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Realizing value within portfolio firms

Realizing value within portfolio firms

Once a deal has closed, PE firms are challenged to take steps to quickly increase asset value. Data science can play an important role in driving performance improvements within an acquired company or across a PE firm’s investment portfolio over time. PE firms can use data science to establish, measure, and monitor relevant, specific, and meaningful KPIs consistently over time so they can more readily assess and enhance their asset valuations. Doing this successfully can drive enhanced revenue and an improved cost structure.

Revenue enhancement

Once a deal is completed, data science should play an essential role post deal in helping PE firms enhance revenue associated with an acquired company or across their portfolio. Using machine learning, models can be built and refined for improved market strategies.

Examples of revenue lever in action

Marketing and sales investment: Using data science and advanced analytical methods to quantify ROI is not a new concept. These methods have long been used by conventional enterprises, whose timelines are longer for conducting such analyses. However, with today’s technology and tools, PE firms now have the ability to conduct similar analyses in much shorter timeframes. Analysis of elements such as price elasticity, marketing mix/effectiveness, and sales decomposition can quickly answer questions about growth and profitability with more precision and certainty.

Example: Improving revenue via data science through an optimized pricing and upgrade strategy

Challenge–A PE firm wanted to assess the pricing and upgrade strategy of a hotel chain within its portfolio. The company, with a series of locations in Europe, provided a limited number of hotel room format offerings. One higher-end room format had flat sales over the previous 18-month period, and the PE firm wanted to understand how it could both sell more higher-end rooms and realize a higher price premium for higher-end rooms over standard rooms.

Actions–Compiled diverse sources of information, such as CRM booking data, internal guest survey data, and external market research data

  • Conducted specialized analytics using the mix of internal and external data, including customer segmentation using statistical clustering to calculate core customers, calculation of total revenue lift by increasing daily rates for specific customer segments, ranking of desired higher-end room benefits based
    on importance and feasibility, and identification of priority locations to achieve the quickest return.

Result–The data science–driven analysis allowed the company to fully understand the nuances in its customer base and target the right customers to optimize revenue through its pricing and upgrade strategy.

Cost optimization

In order to enhance the valuation of an acquired company or across companies within their portfolio, many PE firms focus significantly on cost optimization. Data science can play a critical role in helping PE firms understand opportunities to optimize costs and improve efficiencies.

Example of cost lever in action

Supply chain management: Using data science, PE firms can better manage the growing complexities associated with supply chain management, such as real-time inventories. Driver-based forecasting, using simulation and optimization algorithms can effectively capture shifting customer preferences and likely shifts in customer demand so management can make adjustments more rapidly, identify exceptions, and divert products in real-time, if necessary. This can, in turn, reduce inventory costs, increase efficiency, and generate other tangible benefits.

Example: Driving better pipeline performance in a portfolio company through data science

Challenge–A PE firm needed to improve cash flow, working capital, and revenue forecasts for a carveout manufacturing business. Delivery of order backlog was often delayed with little advance warning to management. At the same time, the sales force was undisciplined in pipeline management, leading to unpredictability in the amount of incoming orders.

Result–The firm established clear action steps to achieve a more capital-efficient and operationally effective company, enabling a reduction of at least $25 million in net working capital. Management was also able to identify the drivers of won/lost/delayed sales opportunities—leading to a 30 percent improvement in forecast for new orders.

Actions–Using data science and analytics, the PE firm assessed backlog delivery, using product-level trends, to identify drivers of past-due backlog and areas of risk in the company’s manufacturing and planning process.

  • Data science was applied to the historical pipeline trends and external data to build a machine learning engine that predicts pipeline results.

In order to enhance the valuation of an acquired company or across companies within their portfolio, many PE firms focus significantly on cost optimization. Data science can play a critical role in helping PE firms understand opportunities to optimize costs and improve efficiencies.

View the Global report or visit the KPMG US website to read the original publication.

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