E-Commerce Analytics

Customer Journey Analytics

Synthetic conversion and retention model showing how SMB commerce teams can see drop-off, channel quality, and lifecycle movement in one place.

AI-generated concept Proprietary synthetic data Client-safe showcase
SectorCommerce & growth
StackPower BI, BigQuery, GA4
Signal+32% conversion lift
ScenarioAI-generated concept
Why this exists

This customer journey case study uses synthetic channel, funnel, and cohort signals to show how Dedolytics can make growth analytics usable for an SMB operator.

The Challenge

Growth teams had plenty of channel data and almost no shared view of what it meant. They could see clicks and spend. They could not quickly see where buyers were dropping off, which channels produced better customers, or what part of the lifecycle actually deserved the next dollar.

Key Business Questions

  • Where is the funnel leaking most right now?
  • Which acquisition channels create the highest-quality customers?
  • How does retention behave by cohort and offer type?
  • What should the next budget move be?

The Solution

We structured a synthetic journey system that connects channel spend, funnel movement, and lifecycle value into one readable operating layer. It is designed to support budget decisions, not just marketing recaps.

Funnel movement

A plain read on where buyers stall from session to checkout to repeat purchase.

Channel quality

A synthetic view of which acquisition lanes are producing cheap clicks versus valuable customers.

Cohort retention

Retention movement by acquisition cohort and offer type.

Budget brief

The channels and offers most likely to deserve more or less spend next.

Buildable product preview

Growth Signal Preview

This preview is more kinetic: funnel stages, cohort behavior, and revenue signal arranged as a growth system rather than another static dashboard screenshot.

Conversion Cohorts Budget shifts
Conversion rate3.8%
Customer LTV$892
Lift modeled+32%
Average order$127

Journey funnel

Session / cart / checkout / purchase

Sessions

1.25M visits with acquisition cost and mix in view.

Cart adds

312K adds keep the mid-funnel health visible.

Checkout

109K users signal the current conversion pressure.

Purchase

47K orders tie directly into lift and LTV quality.

Channel quality

Where the next budget move comes from

Email flows7.1xscale
Paid social2.4xtrim
Search brand5.6xhold

Retention signal

What turns performance into real growth

The point is not prettier channel data. It is a clearer budget decision rooted in cohort quality, repeat behavior, and revenue lift.

Technical Frame

Data model

Synthetic channel, event, and order data are tied together so teams can see both conversion and customer quality in one frame.

Key metrics

  • Conversion rate
  • Average order value
  • Customer lifetime value
  • Retention by cohort

Workflow output

  • Weekly growth review
  • Budget shift notes
  • Offer watchlist
  • Lifecycle experiment queue

Delivery mode

Designed for lean commerce teams that need a growth system, not just another ad dashboard.

The Result

3.8%Conversion rate
$892Customer LTV
+32%Lift modeled
$127Average order

The concept makes the next budget decision easier instead of just showing us prettier channel data.

Anonymous Head of Growth
Anonymous review
4.8/5
Direct and usable

Good balance between performance detail and actual decision-making.

Anonymous growth review