8 minute read 23 Apr 2019
stylus interacting with chart display on screen

Transaction analytics can reveal insights across the divestment life cycle

Authors

Paul Hammes

EY Global Transaction Diligence and Divestiture Advisory Services Leader

Leader in transformational global divestitures. Catalyst for profitable growth. Innovator. Value driver. Passionate about diversity in business. Husband. Father.

Malinda Gentry

EY Global Transaction Analytics Leader

Passionate about innovation. Early adopter of new and disruptive technology. Focused on helping individuals build sustainable and meaningful careers. Loves travel, scuba diving and salsa dancing.

8 minute read 23 Apr 2019

Embracing advanced analytics can help improve strategic agility, divestment decisions and business results.

According to the EY Global Corporate Divestment Study, the intent to divest remains at record levels – 84% of companies plan to divest within the next two years, consistent with last year’s record of 87%. At the same time, 63% admit they have held onto some assets for too long when they should have been divested. What steps can businesses take to improve divestment decisions, timing and follow-through?

Divestments have taken center stage as businesses across all sectors search for alternative growth and transformational strategies. Under a disciplined divestment strategy, knowing when candidates are “divestment ready” can help maximize the value of the divestiture and drive incremental value to the overall portfolio by actively monitoring the portfolio in real time. But divestment decisions – when viewed as part of portfolio optimization and not as a discrete business activity – need more than just knowing what assets to divest and when.

This is an iterative process. It requires frequent portfolio monitoring and assessment, as well as ongoing operational and performance improvements in preparation for sale, identification of potential suitors, establishing the deal perimeter, and understanding where best to reinvest the sale proceeds within the remaining business.

Transaction analytics can play a key role in each of these steps, across the entire divestment life cycle.

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Apply end-to-end analytics to build consistent value

By embedding analytics throughout the divestment process, sellers can maximize the value of the assets being sold. For example, transaction analytics can be used during the diligence and negotiation phases to create a data-driven story, increasing transparency and deal efficiency.

By building this unbiased perspective on the historical performance of a business unit, sellers can quickly identify potential red flags and risk areas ahead of sale, anticipating potential buyer concerns and articulating a unique value proposition. Incorporating transaction analytics in the pre-divestment phase also allows the seller to command a greater exit multiple to maximize transaction returns.

The application of transaction analytics extends beyond the deal and into post-divestment operational and financial decision-making. Once the transaction has been completed, certain stranded costs linger, transition service agreements (TSAs) remain to be fulfilled, and questions arise from the investor community related to RemainCo’s performance.

Just as transaction analytics enables the seller to maximize transaction proceeds pre-divestment, it can help preserve value post-divestment by identifying, managing, and prioritizing entanglements and transition costs. It can also assist RemainCo in understanding, communicating, and managing the financial impacts of the divestment during the transaction life cycle.

Developing an “always-on” data-driven approach to your portfolio

With divestments set to play a key role in growth and transformation strategies, sellers need to have an “always-on” approach – using advanced analytics to regularly assess their portfolios, support buyer negotiations to ensure successful divestment execution, and inform ongoing portfolio and RemainCo decisions.

Half of executives in our survey agree that shortcomings in their portfolio and strategic review process have sometimes resulted in a failure to achieve their intended divestment results. Such failures can cause missed divestment opportunities, lower valuations, and the failure to capitalize on potential growth opportunities elsewhere in the portfolio.

With the benefit of improved efficiency, transaction analytics supports more frequent in-depth portfolio assessment and delivers a more granular understanding of business unit performance. Companies that used portfolio review and performance assessments to determine whether or not the price being offered for an asset was reasonable were four times more likely to exceed price expectations in their divestment.

Those that embrace portfolio analytics can help improve strategic agility, divestment decisions and business results.

The always-on portfolio management and optimization process should be supported by three types of analytics: performance (descriptive); applied (predictive); and dynamic decision-modeling (prescriptive).

  • Performance (descriptive) analytics focuses on the base business and its historical performance, including strategic, financial and operational levers. The use of performance analytics allows for a more intimate knowledge of the business and greater visibility into business performance when monitored continuously. 

    In particular, performance analytics is used to:

    • Enable companies to learn from past behaviors and biases – whether around customers, products, cash flows, logistics, workforce or markets – and understand how they may affect future outcomes
    • Analyze historical customer buying patterns to determine product preferences, which can be used to streamline the sales cycle and cross- or up-sell an expanded range of products to increase customer “share of wallet”
    • Support portfolio decisions, helping to define divestment parameters and present them clearly and efficiently to the board and strategy team
  • Applied (predictive) analytics provides insights into the likely future performance of the business and optimizes decision-making – based on sector predictions and broader market factors. The use of applied analytics allows for a greater insight into future business and overall portfolio performance expectations as a result of forecasted operational improvements and re-investment scenario analyses.

    In particular, applied analytics is used to:

    • Understand the impact of various divestment scenarios in real-time
    • Identify the root cause of underperforming assets and further identify incremental investments or operational improvements ahead of the divestment decision
    • Forecast operating performance under a new buyer    
  • Dynamic decision-modeling (prescriptive) analytics helps make strategic and operational decisions based on predictive scenarios to optimize portfolio performance – including divestment decisions.

