PA Data Analytics
Customer Intelligence

Customer analytics and segmentation that show who to retain, and why

Revenue totals hide what is happening underneath: which customers bring in most of the value, which ones are drifting away, and when. PA Data Analytics builds customer segmentation, RFM analysis, cohort retention and customer lifetime value analysis from the transaction data you already hold.

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Data → Insights → Decisions

The questions customer intelligence answers

  1. What happened?

    How many new customers came back, when, and how much did each acquisition cohort spend over time?

  2. Why did it happen?

    Which customer groups are loyal, which lapse early, and what separates them?

  3. What should happen next?

    Who should be targeted for retention, win-back or loyalty, and how much is each group worth investing in?

What we deliver

Customer Intelligence services

Customer segmentation

Behavioural segments built from purchase history using RFM scoring and, where useful, clustering (K-Means), with a clear profile and suggested action for each segment.

  • Python
  • SQL
  • Power BI

RFM analysis

Recency, frequency and monetary scoring that groups customers into named segments such as Champions, At Risk and Hibernating. Built in SQL so it can be refreshed monthly.

  • SQL
  • BigQuery

Cohort retention analysis

Monthly acquisition cohorts tracked from first purchase to month 12, shown as a retention heatmap, to reveal exactly when customers stop returning.

  • SQL
  • Python

Customer lifetime value (CLV)

Historical CLV by segment and acquisition channel, LTV-to-CAC comparison and payback period analysis.

  • Python
  • SQL
  • Excel

Churn & retention analytics

Churn rate by segment, channel and product category, repeat-purchase intervals and sizing of win-back opportunities.

  • SQL
  • Power BI

Customer behaviour analysis

Purchase patterns, product combinations and journey drop-offs that explain how different customer groups behave.

  • Python
  • GA4
Why it matters

What it helps your business do

  • A clear picture of which customers bring in most of your revenue
  • The point in the customer lifecycle where most customers are lost
  • Segments your marketing team can target directly
  • Retention spend that matches what each customer group is worth
Typical deliverables

What you receive

  • Customer segment table with profiles and recommended actions
  • Cohort retention heatmap
  • CLV by segment and acquisition channel
  • Refreshable SQL or Power BI outputs
  • Retention recommendations and a walkthrough session

Scope depends on your data and question. It is agreed after the discovery call and data audit.

Proof of work

Related case studies

Portfolio projects built on demonstration datasets. They are not client engagements, and their figures describe the datasets and models, not real business results.

