Though many have heard of social data intelligence, very few have a clear understanding of what it is truly all about. This blog aims to demystify digital intelligence by breaking it down into three key components.
- Social media intelligence platforms
- AI-led analytics
- Human-driven insight discovery
Let’s start with a definition, shall we? Social intelligence is the ability to collect, monitor, and analyze available social media data feeds (including social media networks, blogs, forums, comments, and more) to understand what people say about a topic, brand, organization, or other entity. Social intelligence surfaces insights that drive fast, consumer-centric decisions. With this intelligence, companies can deepen their understanding of their ever-changing consumer base, identify their position in the market, and quantify their brand perception.
However, social data intelligence is much more than software, i.e., data collection and tech-enabled analysis. The most mature and highest value form of social intelligence incorporates the unique touch of human-driven research. So, join us as we explore each of these three elements more in-depth to understand better why and how your business should be implementing social intelligence research.
To demonstrate what each of these methodological building blocks brings to a particular question, we will use the COVID-19 pandemic and critical lessons that have emerged for brands as our focus. Together with our colleagues at Ipsos, we have done (and continue to do) extensive work across a wide range of clients and question types to better understand the status and implications of the pandemic on businesses.
Social Media Data Intelligence Platforms
These are software platforms designed to enable social listening from different data sources, from social to news media, providing real-time access to various metrics through the means of interactive dashboards. Historically, Social Media Data Intelligence Platforms collect and visualize data. On the other hand, newer age platforms offer out-of-the-box, AI-led analytics (more on this later).
Synthesio’s world-class social media intelligence platform allows us to uncover some interesting tidbits of information, which we can use to understand the context of the world around us. First of all, it is helpful to see the evolution of the conversations we are interested in compared to historical data. For example, by the middle of May, we had more than one billion tweets alone about COVID-19. Three months later, the figure had doubled.
Using Synthesio, we can also explore more around particular topics to see at a topline level what is standing out before we dig into the data more deeply.
AI-Led Analytics
Data collection and basic visualizations alone offer a first understanding of the main topics that individuals discuss. However, an advanced AI-enabled analytic lens is often needed to get a more granular understanding of the real and spontaneous nature of people’s concerns, needs, and expectations. When we say AI, we mean text, picture, and video analytics designed to make sense of unstructured data using natural language processing (NLP), machine learning, data mining, statistical analysis, and more. Again, AI-led analytics come pre-packed inside your Social Media Intelligence platform, as is the case with Synthesio.
This type of built-in AI that highlights abnormal trends can save a great deal of researcher time in chasing ‘red herrings.’ For example, thanks to the Signals feature of Synthesio; we can see that alcohol was a topic much mentioned with COVID-19, particularly between April and June.
New deep-learning algorithms help us better understand what key themes are coming through and tell us if they differ between different markets or if they are changing within the same market.
We can also use AI to understand emotion. Here we have focused on people’s fears generated by the COVID-19 crisis. A typical top-down topic modeling approach would consist of quantifying the themes we expect to see in the dataset. One could have assumed that people were indeed concerned about their ability to keep their job or fear losing someone.
It is interesting to combine this top-down approach with a more consumer-centric bottom-up one that leverages the power of deep-learning algorithms and AI. Using this model, we surfaced additional fears that we did not expect to see, such as the negative impact of the lockdown period on kids’ education and the emergence of racism and the blame game during the crisis.
Human-Driven Insight Discovery
The third piece to this puzzle is the human touch. This block consists of an individual researcher’s contribution to finding insights from social media data using analytical frameworks.
From the human-led insights perspective, we can begin to drill down more into the whys and hows of what is happening and the implications in terms of changing consumer behavior and activity. This information helps us to understand what it might mean for brands or organizations. Doing this is especially useful when you can tie it back to other frameworks such as the Ipsos Pandemic Adaptability Continuum shown below. Data helps us to orient the findings and anchor them into something bigger.
For example, when we look within food behavior as the COVID-19 crisis emerged and then took hold, we saw how consumers were moving into different phases, as we can see below. These themes mean that new opportunities were arising for companies.
Having a human look at the data can also prevent drawing wrong conclusions. At the start of the pandemic, we began tracking online conversations about COVID, but as our analysts reviewed the data evolution, we saw the decline in mentions. As a result, we might have been inclined to think that the topic was not as important anymore. Instead, however, COVID had morphed to be part of our daily lives. Therefore, people didn’t mention it as often in their social posts. Using our analysts’ strength, we quickly developed a robust lifestyle-focused view to understanding the many layers of our pandemic experience – life in the home, entertainment, health, wellness, etc. More often than not, relying entirely on a trendline does not tell you the whole story. This illustrates how each layer can add new and essential information to understand better what is happening.
Putting it All Together
When we look at these three blocks separately, they show disparate and messy information difficult for an individual to synthesize. However, combined, these three building blocks reveal rich insights about brands and how individuals view their relationship. The key is to use a social media intelligence platform with AI-driven analytics baked in, and finally, to double-check with human expertise.
Want to know more about how leveraging and making sense of unstructured data is crucial in the race to understand consumers?
Click here to download our full Unstructured Data to Intelligence report to explore and uncover the power of social media analytics.
