Customer report · Illustrative sample
Northwind Labs
alex@example.com
Density · Standard
power user
+ goal focused
Health score
7.6
/ 10
Calculated from analysis signals
Churn risk
LOW
Risk tier · not a churn probability
Sentiment
Positive
Overall sentiment for this customer
Archetype
Power user
Secondary: goal focused
Health and churn risk help you decide where to investigate. In LiteLedger, code calculates these indicators from structured analysis inputs; the session story gives you context for interpreting them.
Section 01 — Vitals with percentile context
Spend · tokens · request volume · cost per interaction · cost per session · response latency
Compare this customer’s usage and cost with the workspace distribution. The range bars show the 10th percentile, median, and 90th percentile, so you can judge the scale of the account alongside its experience. These operational metrics come from synced request data.
Total spend$47.10
p10 · $2.40median · $11.80p90 · $52.00
Total tokens9.4M
p10 · 0.3Mmedian · 2.1Mp90 · 11.0M
Request volume1,284
p10 · 41median · 210p90 · 1,460
Cost per interaction$0.061
p10 · $0.018median · $0.082p90 · $0.190
Cost per session$2.21
p10 · $0.55median · $2.80p90 · $7.40
Average response latency3.9s
p10 · 1.8smedian · 5.1sp90 · 11.2s
Here, Northwind combines substantial usage with a lower cost per interaction than the workspace median. Use the operational context to frame a more useful account conversation.
Section 02 — Behavioral profile
Engagement depth · sophistication trend · expansion pattern · secondary archetype
Understand how the customer’s behavior is developing: how deeply they engage, how their requests evolve, and where they begin using more of the product.
Engagement depth
Deep
Multi-step workflows, returns within the same day, picks up where a prior session ended.
Sophistication trend
Increasing
Prompts grew more specific over time; fewer clarifying round-trips per task.
Expansion pattern
Broadening
Started with a single feature; in the last month reached into batch operations and the export path.
Secondary archetype
Goal focused
Customers can blend patterns — a power user who still arrives with one concrete outcome per session.
In this example, increasingly specific requests and broader feature use suggest a useful follow-up: help the customer extend the workflows they already use successfully.
Section 03 — Session timeline
Sentiment arc · success moments · friction points · features discovered
Follow four illustrative sessions to see what changed. Read the customer’s goals, friction, recovery, and feature adoption together before choosing a next action.
Session 01 · onboarding
Frustrated → resolved
Came in to wire up the first integration. Hit an auth misconfiguration early and re-tried the same step five times before finding the setting. Recovered in the same session and completed the connection.
Friction · auth config (root cause: docs gap) · resolved
Feature · integration setup
Session 02 · exploration
Mixed — exploring scope
Tested the product against an adjacent use case it wasn't built for. Confusion about expected output, partially resolved by the end. No goal abandoned, but no clean win either.
Friction · scope mismatch (root cause: feature boundary) · partially resolved
Session 03 · first real workflow
Breakthrough
First end-to-end multi-step workflow completed without a stall. Prompts were noticeably more precise than session 1. This is the inflection point for the recency-weighted health climb.
Success · multi-step workflow completed
Delight · "this is exactly what I needed"
Session 04 · expansion
Confident, broadening
Reached into batch operations and the export path for the first time. Worked through a question independently. Discovered the saved-view feature without prompting.
Success · self-served, no errors
Feature · batch operations
Feature · export
Feature · saved views
The same customer can struggle early and make strong progress later. The session story helps your team recognize both the original friction and the recovery.
Section 04 — Recommended actions
Specific, evidence-cited next steps for this customer
Use proposed actions and their supporting rationale to plan a relevant follow-up. The examples below connect each suggestion to something that happened in the customer’s sessions; your team decides which action to take.
Customer success · low
Surface batch operations earlier for similar new accounts.
This account didn't reach batch operations until session 04, then adopted it immediately and self-served. The capability landed late relative to its fit — worth introducing it in onboarding for goal-focused power users.
Customer success · medium
Close the onboarding auth-config gap before it costs the next account.
The session 01 friction was a documentation gap on auth setup — five retries before recovery. This customer pushed through; an earlier-stage one might not. A doc fix or an inline hint addresses the root cause.
Customer success · low
Use this account as an expansion reference for the export path.
Sophistication is increasing and usage is broadening into export — a strong, low-risk candidate to confirm the export workflow against a real account and to ask for a reference.
Customer success · medium
Clarify the scope boundary tested in session 02.
The session 02 confusion came from an adjacent use case the product isn't built for, and resolved only partially. A short note on where the product's boundary sits would prevent a misalignment-driven drop-off later.
Section 05 — Analysis context
History reviewed · report detail · estimated cost
This report covers conversations from Aug 14, 2026 to Sep 7, 2026. The analysis can be re-run over any window of synced history; the report reflects the window that was analyzed. Each run is dated and stamped with its estimated cost and its density level, so a re-run is always traceable back to what it was generated from.
Window · Aug 14 – Sep 7, 2026
Density · Standard
Run cost · ~$0.38 (estimated)
Sessions illustrated · 4
The illustrated cost describes this sample only. Actual analysis estimates depend on the selected scope and provider. The report’s date and analysis window help you interpret its findings in context.
This is an illustrative walkthrough with fictional data. Your reports reflect your own synced conversations and selected analysis scope.