Earlier this year, Google started telling advertisers it’s going to take a more hands-on approach with its Google Ads management. In late January, the search giant sent out an email to certain account holders, explaining that its Google Ads reps will start optimising their accounts for them – unless they specifically opt out.
Google has introduced a growing number of automation features to its advertising platform in recent years, as it aims to make Google Ads management easier for its customers.
They state that their automated features provide impressive potential for both results and time efficiency. However, the chief concern for advertisers is whether the lack of control and understanding of individual business needs will result in a lower ROI. As every business and every market is different, there remains a need for an expert human eye to optimise strategy based on a deep understanding of these nuances.
‘We’ll focus on your campaigns, so you can focus on your business’
This is what Google told advertisers in the email sent out in January. It appears these emails were only sent to advertisers managing single accounts, not advertisers or agencies managing multiple accounts.
The email was first reported by
advertiser Aaron Levy on Twitter, who received a copy in relation to an old
Well this is new. @GoogleAds basically saying “we’re gonna start messing with your accounts in a week unless you explicitly say no.” pic.twitter.com/zShgxKH62i— aaron levy 🏒 (@bigalittlea) January 24, 2019
Well this is new. @GoogleAds basically saying “we’re gonna start messing with your accounts in a week unless you explicitly say no.” pic.twitter.com/zShgxKH62i
The reaction hasn’t been entirely positive. A lot of advertisers fear Google is going to undercut agencies with this move and there are always concerns about giving away too much control over advertising accounts when it introduces more automation features.
Some of this concern is justified too. After all, Google is in the advertising game to make money, which it does when people click on your ads. The more you spend, the more Google makes and this isn’t in line with your advertising goals, which means there’s a conflict of interest.
However, if you look at who Google has sent these emails to and the automated features it has released in recent years, it’s easy to see who they are aimed at.
The email mentioned above says “Google Ads experts” will start optimising accounts on behalf of advertisers, but this really means they will be implementing automated features and making changes based on Google’s recommendation engine, which is powered by machine learning.
Essentially, Google is going to push these advertisers closer towards automation, which will likely include:
When Google announced smart campaigns last year, it was clear about the fact this feature was designed for small businesses. If you look at the benefits it lists on the Google Ads page for smart campaigns (included below), you can see this is clearly aimed at small businesses who can’t manually create campaigns for themselves.
For small business owners with
very little time to devote to marketing activities and analysis, this sounds
In the same way smart campaigns
are designed to help smaller businesses get involved with Google Ads, the
search giant also rolled out smart
shopping campaigns to get smaller retailers on board.
Here’s what Google says about
Smart Shopping campaigns, your existing product feed and assets are combined
with Google’s machine learning to show a variety of ads across networks… Our
systems will pull from your product feed and test different combinations of the
image and text you provide, then show the most relevant ads across Google
networks, including the Google Search Network, the Google Display Network,
YouTube and Gmail.”
Essentially, Google looks at the product feeds and ad assets of retailers and creates the ads it thinks will be most effective, while automating ad placement and bids at your given budget.
Related reading: Google Shopping – seller vs product ratings
Search Ads (DSAs), Google essentially takes keywords out of the equation
and creates ads for you, based on the content of your landing pages. Google
creates a headline to match the search query of a relevant user and then uses
your page content to create the ad copy.
Google offers the example of a luxury hotel chain using Dynamic Search Ads. When someone searches for “Luxury hotel New York”, Google might create an ad with the headline “Luxury hotel – NYC” and take the most relevant piece of content from your New York landing page for the ad copy.
The idea is to save advertisers
from keyword research, keyword targeting and manual ad creation.
Google Ads has six different
automated bidding strategies advertisers can use for different objectives:
Essentially, Google aims to optimise your bids so that you bid higher at times when your ads are likely to perform most effectively. If your aim is to maximise clicks, for example, Google will up your bids for searches and times where your ads are most likely to generate the highest volume of traffic.
Related reading: Google’s data-driven attribution model
Automation features can certainly be useful for advertisers large and small – with some options being tailored to higher or limited budget levels. They provide the potential for small businesses to ‘set and forget’ their campaigns if they otherwise don’t have the time to manage them themselves.
However, the true value of automation is when it’s combined with an expert eye that understands individual business objectives in detail. For example, automation cannot optimise for high quality leads vs low quality leads. Nor will it distinguish between high margin sales vs low. These are the obvious examples, but the small differences between one business and the next are what often lead to success or failure in the market.
Automation should be used in a controlled environment where manual campaign management techniques are split tested against them to define the highest return on ad spend (ROAS) approach. This needs to be done in liaison with the business stakeholders on the frontline to ensure that good ROAS results in good total ROI.
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Categories: Data Science, Machine Learning, PPC