Executive Summary
- Buy and build strategies lead to numerous source systems and processes having to be combined to create an accurate view of customer level ARR
- This combination process is hard, new systems provide insight on a forward-looking basis but there is a large amount of insight in historic systems that needs to be extracted to support strategic decision making
- It requires a detailed audit of the existing customer cube to be able to rectify and document the calculation process for core metrics
The Challenge
We recently worked with a PE-backed software platform business who were looking to create an accurate historical customer cube to understand their key trends. Multiple acquisitions with different billing systems meant that accurate ARR at the monthly level had not been achieved. The specific challenges faced were:
- Invoice amounts had not been spread across their period appropriately causing gaps and spikes in ARR at the customer level; this led to high and unrealistic month-on-month downsell, churn and GRR.
- A recent ERP implementation had led to customers changing IDs, causing false churn and new business in switch over months.
- The current cube lacked the necessary auditability to understand how ARR had been calculated from invoices.
Our approach
The cube consisted of hundreds of thousands of rows on customer-product level spanning across several business units – this meant that prioritisation of the most impactful cases was key. To find and correct the most impactful cases we took the following three-step approach:
- Define the prioritisation metric: The core aim of the project was to reduce incorrect downsell and churn. Therefore, we prioritised customers by their total ARR loss (churn and downsell) over the period of interest.
- Bottom-up approach: We analysed individual customer’s ARR profiles and used supporting evidence to create adjustments to appropriately distribute ARR across the time period of the invoice, removing false ARR loss at the customer level.
- Top-down approach: We then explored customer segments with high levels of ARR loss to identify systematic business logic errors and rectify them across the whole customer segment, eradicating large clusters of false ARR loss.
This methodology allowed us to create a list of suggested adjustments and customer re-mappings to implement, focussing on the first two challenges the client outlined to us.
Auditability was also a key success criteria. To achieve this, we worked closely with the client through a series of workshops where adjustments would be reviewed and accepted. Supporting documentation was prepared through this to justify adjustments and remapping.
Impact
We reduced average monthly ARR loss by over 50% aligning the overall ARR profile closer to client’s target. The client now have a customer cube with the appropriate granularity to support future strategic decision making.
Specifically, they now have:
- An accurate customer cube that unlocks historical reporting capabilities and snowball reporting, increasing investor confidence
- Reliable GRR and NRR metrics
- A clear audit trail from source systems to calculated ARR across all customers

If you’re interested in improving your data readiness and strategic decision making through a customer cube audit, please contact us at contact@datavisionservices.co.uk to see how we can best work together.