The rise in voice apps means a new avenue for companies to understand what content delights their customers best, and this is done through voice app data analysis.
When digital magazine apps came about, one of the biggest benefits was being able to understand what content is most effective. All this wasn’t possible in the past unless you surveyed your readers.
For reader-centric and innovative digital publishers, analytics and data analysis have opened up a whole process of monitoring and optimisation, constantly discovering insights from their audiences, all while their readers consumed their content.
Now, a new channel is opening up for digital magazines and publications. Voice apps can reveal new insights about readers that will allow publishers to better engage with them as they try to figure out the best way to structure their voice apps.
So what insights can we glean from voice data analysis? Let’s take a look below.
In voice searches made through virtual assistants or smart speakers, users can make either explicit invocations, which are specific commands that call upon a specific app or brand i.e. “Alexa, what are the latest headlines from the CNN?”, or implicit invocations, which are voice commands that are not specific and leave the virtual assistant to draw up a suggestion i.e. “Alexa, what are the latest headlines today?”
While no doubt both provide insights, data coming from implicit invocations could be valuable as unlocking the secrets to securing the top search result spot could mean your voice app or content being discovered by new users.
You can also find out about the language used and perhaps some vital demographic information.
How about the tone or pitch of the users when doing voice searches? By aggregating and analysing voice commands or searches associated with your brand, you can use this to understand what is the general sentiment for your brand.
Today, sentiment analysis is done through text and even emoji usage across social media posts, blogs, and other text-based content. But with advances in voice recognition technology, this is certainly going to become the next frontier in sentiment analysis.
Intent and parameter
Understanding what users mean exactly during voice searches, i.e. “get me the latest news”, which is the intent, and the specific contextual request, i.e. “about politics”, which is the parameter, can provide insights on your audience’s behaviour. It will certainly help when optimising your voice app to deliver the right answers.
Similar to the conversion paths or flows when a visitor goes through your site from the time they enter up to the time they bounce, pathing in a voice app shows the conversational steps that your audience went through as they interacted with your voice app.
Aside from the conversation paths, it also tracks what actions the audience took after. This can provide insights and clues on how to shorten conversion paths and perhaps get your audience to where they want to reach faster.
A statement commonly heard in voice app design is that there are no wrong questions – just questions that weren’t planned for. Errors and null statements, which can be tracked with voice app data analysis, can give insights of what users want that your voice app is not providing.
A frequent check on this and constant adjustment will eventually allow your voice app to deliver the right content to your audience.
Voice apps and voice search are new channels that will eventually be used by your readers to interact with digital content. Any successful digital strategy must include monitoring and continuous optimisation, and this can be done through voice app data analysis.