Data Storytelling for Digital Marketers

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Playwright George Bernard Shaw once said, “The single biggest problem in communication is the illusion that it has taken place.” In today’s digital economy, overcommunication is the default, but how much truly resonates? We can have 100-column charts fill our reports, monotonous speeches fill our conference rooms, and paragraphs of copy fill our emails – yet have little impact. I claim no panacea in the performance marketing world, but I can offer a crucial tool. It’s called data storytelling – creating a purposeful, actionable storyline using data.

Why Stories Work

Have you ever seen a commercial with such a moving story that you still remember it? What emotion did it invoke? Perhaps it was poignant, humorous, or it had such an unexpected turn that the plot twist found a place in your memory. One example is the following.

Why are commercials like the above memorable? They tell a story. The power of storytelling is not only anecdotal but scientific. Neuroscientist Paul Zak discovered that the brain processes stories more impactfully and memorably than raw information¹. Therefore, he recommends using stories whenever possible in business presentations. On the creative side of digital marketing, we understand the efficacy of painting a picture and storytelling. But on the analytical, data-driven side of digital marketing, appreciation for storytelling tends to fall flat.

“So What?”

Before we get into specifics, let’s discuss the most important thing in this post. All data storytelling must answer one short, big question: so what?

In other words – why does your analysis matter? What’s the actionable takeaway? No matter how fancy the presentation or report is, it fails if the audience doesn’t know why you’re sharing it. First, understand the main point you want to drive with your story and ensure every word and number fulfills it. Then, the following outline can guide you in providing details that support the main idea you want to drive.

Data Storytelling: Three Steps

As promised, here is an outline of three simple questions you can use for data storytelling.

1. What were you facing? Every good story has some conflict, struggle, or problem. What account issue did you have, or what question did you answer? Did traffic suddenly spike? Was performance softer than usual YoY? Did CVR drop? Or all the above?
2. How did you respond? How did you, as a digital marketer, handle the problem? As a good novel brings a reader into the scene with vivid imagery, a good analytical story shares generalities and the concrete steps you took to solve the issue.
3. What was the result? What happened after your optimizations? And… now what? History informs an action plan for the future. Given the learnings from this event, what will you continue to do (or not do)?

When sharing your story, we recommend starting with the high-level takeaway (answer the audience’s “so what?” question quickly), walking your audience through the story’s three components, and concluding with a quick summary. This process keeps everyone organized and happy.

Here’s an example of an Adlucent client’s success story for UrbanStems using the above format.

High-level summary: Early and skillful adoption of PMax (Google’s Performance Max) was critical to overcoming and exceeding YoY macro challenges.

1. What were we facing?: UrbanStems saw revenue down 41% and clicks down 46% YoY on PLAs due to technical challenges in the feed and comparing against a booming COVID season.
2. How did we respond?: We resolved the technical changes and decided to dive into converting our Smart Shopping campaign to PMax well before the automatic SSC to PMax conversion deadline.
3. What was the result?: Implementing these optimizations, we saw revenue rebound to 19% up YoY and clicks up 17% YoY. Now, we’re optimizing our PMax campaign even further to test different structures, ad copy, and audiences.

Make Illustrations That Aren’t Frustrations

Congratulations; you have learned half of the basics of key insights through data storytelling. The other half is illustration, which expresses data further than words can alone. For example, a steeply upward slope on a line graph speaks louder than “revenue increased greatly.” A specific metrics chart filled with YoY percent changes above 100% is more impactful than “we had significant YoY growth.”

However, data visualizations need to be done right – lest they become frustrations (hence the title). A strong visualization is:

• Clear: This is perhaps the most crucial criterion for a good visual. It can be cool, colorful, and complex, but it’s not good if it’s not clear.
• Simple: Don’t fall into the unspoken notion in data analysis that the more numbers you have, the better. If you keep it simple, everyone wins – as the storyteller, you get to do less work, and the audience isn’t straining to understand what you’re saying. 
• Accurate: The numbers must be correct. No brainer.
• Not misleading or biased: This is different from accuracy. A visual can have the right numbers and yet inflate performance or lean the audience to a particular point of view. Of course, if we want to highlight good performance, we should make a visual that supports our case – but does so objectively and honestly.
• Aesthetic: Use colors, font, and spacing skillfully – to make the visual clear and pleasing to the reader. 

Finally, put the visual to the test. Ask yourself candidly – if you didn’t have the context behind the visual, would you understand it? If you were in the audience, would this visual help you?

For example, we made a visual to accompany the PMax success story we included in the previous section. Some components, such as time periods and raw data, have been omitted for anonymity, but we can still get the takeaways.  

Note that instead of crowding the chart with unnecessary columns, we only included relevant metrics (traffic and revenue). When the data does not need to be anonymized, we would also recommend including the raw numbers in addition to the percentages; this helps to create a stronger visual. Finally, we used aesthetics by coloring the numbers from red to yellow to green as we saw positive changes. All this results in a simple, effective way to drive our main point (aka answering “so what?”) – shifting to PMax helped us overcome YoY hurdles and beyond.

CONCLUSION

Storytelling is a data analysis technique that puts a heartbeat and a breath into dry numbers. Here at Adlucent, we care about numbers and stories, but ultimately, we care about our clients. Our motto, “better every day,” is not a platitude but an attitude.

We’re always writing our next client success story and want to help you get started on yours. Reach out if you’re interested in partnering with us today.

SOURCES:

1: https://hbr.org/2014/10/why-your-brain-loves-good-storytelling

Rigel Kim

Rigel is an Account Manager at Adlucent with a penchant for creating a story from data and statistical analysis. She has years of experience across various clients and marketing channels (from ecommerce to in-store, B2C to B2B, Search to Display to Video), combining irreplaceable human insights with increasing Martech and automation.