Predicting what is going to impact the industry over the coming year is a common exercise to gain focus and a good overview of the wider market. Often, these predictions tend to come as no surprise, with trends often rumbling in the underbelly of industries before changing the way agencies and individuals behave. This year is set to be no different as the conversation remains around artificial intelligence (AI) and machine learning.
Looking at paid search specifically, 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. Most anticipated for marketers is no doubt the possibility of time saving and greater efficiency. Having algorithms that can determine intent and inform specific targeting based on data is powerful – not only for scientifically supporting targeting strategies, but also to provide accurate information to backup the wider strategy.
The permeating trend
The predication isn’t so much about what will disrupt the industry, but more about what is going to continue to permeate throughout it. Many applications of machine learning are already considered business as usual. 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 enabling marketers to hone in on ad targeting.
This is what is so empowering about AI and machine learning: the ability to have data at your fingertips that can help inform you who you are targeting and to what success. Despite this, many marketers would like to have greater visibility of the data and the reasons why the algorithms make certain decisions. But that’s the thing, as marketers, you often find the efficiencies driven by big data far greater than any necessary concern over the reliability of data itself. 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 focus on audience targeting
In line with this, a lot of clients want to focus on driving new users, and there are a lot more opportunities you can take advantage of moving forwards to help over-value key audience cohorts. 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. Dynamic Search Ads, which enable marketers to automatically insert an ad into relevant search results when not bidding on the keyword, have also been identified as a second area of growth.
With the added benefit of being able to machine learning as a key source for new keywords, the role of Dynamic Search Ads is set to become bigger in 2018. With any hope, algorithms will soon be able to take big sales dates, such as Black Friday, into account and automate bid optimisations for those. This will reduce the need of having to pause strategies and manually optimise. It will be developments such as this that will enable marketers to focus on finding new opportunities to point the machines at, rather than manual builds and implementations.
There’s a clear desire within the industry to get to a place where a deeper understanding of the impact and make-up of audiences is needed. As algorithms are used more, there needs to be a balance between volume and control to keep a grasp on targeting and results. There is speculation around what this could 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. Speculation insinuates there are updates coming and new ways to use algorithms and machine learning, but it’s clear that one way or another, adoption rates across the industry are likely to increase over the coming year. Marketers should see this as a key growth area for marketing overall, as well as specifically for paid search.
The rise of voice search
Voice search is also a key area for machine learning to grow into. While it’s going to take a higher uptake of virtual assistants for this to fundamentally change search, small influences are breaking through the industry. With searches through voice increasing, 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.
It's not only keywords that need to be considered, but more fundamental than that, websites. 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.
While giants such as Amazon are doing a great job at surfacing products based on user interests and previous purchases on its homepage, this could be easier for smaller websites to achieve as well. Agencies should be able to wholly tailor landing pages to target audience and thus increase relevance and value for the user.
The role of PPC experts moving forwards
It goes without saying, there’s been a lot of negative press around this trend. Marketers fear that machines will end up replacing their jobs. Despite this, machine learning is a trend that should be embraced, the core job functions for PPC experts is no doubt going to shift, but by no means obsolete. It will just require the skill set to change slightly. This is already happening as many professionals before more technically skilled in relation to tagging, coding and programming. The obsession for data arising from the growing influence of machines, creates the need for skilled professionals to provide a wider strategy behind that data. Ultimately, the role of PPC experts will become more focused towards strategy, testing, and growth – and the machines will do all the manual labour.
Bring. It. On.
* This piece was contributed by Angela Knibb (Head of Paid Search), Ewelina Ledo (Paid Media Performance Director), Shiblu Ahmed (Paid Media Performance Director), Kit Bienias (Paid Media Manager), Ian Oh (Senior Paid Media Analyst), Dan Walker (Senior Paid Media Analyst), Jonathan Kelterer (Paid Media Analyst), Natasha Hole (Paid Media Analyst), Sacha Dorf (Paid Media Executive), Tristan Schadler (Paid Media Executive), Hannah Reese (Paid Media Executive) and Laura Baldrick (Graduate Trainee).