An article from anthropologists at Arizona State University on the types of methodological challenges of studying online environments and behaviour. The article is an interview with the research team who were investigating online channels for social support for weight loss. The interview covers a range of research challenges they came across, from collecting data to ethics and privacy, and is an interesting read and argument for the evolution of ethnography to incorporate more of online behaviour as it has become so integrated into daily life for many societies.
From one of the researchers, Professor Amber Wutich:
“Ethnographic research has always been adaptable as humans themselves are in the myriad environments we inhabit. Understanding the new, social world humans experience online helps us more scientifically and definitively answer questions like, how do people construct meaning there? What sorts of cultural norms govern online behavior? How do social worlds created online differ from the spaces people physically inhabit, and how does that affect life in the “real world”? ”
Triangulation in social research is a technique to increase credibility of research outcomes. Borrowed from navigational and land surveying techniques, it aims to overcome biases in single method, researchers, data sets and/or theories.
There are 4 types of triangulation:
- Methodological – two or more methods are used to collect data
- Data – two or more different data sets
- Researcher – two or more researchers investigate
- Theoretical – two or more theories approach the often described as a way of ensuring some form of ‘truth’ in research results. This is one reason to use triangulation, but another is to intentionally draw out some of the differences
As an anthropologist I think we constantly aim to reduce the influence of bias in research we do. A competent, self-aware anthropologist is very good at observing phenomena from multiple viewpoints. Of course we can’t always overcome our own biases, so designing your research (and synthesis) with triangulation in mind can contribute to overall outcomes.
A great interview with Gordon Milne on this podcast about what ethnography is, (and what it isn’t), and tips on how to do it. Gordon Milne is IPSOS Asia Pacific’s head of qualitative research and the podcast is part of a series of market research techniques, trends and practices. There are a couple of things mentioned in it that I completely agree with and are worth calling out, especially for those who don’t have the time to listen to the whole podcast (44mins in total).
He distinguishes between three types of qualitative research using ethnography: i) 5-7hrs of participant observation which he terns as “pure ethnography”; ii)”immersions” as 2-3hrs with participants, and ; iii) more traditional qual research incorporating ethnographic elements.
Some key points:
- Ethnography is about seeing things and being there in the moment – not just talking about them (as it is in a question and answer format).
- In ethnographic research you are looking for the unarticulated, and not merely describing what people report that they do.
- Participant-led – This is such an important distinction between ethnographic research and more traditional qualitative methods such as interviews or focus groups. It’s far less about following a discussion guide and much more about being in the moment and taking cues from the research participant, such as matching the participant’s mood and energy levels. Make it a conversation, not a Q&A interview, and follow the order of their day, not them fitting to the order of your topics/questions. Be naiive – let the participant guide you.
- Pre-field preparation – Get clear at the start about what you’re trying to achieve (i.e. the research question); recruit the right people; help prepare the participant so they know what to expect
- “Pure ethnography” requires a trained ethnographer or a senior, experienced qualitative researcher (~15min into the interview)
EPIC have announced great new learning initiatives for its members. A series of courses and talks will be offered throughout the year in order to deliver on EPIC’s purpose to advance ethnography in industry. Some of the courses look to be taken from popular seminars from the 2016 conference. The only downside is the timezone issue if you happen to be based in incompatible timezones with the US, like Oz…
You can find out what courses and talks are offered here
If customer-centricity is the new metric for organisational success, a new study has revealed what a company’s insights team needs to look like in order to make this happen. The Insights 2020 study by global consultancy Kantar Vermeer highlights the evolution of internal insights teams from being part of marketing to becoming an ‘insight engine’ within an organisation. An insight engine has particular ‘alchemy’ of capabilities that were found to be the key differentiator for customer-centric growth across 10,000 companies. Organisations that possess these capabilities (and that ahve obviously proved their worth) have the ear of the CEO twice as often as other functions. Key to these teams success was having independence as a function with direct reporting into executive level. Another KEY capability of these front and centre, empowered and valued insights functions in customer-centric organisations is data. Not just lots of it, but organised, clean, useable, searchable – and sharable. This combination seems to me to lead to the customer having a voice at the table and not just locked away in market research insights reports kept in particular parts of an organisation. Love it!
