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How to increase ROI on Google Ads and scale revenue in 2023

Guest Author
By Guest Author — February 27, 2023 -

Read time: 2 mins

Planning for a new year is always interesting and no more so than in 2023, when we’re facing a recession, cost of living crisis and global supply chain problems to boot. 

If uncertainty does one thing in business, it makes people more risk averse. Plans to make this your biggest year of growth have likely been scuppered by the finance team and their instruction to rein in spending while the global economy plays out. 

Marketing is often first to come under the microscope, especially demand gen channels and campaigns that aren’t delivering direct returns. But we’re operating in a multi-channel environment, where almost every channel impacts another. 

Rein activity in, and you risk your competitors getting ahead and closing the door to your total addressable market. 

Pulling spend in paid media and bowing out of ad auctions gives your competitors lower acquisition costs and a higher volume of sales. It lowers efficacy in your decision stage activity, reducing brand presence earlier in the customer journey. 

That’s a potentially fatal move. 

The data can and should lead your business when deciding which marketing efforts are commercially viable. 

As we head deeper into 2023, here’s how you can improve your marketing efforts, increase ROI and take full potential of the customer advocacy data at your fingertips.

Using customer advocacy data to optimise ads 

Over the past 10 years, Google Ads has become a powerhouse of machine learning and automation. 

The goal you point the campaign towards and the feedback it gets often has a bigger bearing on performance than anything else. 

It’s changed the competitive advantage when it comes to advertising. It’s no longer about who can make the most changes - it’s about who has the most valuable data and knows their business goals. 

The most valuable data point to feed back in? 

A customer, at target cost, who refers. 

Faced with limited marketing spend, stretching it as far as possible becomes the priority. And customer advocacy data offers great value, both directly and indirectly. 

By pushing out data from your customer advocacy platform and creating Smart Audiences, you can feed back into campaigns the signals that surround what a referring customer looks like. 

In a key eCommerce campaign format such as Performance Max, that adds a very powerful string to your audience signal bow. By giving machine-learning access to this insight, you can steer towards acquiring customers who look like referring customers, maximising your return-on-investment. 

As a multi-network campaign format, when using budget outside of search and shopping, you can see lower efficacy on networks that are typically more awareness drivers (such as YouTube) or with audience formats such as Affinity audiences, which generally contain a lower buying propensity out of the box. 

Having such a high value data point for reference in the campaign will help guide your bids towards the users who are going to deliver you the most value. So you can go into higher volume, non-demand restricted territory, with a target that will yield value for you. 

Harnessing data to become a winning business 

As we head deeper into 2023, this is the playing field that we need to be on, regardless of uncertainty and the economic climate. 

Right now, you’re overlooking huge opportunity in our core markets and channels. By bringing together activity, you’ll get the best value out of both and get your team thinking about the commercial bigger picture. 

The days of competing on tactics are gone. Now it’s all about who can use their data most effectively, be both creative and commercially-minded with their marketing strategy and, most importantly, can truly harness the power of multi-channel, bringing together essential channels, such as paid media and referral. 

Want to supercharge your paid media with customer advocacy data?  Talk to us.

This is a guest blog written by Byron Marr, Founder and paid media strategist at ProfitSpring. To learn more about machine-learning in paid media, check out

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