How Does Data-Driven Storytelling Define Brand Relevance?

Storytelling is good business. Data-driven storytelling is smart business.

Why this matters:

When enterprise companies identify their audience and use research to understand language and context, they can tailor a compelling narrative that drives engagement, sells products, and ultimately delivers business value. The narrative must be the vision for all B2B enterprise, consumer, and healthcare companies.

What is Data-Driven Storytelling?

Data-driven storytelling is more than just a buzzword. It’s not just a trend or a bullet point on a slide. It’s a process of using social media data to inform stories and generate insights. These insights must be easy to articulate, digestible, and actionable. While stories are great for Children’s books, Hollywood, and Netflix Originals, in this context, it’s about the stories, brand narrative, and visual content brands create, publishes and distribute into the marketplace.

To be clear, a data story isn’t creating a visual with research or statistics embedded in a video, image, or infographic and then sharing it on social. However, if the data tells you that your audience prefers to share content with visual elements, charts, and dashboards, creating content that aligns with these insights might make sense.

Data Storytelling is All About Relationship Building

Being relevant to another person means having something in common with them. It could be a similar interest and hobby. Or, maybe you look alike, talk alike, or dress the same way. And in some cases, it might just be an attraction. They’re cute; they look good. Or, maybe they’re a six out of ten but have a great personality, and that’s all that matters.

The common denominator for a successful relationship is communication. It is part of the human DNA to connect, communicate and relate to others. Shared interests, characteristics, passion points, values, and culture can manifest into long-term, valuable relationships.

Human relationships and their psychology are no different from the relationships brands should have with their customers. Data-driven storytelling is an intelligent approach that can provide critical insights into an audience and help marketers create a compelling narrative that will be the building blocks of that relationship.

Data Analysis + Storytelling = Brand Relevancy

Building brand relevance is about relating to an audience. The only way to relate to an audience is to know everything about them, including:

  • What do they care about?
  • What interests them and keeps them up at night?
  • What media outlets do they read, podcasts do they listen to, and events do they attend?
  • Which influencers do they follow?
  • What social media channels do they use to talk with friends, family, and colleagues?

On the surface, this may seem like a lot of raw data to process. It is, and it’s not easy. Marketers will need to verify that they have the correct data, interpret the data, identify patterns, understand context, extract key insights, and then package up the research and data in a digestible way.

My experience with data analysis has been primarily focused on digital data points. This includes social analytics, performance marketing, website analytics, and other interactive data. However, primary and secondary research are tools that can generate data-driven audience insights and smart decision-making.

When identifying your target audience, it is critical to understand and segment which audience you want to go after. For example, if you work in public relations, your audience is traditional media and the journalists and reporters who write for those publications. In some cases, your audience may also be influencers. Again, the data will tell you.

If you work in demand gen marketing, your target audience is the potential buyers of your product or existing power users. This means doing a comprehensive audience analysis to create a data story that converts and creates engagement will be business-critical.

Data analysis can be used to understand the stories and trends demanding your audience’s attention. The below model is an example of how data can be used to uncover these insights depending on which audience you want to reach. In some cases, you might want to engage just one of these audiences, and in other cases, you might want to engage all three. The key is to use analytics to build a narrative for each specific one.

The supply and demand of brand relevance is a model that can be used to inform data-driven storytelling.

This data-driven storytelling model was designed to uncover hidden narratives within the context of audience conversations and sharing patterns. For business and enterprise companies looking to revamp and build their future of B2B marketing strategies, these data-driven insights can help inform marketing and communications programs.

The left side of this graphic shows how data can also be used to analyze the stories, topics, and trends a brand is publishing across its entire digital ecosystem and the earned coverage it receives from the media relations programs.

The most important question you must ask yourself is whether you are meeting the demand of users with your supply of data-driven stories. One technique is effectively segmenting the data and analyzing different media outlets individually versus combining the research and data into one report.

Controlling the Brand Narrative with Owned Media

In this context, owned media refers to all the digital channels a brand has complete control over–the look & feel, images, videos, visuals, charts, tools, creative, hosting, and the story they want to tell.

