Today, Iām going to be talking about automation and the changing face of media. Specifically, the trends that weāve seen in updates on products released by Google using machine learning algorithms and AI, and the way that has shaped the tools and platforms that we use as marketers.
How AdWords has changed
But, before we get stuck into that, just to do some scene setting, weāre going to go back to October 2000, when AdWords was launched. If you were lucky enough to be working in paid search back then, this is the interface you had to use. It was much simpler back then, much less data to optimise against. There was no way of doing bulk changes, you had no complex product feeds. Because of that, campaigns tended to be a lot smaller and a lot easier to manage.
Contrast that to today, and the amount of data that Google has access to at its fingertips, things like device, location, browsing habits, previous search terms, salaries, job titles, other demographics data. Thatās not even really scratching the surface about what they know about you. We can use all of that information to target users and to adjust bids, to change targeting, to be more accurate, and to be smarter about what weāre doing. But itās a big task to do that granularly and manage all of that data. Which is why Google have released automated products and machine learning products that can do a lot of that for us.
Automation within AdWords
So just a couple of those, just a handful of things that theyāve released in the past. Things like smart display, which will automate the targeting and the creative thatās used in display campaigns. Bidding strategies and double clicks that will take control of all of the bids that youāre placing based on what it knows about the user. Even dynamic search ads thatāll automate some of the ad copy and decide what keywords to target based on your website copy. Budget plans, automate pacing and distribution of your budget.
Now, thereās pretty much a fully automated workflow for every channel you could want to advertise on. Google arenāt slowing up either. Myself and Deyna went to the Google product kick off meeting last week. And out of 45 new updates that they announced, 27 of them involved machine learning or automation. And thatās across all of their products. Over half of them involved machine learning automation.
Why is Google focusing on machine learning and automation?
So why are they doing this? Thereās a couple of reasons. Firstly, as a human itās impossible to make use of all the data we have access to today, processing every piece of information and making decisions based on what we see. Itās just not efficient to do it manually. And if programmed correctly algorithms can make the same decisions in a fraction of the time. And because of that, they can perform better. Itās not guaranteed that they will perform better ā theyāre not faultless ā but they have potential.
And secondly, if performance is better then advertisers will spend more money and ultimately generate more revenue for Google. And with most of Googleās decisions, somewhere along the line if you trace it back itāll link back to revenue.
What does this mean for media marketers?
So, what does it mean for people working in media? Well itās probably a scary thought handing over all of the control of your campaigns to algorithms and automated processes. When, essentially, itās a performance agency, when the buck stops with us, that can be quite a scary thought. And you might want to control all of your bids and all of your targeting manually, and you might think that you can outperform the algorithm. But the way I see it, thereās only one way that the futureās going really.
I mean weāve seen it with Google in the past trying to remove manual options, things like ad rotation using terminology in the interface. So, for the optimised version of ad rotation, the label is the ideal setting for most advertisers. Without any optimisation, if you set it to rotate indefinitely, not recommended for most advertisers and you get a warning. Little things like that. And theyāre not afraid of making big changes and ultimately turning things off.
Weāve seen with the new AdWords interface that was slowly released over 2017. It received a lot of negative feedback initially and people have continuously been using the old interface and not wanting to switch over. But theyāve used little tips and tactics to try and get people to move over, making it the default interface when you log in, only releasing new products and features in the new interface. Itās been suggested that the old interface will be sun-setted in Q1 of this year. I donāt think thatās going to happen, but they communicate in that way obviously to try and move people over. Theyāre smart, they know what theyāre doing.
So, basically, I donāt think we have a choice, thatās the way the futureās going. And if you want to stay in the game and still be relevant in a few years then I think youāre going to have to embrace it.
How do we embrace automation?
So how do we do that? I think, firstly, when using automation and machine learning algorithms itās important to understand whatās going on, what the algorithmās doing, what data itās using, how itās making its decisions, and essentially allowing us to better understand what the outcome was and why that happened. And, if performance was good or bad, why was it good or bad? Not just sort of taking that and going: āWell it didnāt work for meā.
Granted, fully understanding how some of these products work isnāt always easy, but Google tends to be pretty open with the data that theyāre using to run these algorithms and how we can affect the outcome. Ways we can affect the outcome? So, things like adjusting settings or feeding in richer data sets. For example, if youāre using bidding strategies that are adjusting bids based on audiences that you have in AdWords or DoubleClick, making sure you have lots of audiences set up, useful audiences of bucketed users that itāll be able to analyse and make decisions on. Essentially, giving it every chance of winning.
And lastly, donāt be afraid to test things. I know weāve tested a lot of stuff internally. You need to be scientific with you approach. Run tests, analyse the outcome. If you donāt get what you wanted, try to understand why and not just give up and say: āWell that didnāt work for this clientā without fully investigating. And be resilient.
And ultimately, if we get it right and the algorithms perform well, itās a much more efficient way of managing campaigns, obviously. If we can remove the need to analyse rows and rows of data manually, we free up a lot of time to start focusing on other things, things like strategy, planning, creative and user experience.
And bringing it back to us, so from an agency point of view, I think thatās very exciting. Our position as a data driven performance media agency means that weāve been at the forefront of this for a while. But weāre all on this path of maturity together when it comes to advertisement technology, learning how to best utilise advances in machine learning and AI. And I think weāre in a pretty privileged position as well, working in marketing. Automation can help us do our jobs better, rather than replacing us completely like in some other industries.
So, thinking about that. Whatās going to differentiate you, whether youāre a marketer or a brand, or work in a media at an agency, when automationās dominating many of the activities that we carry out today itās going to be back to the old school. Focusing on core marketing skills like strategy, planning, creative, providing insight. And in that landscape, we need to be better whole brain marketers. Sure, youāre still going to need technical skills but weāll have more time to focus on the creative side of marketing. Thatās my prediction anyway.
Thank you.