In the digital-first content world, constantly aligning with your audience’s habits and preferences is the key to building brands. But figuring out how to do this is the real challenge.

One benefit of working with digital platforms is the sheer volume of demographic and viewership data they collect and make available to their channel owners. But that amount of information can be a blessing and a curse. Too much can lead to decision paralysis—or the opposite, decision overload. Understanding how to interpret and work with so much data is critical for success—and there are a few digital-first brands that seem to have mastered the challenge. CoComelon, Gabby’s Dollhouse, Ms. Rachel and Angry Birds are all strong examples of brands that perform extremely well outside of their platforms of origin, thanks in no small part to their approaches to data management.

FINDING, ENGAGING AND RETAINING THE MOST VALUABLE USERS

Despite the emerging power of AI, fueled by the big-data revolution, I often find myself returning to proven models when I’m looking for insight. A good example of this is understanding the audience or consumer funnel.

Traditionally, brands have been monetized by selling ad space to large, static audiences, which brought in enough fans to support consumer product sales. However, with today’s vast content choices, the return on investment from ads has diminished, and the CP funnel has shrunk. To combat this, we must optimize two key aspects:

• Cost per acquisition (CPA): The cost of acquiring a new fan
• Cost per retention (CPR or CPF): The cost of retaining a fan

The ultimate goal is for a content brand to transition from seeking an audience to being sought by the audience—a key shift in becoming a tentpole brand. At Gone With, we often use traditional funnel models like AIDA (awareness, interest, desire, action), which applies across platforms and brands. Among these stages, interest and desire are the strongest indicators of long-term brand health.

AWARENESS is about maximizing the number of eyeballs viewing the content. In the world of YouTube, for example, this might be measured by total minutes viewed. However, in an era of fragmented content and declining ad value, this metric alone is insufficient and only works with scale. You will always have to spend $1 to get back $1.02, or $1.05 if you do well. To convert awareness into genuine interest, consistency in messaging and content delivery is crucial.

And with content so fragmented these days, it isn’t a case of finding your audience once and then having them continue to return to you. You need to find them once, then re-find them and probably re-find them again. You have to form that outreach habit—or more accurately, you have to hope that the platform’s algorithm will get the hint and promote your content to your viewers.

One of the main marketing messages at this stage is consistency: “We are here, at this time, showing this thing that you like.”

What we want to consider is the conversion rate from awareness to interest. This would show that we have been effective at finding fans in the marketing stage. To measure this, we can try activating small pieces of marketing across different platforms, or we can try different marketing tones or messaging on the same platform.

By isolating activations, we are able to measure which ones best convert into returning viewers. For example, we can A/B test two different images on YouTube or social media. Or we can target different demographics with the same messaging to see which converts the most effectively. By simplifying the question, we are able to accurately answer it.

INTEREST is reflected in metrics like returning viewers on platforms such as YouTube. It’s not just about driving initial engagement (watch time); it’s about ensuring that those viewers return, too. This also is where iterative A/B testing of content and marketing material comes into play, helping to refine strategies that attract the right audience. Success at this stage means effectively acquiring and engaging viewers who are more likely to become loyal fans, thus lowering the CPA over time.

By measuring the performance of the content, we can begin to identify which “content traits” are most effective—not at gaining minutes viewed, but at supporting repeat views.

Along with tried-and-tested funnel models, there are smart AI tools that can identify content patterns to support the analysis of content performance, such as Scriptsee (script analysis), Wantent AI (analyzing content) and Vionlabs AB (contextual content tagging).

By understanding what drives repeat consumption of a video or channel, we are able to tailor the most appropriate episodes, characters and formats (e.g. shorts) for our “fan” audience.

DESIRE is perhaps the most critical modern metric. In the past, knowing a channel number by heart indicated strong brand loyalty. Today, the equivalent is organic search—how often people actively seek out your content. Growth in organic search volume is a key indicator of brand strength because it reflects content that resonates so strongly with audiences that they seek it out without prompting.

How many people type in some or all of our key search words to find the content? Or say the words “Peppa” or “Bluey” to Alexa? Growing this metric in terms of absolute numbers—going from 100 searches in month one to 200 in month two, or improving the percentage of total views from 0.8% in month one to 1.2% in month two, for example—is key to the growth of a brand. Why? Because this is when the cost of acquisition and retention declines. Organic isn’t paid for. You have created content that people want to find, rather than content that needs to find people.

Tracking and steadily growing organic search from day one is essential. It demonstrates that you’re building a loyal audience and reducing future costs, ultimately creating a sustainable, successful digital-first brand.

PETER ROBINSON is the founder of Gone With, a consultancy focused on early adopters and new markets.