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).