Using Iris to track conversations (photo by John Schnobrich)

Using Iris to track conversations around design thinking

I’m writing a content strategy for Design Club. We want to build a network of 50 after school clubs in 2019, and need volunteer mentors from the design community to help us. Designers tend to love the idea of what we’re doing – but most of them haven’t heard of us.

Brandwatch is great at tracking conversations over time on social media. And its new AI analyst, Iris, can pick out a spike in mentions of a specific word or phrase and give an instant summary of the things driving that surge: these could be links, influencers, pieces of viral content (videos, gifs or images) and/ or hashtags.

Design Club is a non-profit social enterprise and we need to find a low cost way to raise our profile. Social media is an obvious channel, but we need to use it effectively. As a starting point, Iris is helping me understand relevant conversations that are already happening online.

Speed to insight

For this exercise, I looked at trends over the past two months for “design thinking”, “future skills” and “STEAM” (Science, Technology, Education, Arts and Maths). These three key words – or terms – are important to both Design Club and the audience we’re trying to reach.

I added each key word as a query in Brandwatch. I set the query locations to London, because that’s where Design Club is based. We’re still small and want to build a solid presence here. Our outreach involves a lot of face to face engagement: we want to meet and chat to as many mentors as possible.

Over the past two months in London, there were around 3000 mentions of “design thinking”, 2000 of “STEAM” and just 300 of “future skills”.

Here’s what the analytics dashboard looked like, with Iris switched on:

Mentions for future skills design thinking and STEAM in Oct and Nov 2018

Brandwatch dashboard showing mentions and peaks for Design Club key words in Oct and Nov 2018

I’d usually have to scroll through hundreds of individual mentions trying to pick out any trends. Iris has saved me a lot of time and effort by immediately pulling out 4 peaks in conversation: (one in “future skills”, one in “STEAM” and two in “design thinking”) and listing the factors behind them.

Stories behind the data

When a topic has relatively few mentions, as with “future skills”, it only takes a handful more for a spike to occur. This is what happened on 21 November with Peak A. Iris quickly tells me that there were no significant drivers – so I can move on.

Peak B is more interesting. Iris shows that the increase in mentions of “STEAM” was driven by Ada Lovelace Day (#ald18, #adalovelaceday, #womenintech and #womeninscience). This is good to know.

The high spike for “design thinking” (Peak C) is driven by two hashtags (#muxl2018 and #muxl). These both represent Mobile UX London. I wasn’t aware of this event until now.

Peak D is driven by two sector news stories. One is Ideo breaking its silence on design thinking’s critics; the other is an academic centre announcing a public debate on design and ethics. The first story is especially relevant to Design Club.

Peak B driven by Ada Lovelace Day 2018

The Iris sidebar shows key factors driving Peak B

How does this help content strategy?

It’s useful for Design Club to be able to join any conversations that are relevant to both us and our audience. It’s good to have an idea of where and when these conversations are likely to take place in future.

There’s an ongoing debate around design thinking and while it’s not Design Club’s place to critique other people’s interpretations, we value design thinking as a life skill. It’s important for us to have a role in that conversation. I’d like to plan some content (blog posts and opinion pieces) for 2019 that set out our ideas more firmly.

Meanwhile, upcoming events like Ada Lovelace Day and Mobile UX London can be added to the content calendar for next year. We could consider contributing to those events, either in person (as speakers, contributing producers or attendees) or by simply supporting (liking, sharing and commenting on) their content.

Bots to the future

AI is becoming established in many ways across social media marketing, from chatbots to automation. With Iris, Brandwatch has introduced AI to its social listening platform for the first time. No doubt there’ll be more AI to come. If you’d like to know more, read Brandwatch’s blog post introducing Iris and listing six jobs it can do for you.

Photo: John Schnobrich via Unsplash

For more on how tracking user behaviour helps content strategy, try:

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