How to Design Sales Processes That Don't Kill Momentum: An Interview with Mark Spasonov

How INB.bio’s CCO builds sales systems that scale across 15 countries without breaking

Mark Spasonov joined INB.bio as Chief Commercial Officer several months ago. His job: make sure sales teams across 15 countries hit their numbers. Not by motivating harder or selling faster, but by building systems that work when you scale them.

We talked about how to design processes that don’t collapse under growth, why documentation matters more than inspiration, and what happens when your sales operation spans Tanzania, Pakistan, and Venezuela at the same time.

What does “Chief Commercial Officer” actually mean in practice?

I own revenue and profit through markets and sales teams. The full cycle: hiring heads of sales, hiring sellers in local markets, building the operational system that launches our customer conversations, setting metrics, working with data. We support markets so they can work. We solve problems and pass them to marketing, IT, operations, logistics. But everything on the sales side, that’s us.

We need to be sure we have people processing incoming leads, that they’re doing it with quality, according to standards, on time, and hitting our internal productivity KPIs, the ones that directly affect revenue.

How do you build a sales system that stays effective when you scale?

When you’re working with volume and scale, you need a well-structured, clear operational framework. We need a sharp understanding of what our critical processes are. They should be written, described, and understandable.

My internal milestone: a person should be able to look at a process and execute 90% of it without problems, relying on internal instructions and flow.

All our processes are written like this: there’s a flowchart to visually understand dependencies between people and tasks. Everything lives in Notion with a RACI matrix. Each stage of the process contains detailed instructions for direct execution. Having that core base, we can always go through checklists day to day. When I’m confident the team knows how it works, we pass it to local markets.

Process maturity is what helps us achieve repeatable results. But it’s also important to be agile and not spend too much time on a process that maybe isn’t critical or doesn’t have a big final impact. We try to follow KAIZEN principles: we continuously look for small improvements in our existing processes, eliminate waste, and make things a little better every day.

We also follow an agile approach to building new processes:

• take a problem,
• look at how to solve it in MVP format,
• test it, see that it actually helps,
• then fix it fully in the process with all the artifacts.

And one more important thing, acceptance criteria. When we work on any task, we ask ourselves: why are we doing this? Having good acceptance criteria and asking the right question, we already have half the answer. This lets us avoid doing extra things and only do those that actually affect results.

Is there a standard sales process for all GEOs, or is each market built separately?

Of course, culture in markets and approaches differ. In some countries we have many Muslims, in some Christians, in some it doesn’t matter at all. Even something like religion already affects the behavior of sellers and clients during the day: holidays, life rhythms. And we haven’t even considered purely cultural and historical context: what the country’s prosperity is, what accepted norms are, how digitized it is.

So processes sometimes differ from each other because that’s what the business and market require. But there’s always a core that we sincerely believe in. That’s consultative selling, sales through helping the client. We really try to learn the client’s pain and understand how our product can close that pain. We always try to preserve this core because it’s first of all about helping, not about pushing products. And the rest, that’s already a superstructure that operationally can change the application of these tools.

What processes should be the same for all markets, and what needs to change?

The same for everyone, processes related to discipline, key sales nuances, and operational system: how we work with CRM, how we work with data. And then superstructures appear related to culture.

In some countries it’s accepted to speak with the client more directly. In others, the opposite, you need to build a close personal connection even during the first call. Things like that, of course, differ.

How do you know when hiring that a person fits? What qualities matter most?

When hiring, I always evaluate several things. The first, classic, necessary skills for the position. And hiring doesn’t start at the moment of evaluating a person, but at the moment of understanding who exactly you need to solve specific tasks. You can find a great person, but without the right formulation of the position, understanding what this person should solve, it’s very easy to choose the wrong one.

