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P2Lab Stats

Customer reports

The Customers report group lives under P2Lab Stats → Customers and contains the analyses you’d otherwise build in a separate BI tool — cohort retention matrix, RFM segmentation, new-vs-returning customer overview.

Every report follows the common chrome — chart, sortable table, filter sidebar, profile sidebar, date range with previous-period comparison.

Customer overview

Section titled “Customer overview”

The landing report for the group. Combines KPIs that classify your customer base.

KPI cards on top:

  • New customers — first order in the range
  • Returning customers — had an earlier order; ordered again in the range
  • Active customers — placed at least one order in the range
  • Orders per customer
  • Average customer revenue
  • Guest orders — orders without a registered customer
  • Retention rate — share of previous-period customers who also ordered in the current period
  • Registration conversion rate — share of guest orders converted to registered customers

The full list and exact definitions are in KPI list.

The table below the chart shows registered customers in the range with order count, total spend, AOV and last-seen date. Sort by any column.

Customer group

Section titled “Customer group”

A simple breakdown by the Shopware customer group assigned to each customer. Useful when you operate B2B + B2C in the same shop or run named partner groups.

Each row shows orders, revenue, customers, AOV plus Δ % against the previous period.

Cohort retention matrix

Section titled “Cohort retention matrix”

Groups customers by the month of their first order and tracks how many of them return in each subsequent month.

  • Rows: cohort month (first-order month).
  • Columns: M0, M+1, M+2, … M+11 — months after the cohort month.
  • Cell value: configurable — either revenue per active cohort customer or orders per active cohort customer.

The heatmap colour-codes retention strength so weak cohorts stand out. Click any cell to drill into the customers who made up that intersection.

Use cases:

  • Compare cohorts side by side — does August 2024 retain better than August 2023?
  • Spot the retention shelf — at which M+N does retention stabilise?
  • Validate marketing campaigns — does a cohort that signed up during a discount push retain at the same rate?

RFM segmentation

Section titled “RFM segmentation”

Assigns every customer to one of ten segments based on quintile scores across three axes:

  • Recency — how recently they ordered (lower = better).
  • Frequency — how often they ordered.
  • Monetary — how much they spent.

Each axis gets a score 1 (worst quintile) to 5 (best quintile). The combination maps to one of these segments:

SegmentDescription
ChampionsBought recently, often, and spent the most
Loyal customersHigh frequency, decent monetary
Potential loyalistsRecent, decent frequency — nurture them
New customersBought recently, low frequency
PromisingRecent first-time buyers with potential
Need attentionAbove-average recency / frequency / monetary, slipping
About to sleepBelow-average values, drifting
At riskSpent big and often but haven’t bought recently
Can’t lose themTop spenders who haven’t bought for a long time
HibernatingLowest scores across the board

The full breakdown of which RFM score combinations map to which segment is in RFM segments.

The report shows:

  • A heatmap with the R×F grid coloured by segment.
  • A segment summary table with customer count, total revenue and share per segment.
  • A drill-down table of customers in any selected segment.

Use cases:

  • Build email campaigns targeted at specific segments (e.g. discount push for “Can’t lose them”).
  • Track segment migration over time — are Champions becoming At risk?
  • Identify VIPs to onboard into a loyalty programme.

Tips & gotchas

Section titled “Tips & gotchas”
Section titled “Related”