Businesses can’t ignore the power of machine learning

The start of the year is famously a time to reflect, look forward and realign focus. However cliché you find it to apply that within your organisation, it is a valuable exercise that can help you prioritise. It is especially an important time to look at industry predictions for the year – what does your business need to do or change to ensure its continued success?

Often, predictions tend to come as no surprise, with trends 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, and how paid search in particular will be impacted. This has been discussed for a while, but there is still work to be done in order for businesses to really maximise the 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.

>See also: From buzzword to boardroom – what’s next for machine learning?

Time saving and efficiency gains are without a doubt the most anticipated benefits of embracing machine learning. Using algorithms to determine intent and inform specific targeting based on data is powerful. Only when armed with this information can marketers build scientific, data-led targeting strategies and have the insight to back up the wider strategy.

More than business as usual

The prediction isn’t so much about what will disrupt the industry, but more about what is going to continue to grow in importance. It will be a case of those who fail to recognise the potential, could be the ones who lose competitive advantage.

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 marketers use to hone in on ad targeting.

>See also: What is machine learning?

Having data at your fingertips is key to gain an understanding into the success and any weaknesses in targeting strategy. 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 areas of growth

In line with this, a lot of brands want to focus on driving new users, and there are a lot more opportunities that can be taken advantage of moving forwards to help over-value key audience cohorts, such as 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. A second area of growth is Dynamic Search Ads, which enable marketers to automatically insert an ad into relevant search results when not bidding on the keyword.

However, marketers shouldn’t underestimate how the role of Dynamic Search Ads is set to grow in 2018. 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. 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.

>See also: CIOs beginning to deliver real value from machine learning

The demand for greater audience insight is immense. Yet, as algorithms are used more, marketers do risk becoming overwhelmed by data. 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.

Whilst speculation surrounding new ways to utilise machine learning bubble away, it’s clear that 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 growth of voice search

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. 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.

>See also: Machine learning and AI is changing how data science is leveraged

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 relevance and value for the user.

The future role of PPC experts

With these changes, it’s clear the role of the PPC expert will shift; but that doesn’t mean it will become obsolete, as many fear. Machine learning is a trend to be embraced, it will just require the skill set of PPC experts to change slightly.

This is already happening as many professionals become 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.


Sourced by Paid Media team at Greenlight

Avatar photo

Nick Ismail

Nick Ismail is a former editor for Information Age (from 2018 to 2022) before moving on to become Global Head of Brand Journalism at HCLTech. He has a particular interest in smart technologies, AI and...

Related Topics

Machine Learning