This blog is based on a webcast hosted in partnership with CAIA Association, where Eagle Alpha’s Director of Dara Strategy and Analytics, Ronan Crosson, spoke alongside Tim Kiely, Lead Data Scientist, at American Securities about using alternative data for post-acquisition value creation.
The driver for Adoption of Alternative Data
In a collaborative whitepaper with PwC titled data-driven value creation in portfolio companies, we identified 6 key drivers of alternative data adoption which apply to private equity portfolio companies.
- Technology is improving and costs are dropping
- Open-source software, cloud storage costs, monetizing data for IoT sensors is becoming an essential element of new business models
- New revenue sources
- Margin pressure getting corporates to diversify revenue streams, traditional customer acquisition methods are proving less effective than the past
- Fear of big data players
- Major data players like Facebook and Google is causing the broader market to react in order to fend off the threat
- Getting asked by others to use their data
- External entities are asking corporates if they can use their data, being a catalyst for companies wanting to monetize their data
- The value of being viewed as a data-centric firm
- Valuations of data-driven firms are higher due to the assumption of competitive advantage.
- Witnessing the success of others
- Several case studies point towards PE firms increasing the value of portfolio companies by using alt data
[You can download Eagle Alpha’s ‘Data-Driven Value Creation in Portfolio Companies’ whitepaper here.]
Building an Alternative Data Strategy
The most successful data strategy transformations come from the C-suite down. This helps significantly with buy-in but not everything is on the C-suite. The next step is to build the team and building the process that will be core to implementing a successful alternative data strategy in the firm. This includes: appointing a lead, allocating a budget, establishing an internal working group, auditing your existing process, identifying the best place to start, and knowing what success looks like and how it will be measured.
Following this, resourcing is usually the next step. Here it is important to understand whether or not outsourcing is needed if intermediaries like Eagle Alpha can benefit the alternative data strategy, and whether the investment required was over-estimated, which is usually the case.
Use Cases by Corporate Department
There are numerous departments alternative data can be used across a company’s organization including marketing, product, sales, procurement, finance, HR, M&A, and investors relations.
Sourcing Alternative Data
There are four key challenges associated with alternative data for private equity funds:
- Data Availability – less data is available on private companies in comparison to public companies.
- Contracting challenges – most vendors start out by selling to public market buyers like asset managers and are used to annual subscriptions. They are becoming more flexible.
- Data cleanliness – alt data can be messy and unstructured for PE companies as it may not be monitored as closely as public company data and not mapped to any particular taxonomy where public companies have tickers.
- Data evaluation – Unlike public companies who disclosure certain information to evaluate, private companies don’t have this access.
Some practical solutions:
- Innovative PE funds are partnering with vendors to overcome data availability and data cleanliness challenges.
- Partnering with vendors to develop a proprietary solution for PE firms.
- Triangulate multiple alternative datasets against each other in the evaluation process.
Systematizing High-Frequency Decision
A company can be considered a collection of individuals, but also a collection of decisions that are made daily. The person in the role is neither good nor bad, they will just make good nor bad decisions. If these individual decisions can be improved daily then how can this impact the overall success of the portfolio company? Using alternative data, this approach can help improve:
- Strategic decisions (high impact and low-frequency decision-making) such as M&A, product launches, re-structuring, setting organizational focus.
- Operational decisions (low impact and high-frequency decision-making) such as data to-day operations, weekly resource allocation, sales targeting, cross-selling, production out.
According to Tim Kiely, data science adds the most value at the high frequency, low impact end of the spectrum.
Use Case: Real Estate
On the webcast, Tim presented a real-world example of data analytics in action. Historically real estate agents would bring potential sites to the team, where the team would review them in an ad-hoc order with a heavy emphasis on visible characteristics. Nowadays, by systematically prioritizing all of the sites in all markets the real estate agents would receive criteria to keep them accountable for the volume and quality of their finds. This way the team has access to more information making it much faster for group decision making.
The initial question was if the team could score potential sites by being proactive and bring the team more visibility around potential revenue and success of the site. By using geospatial data, American Securities built a map of the potential sites and trained a model that estimated revenue based on the location of the potential site. By doing this the team enhanced site selection, whitespace opportunities, maximizes coverage, and allows for “what-if?” analysis.
By implementing a systematic high-frequency decision-making process, it benefits not only operations around asset market planning, profile M&A opportunities, and increased broker productivity, but also increased the enterprise value.
Developing a Project Pipeline
It’s useful to take a business case-centric approach to private equity. American Securities has a “bakers-dozen” amount of business cases that can benefit and be solved through the application of alt data and data science. These include site selection and whitespace, workforce attrition reduction, demand planning and optimization, and production output optimization. The key principles to developing a project pipeline is to 1) size up the opportunity, 2) understand how willing the management team is to undertake a project for them, and 3) look at the data.
If it takes more than 6 months to prove the worth then it’s probably going to fail. Doing analysis, building a model, building analytics, and setting up a process to take advantage of the three principles above. So this should not take more than 6-8 weeks according to Tim Kiely.
Since there is a variety of different types of private equity companies, the area in which they implement alternative data will vary. For example, a real estate firm might want to start on the sourcing side because the data assets they gather will be applicable across all their investments. The best beginning is to start with portfolio value creation and seeing what that looks like, then taking the lessons learned there and moving them upstream to the diligence process and then to the sourcing process.
[If you would like to learn more about any of the data sources mentioned or how Eagle Alpha is helping clients in the current environment then please contact us at email@example.com]