Hey, my name is Laura. I'm Automation Specialist here at Google for the whole Belgian advertising market. And I'm really happy to talk to you about automation today. What it means for me, what it means for Google and what it could potentially mean for advertisers. For me, automation is a very broad term, it can mean many things. It can for example mean automating simple human processes according to a set of rules for example. It could mean automating the necessary reporting processes or dashboards Automatically bringing data sources together is a form of automation. So, there are many different forms and and different ways to automate. But what I think we need to talk about today is automation in ads, and the way we think about that is the use of a technology called machine learning. Machine learning essentially means lots and lots of data and recognizing patterns in that data.
Deriving conclusions from that data, and then using artificial intelligence or AI to make smart decisions based upon that data automatically. So I think there are three, broadly speaking, three reasons why you might want to use automation in ads. The first and for me the most important one is because you want to personalize your marketing at scale. So you want to use all signals and all data you get from your users.
That discerns what context they're in, their identity, their intent. You want to be able to use all that data to inform your decisions, but also to tailor the message that you send to them. Equally, this is the second reason why you could consider using automation, it's because you are focused on efficiency and ROI. If this is important to your business, then using automated tools can really help you improve upon that efficiency and increase that ROI, at least of your marketing investments. And last but not least, when decisions are being taken about where to put those marketing investments, you want to make sure that all of the available data is taken into account into those decisions. It's very tough to do that as a human because there are just so many data sources, and hence you need a layer of machine learning and a layer of automation to help you do that. So, if we look at automation in ads today, and in advertising and marketing, and the product solutions that are on the market and available to us, probably the most famous one - and sometimes infamous one - is automated bidding. So, we would like to make smarter decisions in terms of bidding and where we go and spend our marketing dollars.
Based upon what we know, from the user, from the query, their intent and their identity, but if we do that purely on a rule's basis, it's a very limiting way in which to do that. Rules can only take one aspect into account at a time. Sometimes a few aspects but never the way they influence one another. And so we started thinking about this a couple of years ago and with that machine learning technology we developed product solutions that actually help us take all of those signals into account to really understand the user's context, identity and intent. And help us make better bidding decisions based on all of those signals and the way they interact with each other. But, more importantly, based on the business objectives that we set. So we inform this bidding algorithm or this machine learning technology of what we want to achieve with our investments, and it helps us achieve that. A second example that I can think of is the way in which we target the right traffic online, the right people, the right users. So we know a lot about our own business, what products are we selling, what type of demand are we trying to capture within the market. Users tell us a lot of things about the context that they're in, about the intent that they have.
So we need to bring those two things together in a smart way. The way that we build our brand online is through our online assets, things like our website - hopefully our mobile first website -, but also our apps, and many other assets that you may have. You may have a voice assistant or a chatbot, of sorts. So all of those assets, we spend time to make sure that they reflect our business, so why should we not leverage the content that is within those assets and the way we build them to make sure that we also find people that are relevant to that business. So again, as a user, as a marketeer, your main role here is to make sure that those assets represent our business so that you can use them in an automated way, to define what kind of users are interested in things we have to offer. The third example that I can think of is when it comes to creatives. That's probably one of the most time-consuming things we do as marketeers. It's thinking about our messaging. And so one of the ways in which we would like to bring more automation to that process, is by moving into the space of asset-based advertising. So in this case, what we need to do is provide assets that show our brand image. For example, our brand logo is a typical one, but also the key things that we want to deliver to users in terms of message. And those assets then get combined in a smart way depending on the context and situation the user is in Whether they're on a mobile device or on a desktop device, or - even better - interacting through voice. So all of those assets get combined and actually that whole process is taken out of our hands.
But our role as a marketeer in this case, is to make sure that we provide the right assets and the highest possible quality of assets, so that that message can be tailored and delivered in the right moment. From those three examples we can see that machine learning plays a big role in automating these processes. But it doesn't take away our role as marketeers, it just changes the role we have. We need to make sure that this machine learning algorithm, this technology, knows, or has as a baseline what our business objectives are, what we stand for as a brand, what type of business we're interested in, and what type of users we want to reach. .