Customer lifecycle distribution
See how your customers are distributed across the six lifecycle stages today, with revenue in each stage.
Authored by Intempt Technologies
Recipe curators
Agentic workflow
Coming soon1 step Blu runs end to end. It drafts a plan and nothing runs until you approve it.
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Map customers to lifecycle stages
Coming soonBlu builds a step
About this recipe
When to use it
A single retention number hides which direction your base is actually moving. This buckets every customer into a lifecycle stage and tracks the month-over-month migration between stages, so a slipping base shows up immediately.
What it delivers
Distributes customers across 6 lifecycle stages (At Risk, Needs Attention, New Customers, Promising, Regulars, Champions) and tracks how many move between stages month over month.
How you prompt this
Describe the outcome in your own words and Blu Agent maps it to this recipe, or run it by name. It drafts a plan first and nothing runs until you approve it.
Tell Blu Agent what you want
“See how your customers are distributed across the six lifecycle stages today, with revenue in each stage.”
recipe.md preview
Built from the published catalog. Blu Agent's full step prompts load in your workspace when you run it.
Inputs & outputs
What the recipe needs before it runs, and what it leaves in your workspace.
Inputs
- Nothing declared beyond an Intempt workspace. Blu asks for anything else it needs as it goes.
Outputs
How the recipe runs
Blu follows the steps in order and shows you each one before anything goes live.
- 01Map customers to lifecycle stagesCreate an Insights report called "Customer Lifecycle Distribution". Series A: Count Unique Users grouped by Users.Lifecycle score: the canonical lifecycle enum with exactly six values: - At risk - Needs attention - New customers - Promising - Regulars - Champions Series B: Sum of Users.Lifetime value per lifecycle stage (revenue concentration view) Series C: Computed: Series B / total LTV × 100, unit: %, label: "Share of Total LTV" Series D: Average of Users.Days since last activity per lifecycle stage (recency context) Series E: Average of Users.Average order value per lifecycle stage (basket-size context) Time range: Current snapshot Compare: Same snapshot 30 days ago AND 90 days ago (lifecycle migration over 1 month and 1 quarter) Chart type: Stacked bar chart with each bar = a lifecycle stage, plus side panels showing: - Series C (% of LTV per stage) - Series D (recency average per stage) - Series E (AOV per stage) Migration view (secondary): - For each pair of stages, surface the count of users who moved from stage X to stage Y over the last 30 days - Render as a Sankey-style flow diagram or a simple migration table with the 6×6 transitions - Highlight the dominant migrations: which lifecycle transitions are happening most? Annotations: - Highlight the top 2 stages by share of customers (volume) AND the top 2 by share of LTV (value). In healthy distributions, "Champions" + "Regulars" hold a small share of customers but a disproportionate share of LTV: this is the desired pattern. - Flag if "At risk" + "Needs attention" stages together hold >25% of total LTV: high-value customers are slipping; immediate retention action required. - Flag if "Champions" stage shrunk >10% in customer count vs. snapshot 30 days ago: your best customers are decaying; investigate which migrations they're moving INTO ("Regulars" is healthy aging; "At risk" is alarming). - Flag if "New customers" stage grew but "Promising" stage didn't grow proportionally a month later: the new-customer-to-promising progression is broken; first-purchase users aren't being properly nurtured. - Surface the largest single migration over the period (e.g. "147 users moved from Regulars to At risk this month"): this is the at-risk cohort that warrants targeted re-engagement. - Add benchmarks for healthy share-of-customers distribution: Champions 5-10%, Regulars 15-25%, Promising 15-20%, New customers 10-20%, Needs attention 15-25%, At risk 10-20%. Distributions skewed toward "At risk" + "Needs attention" indicate retention erosion; distributions skewed toward "New customers" without "Regulars" growing indicate weak repeat-purchase mechanics. Use case: the canonical CRM segmentation view. Every direct-to-consumer brand needs lifecycle visibility; this recipe materializes it directly from the platform's pre-computed Users.Lifecycle score attribute (which is maintained automatically based on RFM scoring). The migration view is the high-leverage piece: the single number "Champions: 8%" is less actionable than "147 Regulars moved to At risk this month." Taxonomy notes: - Users.Lifecycle score is the canonical platform-maintained lifecycle enum with exactly the six values listed above. Do NOT invent additional segments (no "Loyal," no "Hibernating," no "Lost," no "Can't Lose Them"): these are common in textbook RFM frameworks but are not part of Intempt's canonical lifecycle taxonomy. - Users.recency_score, frequency_score, monetary_score are separate enum attributes (the inputs that feed Lifecycle score) but should NOT be used as cross-tab dimensions in this recipe: they're the components, not the segmentation. If R/F/M-cube cross-tab analysis is needed, that's a different recipe. - Users.Lifetime value, Days since last activity, Average order value are real computed numeric attributes on the Users object. - Migration analysis is computed by Lovable from snapshots of Lifecycle score taken at different points in time. The platform should retain or version Lifecycle score history to enable this.
Install Intempt and run it
- 1
Sign up
Create a free Intempt workspace.
- 2
Connect your data
Add the sources this recipe reads from.
- 3
Ask Blu
Type /customer-lifecycle-distribution in Blu once this recipe is live there.
- 4
Review and approve
Blu builds each step and waits for your OK before anything goes live.
