Analytics trends in diligence and beyond

Are technology company deal teams leveraging the best insights?

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As private equity (PE) investors and strategic buyers continue their pursuit of M&A opportunities in the highly competitive technology sector, finding the right target and acting quickly are more critical than ever. Acquirers tell us they are using the latest data analytics to assess potential targets and outperform their competition. However, in many cases “analytics” is merely a buzzword, and we find practitioners barely scratching the surface of available capabilities — or worse, not doing anything fundamentally different than before.

Data analytics, when employed correctly as an integral part of business strategy, can strengthen the diligence process and transform the way buyers manage the transaction after closing. In this article, we aim to define best practice in the use of analytics on the buy side, via a case study of an EY client looking to expand their Software as a Service (SaaS) footprint. The Buyer, an enterprise software company, was considering the acquisition of a SaaS-driven productivity business (the Target).

  • Initial due diligence (two to three weeks): achieving speed and certainty

    The Buyer had several key priorities during the initial due diligence phase, including validating the deal rationale and strategic review of the transaction. Since this phase of the transaction is generally only allocated a few weeks, the analytics tools employed had to deliver efficiently and rapidly, with particular focus on descriptive data analytics and visualization.

    A full set of analytics adds dimension to a deal team’s diligence capabilities: combining and manipulating disparate data sets across multiple business units and functions, in a rapid and repeatable fashion; diving into detailed financial and operational metrics, including transaction-level details (such as a detailed look into top customers at the level of a single product purchase); and testing critical hypotheses in days rather than weeks.

    In this case, the analytics EY provided the Buyer resulted in a deeper and more prompt understanding of opportunities and risks within the Target’s business, or when combined with the Buyer’s business. The additional speed enabled testing of more hypotheses during the initial diligence phase to better identify and mitigate potential risks.

    Accordingly, the Target provided transaction-level user engagement data, including data points such as each customer’s company, signup date, number of sign-ins and number of activities performed. Armed with this data, we were able to drill down and analyze (i) the percentage of monthly active users (MAUs) and daily active users (DAUs) segmented by key use cases, usage and frequency; and (ii) user cohorts based on sign-up dates, allowing the client to understand user trends and analyze differences between user cohort or sign-up dates.

    For example, we were able to identify individuals who signed up for the app and used it only once a month, as well as the proportion of users who signed up once and never returned to the app. This allowed the buyer to understand the risks in the app they were looking to purchase and to have a stronger negotiating position.

    Assembling key decision-makers across a buyer organization is difficult enough without losing time while analysts prepare additional data cuts — a scheduling nightmare. Using a full set of analytics with this Buyer, our team hosted a single management briefing with key stakeholders that provided a dynamic, real-time review of the findings complete with deep dives; double-clicks provided access to underlying data, something that would not be available with a static presentation. No subsequent analyses, or arduous scheduling of follow-up meetings, were required.

    Analytics made it possible for meeting attendees to immediately request different data views, such as geographic region or cohort as well as underlying, individual data points. The analyses were presented in easy-to-use visualization packages (via such tools as Spotfire, Tableau or Power BI), allowing management to interact with the data and focus on the most important trends. In the case of our app usage example, a decline in DAUs among the most recent sign-up cohort was highlighted as an area of concern for the future quality of earnings.

  • Sign to close (three to six months): realizing the potential for value creation

    Typically, at the diligence stage, limited data is available. However, after a successful bid and the signing of a purchase or merger agreement, a wealth of data becomes available, as well as more time to dive into the data set and, even more valuably, incorporate other information sources such as the buyer’s own data.

    While in some cases regulatory challenges (such as the Hart–Scott–Rodino Act of 1976) may limit obtainment or sharing of additional data from a target or buyer, this was not a factor in our example. At this stage, more advanced analytics can be brought to bear, with tools designed to offer predictive analytics and key driver identification.

    Depending on the rationale and deal thesis, at this stage, factors that can be explored in greater depth include buyer or third-party data that validate the potential for synergies and dyssynergies. Deal teams can also spend additional time refining prior analyses, addressing and refining any questions raised by the target’s data.

    In the case of our Buyer, the main focus area related to one of the key deal rationales: synergies related to cross-sale opportunities. Our analysis identified groups of the Buyer’s current customers who were likely to purchase and use the Target’s app, based on demographics and usage indicators (e.g., a midsize engineering company located in Ohio). It measured the expected “uptake” — the probability of an organization purchasing the app.

    This analysis allowed the Buyer to cost-effectively target the most promising customers in a tailored marketing campaign, rather than through costly and generic mass marketing. And it set up the combined organization to realize this particular synergy successfully, with a minimal spend. Performing these analyses before deal close allowed for risk mitigation and earlier synergy realization, positioning the buyer for faster value capture after close.

  • Post-close: hitting the ground running

    Once a deal closes, a significant amount of data becomes available from the combined organization that could not be accessed before. Teams can now revisit previously deployed tools, incorporating data that was harder to process (natural language data, for example) or that was external to the organization (such as third-party benchmarking reports).

    This more robust data enable prescriptive analytics, influencing the drivers that lead to desired deal outcomes. In the case of our client, we had previously identified expected uptake in the Target’s app among the Buyer’s existing customers.

    At this point, the Buyer was ready to deploy a custom marketing campaign — using prescriptive analytics — to reach those specific customers. When we reached the post-close stage, additional analysis of historical app offerings uncovered that when new apps were embedded into an existing product suite, they showed significantly higher uptake rates than standalone offerings. As a result, the Buyer made a new plan to incorporate the Target’s app within its existing suite of software on a free-trial basis for certain customer segments.

    After the immediate post-close phase, a buyer can realize significant value by leveraging long-term analytics products. For starters, all analyses prepared during the deal’s early stages should be incorporated into ongoing operations. Dynamic dashboards and visualization tools can track the success of synergy capture, support reporting efforts, and identify new value-creation opportunities over time. For example, the marketing department should have access to customer behavior data and periodically rerun predictive analytics to keep an eye on potential shifts.



In summary

The key for buyers is to deploy a full spectrum of analytics capabilities and view them as an integral part of business strategy, including ongoing use of dashboard tools. This empowers a company to identify additional opportunities or risks and use these tools to track the success of the combined organization over time.