Private equity deal-making is all about staying one step ahead. With the proliferation of data now available online, there is a vast amount of information available to deal teams in evaluating companies before they even get their hands on any confidential, company specific materials. This can present a huge opportunity for those who know how to harness it effectively. We’ve seen web-scraping become a valuable source of advantage in the deal process.
In our experience at Coppett Hill, an increasing number of private equity firms have developed in-house tools and skills in using web-scraping to inform origination activities. However, firms are often not making use of publicly available data in assessing companies during the deal process, e.g. in assessing search marketing headroom, the competitive landscape, and customer sentiment.
What exactly is web scraping?
Web scraping, also known as web harvesting or web data extraction, involves using automated tools to collect information from websites. Web scraping tools navigate through web pages, extract relevant data, and store it in a structured format such as spreadsheets or databases.
More and more company data is now available publicly online, such as products, services, prices, employees, job postings, locations and customer reviews. For private equity teams, web scraping can offer a faster and data-driven avenue to assess markets, competitors, and target companies – gaining that all important speed advantage in a competitive auction process.
Turning masses of data into insight...
In addition to vast amounts of data, we now have access to powerful tools to process and augment this ocean of data. Many of us remember the manual data cleaning era of our early consulting days, spending hours (or even worse, or requiring outsourced teams to spend painstaking days) matching differing data types, ‘tagging’ data and aligning formats across sources. Modern models in Computer Vision and Natural Language Processing (NLP) mean data can be much more easily standardised, augmented and matched.
Interpreting these huge datasets can also now be done much more efficiently, with the right know-how. Visualisation tools, such as Tableau and PowerBI, are able to receive large datasets and can be easily interrogated to drill down into key commercial points. For example, if you are comparing Google Review performance across a portfolio of locations, this can be easily displayed on a dashboard to show over- and under-performers, whilst also filtering for factors such as a minimum number of reviews, or filtering on locations by geography, tenure, etc.
Practical Applications of Web Scraping
Web scraping can be valuable across multiple facets of due diligence and corporate strategy. Especially in a deal process, the key is prioritisation, homing in on the key investment hypotheses that are driving the value creation plan and finding the right data to inform those. Here are some examples of where it can be used:
Reach and online presence: There is rich market and individual company level data in Google Search and third-party tools which can help to understand organic and paid search presence by keyword and geography. This can inform both market headroom and performance versus competitors.
Pricing and product analysis: By gathering data from competitor websites, deal teams can benchmark pricing strategies, compare product offerings, and analyse inventory overlap.
Sentiment: Web scraping tools can gather customer reviews, ratings and engagement on platforms like Google Reviews, Meta, G2/Capterra. This can then be parsed for themes using NLP models. This provides a real-time understanding of brand reputation and customer satisfaction, helping deal teams assess market sentiment toward a target company.
Talent and organisational insights: Platforms such as LinkedIn and Glassdoor contain data on team structures, hiring trends, employee turnover, and organisational focus (e.g. which teams or which locations are hiring). This information can provide early indicators of strategic priorities or potential challenges in talent management.
Geographic and operational footprints: Web scraping tools can analyse Google maps data such as locations, store performance, opening hours, and customer traffic patterns, enabling deal teams to evaluate a target’s regional presence and growth potential.
Web scraping in action – practical examples of how teams can use data extraction for strategic insights
Making practical use of web scraping requires more than just technical skills. In our experience supporting clients with this work, there are four key steps and things to think about at each stage:
Data collection: Selecting the best source online, with trusted high-quality data (remember – Garbage in, Garbage Out). Then choosing, or building, a custom web scraping tool relevant to that data source.
Data cleaning and processing: Using AI-driven techniques, you can process unstructured data to make it ready for analysis. This includes removing duplicates, normalising text, and categorising sentiment or keywords.
Insight generation: Interpreting the data, which requires contextualising it within the company's industry and focussing on the priority hypotheses or strategic objectives. In the time pressured context of a private equity deal, we would recommend combining this with Go To Market due diligence to focus on the critical questions and levers.
Visualisation and reporting: Especially with such large datasets, it is important not to get lost in the data and to drive to actionable insights. Visualisations using tools like Tableau make it easy to understand key findings and make data-driven decisions.
Although this might sound laborious, many of these projects can be completed in an agile and focussed way and take only a few days’ work, especially if you are familiar with the intricacies of each data source. Here are some examples of client questions where Coppett Hill has used web scraping to answer in both B2B and B2C contexts:
How does the company score versus competitors across key purchasing criteria? For a private equity-backed university group, Coppett Hill scraped reviews from multiple competitors to assess performance across key criteria. Using AI, we categorised, tagged and visualised historical data versus competitors.
How does the product and pricing proposition of the target compare to competitors? When evaluating an online adventure travel marketplace, we scraped all holiday listings from the target and its competitors. By harmonising data on trips by destination and activity, we identified pricing trends, inventory focus and inventory overlaps.
How can the business improve NPS and reduce churn? By analysing Trustpilot reviews for hundreds of mid-market telecom providers, Coppett Hill identified key drivers of customer churn and NPS performance, enabling targeted improvements for our client.
The future of data-driven deal-making
As the volume of online data continues to grow, so does the importance of tools like web scraping in private equity. The ability to quickly transform raw data into meaningful insights provides deal teams with a competitive edge, allowing deal teams to extract, process, and analyse complex datasets quickly, helping them make informed decisions with confidence.
If you’d like to discuss how you can use web-scraping in your due diligence process or value creation strategy, please contact us.
All views expressed in this post are the author's own and should not be relied upon for any reason. Clearly.