Owned media can include the company website, blog, content hub, newsroom, mobile site, and campaign landing page.

Like analyzing audience demand, marketers can use data-driven intelligence to cluster all the content and stories published on these owned channels and look for similar insights and trends as if they were analyzing a specific audience. This is typically done as a part of a brand landscape analysis.

The data starts to get interesting when comparing the data-driven insights from owned media to audience conversations.

Using Earned Media Research to Uncover Insights

The supply of content can also be considered the coverage that brands get from earned media. After an analysis, marketers can use the data to visualize whether the media is telling stories that align with the brand message.

Do their stories align with the overall brand message? Unfortunately, with earned media, there is less control over what the media writes about when covering your business, so alignment with the brand message is unlikely.

This will also uncover market white space when comparing this data to the insights from an audience analysis.

Social Media + Insights = Data Storytelling That Adds Value

Data-driven storytelling includes content, visual elements, images, interactive dashboards, and narratives published on a brand’s digital channels.

The great thing about this is that marketers have complete control over all the stories and data visualizations published on digital channels. Unfortunately, while one could look at this as an opportunity, it’s also been abused over the last 15 years by brands that didn’t pay attention to their target customer or users. Most of the time, they only publish content that they think would deliver business value. The poor content performance and slow community growth finally indicated that it was time to adopt this new model of data-driven storytelling.

The point is that data-driven storytelling is more than just a fancy name with no meaning. When done right, it can be a convenient approach to building brand relevance, engagement, and a narrative that enables you to tell better stories than your competitors.

This approach takes time. It takes an investment in data analytics and requires brands to look at themselves and self-evaluate what stories they want to tell and what content they publish. But however painful as this might be, the result of data-driven storytelling is that every business that uses data will have better decision-making and be much more relevant to their target customer.


What is data narrative?

A data narrative conveys complex data or insights through a simple story. By combining data visualization techniques with storytelling elements, data narratives help make abstract or dense information more accessible and engaging for the audience, enabling them to grasp the data’s significance and understand its implications on their decision-making processes.

How to use storytelling with marketing analytics?

Storytelling with marketing analytics involves translating data-driven insights into relatable narratives that resonate with the target audience. By weaving a story around the key findings, marketers can better communicate the value and impact of their marketing programs, making the data more memorable and persuasive.

What is a data story?

A data story is a visual piece of content that combines post copy (or captions) with data visualizations to tell compelling brand stories across social media. An example might include creating animated videos highlighting one data point from a research study that would link back to the more extensive report or infographic. These types of interactive visuals will engage users in their newsfeeds.

What is storytelling with data?

As mentioned above, once a brand aligns with a critical message or narrative, it can use data stories in videos, infograms, infographics, charts, or other visualizations highlighting specific data points. Data storytelling and storytelling with data are synonymous.

What software can you use for data-driven storytelling?

There are several software applications you can use to mine social data to uncover audience insights:

  • Social Media Monitoring: These platforms are also called consumer intelligence platforms and social listening software.
  • Traditional Media Monitoring: These platforms collect data from articles to track business impact, coverage, key trends, and share of voice.
  • Audience Intelligence Platforms: These platforms support data to analyze conversations, cluster the correct data and provide insights and stories across all social channels.

The key is to ensure that any technology investments made into software align with the business and provide value.

What are the challenges of data-driven storytelling?

The challenge with marketing communications as a job function is that two teams manage them. Collaborating and communicating across groups and telling engaging stories across paid, earned, shared, and owned channels is challenging. The power and value of data-driven stories are that the insights can inform public relations and earned media programs. In addition, these critical insights can inform storytelling in any format and on all channels.

Why is data storytelling so important?

Using data to inform content, a story, paid media segmentation, a blog post, or any form of communication. 

Related Content

Michael Brito

Michael Brito is a Digital OG. He’s been building brands online since Al Gore invented the Internet. You can connect with him on LinkedIn or Twitter.