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Using health monitoring as a model for social media insight | Nomensa

Using health monitoring as a model for social media insight

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2 minutes, 57 seconds

Can monitoring social media data help us understand digital behaviour?

In my years working as an intensive care nurse, I saw first-hand that the skills I learned there were transferable to my current role as a social strategist at Nomensa. Let me explain.Measuring human health is no mean feat. Hospitals are filled with a range of devices that measure different aspects of health, including heart rate, oxygenation levels, and blood pressure. The 100 trillion cells that make up the human body must work in a coordinated way within incredibly tight tolerances to achieve a healthy state. Interestingly, we can consider the social media ecosphere in the same way – each person acts both independently and in various groups when they participate in the world of social media.Doctors use health monitoring data to localise health problems, so they can investigate them further and prescribe a treatment. In the same way, UX professionals can delve more deeply into social media data to look beyond the noise thousands of broad observations generate. Similar to usability testing in a lab, social media monitoring helps UX professionals observe what people are actually doing.


Applying the metaphor

Just as we know that one heartbeat does not provide enough information for a clinician to make an accurate diagnosis, we know one tweet does not provide enough data to make a case for redesigning a customer journey. But, with social media monitoring tools, we can build dashboards that let us see trends unfolding in real time. By analysing a broader social dataset, we can understand if a significant amount of people have had a similar experience and, therefore, whether a redesign may be necessary.A great example of applying this macro perspective is the way Foursquare have used their data to predict the day on which everyones New Years resolutions would end abruptlyalso known as Fatty Solstice. By looking at their users exercise habits and visits to fast-food restaurants, Foursquare predicted that, on February 2nd 2017, visits to the gym would drop as trips to fast food restaurants rose. The infographic in Figure 1 shows this reversal in user behaviours, as users exercised less and ate more.


Foursquare data showing gym visits vs fast food outlet visits

Figure 1: The upturn in fast-food visits meets the downturn in trips to the gym


T-Analysis monitoring

T-Analysis is an approach we have designed at Nomensa to help our clients analyse the needs of their audience and to inform our UX design process. Rather than chasing meaningless metrics, the diagram in Figure 2 shows the measurement of broad contrasts meeting deep social-media monitoring.</>

Diagram of T analysis research model

Figure 2: T-Analysis modelIn a comms environment people tend to think responding to social is a comms exercise.

Its seen as simply managing a situation that occurs online with a customer. But Nomensa also looks at it from a design perspective – how could we improve the whole user experience, so a problem doesn’t happen in the first place? Broad analysis can reveal trends in the industry allowing a client to stay ahead. Deep analysis identifies specific problems that may occur in a digital estate, like bad commentary, and allows websites and platforms to grow accordingly. You’ll see the real value in social when you can predict when your customers are going to be facing a crisis. For Foursquare this meant using this insight to help their customers ward off the challenges posed by their local takeaway, help them stick to their goals and most importantly keep them as a subscribing member.The nurse in me knows that prevention is better than cure so dose up on T-Analysis now.

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