    In particular, prescriptive analytics is used to:

    • Understand the current portfolio performance and valuation, and how to best allocate and raise capital
    • Identify investments opportunities, as well as potential divestments, including where the capital raised can be reinvested in the portfolio to drive ongoing growth
    • Understand what business and operational adjustments are required in order to achieve likely outcomes

Make analytics a part of diligence and buyer negotiations

Companies need to know much more about their own businesses than potential investors. They must do their own due diligence before going to market, using at least the same level of analytics employed by investors. Transaction analytics allows the seller to be better prepared for buyer questions, increasing their ability to deliver the full transaction value potential, often in an accelerated time frame.

Many different types of analytics can be used during diligence to support buyer negotiations.

  • The top 5 ways we have seen sellers use transaction analytics in this phase are:

    1. When positioning a non-core business to potential buyers, highlight the performance of the assets to be divested based on the data to avoid management bias or lack of management knowledge.
    2. Anticipate and prepare for buyer questions before diligence and sale negotiations, which accelerates the divestment process and reduces the workload of the management team during this typically compressed time frame.
    3. Understand impacts to various deal parameters in real-time.
    4. Help position the divesting entity in the best possible light based on the anticipated investment thesis for the potential buyers.
    5. Better understand the historical performance of customers, products, markets and geographies, which are not readily discernible in the typically-provided financial information, and where the seller has chosen not to provide transaction-level information.

Market participants are also increasing their reliance on social media analytics to reveal market sentiment, key stakeholder perceptions and trends that may not be evident from internal data. For example, what are customers, suppliers and employees saying about the company’s reputation? What product or pricing strategy is generating positive feedback from customers and the media?

Companies can unlock the value of social media in portfolio decisions by removing functional silos between the strategy team and the marketing team that may be managing social tools.

According to the 2019 EY Global Corporate Divestment Study, companies are more likely to exceed expectations on divestment performance when they spend time up-front to properly capitalize and operationalize the business for potential buyers. Sellers can improve negotiations through greater transparency, using analytics to avoid learning something from the buyer about their business that they should have already known. In addition to preparing a compelling value proposition, creating an effective stakeholder communication plan and focusing on a quality management team can improve divestment speed and value.

Sharing data and analytics output

58%

of companies gave potential buyers access to their data and analytics output, and 20% say it generated more value in the sale than any other single initiative in the divestment process.

Use analytics to support separation and RemainCo value creation

Many companies have a proven track record of successfully acquiring and integrating a target’s operations into their own, but they aren’t necessarily as adept at disentangling and maximizing divestment value.

Just as transaction analytics can help maximize the transaction proceeds from a sale, they can also minimize transition costs. These costs – which include separation costs, stranded costs, and TSA costs – are difficult to quantify and manage pre-sale. Once the revenue-generating assets have been shed, transaction analytics can help identify and quantify additional savings opportunities across various functional and shared service areas (e.g., operations, human resources, procurement, etc.).

Analytics can also be used to prioritize these savings opportunities, taking into account any resource constraints, and monitor and forecast value leakage as implementation plans are put into effect.

In addition to managing entanglement and transition costs post-divestiture, transaction analytics can also help position RemainCo for future growth:

  • Pre-signing phase: transaction analytics can be leveraged in preparing and analyzing pro forma financial information for RemainCo. This enables the seller to better understand historical performance and propose corrective action ahead of the transaction close.
  • Sign-to-close phase: transaction analytics can support the effective external communication of the divestment thesis and RemainCo’s financial position to shareholders, maximizing deal value by preserving and enhancing the equity valuation of RemainCo.
  • Post-close phase: transaction analytics can be used to support the financial modeling and management of continued cash flow entanglements (NEB, stranded costs, TSAs, etc.).

Conclusion

Companies should apply the same scrutiny to their own portfolio as they would to potential acquisition targets. Advanced portfolio analytics provides sellers with an intimate and granular understanding of their business, revealing insights, potentially identifying hidden value and prompting more effective positioning.

Sellers should leverage analytics to offer buyers critical insights, so they can quickly understand the business model, performance, and long-term growth opportunities.

The use of analytics during the divestment process can attract buyers who are demanding greater access to data to support detailed bottom-up modeling, with sellers able to add significant value to the divestment negotiations by providing customer, supplier, operational, workforce, commercial and market data.

Summary

The EY Global Corporate Divestment Study focuses on how companies should approach portfolio strategy, improve divestment execution and future-proof their remaining business.

About this article

Authors

Paul Hammes

EY Global Transaction Diligence and Divestiture Advisory Services Leader

Leader in transformational global divestitures. Catalyst for profitable growth. Innovator. Value driver. Passionate about diversity in business. Husband. Father.

Malinda Gentry

EY Global Transaction Analytics Leader

Passionate about innovation. Early adopter of new and disruptive technology. Focused on helping individuals build sustainable and meaningful careers. Loves travel, scuba diving and salsa dancing.