SafeGraph’s Guide to Data-Driven Investment Decisions

Private equity firms use minute-by-minute analysis to understand market trends and make better investment decisions. Now you can, too.

Data is Everything in the World of Private Equity 

It’s how you understand the market and identify new opportunities to generate strong returns for investors. Accurate data also underpins every stage of the deals you make—from sourcing potential investments to due diligence and company monitoring.

Today, private equity firms have access to more data than ever before. And as data aggregation and analysis techniques have continued to evolve, more companies have begun using alternative data sources—including foot traffic data—to fuel their financial models. 

Even so, some estimates suggest that only 27% of private equity firms are currently using this kind of data in their analyses, thus making this a huge area of untapped opportunity for investors to drive new levels of precision and accuracy around investment decision-making. 

In this guide, we’ll show how measuring foot traffic patterns can empower private equity firms to identify new investment opportunities and manage portfolios more effectively. With SafeGraph’s help, you’ll learn exactly what your data can do and how you can put it to good use to achieve stronger returns for key stakeholders.

>>  Be sure to take a look at our technical workbook that walks you through each critical step of foot traffic analysis and dataset correlation.  <<

Key takeaways at a glance

  • Data analysis, including foot traffic data, is changing the private equity industry, making it easier to identify and measure evolving market trends more accurately.
  • Today, more private equity firms are analyzing foot traffic data to source new deals, conduct due diligence, and monitor the performance of their portfolio companies.
  • Foot traffic analysis is particularly useful when pivoting investment strategies in response to market disruptions, like the COVID-19 pandemic.
  • This kind of analysis might seem daunting at first, but it’s easy to get started. You just need access to the right datasets and an understanding of how to correlate them well.

Data Analysis is Changing Private Equity

The private equity industry has always been data-driven. Accurate information and analysis underpins every decision made about market fluctuations. In fact, the very nature of this analysis is always changing as new data sources, practices, and insights become available.

Unfortunately, traditional analysis methods of the past no longer cut it. Today, private equity firms need better, more accurate data for research reports and portfolio management strategies. Whether you’re an analyst or an experienced VP, you know that foot traffic data and store visit attribution data have the power to unlock actionable insights allowing you to take advantage of market trends, optimize current investments, and expand portfolios into new areas of potential growth. 

In saturated markets, such as the restaurant, real estate, and entertainment sectors, every data source can offer investors a competitive edge. For example, analyzing foot traffic data can quickly identify increases or decreases in demand throughout the day. This can help investors understand when these kinds of businesses are either hitting or missing sales goals.

Here’s what this looks like in action across Starbucks locations in New York City:

Comparing foot traffic data can reveal a lot about the commercial viability of certain business locations and help private equity firms ascribe more accurate long-term value to them.
Comparing foot traffic data can reveal a lot about the commercial viability of certain business locations and help private equity firms ascribe more accurate long-term value to them.

Using these insights, private equity firms can dive deeper into the data and establish the exact times of the day where foot traffic ebbs and flows. In many ways, this kind of information can be the “make or break” that informs decisions around whether to consolidate ownership in coffee shops or allocate investments elsewhere. No wonder why so many firms are now advertising job postings for spatial analysis, data science, and analytics specialists to round out their expertise.

That being said, these new data insights aren’t just about finding the next great company to invest in; they also highlight new opportunities to save resources by building a stronger understanding of company operating costs. For instance, a McKinsey report on data use in private equity calls out a European VC firm that has built a machine-learning model to analyze a database of 400+ characteristics across 30,000+ deals in order to better identify the 20 drivers of a deal’s success. Insights as granular and detailed as this can shed tremendous light on why some deals succeed whereas others fail.

Foot traffic data unlocks minute-by-minute insights

The true value of data in private equity lies in its timeliness. The older the information, the less relevant it is. For insights to be useful, data must be reliable, accurate, and recent.

