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.