SaaS & Technology

From 22% to 34% Trial Conversion. $680k ARR Recovered. In 60 Days.

How we automated Stackify's entire trial-to-paid lifecycle — onboarding, churn detection, product-CRM sync, and RevOps reporting — and turned their biggest operational bottleneck into their fastest-growing revenue lever.

22% → 34%

Trial-to-paid conversion

$680k ARR

Recovered from onboarding

−62%

Monthly churn rate

12 days

Discovery to live systems

Executive Summary

Stackify had strong product-market fit at Series A but was losing recoverable revenue at every stage of the customer lifecycle. Manual onboarding delayed activation. A disconnected product-CRM stack left the GTM team operating blind. Churn was only discovered at cancellation. And 20 hours of senior analyst time was consumed every week building a report that should have taken zero.

We deployed four interconnected AI automation pipelines in 12 days. Within 60 days, trial conversion improved from 22% to 34%, monthly churn fell 62%, and $680k in ARR was recovered — against an engagement cost of approximately £24k. The RevOps reporting function was fully eliminated as a manual task.

At a Glance

IndustrySaaS & Technology

Company size48 employees

ARR at engagement£6.2M

Pipelines built4 systems

60-day ROI~40× project cost

Engagement typeProject + Retainer

Before vs After

The Operational Transformation

Before · Manual Operations

Trial onboarding. Manual · 24–48h delay · no consistent sequence

Product → CRM sync. None — weekly manual export only

Churn detection. At cancellation — too late for intervention

RevOps reporting. 20h/week manual build · 48h stale on delivery

Trial conversion. 22% — stagnant for 2 quarters

After · AI-Automated

Trial onboarding. Automatic · <90 seconds · role-personalised

Product → CRM sync. Real-time · event-driven · 28s latency

Churn detection. 28 days early · 89% accuracy · auto-intervention

RevOps reporting. Auto-delivered Mon 8am · zero analyst time

Trial conversion. 34% — +$680k ARR recovered

Client Snapshot

Stackify SaaS

B2B Project Management · Series A · £6.2M ARR

Series A · £6.2M ARR3,200 active customersRemote-first · 48 employees

Stackify had built a genuinely strong product — NPS above 50, retention metrics improving, and clear product-market fit in the mid-market segment. Their problem wasn't the product. At 3,200 customers, the volume of trials, support interactions, and account changes had outgrown what a 48-person team could handle manually.

The Challenge

Four Operational Failures Compounding Into One Revenue Problem

Every problem below was survivable at low volume. At 40+ trial signups per day, they became a system of compounding losses — each failure amplifying the next.

Manual onboarding with no consistent sequence

New signups received a welcome email then were assigned to whichever CSM saw the Slack notification. Follow-up timing varied from 2 to 48 hours. No structured activation path, no role-based personalisation. Result: 22% trial conversion.

Product analytics and CRM completely disconnected

Mixpanel held behaviour data. HubSpot held commercial data. No connection between them. Sales and CS made account decisions based on memory, not product reality. Result: Blind GTM team.

Churn only discovered at cancellation

Declining logins, dropping feature adoption, support spikes — all sitting in Mixpanel unmonitored. The CS team only found out about churn when the cancellation email arrived. Result: Preventable revenue loss.

RevOps team burning 20 hours per week on manual reporting

Weekly MRR, churn, and cohort reports manually pulled from 4 systems. 3–4 days to produce. Already stale on delivery. Result: 1,040h/yr of skilled analyst time wasted.

The Solution

Four Interconnected Pipelines. Deployed in 12 Days.

Each pipeline solved one challenge — and each shared data with the others, creating a unified operational layer that runs without human intervention.

Pipeline 01

Intelligent Trial Onboarding

Signup triggers enrichment via Clearbit + LinkedIn. GPT-4o classifies user role. Tailored Intercom sequence fires in <90 seconds. HubSpot updated, CS auto-assigned.

n8nGPT-4oIntercomHubSpot

Pipeline 02

Real-Time Product ↔ CRM Sync

Segment event listener maps product actions to HubSpot health properties. Feature adoption score, session frequency, seat utilisation — updated in 28 seconds.

SegmentMixpanelHubSpotPython

Pipeline 03

Churn Prediction & Intervention

Daily scoring job runs gradient boosting model on 14-day usage signals. At-risk accounts trigger AI re-engagement email + CSM Slack alert 28 days before cancellation intent.

Python · sklearnGPT-4oSlack

Pipeline 04

Automated RevOps Reporting

Sunday 11pm: n8n pulls Stripe + HubSpot + product DB. Claude 3.5 synthesises executive summary with anomalies flagged. Delivered to Slack #exec-updates at 8am Monday.

n8nClaude 3.5StripeNotion

Results

Verified at Day 60. Confirmed by VP Revenue.

All numbers pulled from Stackify's production systems at the 60-day mark and reviewed with Marcus Chen before publication.

34%

Trial conversion
was 22%

−62%

Churn reduction
28d predictive lead

$680k

ARR recovered
onboarding alone

20h/wk

Ops freed
reporting eliminated

Performance vs Pre-Engagement Baseline

Trial conversion rate22% → 34% (+55%)

Churn rate reduction−62%

RevOps reporting eliminated100%

Churn prediction accuracy89%

ROI Breakdown

Full ROI

ARR from trial conversion improvement+$680,000

ARR retained from churn intervention+$290,000

Analyst time recovered (20h/wk × £70 × 60d)~£60,000/yr

Total 60-day value~$970,000

Engagement cost (project + 2mo retainer)~£24,000

Implementation Timeline

Discovery to Live

Discovery Audit

Days 1–3

Full stack audit, CRM review, Mixpanel schema analysis. Three stakeholder interviews. ROI prioritisation of 4 automation opportunities.

Architecture Blueprint

Days 4–6

Data flow diagrams, API specs, field mapping, churn model feature set, error handling strategy. All 4 designs approved in one 90-minute review call.

Build & Integration

Days 7–10

2 AI engineers built all 4 pipelines in parallel. <4h total from Stackify's engineering team. Onboarding + sync live day 8; churn + reporting day 10.

QA & Staged Rollout

Day 11

62 test cases. 500-user load test. UAT session with VP Revenue + Head of CS. Signed off same day.

Go-Live + Handoff

Day 12

All pipelines live 9am. First onboarding fired 4 minutes later. 4 Loom walkthroughs, architecture docs, runbook, dashboard access delivered. Monthly retainer active.

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Quick Facts

IndustrySaaS & Technology

Employees48

Pipelines4 production systems

Time to live12 days

Measured at60 days post-launch

ROI~40× project cost

Client Testimonials

I was sceptical we could move the needle on trial conversion without rebuilding our onboarding inside the product. This Platform proved me wrong — purely through smarter operational automation, we went from 22% to 34% in under two months. That's $680k in ARR we were leaving on the table every single year.

Marcus Chen

VP Revenue · Stackify SaaS · Series A

The churn model changed how our CS team operates. Instead of reacting to cancellations, we're having proactive conversations 28 days earlier with full context on what the customer hasn't been doing. Our renewal rate went from 74% to 88% in two months.

Jamie Lee

Head of Customer Success · Stackify SaaS

Your Operational Ceiling Has the Same Solution.

Whether your problem is onboarding, churn, reporting, or all three — the pattern is the same. Let's audit your ops and build the roadmap.

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3.2×

Median client ROI

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