Analysis of ratings and reviews of the Philips Hue app

Signify

  • Customer case
  • Data Analytics
app ratings analyse
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Erik-commercial-manager
Erik van der Kooij
Commercial Manager
3 min
21 Feb 2020

The Internet of Things (IoT) system from Philips Hue, part of lighting manufacturer Signify, includes a mobile app. This allows end users to control their smart-home lamps both locally and remotely. They rate their experiences with the mobile app and the rest of the IoT system via Google Play or the App Store. Due to the large number of ratings and reviews, it is time-consuming to analyse them manually and recognise recurring themes.

Signify asked us to contribute to a solution that makes it easier for them to analyse the ratings and reviews. Based on the insights obtained, the lighting manufacturer can set the right priorities for fixing bugs, improving existing features, and developing missing features.

Our approach

Hundreds of ratings and reviews are received per release of a new app version. The main data points that are collected are the rating (1–5 stars), the date of placement, the app version, the platform, and the text.

To gain insights from these data points, it was important to be able to classify the texts. We did this with a Python script that first translates the review text into English and then determines the categories based on keyword matching.

An example of a 4-star rating + review (Google Play Store)
An example of a 4-star rating + review (Google Play Store)

Using the script, we enriched the available data points with one or more category labels. For example, we can visualize per period and/or per app version how certain categories are assessed.

The result

There is now better insight into the opinions and experiences of the end users with regard to specific parts of the app. We can also use the script to monitor the influence of improvements and bug fixes on the ratings that users give.

This new way of working helps Signify adjust the roadmap based on feedback from its own users. In this way, they can improve the user experience of the IoT system. By focusing on categories with a lot of reviews and a low rating, the most impact can be made. For example, geofencing has been improved on the basis of this process.

The script can be further improved in the future by applying machine learning. For example, it has become possible to automatically detect new categories.

Example of rating distribution by category for an app version (fictitious data). Category 7 would be prioritised in this example: there are a lot of responses with a 1-star rating.
Example of rating distribution by category for an app version (fictitious data). Category 7 would be prioritised in this example: there are a lot of responses with a 1-star rating.

Want to know more?

Commercial Manager Erik would be happy to talk to you about what we can do for you and your organisation as a data partner.

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