Analytics & BICase study
Customer Segmentation and Automation
Automated, weighted customer segmentation with predictive analytics, so the sales team knows which customers need attention without digging through years of history.
Before
- Manual decoding of customer histories
- Payment terms and volumes untangled by hand
- Hard to spot high-value or at-risk customers
- Returning customers researched from scratch
After
- Data consolidated into one repository
- Automated, weighted segmentation
- Predicted payment patterns and volumes
- Dashboards tracking segment performance
01 · The challenge
Where they started
An infrastructure business serves a wide mix of customers: long-standing partners, occasional high-volume buyers, loyal low-volume customers and customers on many different payment terms.
Heavy manual work
Sales representatives spent significant time decoding customer histories, payment terms and transaction volumes.
Finding the right customers
Identifying customers who need personal attention, and the most valuable ones, was hard.
Long histories
When customers returned after a long gap, the team had to sift through history by hand.
02 · The solution
What we built
DATA LEAGUE designed a solution to automate segmentation and give the team forward-looking insight.
How it fits together
- Customer, transaction and payment dataMany years of history
- One repositoryConsolidated and clean
- Weighted segmentationAdjustable factors
- Predictive modelsPayments and volumes
- Power BIDelivered on a schedule
Data integration
Customer histories, transactions and payment terms consolidated into one repository.
Automated segmentation
Algorithms categorise customers using weighted factors such as spend, payment punctuality and volumes.
Predictive analytics
Models forecast payment patterns and transaction volumes for each segment.
Dashboards
Interactive Power BI dashboards to explore customers and track segment performance.
03 · Delivery
How we delivered it
- 1
Integrate
Extracted, transformed and loaded data from every relevant source.
- 2
Design the algorithm
Built segmentation using weighted factors such as spend, punctuality and volume.
- 3
Predict
Trained models on historical data to forecast payment behaviour and volumes.
- 4
Refine
Iterated on the weightings over several rounds to improve accuracy.
04 · Results
What changed
Richer customer profiles
Profiles that reflect many variables and their relative weightings.
Adjustable weightings
Factor weightings can change as the market changes.
Focused engagement
The team knows which customers warrant attention.
Efficiency gains
Automation removed manual segmentation work and reduced errors.
Real-time tracking
Dashboards track segment performance for timely course corrections.
Better planning
Supports resource allocation, revenue forecasting and targeted marketing.
05 · Technology
Built with
- Machine learning
- Segmentation
- Predictive analytics
- Power BI
- Automation
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