Forbes.com

Data Mining Your Way to Better Due Diligence in Private Equity

From scraping social media sites to mining e-commerce traffic, leading private equity firms are using web data to provide a competitive edge during due diligence.

Von Hugh MacArthur, Graham Elton, Daniel Haas and Suvir Varma

First published on April 07, 2017
Min. Lesezeit


This article originally appeared on Forbes.com. In the hypercompetitive auction processes now common to private equity markets, investors look to gain an edge in drawing insights and bidding with greater confidence. One emerging vein of raw material to be mined is the wealth of unstructured data available on the web. From scraping social media sites to mining e-commerce traffic, leading private equity firms are using web data to increase the speed and reliability of insights and thereby provide a competitive edge. These firms have incorporated such data into the due diligence phase, which allows them to develop proprietary insights faster and more comprehensively. That gives them greater confidence in the price they should pay for an asset. What was a long, mostly manual, laborious effort to learn about the strengths and flaws of a business now can happen in just days. Software tools today can organize and analyze customer reviews, geographic information, employee compensation, employee and organizational data, social media sentiment and other web data. By augmenting traditional research such as interviews and store visits, advanced analytics often provide answers that are more exact than traditional sampling or probability ranges. Once the data has been collected, traditional analytical tools such as Excel or SPSS Statistics may not be sufficient, so PE firms should explore additional tools such as Tableau. Software that both analyzes and visually displays data can quickly reveal important patterns that might take too long to produce using traditional spreadsheets and charts. PE firms are applying advanced analytics during due diligence on several fronts. Product assortment. Potential acquirers collect and analyze product assortment data to assess the relative pricing, assortment and discounting strategies of target companies in retail, consumer products and technology. In a due diligence of a women’s clothing retailer, an SKU assortment analysis revealed that the retailer provided a wide selection of items at comparatively lower prices. Employee costs and organization. Online data allows a due diligence team to sort employee counts by role or department, estimate personnel costs and assemble a rough organization chart. This provides a perspective on the employee mix and cost, as well as on the caliber of talent management. A profusion of spans and layers, for instance, may signal an opportunity to reduce headcount and improve how the company manages talent. Working with a PE firm on its due diligence of a sporting goods retailer, Bain & Company combined online data with store visit observations for the potential buyer. We concluded that the target firm, compared with its peers, had a higher proportion of store managers and support staff relative to store employees. Customer reviews. What customers write about vendors can deepen the understanding of differences among competitors in products, service or the overall experience—or among a target company’s locations. One due diligence of an amusement park company analyzed reviews on TripAdvisor to confirm primary research and understand the company’s competitive position. Social media postings. Data compiled from social media postings yields trends in sentiment and share of voice for a brand that people comment about on the web. In the due diligence of a European retail health provider, an analysis of online blogs, forums and news helped a PE firm confirm how familiar people were with the provider. E-commerce. Software firms crunch web traffic data to assess the search effectiveness, conversion rates and purchase sizes of online retailers. The analysis helps to identify the size and nature of post-acquisition sales opportunities. Geographic presence. Geographic analysis can show the competitive presence in individual markets and potentially underserved markets. And the geographic data might be combined with other data for more nuanced analysis. For example, the due diligence of a US industrial products distributor used core location data from the target company and competitors to reveal which postal codes merited expansion or new facilities. Even the best analytical tools have limitations—chiefly, they have access only to materials available on the Internet. They don’t yet replace other methods of research such as market participant interviews, customer surveys and intercepts, store visits and literature searches. For comprehensive insights, the best-informed due diligence will use web scraping to complement traditional research. Moreover, software tools do not replace business judgment. For example, just a few positive or negative reviews could easily skew average review ratings of smaller locations or less popular products. Likewise, in analyzing product assortment, scraping data on sale days or unusual periods such as holiday seasons will skew the results. Algorithms have not yet mastered cultural quirks that only a human can interpret. Hugh MacArthur, Graham Elton, Daniel Haas and Suvir Varma are leaders of Bain & Company’s Private Equity practice.