Foot traffic data allows firms to conduct in-depth analysis for understanding what is happening at a business level—on a day-by-day or even minute-by-minute basis—allowing analysts to build a robust picture of how well a business location is attracting customers in-store as well as how that foot traffic may have been affected by external factors (i.e. statewide COVID-19 lockdowns).

As market observers have noted, these insights help analysts predict company growth more accurately by providing a granular understanding of every customer, transaction, and engagement that takes place. By asking the right questions, firms can sharpen their acquisition strategies and analyze market trends at the local, state, and national levels with more accuracy.

The world’s leading private equity firms are finally taking notice. For example, the firm Two Six Capital is leading the way by tapping into a constant flow of granular insights to power their portfolio performance strategies. As they put it, monitoring the performance of a business is like trying to lose weight: if you really want to see results, you have to step on the scale every day.

Only 27% of private equity firms are using alternative sources of data,  including foot traffic data, in their investment analysis

Jordan Mizrahi, CEO, First to Invest

One of the most challenging parts of any private equity firm’s job is accurately predicting the growth of a portfolio company. Data analytics now allows firms to take bold new approaches to this. As Two Six Capital co-founder Ian Pichache explains, metrics such as foot traffic and geospatial indicators allow firms to be much more exact when assessing company performance: 

“Right now in the industry, when most associates at a private equity firm go to sell [a company], they [just] put ‘12%’ in there. It’s a relatively finger-in-the-air approach. But because we count every customer and every transaction, we get a much more granular perspective.”

Therefore, for a company like Two Six Capital, whose portfolio is rooted in consumer retail by about 45%, these insights aren’t just nice-to-have; they are key to survival.

>>  Be sure to take a look at our technical workbook that walks you through each critical step of foot traffic analysis and dataset correlation. <<

Sourcing, Diligence, Monitoring: 3 Uses of Foot Traffic Data in Private Equity

Foot traffic and spatial analysis offer incredible opportunities for firms to improve their performance. You just need to know how to put these techniques to work in the right way.

There are roughly three ways you can use this data effectively: 

#1: Deal sourcing

Data analysis can help a firm determine which companies to invest in. By identifying long-term trends in customer visits, for example, a firm can make a shortlist of businesses with strong potential for future growth, especially those that may currently be undervalued.

These techniques can do more than just help a firm vet prospective companies for potential investment; they can also inform strategies for how and when to invest in them. A great example of this is how foot traffic analysis for supermarket chains can signal when a company may achieve an increase in market share as a result of seasonal patterns in customer visits.

#2: Company diligence

Oftentimes, firms rely solely on traditional sources of performance information, such as company financial statements, to make diligence-related decisions. However, with foot traffic and geospatial data, firms can build a more detailed picture of long-term company profitability and fold this information into the due diligence process.

McKinsey notes that the growing use of data analysis has made due diligence faster and more accurate. Private equity firms can now use foot traffic data to build clearer models depicting patterns in customer visits to specific store locations and then correlate that data to other information gathered during due diligence to understand the real “big picture.” 

Pichache explains that having varied sources of data is incredibly useful during the due diligence process: “In commercial diligence you’ve got four to six weeks to make a go or no-go decision on the deal. [We can] take in large volumes of data, and quickly come up with a point of view—is this company going to make returns, yes or no?” 

#3: Portfolio company monitoring

The insights offered through foot traffic data aren’t just useful for private equity firms; they’re also helpful for portfolio companies looking to monitor their own performance more actively. In competitive markets, these insights can quickly identify a unique, competitive edge.

By matching geospatial data against projected and actual revenue for each quarter, for example, a company can determine how closely its customer visit data tracks against sales performance—and then follow this relationship over time. This can inform future site selection, customer promotions, marketing strategies, and a lot more.

With the number of private equity-backed companies doubling from 4,000 to 8,000 in the decade leading to 2017, there is undoubtedly growing competition within the industry. In this context, firms need all the data they can get to stay ahead of their closest competitors.

The number of private equity-backed US companies has grown from about 4,000 in 2006 to 8,000 in 2017 - a 106% increase.

