customers perceive and value the differentiation that data creates.
Regulatory Barrier Moats
Regulatory moats exist when compliance requirements, certifications, or government approvals create barriers that prevent or delay competitive entry. Healthcare, financial services, defense, and education are common sectors where regulatory barriers provide meaningful protection.
Regulatory moats are typically the most verifiable of all moat types because the regulatory requirements are public and objective. The interview component tests whether customers value the regulatory compliance itself or merely use it as a selection criterion.
Interview questions that test regulatory barrier moats:
-
“How important was regulatory compliance in your vendor selection process?” If compliance was a hard requirement that eliminated most alternatives, the regulatory moat is functioning. If it was one factor among many, the moat provides less protection than assumed.
-
“Are you aware of new competitors that have recently achieved the same compliance certifications?” Regulatory moats erode as more competitors invest in compliance. If customers report an expanding set of certified alternatives, the regulatory advantage is diminishing.
-
“If compliance requirements changed, how would that affect your vendor evaluation?” This tests whether the moat is the regulatory barrier specifically or whether the company would retain customers on product merit alone. If the answer is that they would immediately reevaluate, the moat is purely regulatory — and therefore vulnerable to regulatory change.
When Moats Are Weaker Than Management Claims
In our experience conducting customer interviews across hundreds of CDD engagements, moat overstatement is the norm rather than the exception. Several patterns recur consistently.
Confusing inertia with preference. The most common error. Management sees low churn and concludes that customers love the product. Interviews reveal that many customers simply have not prioritized switching — they are indifferent, not loyal. The distinction matters because indifferent customers convert to active churners the moment a trigger event occurs: a price increase, a product disappointment, a competitor’s sales outreach.
Overweighting the most engaged customers. Management’s view of customer sentiment is heavily influenced by the accounts they interact with most — typically the largest, most engaged, and most satisfied customers. The silent middle of the customer base, which collectively represents significant revenue, may have a very different view of competitive alternatives.
Ignoring emerging competitors. Management teams dismiss nascent competitors as insignificant. Customers, who operate in the market daily and interact with competitor sales teams, often have a more accurate view of the competitive landscape. Interviews frequently surface competitors that management has not yet taken seriously.
Assuming moats are permanent. Moats erode. Switching cost moats weaken as competitors invest in migration tools. Network effect moats face disruption from interoperability standards. Brand moats deteriorate when product quality declines. Data moats thin as alternative data sources emerge. Interviews capture the current state of moat strength, not the state that existed when the company was last evaluated.
Building the Moat Assessment Into the Deal Model
The moat assessment from customer interviews should translate into specific deal model assumptions.
Strong moat (validated by interviews): Assume current retention rates persist. Model pricing power at or above current levels. Apply competitive discount only for long-term planning horizons.
Moderate moat (partially validated): Assume retention rates decline 2-5 percentage points over the hold period. Model limited pricing power — price increases possible but constrained by competitive alternatives. Build competitive response costs into the operating plan.
Weak moat (contradicted by interviews): Assume retention rates decline materially. Model no pricing power — any price increase accelerates churn. Budget significant investment in product differentiation and customer success. Consider whether the deal thesis depends on a moat that does not exist.
The difference between a strong and weak moat assessment can swing enterprise value by 30-50% in a typical mid-market deal. This is not a theoretical exercise — it is one of the highest-impact analyses in the entire diligence process.
For more on how customer interviews inform competitive positioning analysis, see our competitive intelligence solution. For the full CDD methodology, see our commercial due diligence solution.