Here’s a sobering statistic for marketers: Google, Meta and Amazon took in more than 74% of all global digital ad spending last year. That’s a lot of dollars chasing the same consumers in the same place.
So how do you stand out? There’s still too much uncertainty about what works and what doesn’t and what channels are best for which messages. That’s because to get the most out of each channel, marketers have to experiment — a lot. You have to constantly test and learn; test different messages, test different landing pages, test different designs and learn what works to inform your tactics.
And even once you find what works, you need to continue testing to keep pace with changes in consumer tastes, the competition and the online ecosystem in general.
Experimenting That Much Sounds Radical. It’s Not
It’s essential. In all of my work with companies, agencies and brands, the biggest predictor of campaign success is the utilization of rapid experimentation and real-time measurement. What determines whether you’re going to get the most out of a channel or not is directly connected to the frequency of your experiments, and how well you can measure them to synthesize the insights. Iteration matters more than your budget, your time or even your creative assets.
When you invest in Google or Facebook for example, you can compare them against each other in a rudimentary way. It’s very easy to answer the question of which one earned the most impressions, greatest number of clicks and largest number of conversions.
But if you want to get the most out of your investment in each ad channel and control for variables (for example, which message, which targeting, which time of day, which creative and which tactics worked best), you can’t just stop there; you have to dig deeper. You need to be able to ask more detailed questions, compare different segments and deploy multiples of the same campaign, with slightly varied parameters.
The Most Successful Companies Launch Campaigns Daily
Or more precisely, they launch new experiments every day. And the more experiments they launch, the more they learn about what works and why.
One major European car maker, for example, was able to increase its conversion rate by 30% over their immediate competitor with rapid testing and measuring. And when you’re spending millions of dollars a year, 30% better utilization of marketing budgets is a serious competitive edge. In another case, a marketing agency was able to spot an opportunity for better budget allocation across all its clients. While it would usually take weeks to test such a large-scale hypothesis, experiment-first design and a modern data stack architecture allowed them to validate the hypothesis in one day.
One reason most companies can’t do this yet is that their marketing teams are simply not structured to iterate, experiment and analyze data. Another, as the agency test suggests, is about implementing a relevant data stack architecture to make the difference.
A 2021 survey of more than 1,700 marketers found over than 88% say they spend most of their time on reporting tasks, including tracking performance, creating competitive analyses and producing audience insights.
While the most obvious conclusion lies with the aspects of team design and workstream prioritization, we should also take into account the huge amounts of marketing data within organizations. Then again, it’s mainly about how you handle the data — a backbone for further workflow improvements. You may choose to hire marketing data analysts, but even that weighs down a marketing budget, reducing your pathways to revenue.
A Change is Gonna Come
Enterprise IT is quickly figuring out more effective ways to manage marketing data from multiple sources and present rational insights through no-code or low-code data aggregation tools. It’s making for more immediate insights, more responsive campaign experimentation and a clear picture of what it takes to create more consistent revenue streams.
Top marketers are eager for the opportunity in front of them. To wit: building an experiment-friendly team and data environments, shifting from tracking and reporting and getting back to creating and experimenting for higher conversions.
Nikita Bykadarov is VP of Demand Gen and MarTech for Improvado