• The right construction of understanding who we need, and then pure technical match: does the person fit by soft and hard skills. For me it’s very important that the person understands the essence of sales and is results-oriented.
• The second, more human, it’s easier for me to work with people who want to develop, move through careers, set new challenges. Because I’m like that myself, constantly trying to grow. If I see that a person doesn’t know something but is ready to learn, search for options, become better, that’s my person.
• The third, common values: focus on results, readiness to work, politeness and ability to build communication with the team. And it’s also important to me that the person is fast. I can explain and tell a lot, but I don’t want to repeat the same thing three times.

What role do technologies play in sales processes? Do you use AI?

Technologies today are core for efficiency, whether in B2B or B2C. Work with people remains work with people. Psychology and behavior of people don’t change much. But technologies very strongly accelerate operational work: advanced CRM systems, reporting and so on.

Artificial intelligence helps accelerate work through data analysis, search and validation of hypotheses, it helps compose them faster and faster to understand where to look for the problem. It helps analyze data and bring together things that manually bringing together used to take much more time. Quality control also accelerates: you can transcribe conversations into text, automatically analyze, find patterns. You can’t cancel the AI revolution anymore, so whoever adapts fastest will be on top.

Was there a moment when you had to quickly rebuild the sales process?

Of course, such moments happened. I’m a person who likes to push off from numbers and validate hypotheses based on data, not only empirical reasoning. And once we rebuilt our analytics process. Even though it’s not a direct change in sales, it very strongly improved our work.

It was like this: we had a drop in conversion across many markets, and it became clear that when we help local heads of sales departments focus attention, we chaotically find problems and just as chaotically solve them. 

Then we reconsidered the approach and created a new, simple and fast reporting format by which we track indicators and problem agents. And we choose a specific group, focus on it and watch the dynamics: are results growing or falling.

That’s when we started seeing real results. Feedback and work with operators shifted from the level of “here’s a problem agent, noticed a couple questions, solve it” to the format “here’s a clear problem pattern, here’s a specific group that’s not improving, here we’re working with them purposefully.” We see who’s going up and who’s down. 

Those who don’t want or can’t grow, we part with. Those who are ready, we develop. And we definitely do retro to understand where the gap was, and in the future identify and close the problem faster.

What innovation in sales processes gave the strongest result?

I’d say the biggest innovation is the ability to accelerate operational work and find problems with the help of AI. Making decisions actually isn’t such a complicated story. What to implement, what to change, what to remove, that’s clear. The problem is to really understand what the problem is, based on data. When we have all the information at hand, the decision is made easily. But collecting data and searching for the cause, that’s what’s difficult.

And AI helps very strongly here: in quality control, in automatic analysis and highlighting problems, in transcriptions, in accelerating script writing, and most importantly, in data analysis, highlighting problem patterns in conversion. The biggest result is that before you needed to spend more time to make the right decision. And now you can make more right decisions because the process of getting data accelerates.

What principles help build effective sales processes?

Honestly, it’s hard for me to understand what a “fast” process is, because a process should first of all be effective. And whether it’s already fast or long, depends on the situation.

The main principle is clear understanding of the problem in front of us, and formulation of how we want to solve it and what specifically the deliverable will be. Very often we do many things that don’t lead anywhere because we don’t formulate the final outcome for ourselves. If we formulate the problem poorly, then even a good solution won’t solve it, or it turns out this wasn’t the problem worth solving.

But when the problem is formulated, we can already build an understandable path to solving it, at least in MVP format. We form a clear deliverable, acceptance criteria. When we know how to ask ourselves the question “what exact problem are we solving and how do we see its solution,” that’s when processes are built both quickly and effectively, and really solve the problem.

Final thought for people building sales in multiple markets?

Don’t skip the boring part. Documentation, flowcharts, acceptance criteria, that stuff feels like bureaucracy until you’re trying to scale. Then it’s the only thing that saves you.

And ask better questions. Half the time we’re solving the wrong problem because we didn’t define it right in the first place. Good question, good answer. Bad question, doesn’t matter how smart the solution is.