Elevate pre-deal strategy and financial analysis with rich data sources and advanced analytics to unlock greater value in transactions.

Current market conditions and technological disruption have made executing deals and value creation more complex than ever before. What's more, this disruption impacts buyers and sellers in different ways, making the process less clear.

To better navigate their transactions, dealmakers are turning to the latest developments in advanced analytics, which are helping them analyze previously inaccessible data faster than ever before. This helps reduce risk, empowers teams to develop more informed strategies and builds competitive advantage.

Increasing complexity can limit deal outcomes

To take advantage of these conditions, organizations should understand and address three layers of complexity with respect to the current climate for their growth strategy:

  • Pre-COVID-19 trends drive super-sized deals across several sectors: This has been underpinned by the growth of super funds, private equity (PE) and the consolidation of investment in safe havens like infrastructure, agriculture and healthcare.
  • Lower rates and new marketers emerge: During COVID-19, the global health crisis, supply-and-demand shocks and geopolitics influenced both a low interest-rate environment and the emergence of new markets and investments focused on counter-cyclical sectors.
  • Capital is ready to be deployed: Liquid and 'dry powder' made available from recent raisings for listed companies as well as exits on large PE portfolio companies via IPO or trade sale have flooded the market with capital -- and institutional investors, super funds and PE firms are ready to jump.

Against this backdrop of economic uncertainty and unprecedented market competition, dealmakers should proactively design new investment strategies to secure returns. This could include investing in new verticals to address market change or those that have been turbo-charged by the impact of the pandemic, such as drones, education technology, cyber security, in-home care, medical devices, domestic tourism, radiology and work-from-home-related businesses.

With a broader range of opportunities at stake, new imperatives to grow and a competitive deal environment, it's important to both sides of every transaction to leverage big data and advanced analytics.

Advanced analytics early on unlocks value throughout the lifecycle

Some key insights deal practitioners need to pre-empt other parties in the deal process are buried in sector signals and transaction-level data -- and they're often missed by the traditional, often manual approach to strategy and analysis. And while timing is key to get the best outcomes, it's often based on experience and instinct rather than considering the longer-term impact of evolving market conditions.

Competitive tension and data imperatives illustration

Advanced analytics and emerging technology provide new capabilities to address these new needs and can be deployed at deal speed to deliver richer insights that drive competitive advantage -- where and when you need it most. Examples include natural language processing, topic modelling, sentiment analysis, cognitive search, predictive modelling and business intelligence at scale drawn from public, owned and proprietary data.

These technologies help analysts harness big data and new types of data at new depths to curate information as required. They've revolutionized the old approach of casting a wide net for sector analysis before narrowing to a target. In the same amount of time (and sometimes less), deal practitioners can now conduct comprehensive reviews of data and potential sectors to identify opportunities for growth aligned to strategic objectives that may otherwise have been excluded from a traditional approach.

Beyond sector and target analysis, advanced analytics also help dealmakers consider both buy- and sell-side drivers in a transaction to enrich their strategy. Understanding this competitive tension is key to delivering the right deal outcomes and developing clear strategies for value creation and inorganic growth.

Clarifying deal drivers, aligns decisions with objectives

To create value, it's important to identify and deliver growth opportunities and synergies in a transaction. But this requires deal practitioners to focus on post-deal integration much earlier in the transaction lifecycle than it often is.

Mapping out where and how value can be created in the pre-deal phase helps dealmakers build competitive advantage throughout the process, pre-emptively address risk and focus integration planning and resource distribution. To do this effectively and at deal speed, get a deeper level of intelligence on deal drivers. Make sure your strategy, options and evaluations are supported by the most accurate and full view of data and asset performance possible.

When contextualized by the right sector, market and geographical insight, advanced analytics may also provide deal practitioners with the right intelligence at the right time -- despite limited information from sellers or bidders. Early in the deal process, the right data and analytics tools can help with the workflow and analysis, which includes framing ideas, investment hypotheses and anticipating likely bidders' thinking.

By better quantifying opportunities and risks, these tools can better link pre- and post-deal phases and support the initiation of integration planning and relationship building from the outset as well as best prepare you for the full due diligence process.


KPMG Consumer and Retail M&A Outlook 2021
Buy-side tools
Secure deeper insights for deal origination
Sell-side tools
Evaluate offers with data-informed view of value
Target identification using structured and unstructured data can provide a more complete view of market participants and enable benchmarking at scale and speed. Techniques like automated data collection scripts (e.g. web robots) can help acquire and analyze data points — often as proxies for financial information — that indicate the size and scope of the potential target.

Automated data collection scripts can help create data lakes and repositories of collected structured and unstructured data. Coupled with the right advanced analytics techniques, such as natural language processing, topic modelling and cognitive search, deal practitioners can analyze more data, faster, to yield deeper insights for deal origination.
Sellers can form a view on value for bid defense strategies and evaluating offers. Since this may be based on different assumptions of one or more bidders, it’s important to be able to do scenario modelling and sensitivity analysis.

The timing of a transaction plays a significant role in determining the likely interest of a buyer pool. Market conditions may change, and sellers need to take this into account (alongside the underlying financial performance of the asset) and be able to articulate their view of the future.

Predictive modelling (such as turning-point analysis) can be done using information like the product lifecycle, the maturity of the sector, market shocks and long growth arising from new channels, competition and digital disruption.