Audience intelligence is the study and understanding of both the demographic and psychographic characteristics or behaviors of a group of people or an ‘audience’. The production of audience intelligence involves collecting large amounts of data about a specific group of people, which is then studied using data science techniques such as machine learning and natural language processing (NLP) to develop the relevant insights and understanding for that audience.
Audience intelligence has a variety of applications from purely academic, to both economic and political, as well as for marketing purposes. Today, the most common sources from which audience intelligence is produced are social media platforms, as people tend to share their open and honest opinion about themselves and other topics, products, people and brands without any incentive that might produce bias.
In this article, we are going to focus on the marketing aspect and use of audience intelligence.
Social media has become ubiquitous and critical to modern marketing strategies. Perhaps at one time considered juvenile and diverting, social media platforms do not discriminate when it comes to bringing significant value and measurable ROI to companies across all industries.
The evolution of market research
Yesterday’s social media management platforms are focused on enabling mass communication and, at most, offer baseline insights into their respective audiences. As more information becomes available and companies look to gain further insight into their audience, the trick is to figure out how to efficiently interpret meaning from the data deluge. There are a variety of sources for audience data that can be leveraged to produce audience intelligence. For example, a traditional source of audience data is surveys. With this same data being readily available on social media platforms, we are able to survey thousands to hundreds of thousands of people, and collect their opinions using the right tools and technology.
Yesterday’s social media management platforms are focused on enabling mass communication and, at most, offer baseline insights into their respective audiences. As more information becomes available and companies look to gain further insight into their audience, the trick is to figure out how to efficiently interpret meaning from the data deluge. There are a variety of sources for audience data that can be leveraged to produce audience intelligence. For example, a traditional source of audience data is surveys. With this same data being readily available on social media platforms, we are able to survey thousands to hundreds of thousands of people, and collect their opinions using the right tools and technology.
People have grown accustomed to sharing unprecedented amounts of personal information across a multitude of mediums. According to a recent study by Mintel, 60% of millennials are comfortable sharing details about their habits and preferences with marketing professionals. For many, online sharing and the widespread dissemination and use of personal data it entails is just the cost of doing business.
The data needed to produce audience intelligence doesn’t only have to come from surveys or social media. Over the years, marketing teams have amassed data from quantitative research, email lists, transactional histories, and metadata on customers and prospects. Having the right tools is the key to leveraging this data. These are audience intelligence tools such as People Pattern. Tools that allow for processing of the unstructured data and combining it in a timely manner such that the insights gained can be put into action quickly and smartly before the data becomes stale.
Audience intelligence research has historically been done through surveys. With the emergence of social media and the recent advancements of data processing, this research can be done at scales never seen before. To learn more about the revolution of market research, you can read our whitepaper dedicated to this topic.
What is the difference between social listening and audience intelligence?
Prior to audience intelligence, social listening tools and the basic insights they produced were the only option in leveraging social media platforms for marketing insights. Social listening products are designed to collect conversation around terms or keywords and produce insights or aggregate counts from that conversation. Although these insights can prove useful, there are two main drawbacks to this type of ‘conversation analysis’.
Audience intelligence allows us to understand the audience related to our brand or topic, and also to benchmark them against other audiences such as a competitor’s audience. With the right tools that help us interpret this data properly we are able to extract extremely unique and actionable insights.
- The analysis that is performed is limited to the specific conversation and context that was captured. This is a problem because this leaves out a lot of context. Both related and unrelated to the conversation of interest. For example if the conversation captured was based on a ‘keyword search’, then any post that is related to the topic but doesn’t happen to use the specified keywords is missed. The ‘missed’ conversation can also still be very relevant in helping us understand the audience and their opinion. This ‘missed’ conversation can also help significantly with the development of personalized messaging for campaigns.
- Any information about the audience themselves is left out. We’ve managed to collect a large set of posts about our topic or brand, but who are these posts from? What are the demographic and persona differences for those speaking positively about our topic vs negatively? With these tools alone, we would not be able to segment the conversation and our insights by any audience level attributes such as gender or marital status.
Context is key
Context is critical to understanding your audience. Data science makes it possible to turn characteristics and nuances into quantifiable items that marketers can act upon. From the information derived using data analysis techniques, marketers are able to gain insights into who makes up their audience without missing some of the finer points of individual expression. Advanced methods like predictive analytics, machine learning, and natural language processing are the keys to these innovations.
Researchers and developers operating within the field of natural language processing work on ways to automate algorithms that perform intriguing computations on the things people say. This includes well-known applications such as machine translation, spam filtering, and sentiment analysis.
Marketers who use audience intelligence derived from data analytics to add context to people-based datasets, when combined with demographics, are able to understand personas as combinations of demographics and interests.
Using audience intelligence for audience segmentation
Developing audience segments based on traditional methods of understanding an audience result in inward-focused groupings. Such as grouping customers by how they appear in the organizational data.
Have they purchased?, are they a repeat customer?, what is their average transaction value?
Although useful, these insights are not as relevant for developing marketing content or personalizing campaigns.
Audience intelligence can essentially enrich your target audience with an extraordinary amount of additional data and attributes. Having more information is always valuable. One of the key values that this additional audience intelligence provides is a deeper level of audience segmentation. With audiences enriched with audience intelligence we are able to segment them not only on the demographic attributes but also by their psychographic attributes such as interests, brand preferences and behaviors. With an increasing reliance on personalized marketing having a deeper level of audience segmentation enables the level of personalization that makes the difference.
Using data science techniques, we can create segments from an audience that are much more complex than traditional methods. This more complex segmentation can be used to create personas that are based on behavior and preferences. As opposed to personas based on basic characteristics and demographics.
We can now develop a better understanding of some of the behavioral clusters or sub-groups of people that exist within the audience we are analyzing. Targeting and content decisions are made easier and more reliable when they are based on personas that have been developed around people’s current interests.
Audience intelligence is revolutionizing the way that marketers listen to, understand, and connect with their audiences. It doe this by producing actionable insights that are cheaper, faster to reach, and more scalable than traditional market research techniques. Audience intelligence demystifies content strategy and influencer marketing by delivering data-driven insights. These insights can be delivered down to the individual level, allowing you to make decisions with the best information available. In a rapidly changing space like digital marketing, audience intelligence is the most effective piece in a serious marketer’s toolkit.
To see audience intelligence in practice and get an understanding of the types of insights that are produced contact us for a demo.
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