When the Best Product Is Wrong Choice

Max Sevostianov - CCO B1 (Be One, formerly Betbazar))

After years of selling technology to operators, I’ve learned that the hardest part isn't convincing someone that a product works. It's understanding whether it will work for their business.

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The wrong question is: “Does it work?”

When operators evaluate a new product, conversations naturally move toward performance.

What margin can it generate? How much engagement can it create? How quickly can it be integrated? What results have other operators achieved?

These are reasonable questions. But commercially, I think there is a more important one:

Will it work here?

A product can be technically strong, commercially successful and proven with other operators - and still be the wrong choice for a particular sportsbook.

That is one of the biggest lessons I have learned from working closely with both products and sales. Successful product adoption is rarely about finding the universally “best” solution. It is about finding the right match between product, audience, market and operator economics.

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Start with the player, not the product

The easiest sales conversation begins with features. 

The better one begins with player behaviour.

Take cricket as an example. There are markets where cricket is a major driver of sportsbook activity, but demand is highly concentrated around tournaments and live schedules. When the major competitions stop, player interest does not necessarily disappear with them.

That creates a very different commercial problem from an operator whose audience primarily bets on European football.

For the first operator, additional cricket content outside the traditional sporting calendar may solve a genuine supply gap. For the second, the same product may be technically impressive but commercially irrelevant.

This is why I am skeptical when someone says a product “works for everyone.” 

Very few products do.

Before discussing functionality, I would rather understand what players already do: which sports they follow, when they are active, what they bet on, where activity drops and what is missing from the existing offering.

The strongest product opportunity is often hidden inside that gap.

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A case study is evidence, not a promise

Case studies are valuable in B2B sales, but they are also easy to misuse.

Imagine an operator introduces additional always-on cricket content and sees a significant increase in betting activity outside traditional live hours.

That result tells us something important: the concept can work.

It does not tell us that another operator will achieve the same result.

The second operator may have a different acquisition mix, geography, player profile, frontend, promotional strategy or existing content portfolio.

This is why I never see a successful case as a promise of identical performance. I see it as evidence that allows us to ask better questions.

Why did it work?

Was it because the audience already had a strong cricket affinity? Did the product fill periods when there was little relevant live content? Did short event cycles increase betting frequency? Was the content positioned prominently enough for players to discover it?

Understanding the mechanism behind the result is far more useful than copying the result itself.

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Look beyond revenue

Commercial conversations naturally focus on revenue, but revenue alone can hide a bad product decision.

An operator should also ask what it takes to produce that revenue.

Does the product require additional traders?

How much technical work is needed?

How quickly can it go live?

Does it create a new support burden?

Can it run continuously without additional operational input?

Can it scale if player activity grows?

This is particularly relevant with newer automated content formats. AI-driven sports, for example, can run continuously and automate areas such as tournament generation and trading that would traditionally require greater manual involvement.

That changes the commercial equation.

A product generating slightly more revenue but requiring significant additional operational resources may ultimately be less attractive than one generating similar revenue with much lower overhead.

The real metric isn't revenue alone. It is the value created relative to the complexity introduced.

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Sometimes the best sales decision is to say no

This may sound strange coming from a CCO, but I believe one of the most important parts of selling B2B technology is knowing when not to sell it.

If the audience is wrong, the economics don't make sense or the operator cannot realistically give the product the positioning it needs, forcing the deal rarely creates a good long-term partnership.

You may win the contract.

You probably won't win the client.

The best commercial relationships I have seen begin when both sides are comfortable asking difficult questions before integration rather than after it.

Where exactly will this product sit?

Who is going to use it?

What behaviour are we trying to change?

What does success look like after 30, 60 or 90 days?

And, perhaps most importantly:

What would make us conclude that it isn't working?

If nobody can answer those questions, I would hesitate to integrate anything.

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Sell the fit, not the feature list

The iGaming industry has no shortage of products.

Operators can choose between thousands of games, feeds, platforms, engagement tools and increasingly AI-driven content formats. Adding another product is easy. Adding one that changes the economics of the sportsbook is much harder.

That is why I believe the commercial conversation needs to move away from “Here is what our product can do” toward “Here is the problem we think it can solve for your business.”

Sometimes the answer will be yes.

Sometimes it should be no.

Knowing the difference is what turns a product sale into a product strategy.

Max Sevostianov
CCO, B1 (Be One, formerly Betbazar)

Max Sevostianov is CCO at B1, an iGaming technology company. Working at the intersection of product and commercial strategy, he focuses on how sportsbook technology translates into measurable operator value, from market fit and integration to adoption and long-term performance.

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