Harvard Business Review picked up on the Kantar Vermeer study and uses Unilever as a case study for how its working and to do it. Have a read 🙂
A few key points about “successful insights engines” for those short on time. HBR identifies 10 characteristics of these ‘superior’, strategic insight teams which can be grouped into Operational and People characteristics:
- Data – making sense of it and extracting value
- Independence – sit outside marketing (its traditional home) and report in to C-suite
- Integrated planning – being involved in where to play and how to win strategic conversations within the business
- Collaboration – with customers and other areas with the business (evolving from being an effective service provider to shared goals and partnerships)
- Experimentation – embrace a culture of experimentation such as hackathons, mentoring programs with startups and collaboration platforms and Shark Tank like pitching of ideas to execs
- Forward-looking orientation – less focus on history to predict future performance and more focus on real-time
- Affinity for action – focus as much on strategy as on data and who they recruit
- Whole-brain mindset – recruiting and consciously supporting holistic, creative, right-brain skills as well the more traditional, (and organisationally familiar), left-brain thinking to move away from default thinking and gather multiple perspectives (and strengths)
- Business focus – programs to build business acumen, linking staff bonuses to business performance
- Storytelling – constructing a message through engaging, even provocative, narratives
The article concludes:
Much of what insights engines at any firm do is gather and analyze data. But today that is the minimum needed for success. Being able to translate this capability into customer-centric growth is what distinguishes winners from losers.
I’d add that your insights engine doesn’t need to just analyse data and translate into growth, but also internal insight teams need to know what and how to research otherwise the data just won’t be there to achieve those important strategic insights.
I am always interested to hear the ways fellow researchers, designers or consultants reach their insights. I found this article today that talks about distracting your brain or creating ‘incubation periods’ to allow the creative process to come forth and almost magically give you the clarity and insight you need.
I’ve written on this before about what constitutes productive time as a researcher. This new article has a bunch of links to other research on why disconnecting and doing something different can often create the spark needed for the major a-ha! moment. The article concludes that “what all of this research suggests is that peak creativity happens when we’re pleasantly absent-minded”.
Pleasantly absent-minded – sounds blissful!
One of the suggestions the article puts forward is to plan time for disengagement and distraction. I imagine this is what we intuitively know (but can sometimes forget) when we go for a walk outside to get out of the office, or go eat something or do a load of laundry when working from home. These meditative, repetitive or seemingly mindless tasks carry great power and may in fact lead you to your next insight!
I had a beautiful moment today in customer research of revealing the difference between what people say they do and what they will actually do. I had created an activity for participants to complete in pairs around how they wanted an app to work which involved them choosing desired features.
As I looked around the room and saw the feature wishlists of the pairs forming on the walls around the room, I could see a range of features we had discounted based on earlier user research. I was trying to figure out what we had possibly missed from our earlier analysis, or what may be unique to these particular users we had recruited. We finished the activity and the pairs shared their rationale behind each of the features they had on their lists. Each pair explained their thinking clearly and quite convincingly. While I was listening to them, it made sense why they wanted these features.
However, once I asked them to choose their top three features, the conversations were so rich and full of data about what really mattered that those logical arguments from the activity before started to fall away. Going through a process of prioritising and explaining why they were choosing what they wanted the app to be able to do gave us far more insight into what this app needed to be able to provide. We started to move past rational thinking and tap into how the app could fit into their lives and habits. This activity was really helping to get to the things that really mattered to potential users, and therefore what we needed to focus on. It was a great reminder of how ranking and prioritisation can help to tap into what users really want, and move beyond what they think will be ‘nice-to -haves’, even if they can make a logical reason for wanting a feature.