Case study · Analytics · Databricks on AWS

An automated Customer Lifetime Value engine on Databricks and AWS.

How Tenplus automated CLV analysis for Ambition Data and cut delivery time from months to around three weeks per client.

DatabricksAWSApache AirflowAutomated pipelinesBI dashboards

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Analytics · Databricks on AWS

From months of manual work to weeks, automated.

Months → ~3 wksDelivery time

Days → hoursData prep

AutomatedEnd to end

Client background

Ambition Data: a financial analytics company.

Ambition Data helps businesses understand customer behaviour and growth, centred on Customer Lifetime Value. Working with data from e-commerce platforms, transaction systems and customer databases, they needed a system that could process large volumes and generate reliable CLV insights quickly. The project was coordinated by Managing Director of Customer Growth, Allison Hartsoe.

At a glance

E-commerce platforms and transaction systems
Customer databases
Clients relying on accurate, fast insight

The problem

Manual processes slowing growth and adding risk.

The entire CLV process was manual and Excel-based, which made it hard to scale or stay consistent.

Data cleaning and preparation took days for each client.

High risk of human error in calculations.

No clear data lineage or history of how results were generated.

Repeated work for every new client.

Limited scalability due to manual effort.

Slow delivery timelines, often taking months.

What they needed

A fully automated data pipeline
A centralised system for managing and processing data
Reliable data lineage and governance
Faster turnaround and a scalable platform

Solution overview

An end-to-end platform on Databricks and AWS.

Tenplus automated the whole CLV process, from ingestion to reporting, on a structured and scalable system.

Databricks

for data processing, machine learning and analytics.

AWS

for cloud infrastructure.

Apache Airflow

for pipeline orchestration.

Automated pipelines

for ingestion and transformation.

Built-in BI dashboards

within Databricks for reporting.

Built with DatabricksAWSApache AirflowAutomated pipelinesBI dashboards

How we built it

From ingestion to automated CLV reporting.

Step 1Data ingestion and centralisation

Step 2Data cleaning and transformation

Step 3Machine-learning model for CLV

Step 4Pipeline orchestration with Airflow

Step 5Reporting and BI dashboards

Results

Faster delivery, higher accuracy, real scale.

A fully automated end-to-end CLV analytics engine.
Delivery time reduced from months to around three weeks per client.
Data processing and cleaning reduced from days to a few hours.
A significant reduction in human error.
Centralised data with clear lineage and history.
Ability to onboard more clients without increasing workload.

★★★★★

Working with Tenplus helped us transform a complex and manual process into a structured and automated system. The team built a scalable data platform that allows us to deliver insights faster and with greater accuracy. We can now serve more clients, reduce errors and build on a strong data foundation for future growth.

Managing Director, Customer Growth · Ambition Data

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