A logistics SaaS was losing 18% of its customers annually due to platform reliability. Three engineering leads in two years had not solved the problem.
A Brisbane transport management SaaS serving 340 logistics operators had an 18% annual churn rate — nearly double the industry benchmark. Exit interviews cited “reliability” as the primary reason. Three engineering leads in two years had not solved it.
Month one was diagnostic. Analysis of 14 months of incident data, architecture review, interviews with 22 churned customers, and one week embedded with the support team. Technical diagnosis: no read/write database separation, a queue system that backed up under peak load, and monitoring that detected problems after customers were already impacted. Customer interview insight: they were not just upset about downtime — they were upset about the lack of communication during it.
We redesigned infrastructure over six months: read replicas and query optimisation eliminated the database bottleneck; a properly configured Redis cluster replaced the queueing system; a real-time status page was implemented; and proactive customer communication protocols were established for any incident exceeding 2 minutes. A formal post-incident review process was introduced and monthly reliability reports were published to all customers.
Annual churn dropped from 18% to 6% over 10 months. Uptime reached 99.95%. NPS scores tripled. ARR retained as a result of churn reduction exceeded $1.4M in the first year — a 127x return on the cost of the engagement.
Churn is simultaneously a product problem, an engineering problem, and a communication problem. Solving only one of the three is why the previous engineering leads failed.
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