As with most SaaS businesses, our client had built up an extensive amount of data about their products and customers but crucially lacked a consolidated view of the key functions of the business, namely marketing, sales, and product usage. This view is an essential building block to sophisticated business management in the digital world – understanding the behaviour of a business’s customers today in order to predict what they might do tomorrow.

The client is an ESG-focussed asset manager with a global portfolio and multiple funds. The client has a sophisticated internal data environment but maintaining and updating legacy tools proved to be time-consuming and cumbersome.

A private equity owned B2B SaaS company which had pursued a buy-and-build strategy. Having completed several recent acquisitions, the client could now boast both strong organic as well as acquisition growth. However the constituent business units still used different source systems and business logic across key datasets.

Our client wanted an analytical suite to understand its customers’ behaviour and optimise the performance of its products. They required our modelling and visualisations in order to derive the desired degree of customer and product insight of customer segments, leads funnel performance and product profitability to achieve the desired degree of analysis.

Our client needed an objective assessment of their data engineering stack in order to mitigate risks created by staff turnover, improve institutional knowledge, embed best practice into the rebuild of the data architecture, scope future phases of data engineering work.

A high-street clothing retailer wanted an analytical toolset to allocate digital marketing spend across multiple channels to maximise their return on investment on advertising spend. Our tool reduced CPA by 10% whilst growing profitable acquisition.

A PE-backed price comparison service wanted to optimise their outbound sales funnel with the aim of increasing monthly sales by improving conversion rate. Using machine learning we improved their conversion by 20%.

A PE-backed prosthetics company wanted more accurate forecasts to improve the performance of their supply chain. They business wanted to leverage historic performance trends to improve forecasting accuracy of future sales on automated basis.

A PE-backed industrials manufacturer wanted to create a process for predicting churn by anticipating risk at the customer level and help its sales team pre-empt likely customer churn.

A PE-backed SaaS business needed a robust analysis of revenue and churn in order to support its Vendor Due Diligence process.

A UK high-street retailer wanted to drive incremental growth in email revenues and increase customer retention.

A PE-backed accounting and HR SaaS provider wanted to reduce customer churn and drive value from its existing customer base.