Celebrating the launch of our upcoming Qual and Big Data event, Fusion 2019, ESOMAR is excited to announce the launch of our Qualitative week. Offering a number of specially curated global qual impact case studies and qual event impressions through the years, keep an eye out on Research World and our social media channels (#esomar) for impressions and great content!
And please enjoy piece one from our RW editorial Qual focus below. Many thanks to all that contributed!
–From your Research World Editors.
We’ve heard a lot about social data in recent years and what can be gained from social intelligence as a market research methodology. Whilst this is useful for bringing the world of traditional market research together with big data, there is still, at times, a disconnect. Part of this disconnect occurs as many traditional researchers have not had the opportunity to interact with, or make use of, social data. However, we believe it is possible for those employing even the most traditional of research methods to make use of and gain from social data. Depending on specific research objectives, there are various data streams that can be accessed through social intelligence:
1. Search data
Using search data, we can gain an overview of the keywords and questions people are entering into search engines like Google and Bing, allowing us to identify trends, competitors and key information needs. By exploring search data we can also understand the language used, which can help build effective social listening searches. By recreating Google searches using search data, we can relive user experiences.This can be especially useful for brands trying to improve or maximise their search engine presence as they can address gaps through paid activity or content marketing.
2. Social listening
Social listening allows us to observe what consumers are actually discussing around a topic, product or brand. Not limited to social media only, social listening also pulls in forum conversations and news to collect data from across the web. Searches are built on a set of key terms informed by desk research and existing knowledge. By harnessing social data we can capture in-the-moment, spontaneous responses and behaviours. As conversations are naturally occurring, we gain further insight into the language being used within the context of a set topic, and an understanding of some of the key discussion points.
3. Passive data metering
With informed consent, we can passively collect device usage data (app downloads and usage, website visits, search data, shopping journeys). This data offers the opportunity to gain a detailed view of online behaviours without placing a heavy burden on the participant. Passive data can be used to map online purchase journeys or create a digital footprint (understand where and how a target market spends time online).
4. Social ethnography
How consumers project themselves on social media, and who they choose to engage with, gives an insight into their broader interests and priorities. With permission, we can conduct social media observation amongst individuals, which provides further insight into the broader context of a target audience; their interests, hobbies, content they engage with and brands they choose to follow.
How this helps
We don’t believe social qual is superior to primary qual; rather we believe that using social and big data qualitatively can enhance traditional qual, for example:
- Search data helps to inform how we ask questions in primary research – by uncovering the language being used spontaneously online and the types of challenges being explored. It can help us naturally frame the conversations we contrive, so they feel natural. We’ve found this particularly helpful in framing healthcare research.
- We can join the dots between self-reported behaviour and what we observe happening in the online space through social intelligence and passive data collection. This can help us build a rich picture of the customer journey in particular categories or of specific need states, when participant recall would be a barrier. This has helped us unpick complex customer journeys for clients in both the travel and automotive sectors.
Use case
We recently worked with a football club that had concerns about a lack of female fan engagement. The club had identified that whilst there was high attendance of female fans on match day, this did not translate into engagement with digital activity. In order to understand female fans further we fused a survey with social listening, in-home interviews and cultural context. Our approach allowed us to segment female fans into personas and understand how their relationships with the football club differed, as well as the types of digital content that interested them. The insights informed the club’s activity and media planning.
In our experience the tools outlined above can deliver an additional layer of insight unachievable through primary approaches alone. We therefore encourage everyone to explore the insights available from free-to-use and paid big data tools, in order to inform and improve their primary research stage.
