Why is data science transformation so important for Private Equity?

Given the imperative for private equity firms to steer portfolio businesses to optimal value during a typical three-to-five-year ownership window, data science transformation has become increasingly necessary in recent years. It is one of the most sustainable, impactful ways to accelerate portfolio value creation. Data Science Transformation is one of the most sustainable, impactful ways to accelerate portfolio value creation. It enables them to make smarter, quicker decisions more often and at scale, outperform the market, and so grow quickly and also sustainably. Indeed, data science transformation is today one of the leading routes to optimizing value and enhancing ROI. The sweetspot for private equity portfolio businesses is often in the €100m-€1bn, mid-sized company range. This is precisely the size of business which – with a few, notable exceptions – has yet to fully capitalize on the potential for data science transformation. By starting on the journey towards data science maturity, portfolio companies make themselves more competitive and drive lifts in their valuation multiples. This is because, among other factors, in so doing they enhance their ability to make smarter decisions more quickly (through business intelligence and analytics), and to delight their customers with streamlined experiences (responsive products). Indeed, even before a business has fully implemented its data science vision, the act of starting on the journey can already have a positive impact. Simply creating a clear roadmap that tells the story and concretely lays out the steps to such a transformation can change both how a company itself is perceived and, in time, how it is valued.Discover three examples of Ekimetrics’ work with mid-sized private equity portfolio businesses. Each one demonstrates the immediate results that data science transformation can bring to operational efficiency, profitability, and portfolio value.Case study 1: Improving efficiency in identifying carersFinding suitable, long-term carers for children whose birth parents cannot care for them is challenging – to say the least. For national and local government care services, it can be like finding the proverbial needle in a haystack. Not only are there more children in the care system than carers prepared to volunteer. Of those who do put themselves forward, fewer than one percent end up fulfilling that role, following a stringent series of up to ten different checks, reviews, and interviews. As a result, government care services have traditionally had to invest very heavily at the top of the funnel – with digital advertising, literature, in-person events and so on – to source a pool from which fewer than one in 100 will be successful. Three mini, private equity portfolio company case studies We worked with a leading social care company – itself a private equity portfolio business and the recipient of a government contract in a major, developed economy – to improve the efficiency and radically cut the cost of recruiting carers. This involved a data science transformation project which enabled the business to identify the traits and characteristics of successful carers, and so drive efficiencies throughout the recruitment process. In this way, we dramatically improved ROI.Case study 2: Automating a complex supplier ecosystem Creating products that combine component parts from multiple different suppliers demands that manufacturers keep a close eye on a bewildering array of variables, factors, and contracts simultaneously. With sufficiently comprehensive, accurate, and timely analysis, manufacturers have the opportunity to control and maintain profitability by managing input costs. Yet many businesses try – and fail – to do this manually, an approach especially badly suited to today’s global supply chain, with significant uncertainty and galloping inflation for raw materials. Working with a consumer goods business, we ran a data science transformation program that automated the work of a five-strong team into a comprehensive, near-real-time, Enterprise Resource Planning dashboard. By integrating and automating the pipeline data right across the company’s supply chain, we created an on-demand, self-service platform that enabled it to make the data useable, keep track of all cost inputs as prices fluctuate, and maintain profitability.Case study 3: Breaking down silos to bring data together into a single platform Ekimetrics enabled a leading chain of high street opticians to serve its customers better across all channels by designing and introducing cloud-based, datalake architecture that brought together all of its data from all departments and functions. By mirroring the company’s physical business environment, we transformed its customer experience. But rather than try to change everything at once, we added new data use cases at three-monthly intervals through a bespoke, ‘data as a service’ platform. The platform was straightforward for everyone in the business – including those who were not data-savvy or digitally native – to adopt with ease. New use cases integrated seamlessly with existing operational systems such as Salesforce. The datalake architecture was deployed on Microsoft Azure, with data processing delivered via Databricks and Py-Spark, and reporting using PowerBI. The success of this transformation project stemmed from Ekimetrics converting our deep understanding of our client’s company strategy into a phased, operational roadmap. that enabled it to make the data useable, keep track of all cost inputs as prices fluctuate, and maintain profitability.Structuring and interpreting data to generate insights to enhance performance and ROI In each of these three examples, we demonstrated the value for a mid-sized business in structuring and interpreting data in the right way to generate insights to enhance performance and ROI. In each of these examples, there were immediate benefits which had a flywheel effect, propelling these private equity portfolio companies on the path to wholesale data science transformation. In this way, we enabled their parent private equity firms to accelerate portfolio value creation.Enhancing value in invested companies through data science transformation can take many forms, covering capabilities, sales and marketing effectiveness, and operational excellence. This includes: stock optimization, pricing and promotional strategy, store location optimization, omnichannel sales and marketing strategy, logistics delivery optimization, demand forecasting, customer engagement, marketing mix optimization, and client churn detection and cross sell (see below).

Data science transformation for private equity firms’ own operationsPrivate equity firms are typically lean, nimble operations. Although the companies they own may have tens of thousands of employees, private equity funds themselves tend to be orders of magnitude smaller. Nevertheless, the application of data science also offers opportunities for private equity firms, too, most notably in deal sourcing – assessing and analyzing the essential characteristics of mid-sized businesses to identify, quickly and reliably, potential future targets. As in portfolio businesses, data science has the ability to transform decision-making, making it better, quicker, and more reliable. While the flames are yet to catch across the industry, private equity firms cannot avoid the maturity curve in the long term. Those who have successfully applied the principles of data science transformation to portfolio businesses are in the vanguard of applying it to their own businesses. Those at the forefront reap the biggest rewards, and in deal sourcing they will make it quicker and more straightforward to find the right businesses in which to invest. Private equity funds enjoy an edge if they are able to identify potential targets earlier than their competitors or that their competitors haven’t considered. There can be significant benefits to speed and proprietary knowledge in deal-making, and data science can help to deliver this advantage.Learn more about Data Science Transformation with Ekimetrics

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