Our customers have regularly asked us whether we can index device information against make/model strings in addition to User-Agent strings.

The driver for this in general is that they have an installed base of apps or SDKs which return manufacturer and model information, but they are unable to use this data programmatically. In some cases, the data has been accumulated over time in the form of unstructured data (data lake), and a means is required to convert the data into a structured format whereby it can be usefully analysed.

Accordingly, DeviceAtlas has been extended to support the app environment, in addition to web and mobile network environments. After a successful period in beta, this functionality is now fully available to DeviceAtlas customers.  

How does it work?

DeviceAtlas for Apps uses the existing DeviceAtlas APIs, with a new data file containing the make/model strings indexed against device information. By submitting the make/model string derived from the app to the DeviceAtlas API, the full device details are returned.

For example (the Samsung Galaxy S9):

Example use cases for the data

1. Simplest analytics use case: device naming

Why struggle with unrecognisable model numbers when you don’t have to? The use of the marketing name has value in two areas. Firstly, it is the consumer recognised name for a product, and hence device reports are easily understood.

Secondly, the marketing name provides a powerful roll-up parameter. The reason for this is that there can be many model numbers that share a single marketing name. For example, there are over sixty model numbers that make up the Samsung Galaxy S6 family, and accordingly to understand the population of this device, it is necessary to sum the results for all the model numbers.

DeviceAtlas makes this very straightforward to do by grouping results by marketing name.

2. Richer analytics: providing additional parameters

The availability of a wide range of additional datapoints about devices permits analysis across diverse variables, yielding deeper insights into device and app usage patterns.

Example questions that can be answered:

• Does the age of the device (year released) affect conversions?
• Does screen size correlate with time spent in-app?
• Do results vary by brand across nominally similar devices?
• How significant is the impact of device performance (CPU+ RAM) on results?

3. Cross-environment data comparison

DeviceAtlas data is used in leading web analytics platforms and monetisation platforms, and the extension of DeviceAtlas into the Apps environment permits analysis of data across web and app ecosystems, since there is a key field that links both environments: the DeviceAtlas device ID.

For publishers with both web and app offerings and a data lake, DeviceAtlas provides the means to create integrated views of device activity across environments, effectively merging silos.

4. Alternative lookup mechanism

In the event that the User-Agent string from a device cannot be interpreted, if the make/model string is available this can be used as an alternative input to the DeviceAtlas API.

Similarly, if the app make/model string is not available, the User-Agent string for the app webview may provide an alternative mechanism.

These cases apply when both DeviceAtlas for Apps and DeviceAtlas for Web are used together; a combined file which permits a single API call with either UA string or Make/Model strings to be made.

Summary of benefits

• Easy to use in conjunction with existing DeviceAtlas deployments; the same high performance API is used, with the same robust download mechanism and processes.
• Rich information on devices, using the same DeviceAtlas Device ID to provide easy cross referencing between web and app datasets.

So, if you’d like to take a look at it, let us know: fill out the form at and we’ll set you up with access.