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Buyer-intent advisory

Data Collaboration & Clean Room Advisory.

Plenty of companies now have a clean room, a collaboration capability, or a licensable data asset. Far fewer have a commercial motion around it.

This advisory closes that gap: data products packaged so enterprises can buy them, pricing that holds, a deliberate posture toward Snowflake, Databricks, AWS, and InfoSum, and a sales motion that turns capability into revenue. Operator-led — built on scaling AdTech, MarTech, and data businesses, not on analyst decks.

Start with the Market Entry Audit → Read the data collaboration playbook →
Fit first

Who this is for.

Companies where the data asset is real and the commercial question is open. Primary fit: Series B Scaleup (75–200 employees · $10–30M ARR). Secondary fit: Series A PMF (25–75 employees · $2–10M ARR). Selective Series C / Growth (200–500 · $30–75M ARR) on board-grade decisions — the full fit map is here.

  • You own a data asset, a clean room, or a collaboration capability — and it's expected to become revenue.
  • You're an AdTech or MarTech vendor whose data licensing line has gone flat.
  • You need to package data collaboration so enterprise buyers can actually evaluate it.
  • Your buyers ask about Snowflake, Databricks, or AWS clean rooms and the answer isn't crisp.
  • You're deciding where to sit relative to the platforms — build on them, integrate with them, or compete with them.
  • The measurement story behind your data product doesn't survive contact with a buyer's analytics team.
Common triggers

The sentences that start this work.

Four versions of the same underlying problem: a data capability that was built before its commercial system was.

  • "We have a clean room but no commercial motion."

    The capability shipped; the product, pricing, and sales motion didn't. The clean room is a line in the deck, not a line in the forecast.

  • "Data licensing revenue is flat."

    Usually not a demand problem — a packaging problem. The asset is sold as access instead of as use cases a buyer can budget against.

  • "We need to package data collaboration for enterprises."

    Enterprise buyers don't buy interoperability claims. They buy a named use case, a pricing logic, and a proof path through procurement.

  • "Buyers ask about Snowflake, Databricks, and AWS clean rooms — and we don't have a clear answer."

    Platform posture is now part of the pitch. If you can't say where you sit in the buyer's stack, the buyer decides for you.

What it covers

Six commercial layers.

The work reads the data business as one commercial system — from how the product is packaged to how the deal is renewed.

  1. 01

    Commercial packaging of data products

    From a capability list to products with a named buyer, a named use case, and proof — audience collaboration, measurement, enrichment — each one evaluable on its own.

  2. 02

    Clean-room GTM

    Who actually buys, what they're buying, which team owns the budget, and what the first three deals look like — before headcount gets added.

  3. 03

    Partner & platform strategy

    Where you sit relative to Snowflake, Databricks, AWS Clean Rooms, InfoSum, and the identity layer — build on, integrate with, or route around. A deliberate answer, not a default.

  4. 04

    Pricing logic

    Pricing tied to the value of the collaboration, not the cost of the infrastructure. Licensing, usage, and outcome models tested against how the buyer budgets.

  5. 05

    Enterprise sales motion

    The motion, the roles, and the proof that get a data deal through security review, legal, and procurement — where most data products go to stall.

  6. 06

    Measurement narrative

    The measurement story that makes the data product credible to the buyer's analytics and finance teams — because the deal is renewed on evidence, not on the demo.

Typical outputs

What the work ships.

Outputs vary by engagement — a diagnostic ships a read, a sprint ships the motion. This is the usual shape.

  • 01 Data-product packaging map — products, buyers, use cases
  • 02 Pricing and licensing logic
  • 03 Platform posture read across Snowflake, Databricks, AWS, and InfoSum
  • 04 Buyer narrative for enterprise data collaboration
  • 05 Partner and target-account sequencing plan
  • 06 90-day commercial plan with a named owner
Go deeper

The reference material behind the advisory.

This page is the front door. The depth lives in the Enterprise Data Collaboration playbook and its platform deep dives — the same material the advisory works from.

Why operator-led

Advice from someone who has run the P&L.

Data collaboration advice usually comes from two places: platform vendors selling their own stack, or analysts who have never carried a data revenue number. This is the third option. Evgeny Popov has spent 25+ years scaling AdTech, MarTech, and data businesses across four continents, with three exits — scaling businesses where data had to become revenue. He is a founding member of AdCP (Signals & Measurement working group), currently Global Head of Enterprise at Samba TV, and based in NYC.

The practical difference: the advice is accountable to whether deals close and renew — not to whether the framework looks complete. The operator background and the proof are on the record.

How it runs

Three ways in.

Data collaboration work runs through the same three engagement models as everything else on the services page — sized to how much of the commercial system needs building.

  1. 01

    Market Entry Audit →

    A 2–3 week diagnostic. The right start when the data asset is real but the commercial read isn't — who buys, how it's packaged, what it's worth.

  2. 02

    GTM & BD Sprint →

    6–8 weeks embedded. For teams that have the read and need the motion built — packaging shipped, partners sequenced, first deals moved.

  3. 03

    Advisory Retainer →

    3, 6, or 12 months of operator partnership through the build — pricing decisions, platform posture, and enterprise deals in flight.

Commercializing data as part of a US expansion? Start from US market entry for AdTech and MarTech — the data-product question usually sits inside the market-entry question.

Straight answers

FAQ.

What is data collaboration advisory?

Operator-led help turning a data collaboration capability — clean rooms, identity, measurement, data products — into commercial results: packaging, pricing, partner strategy, and an enterprise sales motion. It is advisory built on operating experience, not a research subscription.

How do companies commercialize clean room capabilities?

By packaging the capability as buyer-ready products with named use cases, pricing tied to the value of the collaboration, a deliberate posture toward platforms like Snowflake, Databricks, AWS, and InfoSum, and a sales motion aimed at the team that owns the budget. The capability alone does not sell; the commercial wrapper does.

When does a data clean room need GTM strategy?

The moment it is expected to produce revenue. Common signals: licensing revenue has gone flat, enterprise buyers ask which platforms you support and the answer rambles, or the clean room lives in the product roadmap but in no seller's pitch.

How should AdTech and MarTech companies package data collaboration?

As use-case-led products a buyer can evaluate — audience collaboration, measurement, enrichment — each with a named buyer, a pricing logic, and a proof path. Not as a feature list, and not as "we're interoperable with everything."

Who is the best-fit client?

Primary fit is Series B Scaleup (75–200 employees · $10–30M ARR). Secondary fit is Series A PMF (25–75 employees · $2–10M ARR). Series C / Growth (200–500 employees · $30–75M ARR) selectively, on board-grade decisions.

Next step

Have the capability.
Need the revenue?

The fastest way to find out where the commercial gap is: a 2–3 week Market Entry Audit scoped to the data business — packaging, pricing, platform posture, and the first 90 days.

Scope the audit → Or talk it through first →