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Case Study: EntSight on Comparing Perceptions of Smartwatch Features Case Study
This article, featuring analysis done by us here at EntSight, compares perceptions of smartwatch features between two groups – Journalists and influential Smartwatch Enthusiasts.
Brandwatch Audiences was used to identify, filter and verify both audience groups – as well as gather initial insights about them. This data was then imported into Brandwatch Analytics to track and categorize conversations from these two groups.
If you have a Brandwatch account yourself, you can also explore the data that was used to create this report in Insights Central.
Step One – Using Audiences to Find and Evaluate Groups
Our first step here was to use the tool to track mentions relating to directly Smartwatches and popular Smartwatch models that Twitter users had mentioned in their posts – we build this using the kind of Boolean normally used in Brandwatch Queries.
We then filtered for individuals using to ‘account type’ feature to ensure that we were identifying actual journalists rather than profiles from news organisations and finally used Audiences ‘Professions’ filter to identify journalists specifically.
The end result, as shown above, was a list of just over 1,000 journalists who met the criteria.
We can see from both the interests (books and technology) and common keywords (Mashable, CNet, Macworld) that we have a representative group of Technology orientated journalists here. Final step here was to save that group in Audiences.
The first step here was to ensure this second group does not contain any of the journalists from the first, and so we used the Boolean ‘NOT’ feature using keywords such as “journalist”, “blogger”, “editor” and “author” to ensure they were filtered out.
Secondly we again applied the keyword list relating to Smartwatches and related products, as well as once again filtering for individuals only.
Finally, in order to find a more focused group we used Audiences to filter further for those interested in Technology and based in the United States.
Step Two – Building Categorisations
Before importing our audience groups into Brandwatch we needed to build the rules and categories which would enable us to identify the Smartwatch features our audiences were talking about.
First of all, we created a Category set in the ‘Tools’ section of the platform which included the most prominent features of Smartwatches:
We then created individual rules for each of these categories that used Boolean to identify keywords, for example:
With these rules and categorisations in place we were then able to focus on importing our audience groups for analysis in Brandwatch.
Step Three – Exporting Audiences and Importing into Brandwatch Analytics
With the two audience groups now identified, we selected 1,000 profiles from each to use as our target group for analysis.
We then used each list to create Brandwatch Query groups using the author: Boolean.
Each query was set to track data from January to September 2016.
Step Four – Dashboard Build
The first step here was to set up a new dashboard, remembering first to filter the dashboard so we were only seeing results relating to our category and rule set created earlier.
This will ensure we only see data from audience conversations relating to Smartwatch features.
Next we create a set of Dashboard features to allow us to compare results from both groups – these were:
- Key Insights
- Overall Conversation for feature categories
- Conversation over time
- Trending vs Fading Topics
Step Five – Analysis
Both audience groups saw a similar level of conversation relating to Smartwatch features here, with Operating Systems seeing the most interest in both cases.
Where there are differences we can dive deeper into those conversations to understand why.
Journalists seemed more interested in ‘Fitness Tracker’ features in general than our focus group of Enthusiasts.
Looking at the topics related to these conversations in particular we can see that this was largely due to the press interest in Apple’s fitness tracking features during the Apple event which the public didn’t talk about as much.
This is an important insight for Smartwatch brands, especially if you compare overall share of voice between the two groups.
Apple was present in a much larger percentage of the Enthusiasts’ conversations. So even though Apple’s fitness tracking features didn’t resonate with buyers, it appealed to them in other ways.
Analysis of conversation over time suggests that both audiences responded to key events relating to Smartwatches such as the announcement of the Michael Kors Android powered Smartwatch in March, WWDC in June when Apple’s WatchOS 3 was announced and the September 7th announcement of the Apple Watch Series 2.
Both audiences saw similar levels of sentiment towards features. But Storage was clearly a more important positive feature for consumers suggesting a different priority for those actually buying the products.
In both cases the lack of positive sentiment relating to Heart Rate Monitors, and even negative sentiment in the case of the Journalists group, would suggest that this feature has yet to impress audiences.
In the case of both audiences, the topics of conversation featured a trend towards the recently announced Apple Watch Series 2 and WatchOS 3 and away from Android Wear and Samsung Gear.
In conclusion, despite some variances it is clear that both audience groups have reacted in similar ways to Smartwatch features and we believe that in many cases the Journalist groups’ coverage of announcements and events has influenced the Smartwatch Enthusiasts to a considerable degree.