Especially in this fast-moving market, companies need a portfolio review process that makes them ready to act. Those that conduct portfolio reviews annually are twice as likely to exceed performance expectations for divesting “at the right time.”
This may be in part because more than two-thirds (69%) of companies find it a challenge to make portfolio reviews a strategic imperative, indicating the need for a more formalized approach. And many don’t regard divestments as a catalyst for growth, or want to admit “failure” in one of their business units.
Companies should start their review process with the following questions:
The answers will help companies develop their divestment road map. This gives the board and the strategy team a framework for further discussion — and action.
Companies should start by assessing their proprietary financial and operational data alongside relevant external data. This combined view supports their ability to understand current valuation, manage company growth objectives, assess the impact of various scenarios, and allocate and manage the return on capital.
Businesses that assess their portfolios to determine business units or brands to grow or divest twice a year — rather than on an opportunistic basis — are 41% likelier to achieve a sale price above expectations. They are also three times more likely to complete an exit sooner than expected.
But, with regular reviews now the norm, the future of portfolio reviews is a real-time process that captures the exponentially increasing amount of external data available to companies and their competitors. Portfolio optimization requires timely and frequent feedback through a decision analytics platform that transforms data into insight. Ideally, this “always on” approach should be results driven and include the ability to manage tactics throughout each phase of portfolio optimization.
Once a business has applied data to the agreed upon metrics, it can then take an unbiased perspective of its assets. This provides greater confidence in divestment decisions, as well as better results.
Companies that apply data-driven analytics consistently to drive decision-making are 33% likelier to exceed price expectations in their divestments.
This always-on review process should be supported by three types of analytics: performance (descriptive), applied (predictive) and decision modeling (prescriptive) analytics.
Nearly two-thirds (64%) of companies struggle to find people with the right blend of technical and analytics skills to lead a data-driven portfolio review process.
Given that a complete set of these skills is rarely found in one person, we recommend building teams with a mix of deep business knowledge, specialized functional skills (e.g., strategy, finance, marketing, supply chain) and analytics skills, including data management, modeling and visualization. With analytics skills in particular, companies will need to consider all options in finding the right talent.
Performance, or descriptive, analytics can summarize a company’s historical data to unearth critical, value-driving insights. Performance analytics enables companies to learn from past behaviors - whether around customers, cash flow, logistics or workforce - and understand how they may affect future outcomes. For example, companies can analyze historical customer buying patterns to determine product preferences, which can be used to streamline the sales cycle.
Performance analytics and visualization tools can also be applied to portfolio decisions, helping to define divestment parameters and presenting them clearly and efficiently to the board and the strategy team. Companies that use these tools are 24% likelier to achieve a sale price above expectations, and 20% likelier to complete the deal faster than expected.
Strategy teams are making greater use of applied, or predictive, analytics capabilities in their portfolio reviews.
In particular, they are using applied analytics to:
Companies with effective predictive analytics capabilities are 81% likelier to achieve a sale price that exceeds their expectations and 35% likelier to close their deals ahead of schedule.
Dynamic decision-modeling, or prescriptive, analytics can help companies determine how to optimize performance across their portfolios, by taking action on operational data outputs and feeding results back into the model.
Companies should use prescriptive analytics to understand their current portfolio’s performance and valuation, and how to best allocate and raise capital.
In our survey, more than two-thirds (69%) of sellers say they expect to make greater use of prescriptive analytics for portfolio decisions over the next two years. Those that use these analytics are 76% likelier to achieve a higher than expected price for the business being sold.
Social media is often overlooked as a vital source of data, despite its potential value to companies — especially those with a strong connection to consumers. Social media can reveal market sentiment, key stakeholder perceptions and trends that may not be evident from internal data.
Just over half (51%) of companies expect to make greater use of social media analytics in the future — more than double the number in our 2017 survey. Removing functional silos between a company’s marketing teams that may be managing social tools, and the strategy team that can benefit from access to the data, will unlock the value of social media in portfolio decisions.
A large pension fund with key investments in life sciences, retail, business services, technology and energy needed to create an automated review process to better understand industry convergence and remove management bias from historical performance.
The fund created a real-time platform that combined industry benchmarks, public domain data (e.g., news feeds), syndicated data, financial filings and a comprehensive set of financial/operational data with machine learning based algorithms.
As a result, they can predict performance of the overall portfolio and a subset of companies, drive the capital allocation process and measure return on invested capital. The fund is also able to simulate growth-related scenarios to stress test any investment thesis. By identifying underperforming parts of the portfolio earlier in the process, the fund is able to provide adequate time to resolve issues or prepare for divestment.
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