Maybe you’ve tested subject lines or obsessed over optimizing a landing page call-to-action. You greet your customer by first name in emails and track campaigns with UTM parameters. You rejoice when you see lifts in engagement like opens and clickthroughs. But how are these adjustments really affecting your business? Are your customers happier and sticking around?
Not all data is created equal — and these days, there’s certainly a lot of it available to use and call ourselves data-driven and data-informed. But how do know we’re using data to do marketing that really matters?
If we can do that, we can better tell our story, gain customer trust, and convey what makes our product truly valuable.
Great content might seem like the product of creative talent, but of course, creative wins can become a powerful positive feedback loop. Even if you’re reviewing past content marketing performance, you may still be operating in the dark as you move forward. Without incorporating insights into a long-term content strategy, you can’t build a cohesive narrative for your business.
Airtable, for example, tracks the performance of every article they publish to find the perfect formula for making their blog as helpful as possible for their readers. That data connects content efforts to a higher-level strategy. As they explain:
Companies that are truly successful with content marketing execute consistently, but they also feed their learnings about what worked and what didn’t back into the machine.
To do this, they created a content marketing pipeline in Airtable that’s separated into three stages: preparation, production, and publication. While the first two stages are helpful for content managers to brainstorm and keep track of what’s in the works, the last stage informs all strategic improvements. This creates a pinwheel in which every new article written is another piece of data on what works and what doesn’t.
The marketing team has a dashboard (in Airtable, of course) with every piece of content published. Each article is a separate entry in the database, and includes details like publication date, persona, and vertical. They also created a view that shows the performance of each piece, using a Google Analytics integration:
When it comes to coming up with new ideas, team members can start with the articles they’ve already written. They can see which types of ideas perform the best and reverse-engineer success. For instance, if they see that the article “SEO for Dummies” got over 2,000 hits, they create a series, breaking down the nitty-gritty of how SEO works while getting further data on what aspects of SEO are most interesting to their users. Or they might investigate deeper in other dimensions, looking at how the topic of SEO has performed for different verticals and audiences.
With a bird’s-eye view on both successes and failures, Airtable learns from their readership and improves the way they tell their story.
You can’t get a competitive advantage doing that stuff anymore. You could say that as the percentage of marketers with a certain tech stack or using a certain tool approaches 100%, the competitive advantage you reap from it approaches 0.
He wanted to find a way to use data in a way his competitors weren’t: to dig deeper into the customer experience.
A few months back, Wistians observed an issue with their onboarding process for their video hosting platform. Using FullStory, a tool that records user session, they discovered that users didn’t complete their walk-through if they didn’t have a video on hand to upload onto the platform. So they created a loaner video featuring Lenny, the adorable office dog and mascot.
Based on the metrics alone, the video was a runaway success: 20% of users viewed the loaner video.
But as the team looked closer at what users were doing, they realized that people were watching the video all the way to the end. The marketing team worried that while people were entertained, they were also distracted from learning about the platform.
The team decided to go ahead and A/B test a different video that still had the same friendly and funny tone, but expressly taught users how to use the platform’s customization features. The new video resulted in a 30% lift on their main goal — which wasn’t, at the end of the day, for new users to watch the video itself but to check out the customization options.
Rather than use data to just give you a final thumbs up or a thumbs down on an idea, use it to double-check your work. You might find that a lift in engagement in the short-term, for instance, doesn’t equal better retention in the long term. Or that there’s a way to get a 40% lift rather than a 4% lift. This mindset will cause you to never be satisfied with a small improvement and help you raise the ceiling with what’s possible with your marketing efforts.
Onboarding is the most important part of the user experience, which is why most marketers look to data to pinpoint and reduce friction during the process. But they often furnish the new user journey with a kitchen-sink approach (here’s everything you need to know!) or what they assume is valuable.
Go deeper than MAU and DAU to see how users are interacting with your product — and longer-term, looking at the behaviors that separate the users who stuck around after month one from the users who churned. The Appcues team recommends focusing only on the behaviors with close correlation to retention:
- Behavior exhibited by most retained users AND by most churned users = no correlation.
- Behavior exhibited by few retained users AND by few churned users = no correlation.
- Behavior exhibited by most retained users AND by few churned users = correlation.
|most retained users + most churned users||no correlation|
|few retained users + few churned users||no correlation|
|most retained users + few churned users||correlation|
Only looking at popular behavior patterns is an incomplete story — no telling how it will end, whether your protagonists will stick around or not. Differentiated behavior that’s correlated with retention, on the other hand, indicates those users have found some sort of value. With that knowledge (or a strong hypothesis), you can design your onboarding to that path of discovery.
For Wistia, that path to discovering value requires a new user to upload (or borrow) their first video. After all, they put all that effort into optimizing the power of that loaner video. So it’s no surprise that there are nudges to upload your first video in their welcome email:
A smart (and subtle) trick of decision design that Wistia uses in this email is to only provide a link to the “upload your first video” step in their getting-started checklist. With an obvious and concrete next step, the reader is more likely to take action at all and on exactly the behavioral conversion you intended.
Relying too much on any ol’ data can put you at risk of missing the forest through the trees.
We work through the thrum of loud declarations about being data-driven and modern movement towards a more analytical, agile approach to marketing. And it can be easy to get lost in numbers and information, even in the name of testing and iteration, obscuring the singular purpose of great marketing: to create a compelling narrative to get people interested and meaningfully engaged in the product.
Data matters for marketers not because it helps you decide between a blue or a red CTA. It’s not even because it helps increase clickthroughs or lift conversion numbers. It matters because it helps you craft a compelling story around their product that’s based in fact and a worthwhile user experience.
Over to you: how do you rely on data in marketing? Share with us in the comments!