About TestMu AI
TestMu AI (Formerly LambdaTest) is a full-stack agentic AI Quality Engineering platform that empowers teams to test intelligently and ship faster. Engineered for scale, it offers end-to-end AI agents to plan, author, execute, and analyze software quality. AI-native by design, the platform enables testing of web, mobile, and enterprise applications at any scale across real devices, real browsers, and custom real-world environments.
TL;DR
Three initiatives the account-management team wanted to drive with RetainSure.
| Challenge | What RetainSure did | Impact |
|---|---|---|
| ChallengeTime-bandwidth crunchSizing up an account and building QBRs meant hours of manual work across disconnected tools. | What RetainSure didAutomated account analysisA single prompt pulls usage, support and history into an account view, and generates the QBR — the busywork is gone. | Impact~20 hrs/AM/week back → +24% ARR per AM |
| ChallengeChurnAt-risk accounts surfaced too late, and it was hard to know which ones to save, and why. | What RetainSure didAccurate churn predictions + agentsPredictions trained on each customer’s full history tell AMs exactly which account to act on — and agents take the first action. | ImpactDollar churn down 29% from the previous period |
| ChallengeIncrease expansion revenueUpsell moments were being missed; AMs lacked a reliable read on who was ready to grow. | What RetainSure didAccurate upsell predictions + agentsThe model flags accounts ready to expand and why; an agent drafts the first upsell plan and outreach. | Impact4.6× higher upsell hit-rate |
Solving for Retention
Retention was something the team wanted to crack like magicians.
The problem
For a business with revenue spread across thousands of accounts, quiet churn is the thing that keeps a AM team up at night - and it was the outcome TestMu’s team most wanted to move. The hard part was spotting churn even before the customer had made up their mind - knowing which accounts were slipping, and understanding the real pain point behind it, in time to help the customer get value and adopt the product more deeply. Doing that by hand, across a large book, simply didn’t scale.
What RetainSure did
We built a churn-prediction model trained on the full picture of each customer - email exchanges, support tickets, product usage, and meeting summaries - rather than any single signal. The predictions were accurate enough that an AM knew exactly which account to go after, and for what reason. We then put agents on top of those predictions: instead of leaving the next step to a stretched AM, agents began taking action on their behalf and drafting the outreach - so the AM can review and approve and right intervention reached the right customer at the right moment.
The impact
AMs stopped reacting to churn and started getting ahead of it - reaching at-risk customers early, with the context to actually help them, and with the first draft already written. The revenue that mattered stayed on the books, and net retention improved – churn reduced by 29% in 6 months alone from the previous 6 months period.
Growing revenue through upsell
Point AMs at the right accounts, and hand them the play.
The problem
Expansion revenue was being left on the table. AMs didn’t have a reliable way to see which accounts were genuinely ready to grow - or what to pitch them - so the moment often passed unnoticed. Finding the upsell meant manually digging through usage for every account, which few had time to do.
What RetainSure did
Accurate upsell predictions surfaced the accounts with real expansion potential and the reason why - hitting concurrency limits, climbing usage, deepening KaneAI adoption, business changes and a lot more. And, as with churn, we put an agent on top: it produces the first version of the upsell plan and drafts the outreach, so the AM starts from a ready-to-refine play instead of a blank page.
The impact
The targeting worked. Accounts RetainSure flagged for upsell converted at 4.6× the rate of accounts it didn’t flag - the signal told AMs exactly where to spend their expansion energy, and nearly all of the period’s expansion came off that list.
Solving the time-bandwidth crunch
Give AMs their week back - and they cover a bigger book.
The problem
To understand a single account - is it healthy, at risk, ready to grow? - an AM had to stitch together product-usage data, support tickets and account history across separate tools, then turn it into a QBR by hand. A usage review took ~20 minutes an account; a QBR deck took hours. Repeated across a large book, that is a near-full-time job of preparation - every hour of which is an hour not spent with a customer.
What RetainSure did
RetainSure automated the two heaviest lifts: account analysis and QBR creation. A single prompt in the copilot pulls usage, support and health into a complete account overview in minutes, and turns a three-hour QBR into a fifteen-minute one. Agents handle the recurring monitoring and first drafts, so the analysis is waiting for the AM rather than being built by them.
The impact
That gave each AM back roughly 20 hours a week — about half their time — redirected from spreadsheets to customers. And the freed capacity showed up on the top line: over the RetainSure era the same count of AMs showed a 24% increase in ARR under management per AM, with no additional headcount. Time saved became capacity, and capacity became revenue coverage.