McKinsey

Foot Traffic Data Helps Private Equity Firms Solve Tough Problems

Now, let’s shift gears and look more closely at how both Points of Interest (POI) data and foot traffic data can be used to help firms solve some of the market’s toughest problems—from predicting business closures and sales downturns to anticipating the long-term impacts of disruptive events, like the COVID-19 pandemic.

Pivoting investment strategies to reflect market insights

A great investment strategy is never static. Firms need to keep a close eye on both short- and long-term market trends in order to adjust their approach to driving stakeholder value in real-time. Foot traffic analysis can help firms identify, quantify, and understand these trends.

Foot traffic analysis also allows private equity firms to understand how people move between urban and rural areas, whether it be for work or pleasure. Salient patterns in this data can give firms an in-depth understanding of how to pivot their investment strategies more effectively.

For example, let’s say a firm is researching grocery stores trends in Philadelphia to better understand the consumer retail operator ecosystem. They may find that stores in Center City have less foot traffic now than a year ago while, during the same period, suburban stores may be experiencing a sharp increase in customer visits. This provides critical information to help them adjust their portfolio in real-time to take advantage of this suburban shift trend—and potentially even focus future investments on suburban retail opportunities in the area.

This can also reveal useful insights about brand affinities, including linkages driven by brand loyalties. Take a look at this brand loyalty analysis for Gold’s Gym and SoulCycle, respectively:

Analyzing linkages in brand loyalty can reveal eye-opening market and investment insights.
Analyzing linkages in brand loyalty can reveal eye-opening market and investment insights.

Firms can use this data to shape investment strategies modeled around broader customer loyalty trends. For example, a firm with investments in health and fitness businesses might want to look for ways to invest in food and beverage brands that are closely aligned with the customers having an affinity for a specific fitness brand. In essence, this is akin to “lookalike audiences” in digital advertising. 

Measuring and predicting business closures

Another key challenge for private equity firms is both predicting business closures and measuring them as they occur. There is often a lag between when these events happen and when data about these events becomes available. For example, business closures due to COVID-19 in mid-2020 won’t be available until late 2021. For private equity firms, this huge delay in the availability of data makes timely adjustments to investment strategies impossible. 

By measuring foot traffic data over time, firms can get a better grasp on current indicators of business closures in order to adjust their investment priorities in the here and now. Because these closures have a huge number of flow-on effects for other businesses, these insights also have the power to help firms understand macro-level market dynamics. For example, if a major quick-service restaurant (QSR) closes a series of locations, this can decrease the demand for plastic utensils. A firm investing in a plastics manufacturer would need to account for this change in order to adjust their investments to limit the financial impact caused by this event.

Tracking foot traffic data over time can also tell investors whether fluctuations follow expected seasonal patterns, illustrate more permanent long-term trends, or end up being just a one-time blip. We saw this play out across major department stores during the COVID-19 pandemic:

Foot traffic to department stores dropped significantly during the COVID-19 pandemic.
Foot traffic to department stores dropped significantly during the COVID-19 pandemic.

Private equity firms can use these insights to make better investment decisions. In the above example, if we take COVID-19 out of the equation, a firm with significant investments in one department store might have initially looked for ways to shift investments to rival stores after seeing a major drop in foot traffic. Having a view like this across competing businesses can reveal comparative market trends that investors can take action on immediately.

Anticipating the long-term impacts of disruptive events

Aside from business closures, other market disruptions can be difficult to understand in real time. Let’s go back to the COVID-19 pandemic for an example of this. A firm may want to get an accurate view of how COVID-19 lockdowns affected businesses in different towns, cities, and states. Once again, foot traffic analysis offers a viable answer.

Foot traffic data can reveal the severity and extent of market disruptions as they happen while also pointing to emerging trends that could have long-term businesses impacts. Data from the Federal Reserve, for example, shows that restaurants in New York City experienced more closures than restaurants in Oklahoma City, likely due to earlier or more restrictive lockdown measures. Using this information, a firm can decide how best to prioritize investments in cities and states where lockdown measures haven’t brought business operations to a complete halt. 

