In a geospatial context, mobility data refers to the aggregated, anonymized measurement of people’s movements around points of interest (POIs) or neighborhoods, such as census block groups or specific areas.
Social mobility data can include where people come from, how long they stay, and where they go afterward. It is also known as “footfall data” or “foot traffic data” and can be collected manually through GPS signals, connections to WiFi networks, mobile beacons, and other methods.
Apps often request users’ consent to share their mobility data before collecting and anonymizing it as geospatial data. Due to privacy concerns, mobility data collection is not aimed at tracking specific individuals or their activities. Instead, the focus is on anonymously tracking the number of devices that enter the vicinity of a point of interest or other area and the timing and duration of their stay. This anonymity ensures that the privacy of individuals is respected and upheld.
In this guide we will cover:
What kind of questions does mobility data answer?
Mobility data covers different aspects of consumer movement patterns, answering questions like:
- Visit time: How many visitors enter a store in the morning, afternoon, and evening? Does a particular location receive more weekend or working week visits?
- Dwell time: How long do people stay in one place? Is it a transient location, like a train station, or somewhere they will spend a few hours?
- Origin-destination: How far have they traveled to reach their destination? Where do they go after?
- Location: Where are they visiting when they’re within a certain geofence (a virtual perimeter for a geographic area)?
- Flows and journeys: What are the flows from a particular retail store to other affinity locations within the same trip?
Main benefits of mobility data for business
The potential benefits of mobility data are countless. The most impactful use of mobility data is to enhance businesses’ decision-making. By providing organizations with precise information, professionals can make well-informed, evidence-based decisions.
Provides better market insights
Remember that knowing a place’s location and appearance may only be helpful if you have information on whether people are actually visiting it. Human mobility data offers insights into people’s activities, enabling you to respond to or even anticipate their movements and actions.
Also, mobility data sources are active daily, and the data is collected and processed rapidly, allowing for analyses without waiting for other less frequent data sources (Like sales reports). This efficiency ensures that professionals can make timely decisions based on current data.
Understand user activity patterns
The continual collection of mobility data offers comprehensive insights over time. Sufficient data identifies patterns in people’s frequent travel destinations at different times, on specific days of the week, during different seasons, and on holidays.
Identify new business opportunities
Mobility data offers decision-makers the potential to capitalize on uncovered opportunities before competitors. It enables organizations to assess local market sizes using catchment areas and footfall metrics, helping identify specific business or marketing prospects based on reliable data. This significantly mitigates the usual risks associated with making such decisions.
Benchmark and forecast results
You can use mobility data to compare locations and physical assets with peers and competitors to assess your business’s performance.
Also, a solid understanding of historical movement and access to current mobility data puts you in a strong position to forecast future outcomes and access revenue prediction models of retail entities.
How companies and businesses are using mobility data?
Mobility datasets are most powerful when combined with other datasets (spatial or otherwise). Here are examples of what global mobility data can do as part of a larger data ecosystem.
Retail site selection
Businesses developing expansion strategies typically allocate their new stores in areas with high foot traffic. This allows their brand to reach a larger pool of potential customers. However, conducting a trade area analysis is essential to determine whether the chosen area is not already saturated with established competitors.
On the other hand, when a brand identifies that some of its stores are experiencing a decline in foot traffic, it might be time to consider rightsizing, closing down, or relocating that store.
You may also like to read: Retail Site Selection Guide: Using Big Data For Better Strategies
Trade area analysis
Trade area analysis involves identifying the types of businesses with potential in a specific geographic area and determining who their competitors or complementary businesses might be. Adding data on human mobility to information about points of interest (POIs) can be very helpful in this process.
For example, if a large volume of people travel a long distance to visit a particular store or use a specific service, it could suggest a need for a similar, more convenient option in the local area. The amount of people coming in and out of an area can also show which types of businesses customers like to visit or avoid. This information can help identify other companies that would work well in the area and those that people might be passing without stopping because they already have what they need.
Consumer insights and analytics
Although mobility data does not reveal sensible personal information, it is still possible to gain a general understanding of consumers based on their movements and activities while complying with privacy regulations.
Footfall data becomes particularly valuable when combined with datasets such as POIs, demographics, and anonymized purchase records. For example, a company could analyze demographic data for the neighborhoods surrounding its stores to gather insights into the average age, gender distribution, income, and other characteristics of the locals.
