The Power of Traffic Data for Retail Predictive Analytics
Recent disruptions in the workplace—including post-COVID population shifts and the effects of remote work on traffic—have left instinctive business decisions using a specific, frozen-in-time market unreliable.
Traditionally, the retail industry, including retail banking and fuel retailers, have relied exclusively on static data such as demographic census data, average annual daily traffic (AADT), and traffic patterns. While still having certain uses, these datasets can no longer accurately measure store success, predict sales, or even help site selection decisions based on market potential, for example.
This new reality is forcing retail companies to evaluate new datasets: pedestrian and vehicle mobility data, including detailed historical traffic (DHT). Combined with static data, these additional datasets are a powerful means of predicting sales, building targeted marketing campaigns, and adapting supply to demand with the utmost accuracy.
What Is Mobility Data?
Mobility data refers to hyperlocal and hyper-detailed pedestrian and vehicle traffic data. It provides information on the historical or near-real-time movements, activities, and purchasing habits of consumers.
The recency, accuracy, and frequency of detailed historical traffic (DHT) data generated primarily by modern vehicle navigation systems make it an invaluable source of information that no retail business can afford to ignore.
In addition, mobility data provides greater insight into consumer behavior over time and adds more context by knowing their departure point, their travel time and speed, and their destination.
Measuring Retail Store Performance With Mobility Data
Store Sales Prediction
Mobility data can be used to identify the periods of peak pedestrian and vehicle traffic and to compare how flows vary from one year, month, week, day, or hour to the next. From this, retailers can discover whether there are specific times when consumers are more likely to go out shopping, such as on payday or the weekend. They can then build predictive sales models by the time of day, day of the week, and time of year.
What’s more, a business can use this data to analyze traffic patterns around competitors’ sites and compare customer traffic with its own sites. By combining mobility data with other hyperlocal data, the business can work out why certain catchment areas and stores outperform others and then adapt some of its own strategies accordingly. For example, a solid marketing strategy is essential to attract consumers to a store during low-traffic periods.
It’s even possible to correlate vehicle traffic volume with geographic barriers or nearby road closures, caused by a local event, for example. Such traffic disruptions could hurt a site’s performance and require it to work harder to meet its financial goals while continuing to provide an exceptional customer experience.
Matching Supply With Demand
Analyzing variations in daily traffic makes it easier for retailers to understand the sales seasonality and consequently the purchasing and travel patterns of consumers for their market segment. From here, they can better plan how many employees need to be available to meet customer needs, as well as how many products and what type of services to offer based on anticipated hourly and daily traffic.
By extension, they can determine whether their own performance—or that of their competitors—is consistent with this seasonality. If experiencing poor performance, a retailer could use personalized marketing campaigns or promotions tailored to the travel patterns of various demographics or to other factors such as an upcoming event to increase sales.
Watch this webinar on traffic analytics »
Hyperlocal Data Aggregation: A Step Not to Be Overlooked
It’s important to remember that to better understand consumer habits and more accurately predict sales and store performance, aggregating data from both mobility and hyperlocal datasets is necessary. This data includes:
- Internal data, meaning your own sales, spending, and investment history.
- Accurate and dynamic demographic data, so you can connect travel patterns in a specific area with the characteristics of your customer base.
- Risk areas, particularly in terms of crime and weather events.
- Competitive analysis and point-of-interest (POI) data, to monitor changes in market share and whether cannibalization is a concern.
- Property data, such as building footprints and various information about the property (roof, parking, electric vehicle chargers, etc.).
Collectively, this data accurately illustrates which target market is most likely to visit an establishment, at what time, on what day, and for how long, giving retailers a clear picture of the vitality and profitability of their business.
With these datasets, predictive models can be developed to optimize the network, supply, pricing, and total sales per square foot. Building on this, retailers may decide to expand to new areas and use their most profitable site as an analog model to predict how successful other locations will be.
Don’t Leave Your Decisions to Chance!
Mobility data, such as HERE real-time traffic data, together with other historical and hyperlocal data, can boost your return on investment by helping you predict the future, make more informed decisions for your business, and correlate your sales and their potential.
Korem’s one-stop shop approach not only helps you find high-quality data, but also select a vendor in a rapidly evolving geospatial market. By combining this approach with our customized Data as a Service (DaaS) offer, we can also process, prepare, and integrate massive volumes of data according to your need, freeing up your employees to focus on value-added tasks that maximize productivity.
Request your location analytics pilot today to find out if you’re headed in the right direction with your use of geospatial technology and how Korem can help!
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