This article originally appeared on Forbes.com.

In the hypercompetitive auction processes now common to private equity markets, investors look to gain an edge in drawing insights and bidding with greater confidence. One emerging vein of raw material to be mined is the wealth of unstructured data available on the web. From scraping social media sites to mining e-commerce traffic, leading private equity firms are using web data to increase the speed and reliability of insights and thereby provide a competitive edge.

These firms have incorporated such data into the due diligence phase, which allows them to develop proprietary insights faster and more comprehensively. That gives them greater confidence in the price they should pay for an asset. What was a long, mostly manual, laborious effort to learn about the strengths and flaws of a business now can happen in just days.

Software tools today can organize and analyze customer reviews, geographic information, employee compensation, employee and organizational data, social media sentiment and other web data. By augmenting traditional research such as interviews and store visits, advanced analytics often provide answers that are more exact than traditional sampling or probability ranges.

Once the data has been collected, traditional analytical tools such as Excel or SPSS Statistics may not be sufficient, so PE firms should explore additional tools such as Tableau. Software that both analyzes and visually displays data can quickly reveal important patterns that might take too long to produce using traditional spreadsheets and charts.

PE firms are applying advanced analytics during due diligence on several fronts.

Product assortment. Potential acquirers collect and analyze product assortment data to assess the relative pricing, assortment and discounting strategies of target companies in retail, consumer products and technology. In a due diligence of a women’s clothing retailer, an SKU assortment analysis revealed that the retailer provided a wide selection of items at comparatively lower prices.

Employee costs and organization. Online data allows a due diligence team to sort employee counts by role or department, estimate personnel costs and assemble a rough organization chart. This provides a perspective on the employee mix and cost, as well as on the caliber of talent management. A profusion of spans and layers, for instance, may signal an opportunity to reduce headcount and improve how the company manages talent. Working with a PE firm on its due diligence of a sporting goods retailer, Bain & Company combined online data with store visit observations for the potential buyer. We concluded that the target firm, compared with its peers, had a higher proportion of store managers and support staff relative to store employees.

Customer reviews. What customers write about vendors can deepen the understanding of differences among competitors in products, service or the overall experience—or among a target company’s locations. One due diligence of an amusement park company analyzed reviews on TripAdvisor to confirm primary research and understand the company’s competitive position.

Social media postings. Data compiled from social media postings yields trends in sentiment and share of voice for a brand that people comment about on the web. In the due diligence of a European retail health provider, an analysis of online blogs, forums and news helped a PE firm confirm how familiar people were with the provider.

E-commerce. Software firms crunch web traffic data to assess the search effectiveness, conversion rates and purchase sizes of online retailers. The analysis helps to identify the size and nature of post-acquisition sales opportunities.

Geographic presence. Geographic analysis can show the competitive presence in individual markets and potentially underserved markets. And the geographic data might be combined with other data for more nuanced analysis. For example, the due diligence of a US industrial products distributor used core location data from the target company and competitors to reveal which postal codes merited expansion or new facilities.

Even the best analytical tools have limitations—chiefly, they have access only to materials available on the Internet. They don’t yet replace other methods of research such as market participant interviews, customer surveys and intercepts, store visits and literature searches. For comprehensive insights, the best-informed due diligence will use web scraping to complement traditional research.

Moreover, software tools do not replace business judgment. For example, just a few positive or negative reviews could easily skew average review ratings of smaller locations or less popular products. Likewise, in analyzing product assortment, scraping data on sale days or unusual periods such as holiday seasons will skew the results. Algorithms have not yet mastered cultural quirks that only a human can interpret.

Hugh MacArthur, Graham Elton, Daniel Haas and Suvir Varma are leaders of Bain & Company’s Private Equity practice.