Axonaut

• Time back to think: Automating data collection and reporting freed up 3–4 hours per week per person, shifting effort from grunt work to analysis and strategy.
• One truth, many lenses: Sales, Marketing, and Comms now read the same clean metrics in role‑specific views, cutting confusion and enabling faster decisions.
• Support that shows up: Axonaut switched from Supermetrics to Catchr, and gained quicker, more involved support during setup and iterations.
• Clarity drives adoption: Looker Studio dashboards make acquisition performance legible to non‑experts, easing cross‑team communication and reducing cognitive load.
• Simple stack, quick wins: A lightweight pipeline, Catchr > Looker Studio and Catchr > Google Sheets > Looker Studio, delivers reliability without heavy engineering.

About Axonaut

Axonaut is an all‑in‑one business management platform for small businesses, offering CRM, ERP, invoicing, and billing. With around 20 employees, Axonaut grows through online acquisition.

Simon Yeche, Head of Growth, collaborates with Product/Dev, Marketing, and Comms to centralize acquisition data, build dashboards, and share the right insights with each team. Today, Simon leads reporting end‑to‑end: he connects the sources, models what’s needed, and publishes decision‑ready views for stakeholders.

The challenge

Axonaut’s previous setup ran on Supermetrics and worked fine, yet several friction points persisted:

• Manual overhead & error risk: Before modern dashboards, the team went through “hours and hours of CSV downloads and pivot tables.” Beyond time loss, manual work multiplied mistakes and made reviews exhausting.
• Slow support loop: “With Supermetrics, support was a bit slower and less involved,” which delayed troubleshooting and slowed experimentation.
• Budget pressure: Leadership wanted the same outcomes for less, without compromising reliability.

The team hesitated about moving to a lower‑cost vendor: would data quality or stability suffer?

“We expected a cheaper tool to be less performant, and it wasn’t.”

How Catchr simplified cross‑team decision‑making

Axonaut adopted Catchr to streamline reporting, reduce costs, and improve the day‑to‑day experience of working with data.

• Seamless switch, no loss in quality: After migration, the team observed no difference in data collection reliability versus their previous stack.
• Hands‑on support: Catchr’s team responded quickly and was deeply involved in solving issues and fine‑tuning connectors, an immediate confidence boost.
• Data that tells a story: With Looker Studio as the front end, Simon can “vulgarize” (demystify) acquisition data, presenting clean, readable dashboards for internal and external reviews (including influencer debriefs).
• Lower cognitive load: Not everyone is fluent in ROAS/CPM. Clear visuals make performance easy to read so conversations focus on what to do next, not what the numbers mean.

Technical solution: Catchr‑powered reporting, built for speed

When Simon took ownership of performance reporting at Axonaut, the brief was simple: keep Looker Studio as the front door, remove as much friction as possible upstream, and make the numbers understandable to anyone in the room. The migration started where impact would be highest (Meta Ads and TikTok Ads) then expanded to LinkedIn Ads and Snapchat Ads. From day one, Catchr worked alongside Simon to map fields, align naming conventions, and set scheduled refreshes so dashboards would always be meeting‑ready.

For about 90% of use cases, data flows straight from Catchr into Looker Studio, where Simon models the story: acquisition funnels, cost and efficiency trends, creative performance, and cohort views. For the remaining edge cases, custom channel groupings, influencer campaign logs, or one‑off blends, Simon inserts a thin Google Sheets layer. The Catchr Sheets add‑on writes clean, normalized tables; Looker Studio reads them without adding warehouse complexity.

In Looker Studio, the dashboards are role‑based. Marketing gets investigative pages with drill‑downs on campaigns and creatives. Sales sees a topline acquisition view that ties spend and intent signals to downstream pipeline. Communications has a high‑level narrative page that’s easy to present externally. Access rules keep things tidy, and read‑only links make it simple to share a dedicated report with an influencer or partner after each campaign. The net result is the same every week: open the deck, and the latest numbers are already there.

The migration itself was deliberately uneventful. Simon ran Catchr in parallel with the previous stack to validate metric parity. Once the figures matched, he flipped the switch. Throughout, Catchr’s team stayed hands‑on, troubleshooting edge‑case fields, advising on Looker modeling patterns, and confirming coverage for upcoming platforms like Pinterest Ads and Reddit Ads. Axonaut kept the automation and data quality they relied on, at roughly one‑third of the previous cost, and gained a support loop that moves at the pace of their questions.

How the workflow evolved

Phase 0: Early days (pre‑Looker, no connectors)

• Manual exports (multiple CSVs), pivot tables, heavy copy‑paste.
• High cognitive load; easy to make mistakes; time sink before reviews.

Phase 1: Looker Studio + DIY attempts

• Looker improves visualization, but building/maintaining connectors by hand remains time‑consuming.

Phase 2: Supermetrics + Looker Studio

• Automation achieved: scheduled refreshes and stable pipelines replace CSV work.
• Pain points: higher cost and slower/less involved support. Data quality and dashboarding are solid.

Phase 3: Catchr + Looker Studio (with Google Sheets when useful)

• Automation and data quality maintained at parity with the previous setup.
• Lower cost (~1/3 of previous) and faster, hands‑on support.
• Same channels covered (Meta, TikTok, LinkedIn, Snapchat; Google Ads often via native), with Pinterest Ads and Reddit Ads next.

What stayed the same (Supermetrics → Catchr)

• Looker Studio front‑end; automated refreshes; consistent schemas; role‑based views for Sales/Marketing/Comms.

What improved specifically with Catchr

• Cost: ~×3 cheaper.
• Support: quicker responses and deeper involvement.
• Scalability: easy to request/connect new platforms when needed.

Net effect vs. the early manual era

• 3–4 hours/week/person reclaimed from CSVs/pivots.
• Lower error risk and clearer meetings focused on decisions.

Results

• Time savings vs. manual workflows: Moving away from CSVs/pivots to automated connectors frees, 3–4 hours per person per week.
• Reliability & confidence: Automated pipelines and a single source of truth reduce manual errors and debates about which number is “right”.
• Cleaner meetings, faster decisions: Stakeholders read the same well‑structured dashboards and focus on what to change.
• Cost & support improvements (Catchr vs. Supermetrics): Parity in data quality at ~1/3 the cost, plus faster, more involved support.
• Cross‑team clarity: Marketing, Sales, and Comms consume the same story through role‑specific views, reducing cognitive load for non‑experts.

“Catchr gave us the automation we needed so our time is spent where it matters, understanding performance and acting on it, not wrangling spreadsheets.”

Why Axonaut recommends Catchr

• Made‑in‑France, world‑class delivery:

“French tools that work this well deserve to be showcased. They hold their own against international players.”

• Low risk, quick value for SMBs:

“For TPE/SMEs worried about stack overload, just test it, it’s quick to set up and the time savings are almost immediate.”

• Support that’s truly supportive: Catchr’s responsiveness and involvement make a tangible difference when teams are iterating fast.

Simon’s advice:

‍“Test it, you won’t be disappointed. The value becomes obvious very quickly.”