Myth 1: Video is slow and expensive to create
Marketers understand that they should be adapting and optimizing creative assets to the specific features of the platform where they’ll run.
The industry myth is that producing video in this way is both expensive and takes lots of time. A director and large crew have to be hired, and they have to shoot somewhere glamorous, taking their expensive equipment with them. By the time you get through post-production, it’s taken many months and a lot of dollars. The final piece of art, beautiful though it may be, now has to run everywhere — one size fits all — with no time or resources to adapt or customize.
It doesn’t have to be that way. There are lots of tools available that allows you to create custom videos on a massive scale e.g. Google Directors Mix. Video doesn't have to be slow or expensive, and is a crucial channel for any brand to build brand awareness and connect with your target audience.
When running campaigns there are three things ultimately all brands want to establish: 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.
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 realised that, when it comes to data, less is more.
DATA IS ABOUT QUALITY NOT QUANTITY
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. 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, it has been discovered through various 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.
Now I’m no mathematician, so it would literally take me a lifetime to figure out what this means. But even the most analytically minded people would take a while to work this out manually, which is why we used to get an updated CLV only every six months.
Then we turned to machine learning. By handing off our data — like traffic sources and previous campaign performance — to a tool called TensorFlow. We went from having access to one CLV every six months, that we then had to use across all our bids, to having 2,000 predicted customer value (pLV) calculations a day. Being able to use a real-time pLV versus a six-month-old CLV has allowed us to better optimize and regularly refresh our Google Ads bidding strategy.
Machines can also free up time in the area of ad creative. For example, we’ve been able to use smart creative technology to optimize display and search ads in real time based on how people respond to them. The format has greatly outperformed the legacy static display and search ads we’d been using for more than a decade.
Machines are well cast for these highly manual, low-level decisions. So we let them do it, freeing us up to focus on the things machines can’t do as well as us — like identifying the next marketing myth we need to bust on our journey to becoming smarter, more effective digital marketers.