Dealmakers increasingly rely on analytics for M&A decision-making and market insights. Many are using cutting-edge tools to learn more about acquisition targets, negotiate deal terms and smooth out the integration process. Despite the growing use of and interest in analytics, some organizations are holding back because of concerns about analytics and the dealmaking process. In a Deloitte survey of corporate executives, 41 percent of the respondents reported using data analytics to analyze deals, and 17 percent were considering the use of analytics. Investors view such analysis as a critical due diligence activity for addressing deal uncertainty. Here are five popular myths about M&A analytics, along with some observations that should help allay the fears of dealmakers and bolster their confidence to pursue the benefits of analytics. (For the video, see below or click here.)
Myth No. 1:
The M&A timeframe is too short to use analytics effectively.
World-class analytics users avoid drowning in a deluge of data by focusing on what's important and not casting too wide a net. They have a very specific question, value proposition or objective in mind, and they focus only on what matters, avoiding what doesn't. In this way, they use analytics to determine the keys to deal success and then bake that into the transaction effort.
Analysis tools assist with rapid data mining, and visualization tools help deliver clear, intuitive and instantaneous results. Increasingly, technology can help resolve some of the tensions between time and accuracy. For example, most survey respondents - nearly 60 percent - cited revenue growth as the most difficult element of business case forecasting to predict. Powerful new analytics software, coupled with a wider variety of Internet-derived customer inputs, can help deal professionals quickly get a better handle on the revenue components of a target and create more confidence in their forecasts.
Myth No. 2:
Sellers can't deliver data in a timely manner.
More often than not, sellers try to fulfill targeted requests for data. Sellers want to realize maximum value for the business being sold, and data that provides increased certainty and confidence to buyers can result in a higher purchase price, making it a win-win proposition. Of course, there are competitive reasons why this may not be possible, but more often than not there are ways to provide greater information transparency that is in the best interests of both buyers and sellers.
Myth No. 3:
Analytics are too complicated.
Meaningful analysis in the deal space is becoming simpler and easier, thanks to recent technology advances such as user-friendly tools for deal analysis. An advanced degree is no longer required to hone in on important details.
To further simplify the process, companies are developing forecast playbooks that establish the game plan, and frame the depth and focus of the analysis, up front. A great source of information for these efforts is data collected from deals previously conducted in the target segment.
Uncovering hidden insights, including insights from unstructured data such as social media, provides a competitive advantage. About one-third of the survey respondents are already using social media channels as part of their M&A activities.
For example, cloud computing leader Salesforce.com uses social media analytics in its M&A process to monitor what people are saying about potential acquisition targets, as well as to assess reactions to acquisition announcements. Many dealmakers use LinkedIn to gather information on the history of key executives and data about employees. The online career community Glassdoor provides insights into the culture of a prospective target through the unvarnished views of its employees. Although there may be concerns about the veracity of information from social media, it provides another useful directional data point to consider as part of the overall due diligence inquiry into a target.
Myth No. 4:
Analytics are a shot in the dark.
A common error is arming people with long due-diligence lists. Those lists end up as check-the-box exercises and don't translate into practical deal insight or advice. Analytics can be more useful in understanding a particular deal factor and translating insights into practical deal direction. Methodologies, tools and techniques have evolved to allow targeted searches for specific data to support an investment thesis.
In particular, predictive analytics, which help organizations use existing data to help predict future behavior, is tailor-made for M&A, especially when the needed information is available in an analyzable format and can be analyzed within the deal timeframe.
Dealmakers may be underutilizing these tools. Seventy percent of survey respondents use just one method of uncertainty analysis, while only 11 percent use three or more approaches. Many companies are using either sensitivity analysis, varying one factor at a time, or scenario analysis, varying multiple factors at a time, to measure the effect of uncertainty on their business cases. Just 8 percent apply more dynamic uncertainty modeling approaches, such as the Monte Carlo simulation, that show the range of possible outcomes and their respective probabilities when random input variables are altered.
The idea isn't to apply every conceivable approach to analyzing uncertainty. Rather, we now have tools available to analyze and manage uncertainty dynamically to better understand the probability of realizing a desired outcome. Choosing the right tool at the right time can be very valuable to the deal-making process.
Myth No. 5:
Analytics are too costly and don't provide enough value to be worthwhile.
Insights that contribute to valuation and pricing precision are the holy grail of data analytics. But analytics can also uncover other patterns in the millions, billions or even trillions of rows and columns of information available on companies under consideration. While not necessarily influencing the deal's critical path, these revelations can help identify future conditions and events with the potential for long-term impact. A key to capturing the value of this data is simply being able to consume, organize and distill the key insights from it. Visualization tools leverage the ability of humans to recognize patterns and pictures as much as 60,000 times faster than comprehending text.
It's rare, if ever, that the outcome of a deal is predicted precisely, but dealmakers should remove as much uncertainty as possible from the decision-making process. Data analytics can provide confidence by helping dealmakers understand the target's profile and what will be controlled upon closing. Predictive analytics can reveal what factors could create a competitive advantage for an acquirer, such as competitive revenue models or product pricing, product configurations, supply chain efficiencies and other factors. Analytics can also show what can and cannot be controlled about that change. By providing insights that give dealmakers more confidence in their decisions, analytics can help unleash additional M&A activity in the current deal market.
David Williams is the CEO of Deloitte Financial Advisory Services, and Chris Ruggeri is the M&A services leader of Deloitte Transactions and Business Analytics LLP.