There is a massive amount of data available to us at any given time. One of the biggest challenges for enterprises can be cutting through the overwhelming volume and focusing on the pieces of information that have a true impact on your business goals.

One type of data that can provide unique insights is location data. Contrary to popular belief, location data isn’t only useful for pulling up Google Maps to find the nearest grocery store, or launching Waze to help you navigate your city’s traffic. While those are perhaps the most obvious uses, the insights that companies can glean from location data - and the ways in which they can then use that data to better market their products and services to customers, among many other applications - are significant.

Understanding Location Data

Location data is geographical information about a specific device’s whereabouts at a specific time. Due to the nature of the devices - smartphones, laptops, fitness trackers, etc. - a large majority of the time this information correlates to a specific person. So, if you know the location of someone’s smartphone, you’re likely to know where they shop, where they workout, where they spend their leisure time, etc.

This data is tracked in a number of ways, most often by mobile carriers, but also through Wi-Fi, beacons, satellites, and more. Consider the smartphone. When your phone’s location services are turned on, it transmits a location via GPS. That information is then used to help you receive accurate weather alerts, power your Google search results so they are relevant to your current location, or to “check in” on social media at a specific venue.

Thinking bigger picture, location data can help airlines monitor planes en route, Amazon assess when your package will be delivered, health organizations track the scope of disease outbreaks, and more.

Location data is perhaps most easily condensed into this concept: a digital footprint. And by following those footprints, businesses can not only tell what kind of journey customers are taking, but can often influence in which direction they head next.

While this might seem a bit invasive, the collection and utilization of location data is perfectly legal - provided that businesses clearly inform users of how their data is used and offer opt-in and opt-out features, as well as ways to manage their consent practices.

Decoding Location Data: Insights, Applications, and Benefits

There are a myriad of benefits to location data. Per Hubspot, 88% of consumers who do a local search on their smartphone visit or call a store within a day. In fact, nearly 46% of all Google searches are seeking local information.

But beyond utilizing location data to help customers find your brick-and-mortar store, businesses can also use this data to analyze consumer movements and much more. Those insights allow companies to determine what kind of investments to make and how to outpace their competitors. For example, if a new neighborhood is built in a suburban area, there may be an increase in foot traffic to the local Target. This would be of interest to Walmart, who might want to consider building a store to service the influx of residents.

Location data also allows companies to improve upon the products and services they provide, and better reach their target audiences. With this data, your Uber can find you faster. Home Depot can send ads to contractors who frequent their stores. Customers who shopped at an Ulta can receive retargeted ads for Sephora. These are just a handful of examples that illustrate how location data can improve customer experience and business strategies.

Location Data and Experience Design

To that point, location data can - and should - have a direct impact on experience design. Experience design uses a deep understanding of how a customer’s experience affects everything, from product design, to marketing, to customer service. It works for any experience, whether that’s how a customer navigates your eCommerce site, or how you design the layout of your store.

Location data therefore provides uniquely powerful insights to inform the way you design these experiences. Consider these examples:

Store Locator - the location data-based feature that most people are familiar with is the ‘store locator’ option on many websites. This feature uses location data that customers supply to help them find a brick-and-mortar store nearby.
Shipping Calculations - location data can help customize the online shopping experience for users, tailoring their shipping costs by location.
Sales Tax Calculations - another price-focused feature, sales tax calculations can be automated using location data, allowing customers to have a more complete picture of potential costs while browsing online.
Product Availability - using location data, eCommerce companies can highlight different products by region. For example, Boston-based users may see snow tires highlighted, while Austin, Texas users may have vehicle sun shades highlighted instead.
Product Promotions - in a similar vein, you may want to email users a different type of promotion depending on their location, like 30% snow tires for your northern shoppers and 15% off sitewide for southerners.
Location Context - the same principle applies to your brick-and-mortar merchandising and promotional strategies. You could want to highlight a specific type of product in a specific store depending on the demographics of visitors to that store.
Marketing Outreach Push Notifications - how great would it be if you could push notifications to users who are near your business? For example, if you’re a retailer with an app, location-based data could help you send an in-store only coupon to users who are within a certain radius of a store.
Time Zones that Dictate Promotions - for national and international enterprises, location data can also help tailor promotions, advertisements, and offers by time zone. For example, a national coffee chain may want to run ads from 5am - 8am every morning, which would vary by timezone.

In all of these examples, the experiences you are creating for customers are informed by location data, helping you to tailor the experiences to deliver the most meaningful and useful results to customers and potential customers.

Location Data Terminology

Before you can utilize location data to inform your business decisions and experience design, you first must understand the specific terminology that location data uses, as it isn’t necessarily intuitive.

Accuracy vs. Precision

While accuracy and precision sound similar, when it comes to location data, they are two distinct concepts.

