Are you as confused as we are…?

If you’ve been looking at buying new SITech recently, it’s probably been a daunting experience

Whether you’re completely new to social listening or a veteran in the space, it’s pretty overwhelming purely because of the sheer amount of technology that’s available now. And whilst choice is a good thing, it means you have to have a very clear idea of what you need from your social listening tools and - more importantly - be able to compare different technologies to work out which one can help you.

This first element, while time consuming, is within your power. You’ll just need to go through an auditing process to get to grips with what your organisation needs from social intelligence and the use cases you should focus on. But that’s completely doable. We even put together a Buyer’s Guide to help you through this process.

What’s trickier is finding the right technology to help you. And that’s partly because there are so many different types of SITech all claiming to do different - yet very important - things, that it’s not clear exactly which one will help you. And this has changed over time…

In the beginning there was social media monitoring. We saw the emergence of social media as we know it today: everyone was still friends with Tom on MySpace and Facebook was the cool new place to hang out online. At this point, the main focus of social listening was to stay informed by monitoring brand mentions and keywords on these platforms. Companies could use basic tracking tools, but there wasn’t any tech that could offer analysis or insight into this new internet data source.

The Early Days

(2000s)

The Rise of Social Listening

(2006-2013)

Enter Twitter and Instagram. This period saw social media usage explode, and brands realised they needed to do more than monitor what was being said online. They needed to understand the sentiment, context and impact of these conversations. This opened the way for technology that incorporated natural language processing (NLP) and sentiment analysis, and brands used it to gauge public opinion, identify influencers, and track competitors.

In 2011, Gartner predicted that by 2014, organisations that refuse to communicate with customers via social media will face the same level of wrath as those that ignore today's basic expectation that they will respond to emails and phone calls.

Advanced Social Analytics Steps Up (2013-2017)

The concept of social listening had been around long enough for organisations to have got to grips with the basics of social data analytics. Now, the focus was on finding actionable insights. Advanced analytics tools enabled brands to dig deeper into the data to uncover trends and patterns that they couldn’t find before. As a result, social analytics became an important input for business intelligence, helping to inform everything from content strategy to product development.

In 2013, Oreo's famous "Dunk in the Dark" tweet during the Super Bowl blackout demonstrated the power of real-time social listening and analytics. By monitoring the conversation and capitalising on the moment, Oreo generated massive buzz and set a new standard for responsive brand marketing.

The social listening space changed again, this time powered by machine learning and big data analytics. Social intelligence tools went beyond listening and analytics offering predictive insights and prescriptive recommendations. With this, brands could anticipate trends and forecast consumer behaviour. This evolution gets social intelligence a seat at the strategy table.

The Era of Social Intelligence

(2017-2020)

Social Intelligence Now and Tomorrow (2020 onwards)

The role social intelligence played for many brands during the COVID-19 pandemic turned it from a nice-to-have to a need-to-have function. This has enabled the technology to continue developing and go further into areas such as predictive analytics, real-time optimisation and large-scale personalisation. For the long-term future…who knows where it could go!

In 2021, Unilever used social listening and AI to create the world's first "Adaptive Deodorant," which adjusted its fragrance based on real-time social media conversations about weather, stress levels, and activities.

Ultimately though, the biggest issue social intelligence practitioners face is being able to compare SITech properly.

It’s almost impossible to compare different technologies on a like-for-like basis. That’s because there hasn’t been a way to categorise the different types of technology based on the functionality they offer and the use cases they solve. And there hasn’t been consistent language that we’ve used as an industry to explain this clearly.

That is, until now…We decided enough was enough, and we wanted to simplify decision-making for all of you that are trying to work with social data who have a hard enough job already. That’s why we put together this SITech Landscape. Over the next chapters, we’re going to take you through the 9 SITech categories and 37 subcategories that we’ve identified as well as the tools that fall under them, to help you understand exactly what the heck everyone does and to make choosing a new tool easier.