Product Strategy in Khmelnytskyi: From Idea to Product-Market Fit

Why product strategy in Khmelnytskyi requires local expertise

Khmelnytskyi is a city of around 270 thousand people, the centre of Podillia on the banks of the Southern Buh river. Three economic layers coexist here: traditional instrument-making and food industries, the powerful 7th Kilometre wholesale market (one of the largest textile hubs in Eastern Europe), and a young IT ecosystem around the Khmelnytskyi IT Cluster. Each layer has its own buying logic, sales cycle and ICP. A product strategy that ignores this context turns into a pretty deck with no impact on the P&L.

I am Oleksandr Filyuk, and I have been working with regional product teams for more than 15 years. Over that time I have led 130+ projects, from early-stage startups in coworking spaces near Proskurivska Street to mature B2B SaaS companies scaling into the EU. Local expertise helps me quickly tell where a team really sees the market and where it is operating on outdated assumptions. That saves clients 3-6 months at the discovery stage.

Methodology: ICP, JTBD, SAM/TAM/SOM, North Star

Every project starts with the foundation — Ideal Customer Profile and Jobs-To-Be-Done. I run 15-25 in-depth interviews focused on past customer behaviour: when the problem last appeared, which alternatives were considered, why this solution was chosen. I code transcripts thematically and build a JTBD map with triggers, barriers and job stories. This gives the team a shared language and removes internal arguments about "who we build the product for".

Next we calculate SAM/TAM/SOM bottom-up rather than "out of thin air": segment the market, lock in density (how many real companies fit the ICP), multiply by ARPU and win rate. For Khmelnytskyi B2B SaaS targeting the EU, SOM is usually 50-200x smaller than TAM — and that is a healthy figure that disciplines the backlog. On top we set the North Star Metric tied to the P&L: it becomes the compass for the whole team and avoids vanity-metric traps.

Finding Product-Market Fit and working with hypotheses

PMF is not an event, it is a state. I use a combination of signals: the Sean Ellis test, retention curves in Mixpanel or Amplitude, NPS, the HEART framework and unit economics. For an early MVP we run 3-5 A/B tests on target ICPs and validate the promise through concept tests and pre-sales. If metrics do not converge in 8-12 weeks, we revisit the problem and segment rather than polish the UI.

I prioritise hypotheses with RICE and ICE. ICE is used at early stages and for the discovery backlog — fast and with minimal data. RICE kicks in when metrics exist and you need to argue with stakeholders using facts. Scoring is always logged in a shared document with reasoning — it removes politics and leaves only logic. For strategic bets I add the Opportunity Solution Tree to avoid confusing solutions with opportunities.

Local cases: IT Cluster, Podillia agritech, 7th Kilometre wholesalers

Khmelnytskyi IT Cluster residents are mostly B2B SaaS and AI/ML tools entering Poland, Germany, the Nordics and the US. The typical transition — from outsourcing to a proprietary product — requires reframing thinking from "we know how to build this" to "the market pays for this". I support such teams for 6-12 months, until the first Series A or stable MRR of 50K+ EUR. For many clients this is the first experience of living by retention and LTV/CAC metrics rather than hours billed.

Podillia agritech teams operate in a different mode: long sales cycles (6-18 months), seasonality, a heavy share of offline channels and SaaS distrust. Here I adapt the methodology — ICP segmentation goes beyond farm size to include crops and digital maturity. The NSM is tied to hectares under management or active agronomists, PMF is validated through pilots on 3-5 farms with clear SLAs. Slower than SaaS, but the discipline pays back in 7-10 year LTV.

A separate category is 7th Kilometre wholesalers digitising their B2B textile portals. Product strategy here intersects with conversion optimisation: you must understand how a buyer in Bucharest or Warsaw actually decides on a 5-50K EUR purchase. That is a different JTBD from self-serve SaaS, and a different funnel.

Toolkit: GA4, Mixpanel, Amplitude, OKR framework

Without the right toolkit strategy stays on paper. I configure analytics in GA4, Mixpanel or Amplitude depending on the product, and add Hotjar or FullStory for qualitative UX diagnostics. For teams where product and marketing analytics are disconnected, I integrate everything into a single attribution model — it allows fair CAC comparison across channels and reveals the true LTV/CAC ratio.

On top sits the OKR framework: quarterly team goals are tied to the NSM and AARRR input metrics. I do not like OKRs as ritual — only as a focus tool. A team usually keeps 3-4 key results per quarter, no more. That allows an honest review every 90 days and a clear decision: scale, optimise or shut down.

How to start and what you get

The first step is a 60-minute free session where we diagnose your context: product stage, key metrics, team pain points, business goals for the next 12 months. I pick a format: a 6-8 week strategy sprint for teams launching or relaunching a product, or 3-6 months of product mentorship for mature teams with metrics. I work in Khmelnytskyi offline (Proskurivska, Kamianetska, Soborna Square, KhNU and PDU spaces) and remotely for teams in other regions.

If a strategy already exists but specific areas need strengthening — see also conversion optimisation in Khmelnytskyi or UX research in Khmelnytskyi. Product strategy often works in tandem with these services and produces a stronger effect than any single one. Details and booking on the product strategy page.