Accuracy determines how close the device’s geographical reading is to the actual location of the device. Accuracy varies depending upon the type of signal and type of device.

Precision is the level of detail that is associated with the location measurement, and the more that the details provided correlate with other details in the dataset, the more precise the data.

Put another way, accuracy measures how close a device is to the location where it was pinged. Precision means how similar a number of separate pings are.

Real Time vs. Historical

Beyond understanding the difference between accurate and precise data, it’s also important to make a distinction between real-time location data and historical location data. Real-time data is tracking a device’s movements as they are happening. Historical data provides a detailed report on where a device has been over the course of a specific time period.

Deterministic vs. Probabilistic

Deterministic location data is data that relies on human movements, such as when a person drives from one place to another. Probabilistic location data is based on probability, or a prediction as to where a device will be given its past movements.

Accessing, Analyzing, & Using Location Data

While you can certainly collect your own location data, this information can also often be purchased. Purchased location data typically includes information such as latitude, longitude, timestamps, place names, etc. Location data is also sold based upon IP addresses, brand IDs, and more.

Regardless of how you obtain the data, you’ll need to analyze it. Think of location data as a way to identify patterns and trends on a large scale. You may want to analyze this data to help you determine:

Customer demographics
Customer behaviors
Insights to specific brick-and-mortar retail locations (i.e. number of repeat visits, average visit length)
Emerging markets
Unmet customer needs (ex. a group of people who live in zip code X always have to go to zip code Y to get to a grocery store)
Target audiences for specific marketing campaigns

These insights can then inform:

Audience segmentation
Targeted marketing
Retargeting
Interest targeting
Personalization
Location-based targeting
Predictive personalization
Measurement and attribution

This is just the tip of the iceberg when it comes to how location data can impact business strategy and marketing campaigns.

Purchasing & Analyzing Location Data

Customer demographics
Customer behaviors
Insights to specific brick-and-mortar retail locations (i.e. number of repeat visits, average visit length)
Emerging markets
Unmet customer needs (ex. a group of people who live in zip code X always have to go to zip code Y to get to a grocery store)
Target audiences for specific marketing campaigns

These insights can then inform:

Audience segmentation
Targeted marketing
Retargeting
Interest targeting
Personalization
Location-based targeting
Predictive personalization
Measurement and attribution

This is just the tip of the iceberg when it comes to how location data can impact business strategy and marketing campaigns.

Capturing, Organizing, and Utilizing Your Own Location Data

If you want to capture, assess, and utilize your own location data, it’s critically important to have a strategy.

Defining Location Data

The first step is to define what location data means to your company. In the B2B world, some examples may include:

Locations “I do business in”
Locations “I do business with”
Fringe Locations (where things are installed: cameras, access points, or equipment.)

You should then further define these categories. For example:

Locations I Do Business in can be further broken down into:
Warehouses/Distribution Centers
Operating Locations (i.e. physical store fronts and mobile store fronts, pop-ups, vending machines, etc.)

Locations I Do Business with can be further broken down into:
Warehouses, distribution centers, logistics chains, etc.
Customer locations (such as where you ship to or bill to)

You’ll also want to identify the key features of location data and capture this information, too. Nearly all location data will contain the following:

Classification

Typically there are multiple classifications that segment the location. These may include: geographic region, market segmentation, organization structure (ex. Disney > Marvel).

Physical Location

This may mean the physical address, the email address, a phone number, etc.

Generally, physical location data also includes enrichment data about the locations, such as hours of operation for a physical location, or the contact information for an email address, like the name, title, and company associated with that email.

Depending on the type of the location, you may include information such as what can be done at the location, or physical characteristics of the location. Home Depot is a great example, as they keep track of which facilities can finish doors. Quaker Houghton monitors where they can roll steel. Compass Group tracks where their vending machines are located throughout Disney theme parks. Grocery stores note how many delivery bays they have, so they can accurately schedule deliveries.

Another type of enrichment data for physical locations is features or menus. For example, Marriott tracks which hotels have fitness centers, pools, and more. Chick-fil-A tracks which locations serve breakfast and which do not. Many retailers track which storefronts are accepting which promotions.

Relationships

With Master Data Management, you may even begin to track relationships between locations, products, and users.

Some examples include:

What products are in stock at what warehouses?
What products are manufactured at specific locations?
What products can be found on what aisles?
What lead times are for receiving products from different vendors?
What customers are impacted if you no longer sell a certain item?

These are just some examples of the ways in which you can utilize location data to inform decision-making.

Location Data to Drive Strategy

Location data provides valuable context to understand customer behavior, optimize supply chains, identify market trends, reach your customers, and make informed business decisions. By effectively managing location data, organizations can gain insights, improve operational efficiency, and deliver personalized experiences.

Interested in learning more about the impact location data can have on your business? Reach out to the team of experts at Object Edge for a free consultation.