Dealmakers go on the offensive with advanced data & analytics

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Today’s M&A environment is a uniquely challenging one for acquirers.

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Record stock prices are creating high valuations, increasing risk for buyers. And there is greater competition between traditional and nontraditional bidders; both types of acquirers are sitting on record levels of cash to invest.1 Aggressive bidding wars have become the norm. As deal timelines continue to compress, bidders have limited time frames to evaluate highly sought after targets. Many acquirers are currently under tremendous pressure to justify high-multiple deals without the time to reinforce their investment case with the necessary data and conviction. Because of the increased competition, successful acquirers must take an offensive stance. Instead of pursuing deals simply because they may cut costs or offer operational improvements, companies are seeking deals that emphasize a growth thesis. Correctly and accurately identifying the sources of growth and predicting future

revenue growth (including growth in new and unfamiliar markets) is a compulsory step in the bidding game. The playbooks being used by successful acquirers are becoming more and more technologically sophisticated and data-centric. The competitive benchmark has been raised.

1For example, in May, SoftBank Group Corp. and Saudi Arabia announced the largest ever technology investment fund with over $93 billion and approaching its $100 billion target. CNN Tech, “Saudi-SoftBank tech fund nears $100 billion target,” May 21, 2017. In addition, there should be an influx of cash that is repatriated as a result of the recently passed tax legislation.

Information challenges continue to plague buyersBuyers are at an information disadvantage during the due diligence process. Sellers and their advisers control and curate data and present their version of the business narrative, which tends to camouflage the negatives. Accelerated deal time lines further limit the due diligence process and increase the probability that an acquirer may not uncover substantial deal risks or other issues that would affect the target’s long-term valuation. Despite today’s more intense deal conditions, a large percentage of acquirers have not evolved their due diligence tools and continue

to rely on traditional, manual, and defensive methods of performing due diligence. Focusing on historical earnings and using data summarized in an Excel spreadsheet is not the approach that will yield results that companies need to succeed. Acquirers need to adopt a more sophisticated, technologically advanced and data-driven approach to gain a competitive edge in the bidding process and to realize a deal’s full value post-close.

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The use of data & analytics has taken on a particular importance in M&A and when used properly can transform the due diligence process and give acquirers much more control of the most important types of information. In a recent KPMG survey, over 55 percent of respondents said that data & analytics was a critical component of their due diligence, and almost 70 percent of respondents said their companies received raw data from the target and used their own team to analyze the data. The data & analytics capability that is now available is more sophisticated and comprehensive and therefore much more valuable than prior iterations.

Data & analytics provides the competitive edge

The proliferation of data has been an ongoing trend for the last several years. Companies are generating more and more pieces of electronic information. One recent white paper estimates that by 2025, the global data sphere will grow to 163 zettabytes (i.e., a trillion gigabytes). That is ten times the 16.1 ZB of data generated in 2016. In addition, the types of data sources are also increasing as a company’s digital footprint continues to expand across the organization’s functions and value chain. For example, a company’s digital footprint can include customer service tickets, including response times; human resources data, such as retention patterns; social media chatter concerning customer satisfaction; consumer-facing CRM data; marketing performance data; user behavior data; and other transaction-related information.

100100100

Over 55 percentof respondents said that data & analytics was a critical component of their due diligence

Almost 70 percentof respondents said their companies received raw data from the target and used their own team to analyze the data

Global data sphere | 2016–2025

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In addition, external data are increasingly being combined with internal company data to create a more comprehensive picture of the target’s business and a better understanding of key deal issues. This improved triangulation from multiple sources of financial and operational performance data can provide more accurate and reliable diligence information. And large amounts of data are now easier to access and update. The proliferation of cloud storage and smart devices makes it more feasible and cost-effective to capture, store, and analyze relevant information in real time. These factors have contributed to a democratization of data and provide savvy acquirers with the ability to decrease information asymmetry during due diligence.

