Case study

in4mation insights Uses SafeGraph Data to Understand Changing Consumer Mobility During the Pandemic


in4mation insights


Needham, MA


Contact Sales

The Problem: How to Better Predict Changes in Consumer Behavior During the Pandemic

The COVID-19 pandemic created a seismic shift in consumer behavior across all industries and sectors. This led to traditional marketing mix models, once used to predict the future based on past trends or seasonal patterns, getting thrown out the window. As Mark Garratt, Partner and Co-Founder of in4mation insights put it, “The baseline was completely lost as well as our ability to predict into the future; we needed a new measure to capture changes in consumer behavior.”

The Problem-Solver: in4mation insights

The talented team of data scientists and modeling experts at in4mation insights is driven by the concept of aspirational analytics: the power to support foundational business decision making by challenging long-held assumptions and changing current practices. They work on behalf of their customers, many within the quick-service restaurant (QSR) sector, to unlock the potential of ‘knowing more’ and provide strategic guidance to optimize media across multiple marketing channels. So, when the COVID-19 pandemic hit, they needed to act fast to help their customers make sense of—and adapt to—a rapidly changing business environment. And indeed, they did!

The Challenge: Creating a New ‘Variable’ to Explain Changes in Consumer Movement Patterns

Although indoor dining took a bigger ‘hit’ in the first wave of the pandemic than in subsequent waves, that first lockdown experience would fundamentally shift the relationship between consumers and restaurants. With indoor dining out of the question, restaurants had to adapt quickly by offering drive-through, delivery, or curbside pick-up options to stay in business.

However, as the pandemic continued on, simply using the COVID reproduction rate as the proxy for understanding potential shifts in consumer mobility was no longer sufficient. The team at in4mation insights, therefore, had to look to other trends—like working from home, for example—to begin painting a more realistic picture of evolving consumer behaviors.  

The Solution: SafeGraph Core Places and Weekly Patterns Data

With the help of SafeGraph data, in4mation insights was able to build a ‘work-from-home variable’ to understand the impact of changing commuter mobility patterns on the usage of QSRs.  in4mation insights needed to create a baseline measure (with holidays, weekends, and seasonality stripped out) that could better predict future movement patterns given the evolving circumstances. In other words, they needed to figure out how people were moving around at various phases of the pandemic.

To do this, they looked closely at foot traffic data to McDonald’s locations around the country at different dayparts. Any visits before 9am and after 5pm could easily be attributed to commuter traffic, either before or after work. All other hours in between could shed light on new behaviors resulting from the massive shift to remote work.

But you may be wondering why McDonald’s was used to create this work-from-home variable. Explains Garratt, “a precedent for using McDonald’s as a gold standard was first tried in economics using the metric Purchasing Power Parity; we felt that foot traffic to a QSR as ubiquitous as McDonald’s could serve as a bellwether for many other consumer behaviors.” And this quickly proved to be true. “Our work-from-home variable, fueled by SafeGraph data, became the dominant predictor of a new baseline and, even more, was stronger than any other economic variables we had previously used,” added Garratt.

The Result : A Better and More Accurate Way to Link the Past with the Present

Creating this work-from-home variable enabled in4mation insights to achieve two big things:

  1. Run a single model over the entire pandemic to make more accurate year-over-year comparisons (with improved precision versus previously used macro-economic and disease transmission variables).

  2. Allowed the baseline to be stable enough through the pandemic so that KPIs such as return on investment could be measured without interruption.
We needed a smooth curve across the entire COVID-19 pandemic to understand rapid channel shifting and to help our QSR customers take relevant and meaningful action on their TV and digital media investments, loyalty programs, and digital ordering apps.
Mark Garratt

Partner and Co-Founder

The Future: Gaining More Clarity as the Economy Begins to Open Up Again

While this work-from-home variable and data modeling was critical for understanding the initial impact of the COVID-19 pandemic on consumer behaviors and mobility, it will continue to allow the team at in4mation insights to predict, with greater accuracy, how these behaviors will continue to shift as the economy begins opening up again. Mobility turns out to be a critical addition to the model, especially as macro-economic measures became skewed and seasonal patterns were disrupted.

“We’re now able to measure these baseline trends in a consistent way ,” reiterated Garratt. “And even though people are starting to move about a lot more than they have over the past year, remote working is likely to be a ‘sticky behavior’ that will outlive the pandemic.” And only time will tell how these sticky behaviors will impact the QSR sector in the long-term.

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