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Analytics & SaaS

Conalytic.

A raw analytics engine turned into a product buyers could evaluate, trial and expand on without a sales call.

Scope
Platform + GTM
Timeline
14 weeks
Squad
5 people
Year
2024
Conalytic analytics dashboard built by Peakovate
2.9×

qualified trials per month

-37%

time to first value in-product

+112%

non-brand organic clicks

97

Lighthouse performance

The brief, in their words and ours.

Conalytic came to us eleven months after their seed round with 40 paying accounts, all of them acquired by the two founders personally. The engine was genuinely good — sub-second queries across billions of events — but every part of the buying experience assumed a human would explain it. Our brief was to make the product sellable without the founders in the room, and to leave behind a measurement layer they could run themselves.

The challenge

Conalytic had a capable data engine and almost no way to sell it. Trials required a manual setup call, the marketing site described features instead of outcomes, and nothing in the funnel was instrumented — so growth decisions came down to opinion.

Next.jsNode.jsPostgresStripeGA4 + server-side

What we shipped

  • Self-serve trial provisioning with billing and role management
  • Guided onboarding with in-app activation checkpoints
  • 180 programmatic integration and use-case pages
  • Unified GA4 + server-side conversion measurement layer

How we worked

  1. 01

    Instrument before rebuilding

    We wired product and marketing analytics into one model so activation, trial-to-paid and channel spend could finally be compared against each other.

  2. 02

    Rebuild the evaluation path

    Self-serve trial provisioning, a guided first-run flow and pricing that survives procurement replaced the demo-call bottleneck.

  3. 03

    Make the platform indexable

    Integration, use-case and comparison pages were generated from the product catalogue with schema, so non-brand search started carrying demand.

  4. 04

    Hand over the machinery

    Dashboards, experiment backlog and a documented release process moved to the internal team at the end of week 14.

How the engagement actually ran.

Every phase ended with something shipped and something measured. Nothing waited for a big-bang launch.

  1. Weeks 1–2

    Audit & baseline

    Analytics, crawl, funnel and codebase review; agreed the four numbers the engagement would be judged on.

  2. Weeks 3–6

    Self-serve foundation

    Provisioning, billing, roles and the guided first-run flow built behind a flag.

  3. Weeks 7–11

    Search layer

    Programmatic page system, schema and internal linking shipped alongside product releases.

  4. Weeks 12–14

    Handover

    Dashboards, experiment backlog, runbooks and two weeks of paired releases with the internal team.

Before and after

MeasureAt kickoffAfter
Trial activationManual setup call, 3–5 daysSelf-serve, under 10 minutes
Non-brand organic clicks~900 / month1,900 / month
AttributionSpreadsheet, monthlyUnified model, live
Release cadenceEvery 3–4 weeksTwice weekly
Conalytic self-serve onboarding flow
First-run flow: workspace, data source and first chart in under ten minutes.
Unified measurement dashboard for Conalytic
One measurement model covering product activation and marketing spend.

Beyond launch

Conalytic now runs the growth programme in-house on a fortnightly experiment cadence. We stayed on a light retainer for two quarters to review results and cover the platform migration to their own release pipeline.

Services on this engagement

  • Web application development
  • Digital strategy
  • Technical SEO
  • Measurement & analytics
  • Compliance & QA

We stopped guessing which channel was working. Within a quarter we could see the whole path from first click to expansion revenue — and fix the part that was leaking.

Sarah MitchellFounder, Conalytic