Case study Brand valuation

ProblemLimited conventional information available

D&A solutionTeam was able to collect data through specialty tools and social media information to get an accurate picture of brand strength, distribution channels, and sell-through metrics.

Study 1

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What should acquirers do with all of this available data? The key is to identify the most important pieces of information and to use it in a way that improves deal results. In one recent transaction, KPMG helped an investor in a fast food franchise use multiple data sources (both internal and external) to gain a much clearer understanding of the business’s future profitability. In this case, the focus was on optimizing pricing and gaining insights into customer satisfaction across multiple locations. KPMG was able to screen scrape pricing from local competitors in relevant markets and derive consumer sentiment from social media sites in the corresponding markets. KPMG then combined the pricing information with consumer sentiment to develop a predictive model to determine potential customer reaction to pricing changes. This analysis revealed opportunities to optimize pricing and increase revenue.

In another recent case, KPMG analyzed the competitive landscape for a retail chain’s geographic footprint. In addition to using company data, KPMG utilized geospatial analytics, i.e., geographic data points, as well as demographic data, to gain a deeper understanding of competitive threats and customer behavior at each of the company’s individual locations. Specifically, the company was able to determine how many competitors were operating within a 15-minute drive time trade area of all of its locations and the consumer spend using commercially available databases. The combined analyses helped the company assess the potential adverse impact at each location of a competitor’s recent move into regional markets. This in turn helped the company devise countermeasures to the new threat imposed by competitors at each location.

Competitive landscape analysis using geospatial analytics Retail chain Competitors

Geographic data points and demographic information provided a real-time understanding of the competitive retail landscape

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Analytical tools and technologies have advanced significantly

While the sources, quantity, and availability of data have multiplied, the analytical tools and technologies needed to ingest and analyze this rapid trove of information has also been quickly advancing. Analytical tools can now transform massive volumes of raw transactional data into meaningful financial and operational insights in record time.

For example, KPMG recently worked with a consumer markets company and was able to quickly collect and analyze a billion rows of data, which was simply not possible using more common and traditional methods. The deal team was able to dive deep into the company’s

Case study Deal valuation

Problem Overwhelming amount of data

D&A solution Team was able to use KPMG proprietary tools to sort and analyze key financial metrics in a very limited time frame.

various sales channels by analyzing five years of point-of-sale data for both the brick-and-mortar stores as well as the e-commerce sites. The team also coupled this POS data with supplemental data on the use of its consumer loyalty program. The combined data set revealed insights that enabled the acquirer to devise better tactics to attract and retain customers while disrupting the business to protect against the threat of nontraditional pure play e-commerce competitors. All of these computations were done in a compressed time frame and at a granular level that told a much richer story than the typical summaries provided in traditional due diligence.

Today’s analytical tools are built on more sophisticated platforms for enhanced processing power and use more sophisticated analytical techniques that provide acquirers with exactly the types of information that can help them make meaningful and more accurate projections. As part of the evolution of these new tools, there has been a shift away from traditional corporate finance professionals looking at Excel spreadsheets to a more advanced team of data scientists and technology engineers sorting and analyzing data into useful components. Deal professionals now need to be able to understand and utilize these tools, and MBA programs are currently developing dedicated courses that focus on the application of data science to real-world business problems. According to a recent KPMG study, 60 percent of respondents used accounting, finance, or strategy professionals to analyze data in M&A, and 29 percent used data analysts or business intelligence analysts.

Less sophisticated buyers use the diligence phase to create descriptive insights into a target company’s historical performance. However, those with more advanced analytical tools can use available data to create predictive analytics that can greatly enhance the diligence process and by virtue of the increased input can provide more accurate financial and operational forecasts.

Study 2

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Leading acquirers continue their diligence and intelligence gathering even after the deal is signed and executed. This approach maintains the data sets and analytical tools established during the diligence phase and continuously updates the analytical process as the merged entity’s business continues to operate and newer data are generated.