This also allows firms to track the comparative impact of the same market disruption across competing brands within a given category. Let’s now take a look at the relevant change in foot traffic to national grocery store chains during COVID-19 (compared to pre-pandemic levels):

Relative foot traffic vs. pre-COVID-19 levels tells us which grocery chains fared better than others.
Relative foot traffic vs. pre-COVID-19 levels tells us which grocery chains fared better than others.

With big lags between real-world events and official statistics being released, foot traffic analysis offers a way to identify and understand the effects of these market disruptions, quickly and clearly. You can apply the same thinking to understanding emerging trends and patterns driving economic recovery during the pandemic, too. That’s what makes foot traffic analysis so useful and compelling for investment decision-making.

SafeGraph Patterns Data for the Win

The SafeGraph Patterns dataset offers detailed foot traffic information on over 4M+ POIs in the United States and Canada. It aggregates anonymized mobile device activity associated with brand locations, allowing data scientists to understand how often people visit a specific location, where they come from, where else they go afterwards, and beyond. Using SafeGraph Patterns data, companies can visualize catchment analysis, understand market penetration, and create great maps to tell compelling data stories

SafeGraph Patterns provides the most up-to-date view of foot traffic available today, with hourly breakdowns of POI visits. This dataset is updated each month, providing unparalleled access to an accurate and detailed view of current market trends, even at the store or brand level: 

Visualizing foot traffic to NYC’s Fifth Avenue Apple store sheds light on customer visit patterns.
Visualizing foot traffic to NYC’s Fifth Avenue Apple store sheds light on customer visit patterns.

Correlating SafeGraph Data with Third-Party Transaction Datasets

These six steps are covered in more detail in our technical workbook, so be sure to check that out when you’re ready to tackle this on your own. For now, though, here’s a quick rundown:

  1. Process, clean, and explode SafeGraph Patterns data.
  2. Adjust SafeGraph Patterns data for known sources of methodological bias (both at the monthly and day-to-day levels).
  3. Join SafeGraph Patterns data with a third party transaction dataset
  4. Encode common sources of known variance based on historical trends.
  5. Use regression analysis to control for historical and methodological sources of variance to optimize the predictions of transaction data using SafeGraph Patterns.
  6. Demonstrate that models using SafeGraph data perform better than models relying on historical trends only.

A few things to keep in mind

Be sure to keep these things in mind as you go through this process on you own:

  • Control for methodology: SafeGraph Patterns data is collected from a range of sources over time. To use this data to compare different time points, you must take into account the underlying changes in data collection. If you don’t control for methodology changes over time, it may be hard to produce strong correlations with real-world behavior.
  • Regression: Regression analysis is a useful tool to control for changes in methodology. Applying this within your foot traffic analysis will yield more accurate results, helping to isolate the true signal in SafeGraph data and separate it from variance, as otherwise explained by changes in methodology (i.e. changes in sample size).
  • Control for variability: Controlling for other known sources of variability in retail consumer behavior (i.e. day-of-month, month-of-year, popularity of a given venue) can make your analysis more predictive. Using a combination of historical data, methodology controls, and foot traffic signals can yield high correlations with transaction data.

Pinpoint Actionable Market Insights With Foot Traffic Data

The growing use of alternative data sources, like foot traffic data, is revolutionizing how private equity firms now anticipate and measure market disruptions, understand trends around business closures and economic recovery, streamline due diligence processes, and pinpoint the next great investment in ways that hadn’t been possible before.

Analyzing foot traffic data can seem intimidating for those of you who haven’t used it before in your analysis. But it really doesn’t have to be. With the right tools and techniques, including those we’ve shared here, you can make foot traffic analysis an ongoing part of your analytics arsenal to gather critical insights that can help make even more winning investment decisions.

Ready to analyze foot traffic over time? Check out the notebook >> Best Practices for Correlating SafeGraph Patterns with Other Datasets Across Time.

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