This data can then be compared with other behavioral information, like purchase behavior or visitation patterns around the stores, to uncover shopping and other activity trends that align with the lifestyles of their customer base.
Based on data about its stores, the company can adjust its operations better to serve the residents’ demographics and shopping behaviors. For instance, they could organize the store layout to make commonly purchased products readily available to local customers or customize their advertising to highlight popular products among the nearby demographics and lifestyles.
Location-based marketing
If a business cannot set up its store in an area with high foot traffic, it can still attract a more extensive customer base by placing advertisements there. This way, people will know where to go if they can’t find what they need at stores in the high-traffic area.
The company must also examine property data to determine the available advertising space. It may also need to analyze street-level data to determine how accessible its store is from where it places its ads.
Store evaluation
Accurate property data to perform visit attribution analysis involves measuring how many people in a geographic area around a store actually entered a store and how long they stayed versus how many simply walked past the store.
Understanding mobility based on GPS data alongside POI data, property data, white space analytics, transactional data, and other metrics provides a clearer picture of an individual store’s sales performance. When performed on several stores, visit attribution can give clues regarding a company’s financial health long before quarterly reports or other official indicators are released.
You may also like to read: How to Increase Foot Traffic to Your Retail Store Using Data Analytics?
Real estate planning
Mobility data can also be used for real estate analytics. Developers can combine mobility patterns, big data, and transport analytics to compare where people go throughout the day versus how easily they can get to each place with existing transportation routes and methods.
Best practices when using mobility data
Keep up with location planning
The consumer landscape is constantly changing, so it’s crucial to stay updated with the latest datasets for location planning. For example, you might have excluded a particular area from your site selection strategy based on previous data showing that your target demographic doesn’t visit that area.
However, over the last year, some new businesses that your target demographic is interested in may have opened, causing them to visit that street daily now. Remember, if your data is even a couple of years old, you could fall behind and lose out to your competitors.
Prioritize granular data
Having detailed, granular data is essential for making decisions at both macro and micro levels. It’s important to go beyond looking at overall footfall numbers and instead delve into detailed interactions at different times and days.
Granular data can help your brand approach different visitor segments more effectively.
Partner with the data provider
When integrating new datasets like mobility data, it’s important to partner with a data provider that understands your commercial needs and objectives.
The right partner can help turn insights into actionable strategies to ensure the effective use of data for decision-making. Partnering with the right provider will also ensure appropriate due diligence, ensuring that only information covered under a robust consent management framework is collected and that data is anonymized and used in an aggregated format.
Think of different ways to use mobility data
Mobility data can be helpful across different areas of your business. From site selection and marketing to reshaping internal operations, there are multiple ways to apply this data innovatively.
Questioning and exploring the potential of your data will unlock new opportunities and enhance several business aspects.
Leverage insights across your organization
Mobility data is used by more than just expansion or marketing teams. It can also inform internal operations, such as HR strategies. For example, understanding employee commuting patterns and preferences can lead to more supportive workplace policies.
Applying mobility data holistically across the organization allows for extracting maximum value and fostering a comprehensive understanding of customers and employees.
Don’t pass by the latest mobility trends
Consumer mobility patterns and behavior constantly evolve due to different factors, from technological advancements to events like the pandemic. Keeping updated with emerging mobility trends will help you understand how people interact with their local environments, city centers, and retail spaces.
Challenges when using mobility data
Improvements in technology have significantly increased the availability of different data sources for mobility, offering benefits such as speed, on-demand access, detail, and cost-effectiveness. However, the reliability of the statistical information can be a significant concern, as it may not always accurately reflect real-life situations.
When collecting and processing mobility data, different challenges arise. The types of data and their combinations, along with the data’s frequency and fairness, can introduce known and unknown biases. PREDIK Data-Driven addresses these challenges by leveraging millions of mobility data points and integrating them with internal, external, and alternative data sources to produce more accurate and reliable insights.
Start using mobility data in your favor
Mobility data can explain why users frequent specific places and prefer certain activities at particular times and locations. These insights can give us a significant and timely advantage when making decisions. This understanding can be extremely beneficial whether you are expanding your business, attracting more customers, making safer and more profitable investments, accurately assessing risk, or better serving your users.
