You set the tiers. The Lifecycle Marketer agent runs the play the moment a customer crosses one. One profile, every channel you own.
0.0on G2Your ten-year customer and your ten-day trial user get the same message. Your VIP segment is a spreadsheet from last quarter. The scores live in your analytics tool and the sends live in your email tool, so the tier never reaches the email. You find out someone churned in the cancellation survey.
Intempt scores every customer on recency, frequency and monetary value continuously. Champions, Loyal, At-Risk, Hibernating: every tier live, not from a static export. Scoring is a standing skill, not a quarterly model build.
Crossing an LTV threshold triggers the right experience automatically: a personalized offer, an upgrade path, a dedicated touchpoint, or suppression from a generic campaign. You write the guardrails once and every journey respects them.
The email, the SMS, the push and the landing page come out of one brief, on brand and personalized per segment, so the campaign says the same thing on every channel it reaches. Suppression holds across channels, so a big send does not cost you deliverability.
| Formula | Use when |
|---|---|
| Historic LTV = sum of gross profit per customer | You have full transaction history and a stable product |
| Simple predictive = (ARPA × gross margin) ÷ churn rate | Subscription, steady churn, quick directional read |
| Cohort LTV = revenue per acquisition cohort over time | Churn varies by acquisition period or channel |
| Predictive LTV (behavioral model) | Repeat-purchase, variable basket, long tail |
The mistake is using the simple formula on a business with lumpy churn, which either flatters or guts the number depending on which month you calculate it in. If your churn rate moves more than a point between cohorts, use cohort LTV.
One thing worth knowing about AOV: raising average order value with a discount can cost more margin than the basket gains. Recommendation-driven lift is the alternative, and documented category results put checkout recommendations at 10-30% AOV lift, with cross-sell around 10-15%.
Sources: Nosto ecommerce recommendation benchmarks · Rebuy cart merchandising data.
Connected outcomes across the platform.
“We were losing visitors before they signed up. Intempt's personalized experiences changed that - we started meeting people where they were instead of guessing. Once they're in, Intempt's automated email takes over and keeps the relationship moving. Acquisition and retention finally feel like one connected motion instead of two separate problems.”
Jim Stromberg
CEO, StockInvest
“Intempt helped us turn real browsing and purchase signals into personalized experiences that drive repeat buying. We finally have one system that sees the whole customer journey.”
Eric Gardner
COO, FieldsUSA
“With Intempt, we built a signal-led pipeline driven by real behaviors. Follow-ups are triggered by intent signals instead of timelines, so we only focus on users who are truly engaging.”
Tadas Kertenis
Co-founder, Hoperfy
Connect your store or product. RFM tiers and predictive LTV are live in the first session, and Blu runs the journey for each tier without you rebuilding a list. No data scientist, no consultant, no export.
