“Harnessing data and data science is an important strategic enabler for companies and also provides a real competitive edge. At Hg, we make available the expertise and resourcing to help our companies get up the curve rapidly.”Christopher Kindt, OI Principal, Head of Data Analytics team
The recent revolution in data technology has enabled Hg to help deliver end-to-end visibility to Hg’s portfolio companies whilst weaving data science into core business processes.
The data potential
The Hg portfolio of technology and service businesses are particularly ‘data rich’. Each produces a stream of information from their usage of our software, service calls, marketing engagement and so on. These businesses have the potential to become uniquely data driven — therefore achieving higher growth and becoming more valuable — by offering a better proposition to customers, or by delivering more efficient marketing, sales, retention and operations.
The data opportunity
In the past, stitching these data streams together to form an end-to-end view of the customer would have been technically unrealistic — especially for a medium-sized business with a three to five-year window in which to maximise shareholder returns.
However, the past few years have seen a data and technology revolution. Amazon, Google and Microsoft’s cloud data platforms have made data storage and processing power quick, cheap and secure; new data transformation tools enable disparate legacy IT systems to be joined up. Data science skills are now readily available to deploy.
Data Analytics team
In response to this shift, over the past two years we have established a Data Analytics team as part of our Hg Portfolio Team. This team brings these new capabilities to our portfolio, with in-house data scientists, a suite of pre-configured cloud platforms and tools and a dedicated near-shore Romanian team to provide scale.
Unlocking the opportunity
The impact has been significant, with each of our first 10 projects delivering (or aiming for) EBITDA impact in the multiple £ millions.
Step 1 — the work typically starts by setting up what we label the ‘data platform’: cleaning and connecting all the key data sources into a single data set in the cloud to give management automated, end-to-end visibility of the business. We find the Management Information (MI) from just this first step typically adds significant value to management, and we typically roll out a series of automated KPI dashboards as part of it.
Step 2 — the next step is to deploy Machine Learning and AI techniques onto this data platform to help shape how the business is run — typically via two or three projects per portfolio company. For example:
- Reducing customer churn: in one business, a machine learning early warning system to combat loss of customers has saved more than £1 million in EBITDA. The algorithm looked at past service usage patterns, billing behaviour, combined with static factors such as the end industry and size of the customer, and the products they held.
- Increasing customer wins: we deployed similar principles to develop a predictive algorithm for a sales team to help them prioritise the large volume of leads in the funnel more effectively.
- Maximising renewal efficiency and rates: for one company, a machine-learning model is informing a team of the best day and time to call clients – and how many times to try – in order to maximise renewal rates.
Looking ahead, the Data Analytics team will continue to act as a data science ‘centre of excellence’ for our portfolio, scaling as more projects are rolled out and evolving as new technologies emerge. Our ambition is not only to drive EBITDA impact, but also to offer a truly differentiated, value-adding capability to our portfolio companies.
“The hype around Machine Learning and AIDr Amr Ellabban, Hg lead Data Scientist
makesit seem impenetrable. However at their core, they’re simply about deploying data-driven rules to improve outcomes — naturallythis technique can add value across almost any high-volume business processes.”