Inside Google Marketing: 3 marketing myths we busted this past year

Inside Google Marketing: 3 marketing myths we busted this past year

November 26, 2019 Off By esential1@

The best part? Each of the videos ended up costing just $1,800 to create. We spent $144,000 to build 80 contextually relevant ads, proof that video doesn’t have to be slow or expensive.

Marketing myth 2: The more data you have, the better

In digital marketing, we gather all sorts of data points to understand whether our creative and media strategies are going to plan. We can see how long someone spent watching a video, how far someone scrolled down a page, or how many of our website visitors are bouncing. The list goes on.

But just because you can measure something, does that mean you should? We’ve realized that, when it comes to data, less is more.

It all started when we audited the analyses being shared with our leadership by teams across Google Marketing. We discovered that, collectively, we were reporting on 70 different metrics globally. How did we expect our CMO and VPs to make coherent decisions, to compare one campaign or strategy to another, when our teams weren’t speaking the same language?

Instead, as my colleague Avinash Kaushik wrote on Think with Google earlier this year, we’ve whittled down all those data points to just six metrics that matter. Why that number? Because we run two types of campaigns: brand and performance. Across those campaigns, we care about three things: whether we’re capturing people’s attention, how they’re behaving in response, and what the outcome is. So now, rather than drowning in metrics, we have just one for each of the things we’re interested in measuring.

Marketing myth 3: Humans are being replaced by machines

“As advertisers in the age of machine learning and artificial intelligence, it’s easy to think of ourselves in an epic faceoff with these machines,” Ben Jones wrote in a Think with Google piece last year. This fear that machines will displace us is a normal one, and it’s certainly not limited to the marketing industry. But the fear is unfounded. Instead, as we’ve discovered through our experiments this past year, it’s about understanding what machines do better than us and letting them get on with it, freeing up humans to lean into what we do uniquely well: insights, inspiration, and creativity.

Here’s an example. This is the equation for calculating customer lifetime value (CLV) — a way of identifying who your most valuable customers are, something all marketers need to know.