Case studies

Work that moves the numbers.

Lifecycle marketing is only worth doing when it changes something that matters. Below are two programmes written up in full: the situation, the thinking behind the system, and what shifts once it runs.

Alto Lifecycle SystemDATA → INSIGHT → GROWTH

TouchpointsPersonalisationMetricsSegmentationCampaignsAutomation

01 / DTC skincareRetain

Closing the gap between the first order and the second

The challenge. Acquisition was working. What happened afterwards was not. A customer who had bought the same cleanser four times sat in the same segment as someone who signed up nine minutes ago, and both were sent the same twenty percent off email. The data to tell them apart was already in the account. Nobody was asking it anything.

Our approach. Build the programme around the customer's calendar rather than the brand's. Purchase history, engagement, expected usage and reorder timing feed one customer record, and that record decides what each person gets next. Skincare runs out on a schedule you can estimate, so replenishment fires on the product someone actually bought instead of one interval applied to everyone.

One customer record across Shopify and Klaviyo
Segments built on lifecycle stage, not demographics
Post-purchase journeys that teach before they sell
Replenishment timed to the product, not the calendar
Reactivation starting when the reorder window passes quietly
A calendar with education and brand in it, not only offers

A narrowing gap between the first order and the second
Automation taking a rising share of revenue in the background
Unsubscribes falling because relevance went up, not because volume went down
Reporting on repeat purchase rate and lifetime value instead of emails sent

Illustrative. Written from a full strategy document, not a commissioned engagement. No brand, customer, metric or result here is real.

02 / B2B SaaSOnboard

Everyone logs in, and the CRM still cannot tell them apart

The challenge. Trial signups were healthy and activation looked fine from a distance. Underneath it, an account using two of eight core features sat in the same segment as one using all eight and about to buy more seats. Both received the same product tips email. Churn stayed silent for weeks before it ever showed up as a cancellation.

Our approach. Let usage decide. Product analytics, billing and support data feed one record carrying lifecycle stage, feature depth, plan and health signal, and a decision rule turns each signal into a next step before anyone has to notice it. Onboarding aims at the single action that predicts retention for that account type, not at a tour of the whole product.

One record joining product, billing and support data
Segments built on feature depth, not plan tier
Onboarding aimed at one action rather than eight
Trial conversion that shows the account its own results
Renewal opening ninety days out, not at the invoice
Expansion triggered when usage nears a seat or plan limit
Churn risk read from usage, not from a support ticket

Activation measured by real first value, not by an account existing
Trial to paid conversion improving because the trial proves itself
Expansion conversations arriving before the account hits its ceiling
Reporting on account health and revenue movement instead of send volume

Illustrative. Written from a full strategy document, not a commissioned engagement. No account, customer, metric or result here is real.

More studies in progress

03 / Consumer brandTurning customer data into a retention engine
04 / RetailA smarter customer journey at scale
05 / B2BMaking CRM a measurable revenue channel

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