A Social Media Manager’s Guide to ChatGPT [Best Prompts for SMMs]
By Ksenia NewtonMar 9
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Published March 1st 2018
An influx of self service analytics products has meant that vital business data is no longer the sole preserve of top level financial and sales executives. More and more of us are handling data and, with so much of it floating around, the ability to take what matters and communicate it effectively is becoming an increasingly valuable skill.
While storytelling has become another marketing buzz word du jour, the overwhelming amount of data companies produce is certainly not going to subside any time soon. We need to present that data in an easily navigable, detailed (but memorable), human way. With stories.
Stories give context. They take people on journeys. They set up expectations and then surprise or delight by exceeding them. They create a more emotional investment in what’s being said.
Telling effective stories with data will always require a certain level of skill, and you’ll find plenty of tutorials on the web, but social data presents its own set of challenges and opportunities when it comes to storytelling.
Social data, in its own messy way, can give rise to all sorts of different modes of storytelling, firstly because it allows you to get incredibly granular.
For example, let’s say you’re presenting social data on how an airport might improve the experiences of customers as they make their way around its precincts. You’ve noticed that over the last few months lots of people have been complaining about getting lost in the airport, taking photographs of bad signage or complaining that something had led them the wrong way.
A dry presentation of that data could simply explain that 72 complaints had been received about misleading signage in the airport and that a review ought to be implemented.
An engaging one would take an example from a single customer who had gone around the airport photographing the points they found most confusing and live tweeting their bad experience. Their words and view point can make that problem seem a lot more immediate to those listening to your presentation. Verbatim examples of people complaining they missed flights because of that poor signage, and the impact that had on their onward journey and subsequent other things they missed (a child’s birthday, an important job interview, perhaps) would go even further.
Why must you be such a confusing airport!! Got lost. Missed my flight back to base. Go me. #killinit
— Andrea Elizabeth (@FlyGirlAndrea) October 14, 2017
I spoke to Peter Fairfax, a senior research analyst from Brandwatch who regularly presents to clients. He’s a big fan of using verbatim examples to give reports more color.
Verbatim is impactful. Where high level stake holders are involved, they don’t always have time to read and digest the whole report, but if you’ve got recurring themes with example mentions they add more colour and detail. It’s the same story, but with a more human angle.
With social data, each snippet provides its own story you can take advantage of.
Screenshots with photographs or text from the voice of the customer alongside more mathematical visualisations of the phenomena found in the data can also add much needed breaks from never ending, similar looking charts in a long presentation.
On the other hand, social data can present it’s own difficulties when it comes to showing it to an audience.
For example, findings from social data research can still be treated with a level of scepticism or distrust from those who don’t work with it each day. That attitude may be eroding as more and more examples that prove the value of social data are shared, but presenting data in the wrong way to a social sceptic could reinforce their opinions. I’ve done it myself.
When presenting to someone I thought might be hostile to social data, I found myself telling stories that I thought would confirm my audience’s thoughts about how the data would look. Any unexpected insights that I found were moved to the back, while I highlighted data points that would appear reflective of what they already consider to be true. I was, in a way, saying “look, social data is reliable because it reflects all the other data you have.”
But, of course, the value of social data isn’t proved by how closely it mirrors the results we already have from other sources. If you’re hearing something you already know it’s not an insight. It’s the unexpected nuggets – the ones that surprise and present new outlooks or opportunities – that show its real value.
The smaller things are surprising – the little unexpected bits are what get clients most excited. Try not to focus on the top level too much – generally, that stuff is obvious and doesn’t tend to be as insightful as the findings that make the data so unique.
Peter and I discussed the benefits of taking a bottom up approach to social research from the very beginning in order to get to these unexpected golden nuggets. Those are the ones you want to lead with – tell your audience what they don’t already know (or think they know) especially if you don’t know how much time you have to get your point across.
Not all presentations of data are done face to face. Some are done via email. Some are visualised live to screens across the world.
Making it beautiful is important – if you can get people to stop and pay attention to what you’ve got to say then you’re on to a winner.
But however you’re presenting your social data, remember two core lessons: lead with what the audience doesn’t already know, and use social data’s unique granularity to add color where you can. That’s how to make it stick.
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