RETENTION BY COHORTOct 232023-10 cohort, month 1: 39% retained2023-10 cohort, month 2: 22.9% retained2023-10 cohort, month 3: 18.5% retained2023-10 cohort, month 4: 17.1% retained2023-10 cohort, month 5: 15.1% retained2023-10 cohort, month 6: 10.2% retained2023-10 cohort, month 7: 14.6% retained2023-10 cohort, month 8: 10.7% retained2023-10 cohort, month 9: 12.2% retained2023-10 cohort, month 10: 10.2% retained2023-10 cohort, month 11: 7.3% retained2023-10 cohort, month 12: 8.3% retained2023-10 cohort, month 13: 7.8% retained2023-10 cohort, month 14: 5.4% retained2023-11 cohort, month 1: 40.5% retained2023-11 cohort, month 2: 20.3% retained2023-11 cohort, month 3: 21.2% retained2023-11 cohort, month 4: 16.2% retained2023-11 cohort, month 5: 12.6% retained2023-11 cohort, month 6: 12.2% retained2023-11 cohort, month 7: 11.3% retained2023-11 cohort, month 8: 9.9% retained2023-11 cohort, month 9: 10.4% retained2023-11 cohort, month 10: 10.4% retained2023-11 cohort, month 11: 6.3% retained2023-11 cohort, month 12: 9% retained2023-11 cohort, month 13: 8.6% retained2023-12 cohort, month 1: 43.1% retained2023-12 cohort, month 2: 23.9% retained2023-12 cohort, month 3: 22.3% retained2023-12 cohort, month 4: 12.2% retained2023-12 cohort, month 5: 16% retained2023-12 cohort, month 6: 9% retained2023-12 cohort, month 7: 13.8% retained2023-12 cohort, month 8: 9.6% retained2023-12 cohort, month 9: 9.6% retained2023-12 cohort, month 10: 7.4% retained2023-12 cohort, month 11: 9% retained2023-12 cohort, month 12: 9.6% retainedJan 242024-01 cohort, month 1: 35.2% retained2024-01 cohort, month 2: 20% retained2024-01 cohort, month 3: 20.5% retained2024-01 cohort, month 4: 15.2% retained2024-01 cohort, month 5: 14.8% retained2024-01 cohort, month 6: 9% retained2024-01 cohort, month 7: 9.5% retained2024-01 cohort, month 8: 9% retained2024-01 cohort, month 9: 12.4% retained2024-01 cohort, month 10: 10.5% retained2024-01 cohort, month 11: 10.5% retained2024-02 cohort, month 1: 39.1% retained2024-02 cohort, month 2: 25.5% retained2024-02 cohort, month 3: 21.1% retained2024-02 cohort, month 4: 17.4% retained2024-02 cohort, month 5: 15.5% retained2024-02 cohort, month 6: 9.9% retained2024-02 cohort, month 7: 12.4% retained2024-02 cohort, month 8: 8.1% retained2024-02 cohort, month 9: 6.2% retained2024-02 cohort, month 10: 7.5% retained2024-03 cohort, month 1: 33.9% retained2024-03 cohort, month 2: 22.4% retained2024-03 cohort, month 3: 18.8% retained2024-03 cohort, month 4: 10.4% retained2024-03 cohort, month 5: 9.4% retained2024-03 cohort, month 6: 13.5% retained2024-03 cohort, month 7: 10.9% retained2024-03 cohort, month 8: 8.3% retained2024-03 cohort, month 9: 10.4% retainedApr 242024-04 cohort, month 1: 34.1% retained2024-04 cohort, month 2: 22.5% retained2024-04 cohort, month 3: 17.6% retained2024-04 cohort, month 4: 13.7% retained2024-04 cohort, month 5: 15.4% retained2024-04 cohort, month 6: 9.9% retained2024-04 cohort, month 7: 12.1% retained2024-04 cohort, month 8: 10.4% retained2024-05 cohort, month 1: 36.8% retained2024-05 cohort, month 2: 20.3% retained2024-05 cohort, month 3: 13.4% retained2024-05 cohort, month 4: 11.7% retained2024-05 cohort, month 5: 13% retained2024-05 cohort, month 6: 10.4% retained2024-05 cohort, month 7: 6.5% retained2024-06 cohort, month 1: 37.8% retained2024-06 cohort, month 2: 27.9% retained2024-06 cohort, month 3: 16.9% retained2024-06 cohort, month 4: 12.9% retained2024-06 cohort, month 5: 9.5% retained2024-06 cohort, month 6: 16.9% retainedJul 242024-07 cohort, month 1: 38.8% retained2024-07 cohort, month 2: 27% retained2024-07 cohort, month 3: 19.4% retained2024-07 cohort, month 4: 15.8% retained2024-07 cohort, month 5: 13.8% retained2024-08 cohort, month 1: 39.5% retained2024-08 cohort, month 2: 20.6% retained2024-08 cohort, month 3: 14.9% retained2024-08 cohort, month 4: 15.8% retained2024-09 cohort, month 1: 38% retained2024-09 cohort, month 2: 25.9% retained2024-09 cohort, month 3: 20% retainedOct 242024-10 cohort, month 1: 38.2% retained2024-10 cohort, month 2: 21.3% retained2024-11 cohort, month 1: 36.9% retainedM1M3M6M9M125%45%+Portfolio project · Synthetic dataset · Avg M1 37.9%
  • Portfolio project
  • Synthetic dataset
  • Customer Intelligence

Cohort retention analysis: when do customers stop coming back?

A portfolio cohort analysis of 15 months of e-commerce orders, with a reusable SQL pipeline and retention heatmaps showing when customers stop returning.

Synthetic dataset: 2,810 customers · 6,404 orders · 15 cohorts

Read the cohort retention case study
REVENUE HEALTH & RETENTIONMRR by customer health scoreHealthy: 1,315 accounts, $288,465 MRRMedium Risk: 332 accounts, $92,598 MRRHigh Risk: 131 accounts, $21,489 MRRHealthy$288K · 1,315 acctsMedium + high risk$114,087 · 463 acctsAverage cohort retentionMonth 1: 91.6%91.6%M1Month 3: 81.3%81.3%M3Month 6: 72.0%72%M6Month 12: 60.2%60.2%M12Portfolio project · Synthetic dataset
  • Portfolio project
  • Synthetic dataset
  • Customer Intelligence

SaaS revenue health and churn risk analysis

A portfolio analysis of subscription revenue health (MRR movements, retention, acquisition channels, usage and support signals) combined into a rule-based customer health score.

Synthetic dataset: 86,402 rows · 6 tables · MRR at risk quantified

Read the SaaS churn case study
Questions

Common questions about customer intelligence

What data is needed for RFM and cohort analysis?

An order or transaction table with a customer ID, order ID, order date and order value. That is enough for RFM, cohort retention and historical CLV. Product, channel and marketing data add depth but are optional.

How is this different from predictive churn modelling?

Customer intelligence describes and explains behaviour: who your customers are and when they leave. Predictive analytics uses that history to estimate which individual customers are likely to churn next. Projects often start with segmentation and cohort analysis, then add prediction if the data supports it.

Can the segments be used in our email or CRM tool?

Yes. Outputs can be delivered as a customer-level table with segment labels, in a format most email and CRM platforms can import.

InsightA Beginner's Guide to RFM Segmentation in SQLRelated serviceMarketing AnalyticsRelated servicePredictive Analytics

Discuss a customer intelligence project

Share the question you are trying to answer and the data you have. We will reply with practical next steps.

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