Expert scenario

Convert more trials into paid accounts and see churn risk before the customer stops logging in.

Subscription product companies: demo, trial, onboarding, renewal. The person who uses the product is not the only one who decides.

Developers at a pair of monitors: one points a colleague to an interface on screen.

Key constraints

renewal depends on whether the customer reached value in the first weeks
churn signals are scattered across the product, support and correspondence
the buyer is not the user, but the person who pays

Industry economics

Churn costs more than the same percentage in conversion: a subscription is lost not once but every month.
A support hour spent on a question already answered in the docs creates no value and scales with the customer count.
A trial where the customer never reached the key scenario was paid for by marketing and returns nothing.

Typical processes and pains

Symptom
The reasons trials do not convert are known from a sample.
Economic consequence
Product and sales argue about what to fix, and both rely on impressions.
Can you name the scenario the customers who did not buy never reached?
Symptom
Support answers the same questions by hand.
Economic consequence
Cost to serve grows with the customer count, and complex enquiries wait in the same queue.
Symptom
Churn risk shows up in the monthly report rather than as it happens.
Economic consequence
The account manager reaches the customer after the decision not to renew has been made.

Priority AI scenarios

Understand why a trial did not convert

1Collect signals
2match against activation criteria
3review conversations
4a loss reason with an example

Inputs: Product events, customer conversations, support correspondence, activation criteria.
Output: A review of every lost account, naming the step where the customer stopped.
Where the human stays: The product owner calibrates the activation criteria; contested cases are reviewed by hand.
Integrations: Product analytics, CRM, helpdesk
Metrics: Share of trials that activate, trial-to-paid conversion, time to the key scenario.
Limitations: If the product emits no events, they have to be added first — and that is the product team's work, not ours.

Take repeat enquiries off support

1Classify the enquiry
2answer from the knowledge base
3ask a clarifying question
4hand over to an operator by rule

Inputs: Enquiry history, the knowledge base, documentation, escalation rules.
Output: An answer to the customer and a ticket with history and context prepared for the operator.
Where the human stays: Complex and risky topics go to an operator by a rule written in advance, not at the model's discretion.
Integrations: Helpdesk, knowledge base, the product
Metrics: Share of enquiries closed without an operator, first response time, escalation rate.
Limitations: A stale knowledge base produces stale answers: keeping it current is a precondition, not an outcome.

Where teams usually start

This is an observation across similar companies, not a universal recommendation: the order follows where your bottleneck actually is.

1Reviewing lost accounts is the usual start: the data is already there and the hypothesis tests quickly.
2Support comes next, if cost to serve grows faster than revenue.
3Live churn-risk work comes last: it needs both product events and a populated CRM.
4Start from the end where the subscription is lost faster: for some that is the first two weeks, for others the month before renewal.

Industry systems and data

CRM
product analytics
helpdesk
knowledge base
billing
e-mail and messengers

Risks and constraints

Product events are incomplete or inconsistently labelled — the first stage goes into cleaning that up.
Customer personal data in correspondence: access model and retention period are fixed before work begins.
Adoption: if support sees the assistant as a replacement rather than relief, they will route around it.
Churn is measured over the subscription horizon: a monthly slice shows noise rather than effect.

Which solutions apply

Frequently asked questions

We have no product analytics. Is that a blocker?

Not a blocker, but it changes the order. Some signals can be recovered from conversations, correspondence and billing — enough to work out why trials do not convert. Live churn-risk work will still need events, and we say so before work begins rather than halfway through.

We are self-service with no sales team. Does this fit?

Yes, but the focus shifts: instead of reviewing conversations we work with product events, correspondence and support. The point is the same — find the step where customers stop and remove it. We check during the assessment whether the data supports conclusions.

Let's work through one workflow in b2b saas

Thirty to forty-five minutes on your specific case. If it isn't a fit, we'll say so on the call.