A place for everything
Editor's note: Ed Lorenzini is CEO of Analyze Corporation and has spent most of his career in big data analytics and SaaS program management. He specializes in applying large-scale consumer datasets to identify real-world behavior and improve marketing effectiveness. His experience spans commercial enterprises and federal programs. Find Lorenzini on LinkedIn.
Marketing research has long relied on two primary sources of behavioral insight: what people say and what they buy. Surveys, interviews and panels capture intentions, attitudes and self-reported behavior. Transaction data shows completed purchases.
But many research questions unfold in the space between those two points. Researchers studying retail, events, services or place-based behavior often need to understand not only what people say they intend to purchase or ultimately purchase but how they move through the physical world in between. Observed movement and visitation patterns can provide additional context for interpreting emerging intent, showing where interest may be forming before a transaction occurs. Where do potential customers travel from? How often do they visit certain locations? What other destinations are part of the same routine or trip? These questions have traditionally required inference, small-sample observation or proxy measures.
Over the past several years, opt-in mobile location data has emerged as a complementary observational source that can help fill some of these gaps. When used carefully and interpreted in context, this type of data can add a behavioral layer to traditional research programs. It does not replace surveys or transactional data. Instead, it provides an additional lens on real-world activity that can be particularly useful in projects where location and movement are central to the research question.
This article looks at how researchers are incorporating observational location data into applied studies and where it can strengthen – and where it should be used cautiously within – the broader research toolkit. The goal is not to introduce a new technology but to examine how an additional observational source can bolster familiar research tasks.
Observing behavior in context
In many research programs, understanding physical-world behavior requires combining multiple sources. A brand may conduct surveys to understand awareness and intent, analyze transaction data to see who purchased and when and then model likely trade areas or audience composition. These approaches are well established and remain essential.
Observational location data can add context to this process by providing aggregated, opt-in records of where devices have been observed over time. When analyzed in aggregate and with appropriate safeguards, these records can be used to identify patterns such as visitation frequency, travel distance, timing of visits and overlap between different types of locations.
For researchers, the value of this data lies less in the novelty of the source and more in how it can support familiar research tasks. It can help validate assumptions about trade areas, provide independent estimates of attendance or visitation and offer additional evidence when interpreting survey responses. In most applications, it works best as one input among several rather than as a stand-alone answer.
The following examples illustrate how this observational layer is being used in applied research settings.
Estimating event attendance and audience composition
One common challenge in event-related research is estimating how many people attended and where they came from. Organizers may have ticket scans or registration data for some events, but many gatherings – such as festivals, trade shows or open public events – involve a mix of registered and unregistered visitors. Even when attendance counts are available, understanding the geographic draw and composition of attendees can be difficult.
Researchers are beginning to use observational location data to develop independent estimates of attendance and visitor origins. By defining the physical footprint of an event venue and examining observed visitation during the event window, analysts can estimate the number of devices present, how long they remained and where they were observed before and after the event.
In one recent project involving a regional home and garden show, researchers combined organizer-provided attendance estimates with observed visitation patterns. The location-based analysis provided a time-of-day view of traffic flow across the event days and an estimate of how many visitors came from within the local metro area versus surrounding regions. This helped organizers and sponsors understand the geographic reach of the event and evaluate whether it was drawing primarily local attendees or a broader regional audience.
The analysis also provided context for follow-up survey work. Knowing the relative share of local versus out-of-market visitors allowed researchers to weight responses more accurately and interpret satisfaction and purchase-intent measures in light of the audience composition. In this case, the observational data did not replace survey-based feedback; it helped frame it.
Validating trade areas and destination draw
Trade-area analysis is another area where observational data can complement traditional methods. Researchers often rely on customer address files, loyalty program data or survey responses to estimate where visitors originate. These sources are valuable but may not fully capture occasional visitors or those who are not part of a brand’s customer database.
