Peak for Peak: Ad optimization

Peak for Peak: Ad optimization

Taking our own solution for a spin

Ad Optimization is built to drive higher returns from digital advertising by leveraging a business’ data with the power of AI. It allows brands to regain control of their ad spend from the likes of Google and Facebook and run intelligent, AI-powered campaigns which drive cost-effective customer acquisition.

As a new solution, we wanted to take it for a spin in-house. So, what better way to do this than to integrate the solution into our own digital advertising strategy? 

Here, Peak’s Adam Cobb (Digital Marketing Manager) and Mark Douthwaite (AI Engineer) talk us through the solution, why it’s needed, and what it’s achieved for Peak’s marketing team so far.

Peak ad optimization case study

What was the challenge?

Adam: Running digital advertising campaigns in our industry – which is still relatively niche – is challenging. Data is siloed and it’s very hard for a single person to make quick, intelligent decisions based on how Google Ads are actually performing. As a growing business working with a modest budget and no dedicated PPC management function, our campaigns and ad strategy were often put at the bottom of the list, neglected to an extent, and weren’t getting the time and attention they needed.

Putting an AI-powered solution in place was a way of dealing with this problem and taking out some of the manual elements of PPC management.

What did Peak do?

Mark: We looked at some of the in-built Google Ads capabilities, particularly Bid Simulator, which looks at your historical data to predict an expected return in terms of clicks or conversions. It’s useful, but very hands on – you have to go into every single campaign and ad group and manually change bids, and it doesn’t take into account other information about your business.

We see Ad Optimization as a supercharged, AI-powered version of this. It takes into account historical data, customer data, your own custom goals and objectives, and automatically sets optimal bids and budgets across all campaigns simultaneously. You can either let it do this without manual intervention, or accept and reject its recommendations as you wish. In summary, it makes it quicker for users to be reactive and make decisions to optimize their campaigns, without having to trawl through the configuration of endless campaigns.

Adam: We did initially doubt how effective it would be due to the size of our account, we worried whether there might not be enough data to make useful decisions. But, so far, the solution has proved to be incredibly effective despite our relatively low amounts of data. It doesn’t need to be a six-figure ads account in order to deliver useful findings.

What’s the upshot?

Mark: As a starting point, we looked at optimizing our remarketing and brand advertisements for increased clicks. It worked as we hoped it would, driving an increase in clicks for the same ad spend for certain campaigns. 

What’s interesting, though, is that Ad Optimization also automatically tries to move money away from those campaigns that aren’t performing as well. It reallocates spend towards more profitable campaigns that better fit the objectives set – for example, if the objective is to maximize clicks across all campaigns, it will reduce and reallocate spend from the struggling campaigns with rising cost-per-clicks (CPC) to drive cost savings.

Adam: It’s also worth noting that if you set a target budget it will optimize for that, too. It’s not just an engine that tells you to spend more money, it can make your existing spend more efficient and effective.

Mark: To summarize some of the results we’ve achieved so far across a selection of campaigns, Ad Optimization has driven a 12% increase in clicks and a 20% reduction in overall costs. Average CPC has also fallen to £0.93 from £1.32, too.

AI and advertising: increase marketing ROAS

What’s next for Ad Optimization?

Mark: We’re going to take a similar approach to Facebook advertising and its audience network. We’ll take data down to a device and location level and optimize all aspects simultaneously – something that is more or less impossible to do by hand. 

In the end, we plan to roll our audience generation capabilities into Ad Optimization, with our ad engine automatically generating smart audience segments to assist with smarter targeting. Also on the roadmap is to integrate Peak’s Predictive Customer View into this project. We’re aiming to pull marketing information from systems like Salesforce, and plug it into the Peak solution to help further optimize campaigns in terms of sales conversion rates and customer lifetime values.

To summarize…why do marketers need Ad Optimization?

Adam: The fact it’s such a low intensity process is a game-changer for people in roles like mine. The engine makes suggestions, and it’s a case of clicking, implementing and reviewing. To do manually, you’re looking at hours and hours trawling through spreadsheets and Google Ads itself looking for those small changes that can make a difference. The time-saving element is key; it frees up space for users to focus on implementing new creative ideas, try new things and progress the marketing operation rather than being tied up in reporting and making manual minor tweaks.

AI and advertising - download the Ad Optimization guide

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