How future trends are being shaped by digital conversations
As people get exposed to new ideas through online communities, new trends emerge. This is why online behaviour across different channels can provide us with data to predict new and future trends.
At the Observe Summit 2022, we discussed this with Alessia Clusini, Strategist and Co-Founder of Trybes Agency; Rodrigo Milanez, Director of Consumer Insights at PVH Europe; Rose Crabb, Director of Clients & Projects at Discover AI and Bobby Taylor, Managing Director UKI at Talkwalker.
Social data implications on speed and agility
With tons of digital conversations taking place at any moment, there’s an abundance of data that needs to be analysed, which has an impact on the speed at which we can uncover valuable insights.
“The next thing, from my perspective, is about how we interpret and shape this huge graft of content that we’re being exposed to,” explained Rose. “So, finding agile ways of working across lots of online content, lots of discourse, lots of online narratives and conversations to accelerate the process of finding the interesting cultural nuggets within there - the interesting cultural shifts or category shifts or ways of getting closer to consumers.”
Rodrigo agreed, saying, “You are bombarded with data, and precisely because of that I think it’s important to keep social intelligence or any kind of digital insights front and centre whenever you’re discussing brand strategy or new business opportunities.” He further elaborated that, whilst there’s no speed-related challenge with collecting the data, you can’t translate the data into insights at the same speed.
Alessia pointed out how this has created new data translation needs because more and more departments need to handle first-hand consumer insights. She also highlighted the importance of data synthesis, “it comes from so many different sources and in different formats that at the end of the day, we do this to help decision makers to understand what trends and signals are important for their business.”
Ensuring quality around data and discovery
Considering the challenges of analysing online data, it’s crucial to ensure that you collect high-quality data for high-quality insights while maintaining the human element.
Rodrigo explained that having an initial briefing and making it as focused as possible is useful because you know exactly what problems you’re trying to tackle. “But I think where we can provide the most value is actually when we’re free to explore…let the data surprise us,” he explained. “Sometimes, you don’t even know what questions to ask, so the data will actually help you ask the right questions and lead the way especially if you don’t have a very specific topic to tackle.”
Alessia agreed, saying, “It’s the hardest part about our job - to ask the right questions.” While the tech and tools are there, it’s easy to get overwhelmed when narrowing down on the right questions.
“So our way is, again, through netnography. We created this methodology called topic-graphic data and tribes analysis,” she explained. “Topic-graphic means people that gather around a topic as opposed to gender, age, etc. demographic…” It involves looking at a macro theme and then decoding it to create a map of topics that eventually become keywords or queries. These can be plugged into the platform or social channels.
Rose explained how they take a similar approach by “making the questions into one single, intractable question and that sometimes involves taking a step back so it’s almost looking at things from a slightly high order...”
Taking a multi-source approach
Casting a really wide net is also important. Rose explained how in addition to looking at the social data for Discover AI, they also bring in things like emerging brand websites, blogs, or experts talking about certain subjects or people discussing things in forums.
On the same note, Rodrigo explained how the best insights come from multi-data source analysis because it gives you a 360-degree view of what’s going on. “People lie in surveys and social media but not necessarily in search. So if you’re using all those multi-sources then you can actually compare them against each other and see ‘Do I see different things or are they actually working in the same direction?’”
Alessia agreed, pointing out the huge opportunity for social intelligence is to basically give breadth to qual and give depth to quant, and actually complete them.