The reseller briefing.
Everything you need to position Adasight in a sales conversation: who it's for, how to price it, how the engagements connect, and how to handle the objections you'll actually hear.
Positioning
Adasight productises data, analytics and experimentation work for growth teams that don't yet have a serious data function. Every engagement fits one of two intents — Diagnose or Build — and follows the same pattern: understand the system, then build the part that's missing.
The hook is the Data Stack Audit. Low cost, high clarity, no implementation. Almost every long-term engagement starts there.
Ideal client profile
Any digital-first business. SaaS and subscription models benefit most. Stack mapping is most valuable where there are multiple tools with overlapping data.
Pre-seed to seed, or small teams without a technical data person. Also relevant for slightly larger companies that grew fast and never audited what they have.
None expected. This is explicitly the starting point for companies with no data function.
About to invest in data infrastructure, starting to think about AI use cases, or just inherited a messy stack and want to understand it before doing anything.
Pricing & engagement model
| Offering | Price | Duration |
|---|---|---|
| 1.1 Data Stack Audit | €2,000 – €5,000 | 2–4 weeks |
| 2.1 Tier 1 — Data Foundation Setup (Mapping & Integrations) | €7,500 – €15,000 | 2 months |
| 2.2 Tier 2 — Data Analytics Foundation for Growth Teams | €5,500 – €12,000/ month (6 months) | 6 months |
| 3.1 Analytics Retainer for Growth Teams | Starting at €3500 / month | Monthly · rolling |
| 3.2 Data Infrastructure + Analytics Retainer | Starting at €6,000 / month | 6-month minimum |
| A.1 Experimentation Audit | €5,500 – €12,000/month | 2–4 weeks |
| A.2 Experimentation Setup Sprint | €5,500 – €12,000/month | ~2 months |
| A.3 Experimentation Data System | €5,500 – €12,000/month | 3–6 months |
€2,000–€5,000 fixed for the audit: Designed to be a no-brainer entry point — low enough that it doesn't require budget approval at most companies. The conversion goal is always a natural conversation about Tier 1, Tier 2 or the Data Infrastructure + Analytics Retainer.
Conversion path
- 1Data Stack Audit
The hook. Diagnose-only, no implementation. Ends with the Data Stack Report — designed to make the next step obvious.
- 2Tier 1 — Data Foundation Setup (Mapping & Integrations)
Architecture, connections and validation across product, marketing and revenue tools. Requires a schema in place — which is exactly what the audit produces.
- 3Tier 2 — Data Analytics Foundation for Growth Teams
6 months. Multi-team. Up to 20 KPIs across up to 2 teams, two dashboards, three analyses, cleaned and documented data model.
- 4Scale — Analytics Retainer for Growth Teams
Lightweight monthly support after the build. Dashboard upkeep, monthly analyses, async Slack. Keeps the system healthy without another fixed-scope project.
- 5Scale — Data Infrastructure + Analytics Retainer
Embedded, strategic retainer for clients treating data as a function. Roadmap ownership, architecture evolution, AI enablement — accountable for the whole layer.
Objection handling
Can't we just start with Tier 1?+
You can — but Tier 1 requires a schema in place. If you skip the audit and the schema is wrong, we end up fixing it inside the Tier 1 budget. The audit is cheap insurance.
Will you implement any changes during the audit?+
No — the audit is advisory only. We map, validate and recommend. Your team implements, or we do it together in a follow-on engagement.
What if we don't have many tools yet?+
That's fine. We work with what you have and recommend what to add or change based on your goals.
Does the audit lock us in?+
Not at all. The deliverable is yours. Most clients find the next steps clearer after the audit, and many move into Tier 1 or Tier 2 — but it's never required.
What if we don't have a technical person to implement?+
Tier 1 requires someone on the client side who can implement changes. If that's a blocker, raise it in scoping — we can discuss options.
Why a retainer instead of another sprint?+
Sprints solve a known problem with a defined scope. Retainers absorb the unknown ones — drift, new questions, evolving dashboards — and keep the system healthy as the business changes. Most teams need both, in sequence.
What's the difference between the two retainers?+
Analytics Retainer for Growth Teams keeps the lights on — dashboards, monthly analyses, async support. Data Infrastructure + Analytics Retainer owns the roadmap — strategy, architecture evolution, AI enablement, embedded leadership cadence.
The AI angle (use this)
The audit isn't an AI product — but it's the first step toward one. The recommended schema is designed with future AI use cases in mind: connecting data sources, building dashboards, feeding data into LLM workflows. A well-structured schema now means far less rework later.
"If you're thinking about AI and don't know where to start, the answer is almost always: start here."
Recurring revenue lever
Fixed-scope projects close once. Retainers compound. The Growth Analytics Retainer and Data Infrastructure + Analytics Retainer are designed to convert successful sprints into predictable monthly engagements — which means predictable monthly commission for partners who stay close to the account.
The natural pitch sequence: Audit → Tier 1 or Tier 2 → Retainer. By the time a client finishes a build, they already trust the team and they have real questions every month. The retainer answer is almost obvious — your job is to make sure it's on the table before someone hires the wrong in-house junior.