The field of paid search is constantly evolving. The latest wave of changes in SEM affects not just how marketers design and manage their campaigns, they also point to some fundamental changes in technology and consumer behaviour.
Here’s our look at the five most important paid search trends and predictions for 2018.
1. Machine learning
Artificial intelligence (AI) is powering an ever-increasing amount of automation in paid search. Here are just some of the examples that are constantly evolving:
Dynamic Search Ads
Dynamic Search Ads mine your website’s copy to find the most relevant content and serve these pages through targeted Google AdWords. Google will search your selected pages to find the most relevant one that matches the searched keywords and serve an ad that reflects the terms searched. It does this by changing the Google AdWords headline to reflect the keywords searched. This makes it the ideal feature to save time and automatically update your ads based on the content of your website.
Smart display campaigns
Reach more people on the Google Display Network (GDN) with Smart Display Campaigns. These use Google’s machine learning capability to automate targeting, bidding and ad creation to help you reach more customers easily and drive performance.
Smart Bidding uses advanced machine learning to set the right bid to each auction in order to meet your predefined target CPA or ROAS.
Machine learning still has a long way to go, and its impact will doubtless be huge. For example, the use of voice-activated assistants for online shopping may eventually lead to ads that can fit seamlessly into human conversations and steer customers to what they are looking for.
2. Bridging online and offline activity
Remarkably, in-store sales still make up 90% of total retail revenue in the US, according to Census data released in August 2017. But the customer’s journey has also become much more complex, and will often include engagement with online ads.
Retailers that use online advertising can now get more information on how their paid search campaigns affect in-store purchases. Since 2014, Google has been using anonymous geolocation and contextual data from users’ devices – enhanced by machine learning – to estimate online-to-offline sales conversions.
Now Google allows in-store customer information collected at the point of sale (such as email addresses and credit card details) to be uploaded to AdWords for more accurate linking of store revenue to online ad clicks. Google has also added location extensions to YouTube campaigns to help track store visits based on YouTube ad views.
While it’s useful to know when an ad click results in a store visit, it’s even more helpful to know which specific triggers and activities led the customer to that ad. For example, did a car sale result from a single click on an ad? Or was that click the final step in a long journey through other clicks and conversions?
Google’s recently released Attribution tool combines multichannel data from Google Analytics, AdWords, and DoubleClick Search to give marketers a more holistic view of how their online advertising activities are performing across different channels and devices.
Although this update is still in the beta testing stage, it’s expected to improve the performance of automated bidding and enable marketers to make better decisions about how they spend their ad dollars.
Read our guide to attribution modelling here: https://www.salmat.com.au/blog/why-you-should-move-away-from-last-click-attribution
4. In-market and similar audiences
More accurate and more timely audience targeting has long been a hot topic in paid search. Google’s In-market Audiences is intended to help you reach consumers who are ready to buy, based on search queries they’ve used and recent website visits. It can now be used in search campaigns in addition to YouTube campaigns and the Display Network.
Another new trend is the ability to reach out to people whose online behaviour matches those who previously browsed and/or purchased on your website. Google’s Similar Audiences achieves this by targeting people who are looking for the same things as users who were recently added to your search ad remarketing list (RLSA). For example, if someone in your RLSA searched for ‘comfortable running shoes’ and ‘buy supportive runners’, Similar Audiences will help find people who are looking for similar products.
In the coming months, marketers are set to benefit from a raft of new tools that make it easier to manage and optimise online shopping campaigns.
This includes being able to extend the reach of retailers from their own shopping websites to a plethora of other websites and apps. For instance, Google now allows marketers to target their shopping ads to YouTube viewers. For example, it is now possible to display an ad for a brand featured in a video.
With machine learning, more powerful tools, and better audience targeting leading the way, the latest changes in paid search should make for an easier shopping experience for consumers, and significantly better value for marketers.
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