Aggregated location observations can provide an additional perspective by identifying the general areas where devices observed at a location are typically present at other times. When examined at an appropriate geographic level, this can help researchers estimate the distribution of visitor origins and travel distances.
In a study involving a destination retailer known for drawing customers from a wide region, researchers used this approach to better understand the store’s geographic pull. Traditional customer data suggested a strong local base, but the observational analysis indicated that a substantial share of visitors were traveling from outside the immediate trade area, often combining the visit with other regional destinations.
This information helped the research team refine assumptions about the retailer’s market reach and evaluate the potential impact of regional advertising. It also informed the design of a subsequent survey, which included questions tailored to visitors who traveled longer distances. By grounding the survey design in observed behavior, the researchers were better able to capture the full range of visitor motivations.
Examining competitive and co-visitation patterns
Understanding how different locations fit into the same consumer routine is another recurring research challenge. Surveys can ask respondents which stores or venues they visit, but recall may be incomplete and the sequence or timing of visits can be difficult to capture accurately.
Observational location data can be used to examine patterns of co-visitation at an aggregate level. For example, researchers may analyze which types of locations are commonly visited by the same group of devices over a defined period. This can reveal overlap between competitors, complementary destinations or lifestyle patterns associated with certain types of visitors.
In a competitive retail study, analysts examined visitation patterns across several stores within a category. The observational analysis suggested that a meaningful portion of visitors to one retailer were also frequenting a nearby competitor within the same time frame. This finding prompted a closer look at survey data to understand how shoppers perceived differences between the stores and what factors influenced their choice on a given trip.
By combining the two sources, the research team was able to develop a more nuanced view of cross-shopping behavior. The observational data identified the overlap; the survey data explained the reasons behind it.
Integrating observational data with traditional methods
In each of these examples, location-based observation served as a complement rather than a replacement for established research approaches. Surveys provided attitudinal and motivational context. Transaction data showed actual purchases. Observational data added a view of movement and presence in the physical world.
For research teams, the key is integration. Observational findings can be used to inform sampling, validate assumptions, identify segments for deeper study or provide context when interpreting results. They can also help highlight discrepancies between reported and observed behavior, which can then be explored through qualitative or quantitative follow-up.
As with any data source, interpretation requires care. Observational location data typically represents a subset of devices and reflects opt-in participation from mobile applications. Coverage and density can vary by geography and context. Researchers should treat findings as indicative rather than definitive and consider them alongside other available data.
Limitations and considerations
Several considerations are important when incorporating observational location data into research design:
Representativeness. Not all consumers are equally represented in opt-in mobile data. Researchers should assess coverage relative to the population of interest and avoid overgeneralizing from small samples.
Context. Presence at a location does not necessarily indicate intent or engagement. A device observed within a geofenced area may reflect a brief stop, a pass-through or a longer visit. Careful definition of visit criteria and interpretation of results is essential.
Privacy and aggregation. Responsible use of location data requires adherence to privacy standards and aggregation practices that prevent identification of individuals. Most research applications rely on aggregated patterns rather than individual-level tracking.
Triangulation. Observational findings should be compared with survey, transactional or other data sources whenever possible. Differences between sources can be informative but should be examined carefully.
By approaching observational data as one component of a broader evidence base, researchers can make use of its strengths while mitigating its limitations.
A growing role for observational data
As mobile devices have become a routine part of daily life, the volume of opt-in location data available for analysis has increased. For researchers working on projects where physical-world behavior matters – retail, events, travel, services or community-based programs – this has created an opportunity to observe patterns that were previously difficult to measure at scale.
The value of this data lies not in replacing established research methods but in enriching them. Surveys remain essential for understanding attitudes and motivations. Transaction data remains the most direct record of purchases. Observational location data adds context by showing how people move through the environments where decisions occur.
Used thoughtfully, it can help researchers move closer to answering a familiar question: not only what consumers say they will do or what they ultimately purchase but how their decisions unfold in the spaces in between.