For example, a cloud software company wanted to determine how to increase subscriptions, optimize pricing, improve sales productivity, and ultimately maximize its returns from its sales and marketing spend. Through deep analysis of data on various customer touch points, including chat logs, KPMG helped the company create a multitouch attribution predictive model for determining how customers would likely react to changes in multiple marketing and customer experience levers. Instead of simply pursuing the largest customers, a predictive model that utilized machine learning enabled the company to identify and pursue the “most valuable” customers through optimal changes in marketing concentration in various customer acquisition and retention channels. These are the customers

who—on a relative basis—cost less to acquire but return more incremental revenue. Machine learning therefore enabled not only top-line growth but also substantial bottom-line improvement through the advancement of predictive models.

The analytical tools set up during diligence for the M&A evaluation should also be included in the company’s everyday business intelligence platforms and processes to retain the key insights and data sources that underpinned the deal thesis and value creation plan. Doing so enables acquirers to continuously enhance their outlook and predictions on future performance, then iterate on the preferred new course of action to achieve their deal objectives. As new insights are generated during the integration and value creation phase, new business optimization opportunities, as well as risks, will arise. Continually being exposed to and understanding important business data points gives companies the opportunity to quickly adapt to new situations and to capitalize on new opportunities.

Expanding the role of D&A beyond diligenceM&A professionals should also be aware that data & analytics is much more than a one-time-use disposable tool that is limited to the diligence stage of a deal. While the capabilities of data & analytics in the M&A context have advanced considerably in the last few years, not every deal professional is aware of the tremendous value these tools can provide to acquirers in the post-sign, integration, and value creation phases of a deal.

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Today’s data & analytics tools are evolving rapidly and have become a key component in the M&A process. Leading acquirers are using these tools to harness the value of multiple and varied data sources and the power of advanced analytics to adapt a more offensive, growth-oriented mind-set to gain deal conviction, even in time-pressured situations. In addition, leading acquirers extend the use of data & analytics from deal evaluation into the post-sign integration and post-close value creation stages.

Continuing to use and evaluate this real-time data provides continuous insights and operational improvement designed to protect against value erosion caused by changing conditions and to extract value from newly revealed opportunities. Identifying and capturing these incremental opportunities is becoming a critical component in winning in today’s increasingly hypercompetitive M&A market.

Conclusion

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How KPMG can help

For example, KPMG’s Integrated Value Delivery (IVD) solution leverages Strategic Profitability Insights (SPI), which is a proprietary software to rapidly process and analyze transaction data. IVD provides a holistic view of a business and is focused on identifying revenue growth and profitability management levers utilizing a heavily data and analytics driven approach.

KPMG has deployed IVD with most of our major clients, and improvement opportunities and/or synergies identified have typically focused on key revenue and cost elements across the value chain in the areas of pricing and discount management, trade and marketing spend ROI, operational efficiency, cost-to-serve and channel strategy. It is deployed in a very surgical manner that is enabled by the granular

level of data modeling and analytics that historically has not been feasible in a diligence environment.

The SPI platform is also used by the Financial Due Diligence team for quality of earnings procedures (QoE). This provides substantial efficiencies and synergies during the deal, and also helps ensure IVD findings are presented together with the QoE to provide acquirers with a thorough understanding of the target including both the risks and the value creation levers. Several of our clients have also deployed IVD during the sign-to-close and post-close implementation phase as well as on their existing portfolio companies/corporate business units to drive enhanced EBITDA performance.

For more information, please contact:

Nathan Saegesser Principal, Deal Advisory nsaegesser@kpmg.com 312-665-8396

Cyrus Yee Managing Director, Deal Advisory cjyee@kpmg.com 408-367-7656

KPMG has developed a broad data & analytics platform that is deeply embedded and supports all services, which are designed to help deal makers increase their M&A success rate and business functions.

Jonathan Merten Specialist Director, Analytics Deal Advisory jonathanmerten@kpmg.com 415-963-7453

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