Anonymized mobile data is unrivaled when it comes to analyzing the movement of people around locations and providing deep location-based insights. Indeed, the level of insights this data provides is simply unavailable using other methods. However, be aware, not all mobile data is created equally.
There are two main methods for producing mobile phone data used for location-based analytics — GSM and GPS. But how do these two methods match up?
Cellular network data (aka GSM data) uses cellular tower triangulation to identify the location of a phone. The phone signal is picked up by three or more towers making it possible to estimate the location of the phone based on its distance from the towers. The important word here is “estimation”. This is because the average accuracy of GSM data varies between 500m to 1,500m. The closer the phone is to an urban area the more accurate the “estimation” due to the greater number of cellular towers in these locations.
GPS Gives Granularity, GSM Doesn’t
GPS suffers much less from this lack of accuracy. It usually precisely locates a phone within the accuracy of meters. This is key as it means GPS can be used to accurately analyze specific locations, such as highstreets and individual buildings. This accuracy means you can measure the amount of people that go into each retail outlet. You can see where they came from, where they went afterwards and how long they spent in each location. Such incredibly granular and rich data allows for highly precise visitor maps to be developed, plotting journeys and habits.
So, for example, because you know where visitors traveled from you can identify catchment areas. Because you know what other locations they visited before and after walking into your store, you will know the cross-shopping patterns of your customers. This reflects one of the main advantages of GPS — you can work at the level of an individual property or location. With GSM, you cannot work at this level of granularity. GSM is therefore simply not an option for someone looking for location-based information concerning specific retail locations, office blocks or properties.
GSM Lacks Consistent Signal Coverage
Another limitation with GSM is the lack of consistent device ID and, therefore, signal coverage. This is because many telecom companies replace the original device ID with an ID that is rehashed around every 15 minutes. This means you cannot track the complete journey of a given device, because you lose its continuity. This dramatically limits the data available for analysis. For example, if you don’t have the continuous sequence of IDs for a phone, it’s hard for you to track and identify where people came from and where they go to after visiting a given location. This makes catchment areas difficult if not almost impossible to specify using GSM data.
Cross visitation is also impossible to calculate using GSM. This data point allows you to see for each store, where else its customers visit and how often. Cross visitation data enables business owners to build a competitive matrix to visualize how loyal their customers are and their relationship with other stores. You are only able to develop such insights if you have the granularity of the geography that GPS offers. Using GSM this is just not possible.
Managing and optimizing app data
GPS uses the data from consenting mobile app users and enriches it by algorithms to provide unrivaled accuracy. While the app data scene is continuously changing, access to this data source is growing as apps are constantly being developed. The installation of apps increased by 50% from 2019 to 2020, with growth rising a further 31% in the first quarter of 2021, according to app analytics firm Adjust.
The optimum app mix and user base is consistently maintained through the constant monitoring of this data source and reviewing the number and type of apps that are partnered with. The ability to manage and optimize app data, together with the application of advanced machine learning methods, allows GPS to offer a stable and consistent trend of data, with geographical accuracy far beyond what is available using other technologies, such as GSM.
The limited advantage of GSM
So, are there instances where GSM would be a suitable option? Well, GSM definitely has its advantages. But the advantages mainly reside in the cases where you want to analyze a larger area, where meter level granularity is less critical. For example, let’s say you want to see how many people are in the city during night, and during the daytime. GSM is a good option for that. But because it lacks accuracy, it cannot provide data for specific properties or locations. This is why providers of GSM data partner with PlaceSense. They need the addition of more precise and clinical data, because they can’t match the data GPS provides.
Access to such precise data is a game changer for the many industries and sectors that benefit from accurate location-based intelligence, such as commercial real estate, retail, out-of-home advertising (OOH) and smart cities.
The overall superiority and accuracy of GPS data has already been endorsed by many major players in retail, commercial real estate and smart cities. It has also been endorsed by Fachverband Aussenwerbung e. V. (FAW), the professional association for outdoor advertising in Germany. FAW uses PlaceSense as the location intelligence data experts for OOH for Germany to understand the dynamic frequency of people around OOH locations.
For the first time companies have cost-effective access to highly detailed and up-to-date data about locations and the characteristics of the people who visit them. So, when it comes to using technology to empower decision-making with precise location-based visitor data, GPS is literally streets ahead.
About Eyal Lanxner
Eyal is Chief Technology Officer at PlaceSense and has over 20 years of experience in data mining and business intelligence across multiple business domains.