Unstructured Data and the Insights Ecosystem
Social data has been part of the business world for well over a decade now. And yet, it seems as though today’s marketing and communications leaders aren’t getting all they could from it; per the 2019 Business & Innovation GRIT Report, fewer than 5% of market research buyers view social media analytics as representing the greatest opportunity for innovation in the industry. Why is this, and how can we help to address this issue?
More data is produced today than ever before. It’s estimated that 90% of the world’s data has been produced in the last 2 years. Every day, 100 million photos and videos are shared on Instagram; 500 million tweets are posted; and 3.5 billion snaps are created. Businesses are hungry for the learnings that can be derived from these data, and from publicly available data on other social platforms, blogs and forums. They represent a treasure trove of consumer feedback, if we use them properly.
The original use case for social data in the marketing and communications world was ‘transactional,’ i.e., focused on the resolution of specific individual issues, typically either customer service- or PR-related. Then the market matured to a certain extent, leading to a lot of excitement around the myriad possibilities of how to use these data: dreaming up ways in which, to name just a few examples, they could be used to predict stock prices, to spot emerging issues of national security relevance, to support HR, and to feed in to market research.
Social data suddenly represented an all-purpose panacea, this perception driven not only by the promises of vendors but also because of internal corporate dynamics where becoming an expert in the relatively arcane world of social data was a quick way to get noticed and promoted. However, not only were some of the tools in use not actually worthy of being standalone products, but were merely useful features – arguably logo-finding falls into this category, for example – but more importantly, social data crashed into what Gartner calls the Trough of Disillusionment (the part of their ‘Hype Cycle’ for emerging technologies) because it didn’t live up to the promises of the vendor community. The emperor wasn’t naked but he didn’t leave much room to the imagination, and it was a little underwhelming. The market acted on this, and consolidation took over: among others, Salesforce acquired Radian6, Twitter acquired Gnip, Crimson Hexagon merged with Brandwatch, Meltwater acquired Sysomos, and recently Ipsos acquired Synthesio. Yet there is still a preponderance of social listening firms out there; do we really need a list of the Top 48 social media monitoring companies?
The critical success factor is to make sure you’re using social data in a focused way, and not just to replace something else for the sake of it. That’s absolutely not to say disruption is bad – there are a number of areas where the analysis of social data can efficiently improve ‘legacy’ insights approaches – but it’s important to be realistic and practical about this, and to figure out how this analysis can plug gaps or act in a complementary fashion. The best way to think about how to do this is to do so in the context of how the analysis of these data fit into the existing insights and data ecosystem:
- The first, and most well-established type of research is survey-based quantitative research, i.e., studying how consumers respond to a set of predefined questions focused on your brand, product, or service. Suppliers here include Ipsos, GfK, Kantar Millward Brown, and many others.
- The second type of measurement we come across is focused on behavior; either purchase behavior – think of services like Shopcom, IRI and so on – or tune-in / browsing behavior as measured by Nielsen and comScore here in the US, and often by Kantar in other parts of the world.
- Third, we have a newer type of measurement, ‘customer experience measurement,’ focused on understanding customer reactions to a particular aspect of the sales or service experience. Suppliers here include Qualtrics and Medallia; it’s the type of research that you’ll see in action if you buy something at an Apple Store, when you’re emailed a survey asking how your store experience was. Those data are used at the store level but also rolled up for business-wide learnings.
Where we see leading business shining today in is in their rigorous approach to how to use social and other types of unstructured data in this context. The key is to ask yourself these two questions:
- In the context of each the three measurement approaches, what specific knowledge would drive competitive advantage? In other words, what are the gaps in my existing measurement framework?
- Can I use social data to get to the answers provided by my existing measurement stack more quickly, more economically, or with more accuracy?
To use just one example, if we focus for a moment on customer experience measurement and potential gaps, what can social data tell me about the experience of those who visit my stores but don’t buy anything? What can social data tell me about the buyer experience at competitor stores? I’m unlikely to be getting those insights from my existing customer experience measurement. A range of possibilities is immediately opened up here, with implications not just from an operational perspective, but also in terms of communications.
Using a focused approach that’s designed to answer specific questions will help you to get the most out of your analysis of unstructured data and transform it from a shiny object into strategic intelligence. It will help you make better decisions in terms of your product, your service, the experience you’re giving to both customers and non-customers, and to the communications around all of these elements of your business.
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