Regarding the challenge of interpreting data, I've felt something wasn't quite right for years while managing content marketing alongside analytics teams.
The reason was that I rarely saw "discovery-driven" information that could inform the next communication strategy, content strategy, or planning. Why did this happen?
I'd been feeling this frustration for a long time, but at a forum held at our company earlier this month, someone from a business company said one thing:
"Ultimately, what we're doing now is just searching for a winning strategy, isn't it?"
and it felt like a weight was lifted.
A common pitfall
Obviously, data is primarily numbers. When numbers appear, we instinctively tend to think "more is better." This leads us to improve low numbers and increase high ones. We lean toward strategies with higher numbers. This approach itself isn't wrong, but if we focus solely on this, we end up merely improving numbers without truly connecting to the larger purpose. It becomes difficult to genuinely improve communication strategies and planning.
Even before that, there are cases where seeing a high number instantly leads to the judgment "It worked!" and thinking stops right there. Especially for those who have made advertising their livelihood, the "C" in PDCA is often confused with effect verification. Without realizing this confusion, discussions when looking at numbers frequently get stuck solely on whether it was a success or failure, often ending without any talk about what should be done next.
What we truly want to know isn't just for verification or optimization—it's about the other person
When planning content—that is, when thinking about communication—what we truly want to know is about the people we're trying to reach.
What kind of people are currently with the brand? What kind of people passionately support our brand? Or, what kind of people remain completely indifferent? Can we constantly be aware of, understand, and keep satisfying the people on the other side of the content? This is the constant challenge for content teams. Website analytics alone are often insufficient. Yet it's not as simple as saying we can plan based on knowing gender, age, interests, or location, or that identifying "that person" via an ID is enough.
What the content team should do
People with content creation experience often seem skilled at interpreting data. This is likely because they are accustomed to working while imagining the emotional shifts and behaviors of people on the other side of the content. It's not that gathering ample data will reveal everything about the target audience. Numbers and imagination. The constant back-and-forth, the interplay between them, is always necessary.
Furthermore, the content team is also tasked with planning content that can effectively prompt action based on the insights gained about the audience, and then ensuring that plan is translated into the intended final product.
Evaluating and verifying initiatives is crucial. Continuously improving each small issue revealed by data is also vital. Yet, I often feel, while wrestling with this on the front lines, that focusing solely on these aspects rarely leads to fundamental improvement or evolution.
The true purpose of content marketing is to use content to prompt customers and prospects to take actions that drive business. What kind of data should connect to that?
I sometimes wonder if merely chasing the main KPIs often emphasized overseas—metrics like content consumption, shares, lead generation, and sales contribution—might not be sufficient.
From the Front Lines of Content Marketing [33]
