April 26, 2017
With the emergence of sophisticated digital platforms, companies today are collecting granular transactional information on their customers, suppliers and operations. When harnessed and analyzed properly, these large pools of data can improve margins and fuel growth, and they’re fundamental to how companies constantly make better decisions to help deliver higher shareholder returns.
Many executives know that deep insights from data and advanced analytics can help make their companies more efficient and effective. They also recognize that the right deal can lift profits and drive growth. And if you can combine the two, you’ve hit the bull’s eye.
Unfortunately, most companies are only scratching the surface when it comes to using data and analytics in deals. For those businesses, data and analytics often play a minor, supporting role – usually to validate their underlying investment thesis or confirm their decision to walk away.
With divestitures, this tendency is a missed opportunity. Instead of being an isolated transaction disconnected from the overall enterprise, a divestiture should be aligned with the broader corporate strategy. Doing so can make the divestiture a transformative event, as well as a model for future deals.
The key is to harness insights generated from data. Useful information is often there but just not uniform or consistent across an enterprise. By using advanced analysis, companies can connect disjointed functional groups and provide a consistent and comprehensive view to multiple stakeholders. That not only improves the speed of a divestiture, but can make the deal more efficient, boosting its value.
Segregated data and reactive analytics
Companies frequently use internal data during deals to analyze customers and markets, synergies and sizing, workforce and compensation, contracts, and other factors. But these analytics often are done in succession and in a fragmented manner instead of simultaneously, due mainly to the need to manually retrieve and review data from separate silos.
Consider these comments from PwC’s Global Data and Analytics Survey 2016. “The current approach is to use them on a marginal basis,” one respondent said. “We tend to be reactive in nature and use data to determine why something happened vs. using it to make proactive decisions.” Another was more blunt: “The use of data is confined to very small areas within the corporation and not widely used.”
This segregated approach to data in deals doesn’t align with the corporate strategies of many companies. Recent years have brought more focus on connecting different businesses within an enterprise and creating a holistic view. This is partly in response to shareholders who want a clearer picture of company strategy and risk beyond the financial reports. In other cases, executives use integrated reporting to better articulate strategy and make more informed decisions.
Assessing the entire portfolio
When divestitures aren’t aligned with the overall corporate strategy, each deal is like starting your car in freezing weather: It takes time to heat up and move forward. Data and analytics can keep the engine warm, allowing the car to idle until it’s time to accelerate on a carve-out, spin-off or other divestiture.
To start, company leaders should assess their entire portfolio and determine (a) what parts of the company are most important strategically and (b) what parts are performing well relative to the industry. Even if your company isn’t actively considering a deal, this assessment could encourage a reorganization that reveals a potential divestiture opportunity.
Reviewing and ranking businesses on a portfolio map allows management to understand the competitive position for all businesses and rank the shareholder value impact of actions for each business. Supplemental businesses that don’t align with core strategy may deserve a closer look. Spinning off or selling a top performer could deliver a significant capital boost, while divesting a poor performer – even if it doesn’t yield much value – could remove the cost of keeping that business.
In this portfolio map example, parts of the company that are in the groups on the left side don’t support the company’s core capabilities and could be candidates for a divestiture.
Data analytics reveal facts, remove biases
Once a divestiture opportunity has emerged, data and analytics can help with the different stages of deal – and execute some stages at the same time. Tapping the massive amounts of data now available to many companies can result in a bottom-up assessment in the same amount of time that it takes to do a high-level assessment.
For instance, talent optimization leverages the wealth of human resources data available during a separation, while a customer propensity analysis can help identify the risk of customer churn in a divestiture. In industries such as manufacturing and distribution, there may be opportunities for supply chain optimization. With financial reporting, a data-driven carve-out model can automate large parts of the process, significantly reducing the preparation time and risk of missing SEC deadlines.
Analyzing external data also can better inform a divestiture and boost value. For consumer and retail companies, market sentiment around brands is critical and can be determined through analysis of customer reviews, product launches and other factors. Talent decisions can benefit from analyzing job openings, skills in demand and other benchmarks within a particular sector.
These possibilities cut across the enterprise and the market, pulling together different stakeholders to mitigate delays. They also help remove biases that could influence a deal, instead enabling fact-based decisions that increase confidence in the divestiture.
Deeper insights in less time = better decisions
Consider companies that have leveraged large amounts of data to help execute deals. One major pharmaceutical company created a real-time deal intelligence platform in which IT systems provided business unit financial data and operating data at a granular level. That allowed senior managers to decide which business to sell, keep or spin off.
In another case, a multinational IT company pulled and parsed massive amounts of internal data for a strategic portfolio assessment. By increasing visibility into the financials of each business unit and overall operations, the company was able to complete multiple restructurings and strategic divestitures. Along with unlocking value, the transactions created more focused companies that could better respond to competitors and shifting markets.
Creating a thoughtful data strategy clearly gives companies more options for reorganization and growth. Building an interconnected web of analytics that provides deeper insights in less time can help businesses make the right decisions at each stage of a divestiture. That not only can enhance the value of the specific deal but also can become a repeatable and scalable capability, allowing a company to get deals done more quickly and efficiently and deliver better returns. This especially could benefit companies considering a series of divestitures.
Imagine being able to minimize decisions based only on intuition and experience. When you dive into different pools of data during a divestiture, you can ground your strategy in facts and figures. From the initial portfolio assessment to separation, data-driven insights can be invaluable in making crucial decisions on the divestiture at hand and preserving shareholder value in future deals.
For more on trends driving divestitures and other deals activity, please visit our PwC Divestitures page.