The conversation around artificial intelligence and exactly what impact it will have on digital marketing continues to pervade the industry.
There are many predictions about how it could change the way digital marketers do business but there is still work to be done in order for businesses to really maximise its potential. In fact, machine learning should be considered one of, if not the, most exciting application of AI, and certainly the easiest to apply directly to paid search.
Paid search is just one tactic in the arsenal of digital marketing strategies but perhaps the one that can benefit most in terms of both the efficiency and time saving aspect of AI. Using algorithms to help target and reach your desired audiences is powerful and can provide marketers with the data needed to drive wider strategy.
Moving from trend to trust
This isn’t a prediction of what is going to disrupt the industry, it’s more about what is going to continue to grow in importance. Those who fail to recognise the potential that AI can offer could be the ones being left behind when it comes to evolving your campaigns and targeting.
Many applications of machine learning are already considered business as usual – and for good reason. For example, Smart Bidding and bid strategies in AdWords or DoubleClick are already providing marketers with a good level of targeting and insight.
Similarly, Adaptive Shopping, Smart Display Campaigns, adaptive location targets, and Google Analytics smart lists are just a handful of the tools marketers can use to hone in on ad targeting.
The thing is, with increased data being collected through these platforms and tools, there needs to be some assurance that the algorithms are making reliable decisions. As marketers, you often find the efficiencies so appealing that you don’t question the reliability.
That is what is going to take AI from a trend to a fundamental business tool, gaining a deeper understanding in to why the algorithms make certain decisions. Admittedly, whilst you ideally want to learn to put your trust into algorithms, they don’t always get it right, so there is still that level of monitoring and trial needed.
The areas of growth
Where the value of AI will continue to grow is when considering how to over-value key audience cohorts. Being able to do this will help brands drive new users with the ultimate aim of converting them.
One way to do this is by up-weighting revenue driven from new users and pulling that into bid strategies to increase bids more aggressively for new users more likely to convert. This is going to be a key growth area.
However, marketers shouldn’t underestimate how the role of Dynamic Search Ads can help marketers to automatically insert an ad into relevant search results when not bidding on the keyword.
For big calendar dates such as Black Friday, the hope is that algorithms will soon be able to take these dates into account and automate bid optimisations for them. This will reduce the need of having to pause strategies and manually optimise.
Audience insight is invaluable but it needs to be insight that can be actionable. Filtering through an increasing amount of data is the part that can be overwhelming. There needs to be a balance between volume and control to keep a grasp on targeting and results.
Speculation remains in terms of what this is likely to look like, and this could be having a less granular breakdown of audiences. But there is also speculation that Google or DoubleClick might consider creating an ‘Adaptive Audiences’ functionality, which would automatically break out top performing audiences from a broad audience base.
Machine learning will continue to be an area of speculation and intrigue but it’s clear that adoption rates across the industry are likely to increase over the coming year. Marketers should see this as an exciting opportunity to better understand audiences and trust that you’re targeting them in the right way.
Preparing for voice domination
Voice search is also a key area for machine learning to grow into. The rate of growth is still considered relatively slow until uptake of virtual assistants increases, but small influences are breaking through the industry and marketers should be considering what impact this might have on their business.
In order for voice search to be effective, marketers need to adapt to how they’re building long-tail keywords to reflect how users speak as opposed to type. This also needs to be considered for product titles.
But before that, there is a fundamental change that needs to happen. The consideration shouldn’t just be on keywords, it should first be for how websites are currently set up. They will need to be optimised for voice search to align with consumer search preferences.
But this is a good thing – something that should be relished as it means sites would become more informative and therefore give dynamic search ads more information to increase visibility.
It’s easy to think that this is only going to impact giants such as Amazon, but this is a great opportunity for smaller websites to emulate. Amazon is already doing a great job at developing its website to maximise this by surfacing products based on user interests and previous purchases on its homepage.
There is no reason as to why smaller websites cannot wholly tailor its landing pages to target its audience. This will enable them to increase relevancy and value for the user.
PPC experts: what’s needed?
Machines aren’t going to replace PPC experts, at least not in the foreseeable. This shift and growing relationship with technology will mean that the role of the PPC will shift.
Machine learning is a trend to be embraced, it will just require the skillset of PPC experts to change slightly. This is already happening as many professionals become more technically skilled in relation to tagging, coding and programming.
Becoming professionally skilled in that way will ensure that PPC experts can continue to develop data driven strategy – just with more data.
That should be celebrated and embraced from large corporation to small. Ultimately, the role of PPC experts will become more focused towards strategy, testing, and growth – and the machines will do all the manual labour.