Validating a PE-backed SaaS business’s buy-and-build strategy using advanced analytics and robust data engineering to justify further group investment
Profile
The client was a private equity owned B2B SaaS company which had pursued a buy-and-build strategy: buying up smaller but fast-growing rivals to integrate them into a larger conglomerate. 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.
Situation
For companies pursuing buy-and-build strategies, their success rests on their ability to integrate data from their acquisitions into the larger company. That integration in turn depends on sophisticated data engineering to ensure the infrastructure runs smoothly and executives can tap into key datasets in order to drive decisions and benefit from the new growth they have acquired.
By acquiring smaller, high-growth companies, our client’s investors wanted to benefit both from that growth upside and to validate their buy-and-build thesis. This second point required the ability to track key annual recurring revenue (ARR) metrics across all its business units in a consistent and comparable way to get a consolidated view of business performance & highlight growth drivers. Historically, each separate business unit had a unique data model for tracking its customer transactions and movements that pre-dated the acquisition. Now, as part of a larger conglomerate, those different data sources needed to be engineered into a consistent format to allow company-wide analysis to take place. This included historic views alongside visualisations of ARR and other key metrics such as new customers, churn, and reactivations.
Additionally, we developed a suite of SaaS-specific supplementary KPIs to facilitate high-level management reporting and strategic planning. These would include financial, sales and marketing, HR, and product usage metrics. To do so would require a clean, accessible, future-proof data model, and standardisation across businesses.
The project would conclude with a phase of detailed reconciliation, with two predominant aims. The first aim was to ensure all KPIs and novel reporting was accurate and consistent with internal figures. The second aim was to provide support in reconciling ARR reporting with invoicing values and recognised revenue, allowing for holistic understanding of ARR and its relation to revenue.
Action
To kick-off, we met the executive and technical teams of each constituent business unit on-site to understand their strategic aims, products, and data. These workshops enabled us to align on 3 core priorities across the group:
A forensic understanding of ARR
A consistent method for measuring sales efficiency for partners and in-house sales teams]
The ability to monitor unit economics to optimise both customer acquisition costs and customer lifetime value across customer cohorts
Working with each individual business unit, we engineered bespoke algorithms to take in contractual recurrent revenue data at the client-level and break it down into a month-by-month view. This enabled a detailed picture of the growth of both individual businesses and the entire group to be visualised.
We held workshops with each individual business unit to understand their unique data model and set out a roadmap for uploading relevant transactional and contractual data into a common data warehouse. Data cleaning was carried out with a high level of attention to detail to ensure contractual data was processed according to the nuances of each business unit (e.g. ensuring clients only have one active contract if the business unit offers a single product).
We broke down the contracts into a month-by-month view, with ARR changes displayed at the client-level alongside visibility of their chosen metrics. We ensured our queries were future-proof and reusable by including clauses to catch all edge cases that could arise in the data. Queries were also validated by each respective business to ensure they are accurate and in line with each businesses’ internal reporting.
Tools and techniques used in this work
Capturing and standardisation of business logic via bespoke SQL queries
Data Engineering to unite differing business models (Azure Data Studio, MySQL)
Data modelling to generate ARR and KPI tables, optimised for BI software
Impact
Our detailed SQL queries allow the evolution of key metrics such as churn and new customers to be visualised over time, offering high-value insights in areas such as customer retention, new business generated and marketing efficiency to be realised.
Senior management can view the ARR breakdown for each business unit, allowing strategic decisions to be made. Our data consolidation of several business units allows the group to unlock growth potential through both organic growth as well as future acquisitions, allowing them to upsell and cross-sell to additional customers.
Our additional KPIs further allowed for dynamic insight into rapidly changing business metrics, providing actionable direction for management level decisions. Trust in the data built through reconciliation and revenue comparison allowed for decisions to be taken quickly and with confidence. This has enabled our client to buy further businesses more comfortably, rapidly acquiring their customers who were using similar software to their main product.
So what?
QuantSpark’s ability to understand and consolidate several different data models & complex revenue recognition logic into a cohesive view of ARR and financial growth was integral to our client’s understanding of its financial performance and key revenue drivers. In turn this understanding validated their investors’ original buy-and-build strategy, which led to the financing of further acquisitions to drive growth.
To discuss how we use advanced analytics for recurring revenue, contact us.
Get in touch
Are you looking for a team with deep expertise in advanced analytics and modelling techniques to drive value in your business? We can support you.
Related areas of QuantSpark expertise
Data Engineering
Similar Case Studies
