Predictive analytics are the next frontier for many private equity and investment banking firms. During the pre-transaction process of completing valuations, both private equity deal teams and investment bankers are likely to compare the prospective M&A deal at play to similar deals that have entered the market in the past. One aspect of this “relative valuation” or “comparable companies search” practice is to generate an estimated size of the deal by analyzing the attributes of the current offering against data from completed or similar transactions. This data can come from either proprietary or external sources and is analyzed in a variety of methods. Dealmakers and bankers also look at similar deals completed by other sponsors who are executing a similar strategy or specialize in the same industry, geography, or vertical.
DealCloud helps facilitate these exercises by centralizing unorganized data and transforming it into meaningful intelligence with easy-to-read, interactive dashboards and automated reports. Users can elect to receive notifications and automated reports with predictive insights by email, web, and mobile. Notifications can be scheduled, such as weekly pipeline reports, or triggered by certain activities within the platform, such as the creation of a new deal or logged note.
Without an institutional deal management system, the valuation process and many other connected processes can become overly tedious, especially as deal professionals sift through disconnected spreadsheets and third-party data providers across various stratified platforms. Below, we provide a guide for enabling the powerful predictive analytics capabilities of DealCloud at your firm.
Why are predictive analytics important?
We are at a turning point in the maturity of private capital markets where firms can no longer lead the way without adopting technologies that both maintain and optimize the data created internally and in the market. Not only is the amount of data produced exhibiting no signs of slowing, but governments and other regulatory bodies are adopting policies and practices to better address the amount of data that is being tracked and shared internally and externally. Firms that understand the information and data they collect and own are better equipped to make more confident decisions. Early adopters of predictive technologies and business intelligence solutions will foster clear competitive advantages over those who do not. Late adopters risk not only sustaining competitive disadvantages but also facing regulatory pressures.
How do we do it?
DealCloud’s DataCortex allows clients to supplement and enhance their proprietary data with third-party data which they either purchase or subscribe to. Clients leveraging DataCortex can seamlessly gain insight into useful metrics such as deal size without having to export data to Excel, merge with existing data and perform intensive sorting, filtering, and logic statements for each new deal being evaluated. Through pre-defined parameters in DealCloud, originators can create a newly sourced deal in-platform, on the mobile app, or through the Outlook Add-In, and instantly see predictive analytics based on similar attributes the deal shares with historical transactions. These attributes might include industry, sector, revenue, revenue growth, EBITDA, EBITDA growth, employee count, deal type (e.g., platform vs. add-on), and much more. DealCloud assigns a score based on the similarity of the attributes between the deal in diligence and the historical market transactions. Deal teams can control the accuracy of the forecasts by applying weights to the importance of attributes in the predictive valuation model (Fig. 2).
Another popular value-add of DealCloud’s predictive capability is for investment bankers to view suggested buyers for an engagement based on several factors such as industry, sector, and deal size preference. Coupled with powerful market data, investment bankers can create a potential or live engagement in the platform and have DealCloud’s technology scan through recent market transactions, as well as the firm’s proprietary data, to suggest the best buyers (Fig. 3). As the market-leading relationship management system, DealCloud centralizes buyer preferences across the investment bank’s proprietary network, elevates it with market data providers such as Preqin and Pitchbook through DataCortex, and assigns suggested scores based on the correspondence of the buyer preference and client attribute. Bankers can influence the list of suggested buyers by applying greater importance to buyer characteristics such as available dry powder, deal type preference, and strength of the relationship with the firm, for example.
Conclusion
DealCloud’s DataCortex solution coupled with DealCloud’s powerful calculation engine can enrich your firm’s decision-making process and operational efficiency. As a single source of truth for proprietary and third-party information, DealCloud uses historical data to predict outcomes and identify market developments. Whether you’re a fund of funds manager looking to identify a comparable yield for a commingled fund, or a placement agent developing a list of potential Limited Partners to reach out to for a client’s new fund, our deal management and CRM platform utilizes internal and marketplace data to suggests relationships to leverage and forecasts the metrics that are relevant to your business.
To learn more about how DealCloud can implement business intelligent decision making for your firm, contact us at sales@dealcloud.com