In the fast-paced world of content marketing, first impressions matter. You hit publish on that painstakingly-crafted blog post, infographic, or video, and it's out there in the wild. There's no going back and fiddling with the headline once someone's clicked away, no chance to rewrite that intro after a wave of disinterested scrolling. It's one shot. Yes, the pressure can be immense.
But hold the presses! Before you succumb to content creation paralysis, there's a superhero in the marketing toolbox waiting to swoop in: A/B testing. As an analytical tool, A/B testing has had a fairly storied history; first emerging in drug clinical trials during the 19th century, then becoming a staple of advertising and marketing over the course of the 20th century.
A common refrain of proponents of data-led content marketing is that A/B testing is an invaluable tool for measuring what content will best capture attention. The value proposition is that A/B testing can significantly improve key metrics like conversion rates, click-through rates, and engagement.
Can a slight tweak to your title could lead to a rise in traffic? Could a more compelling CTA skyrocket sign-ups? Might a more concise and clickbait-y subject header lead to a higher open rate? A/B testing can help you track which version performs better, you can gain valuable data on what resonates most with your target audience. No longer are you beholden to the tyranny of creative intuition.
However, many content marketing practitioners have come to realise that while A/B testing provides a solid dataset from which one can make informed and - perhaps more importantly - empirical decisions, it is not without its drawbacks.
First, A/B tests need to be designed carefully to ensure the results are statistically significant. This means having a large enough sample size to avoid drawing conclusions from random fluctuations. Therefore, there needs to be a sizable amount of traffic to your website or content, otherwise it can take a frustratingly long time to gather sufficient data to draw statistically significant conclusions.
Second, you can get lost in the weeds of testing every little detail, and never break out of the analysis-paralysis loop. You and your team can endlessly deliberate over how many variables to change at once, and constantly fuss over the appropriate campaign trigger. You could very well be devoting significant time crafting suitable experiments to ensure that the data you gather is viable, instead of just creating new copy.
Lastly, A/B testing can tell you which version of your content performs better, but it can't tell you why. A/B tests typically focus on one or two variables at a time. They may not reveal broader trends or underlying issues with your content strategy. It won't reveal if your content is confusing or lacks depth.
In the long-run, however, any public-facing content will have an audience, and a dynamic, ever changing audience at that.
Instead of having to develop dual-track campaigns, Onside's unique methodology can ensure all your content is not in service of a data-gathering experiment, while staying geared to what your intended customer needs to hear.
At Onside Content, we have two proprietary indices around assessing your audience. Our first index identifies where audiences are most engaged in topics related to your product or service, while our second index reveals where they are most curious. The former is indicative of current engagement, while the latter offers predictive potential to show what your audience is looking for next.
Onside Content is your bridge between data-led insights and the untapped audience you seek. Using our proprietary technology, we can help you find, create, and lead the smartest conversations in your space with a bespoke content strategy. We'd love to support you! Drop us an email at: info@onsidecontent.com
