How to Optimize Your Digital Marketing Campaigns (Hint: It’s All About Data) 

August 17, 2022
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There’s a common adage among digital marketers that the right ad needs to reach the right audience at the right time. For example, an ad communicating a professional service needs to reach a decision maker who is in-market for the promoted offering. 

Media partners like Google and Facebook have a shareholder responsibility to build solutions that meet the increasingly-complex needs of the marketer, who are tasked with driving business growth. The days of basic targeting and limited measurement oversight are long gone. Today, campaigns are powered by machine learning, automatically serving + optimizing against billions of real-time, audience-based signals like online behaviors, location, and time of day, whose impact can be measured in business not just marketing goals.

Despite these advances, advertisers cannot just hand over budget and assets to platforms and let data science machines do the work. Marketers remain artists and scientists, who must know how to manage platform settings appropriately, read data, and analyze accordingly. From an artist’s perspective, they need to create copy & images that are compelling, craft narratives explaining “why” something happened, and more.


Seasoned marketers leverage their scientist and artist skills to refine campaigns and unlock incremental performance gains. This process is known as the “Optimization Cycle” and is a never-ending practice.

The Optimization Cycle’s first step is capturing traffic from ads. A portion of that traffic then converts into relevant online events related to the advertiser’s ultimate business goals. Over time, metrics associated with traffic and conversions grow, and the campaign manager analyzes the data to determine what is or is not working. Adjustments are made based on learnings gleaned from the data, and the cycle begins again to determine if the changes will or will not improve performance.


Optimizations can be applied to all parts of a campaign as publishers collect granular user data and allow advertisers to leverage that data accordingly with settings. Optimization cycles are dependent on driving demand to fill the top of the funnel — here are some example ways cycles can be manually applied:


Recently, Google made Responsive Search Ads search’s default ad type. This ad format allows advertisers to upload up to 15 headlines and four description lines, and Google will mix and match these assets to identify which combinations produce the best results. This ad unit greatly simplifies ad copy testing for advertisers because previous ad types did not adjust copy dynamically, but there are drawbacks. For example, Google does not report which specific mixed-and-matched ad variation produces the best results. The platform only reports on how the overall collective headlines and descriptions perform. This limits an understanding of what copy performs the best.

To get around this, marketers can implement more than one Responsive Search Ad variation per group of keywords, and each Responsive Search Ad variation can be established by theme. For example, Responsive Search Ad variation #1 can contain messaging elements that highlight one value proposition, and Responsive Search Ad variation #2 can highlight another value proposition. Both ads can be implemented as an experiment, and collected data can then be used to determine what performs best. The winning ad can continue to be optimized for additional learnings while the weaker ad can be retired.


Like text ads, experiments can also be run for landing pages. Marketers can split clicks between two pages to identify which generates the best results, and the weaker page can be retired in favor of the higher-converting page. Additional variations of the stronger page can be implemented with different text, images, or conversion-related forms to further encourage the desired action.


Google captures extensive user data, and marketers can reach individuals based on their affinities, in-market behaviors, previous site behaviors, and more. Within the ad platform, marketers can set these segments to “Observation” mode, which allows them to collect performance data by audience segment. This data can then be analyzed to identify how specific audiences ultimately perform against business goals.

Google’s machine learning capabilities can be used to serve ads against audiences that perform automatically, but additional settings can be manually updated to ensure the right targets are reached. For example, if a particular audience performs poorly, exclusions can be implemented to suppress unwanted users.


As campaigns run, marketers can identify many areas where performance could be improved. For example, a campaign may have ad assets, landing pages, and audience settings that are underperforming compared to other parts of the campaign. While strategies can be developed to address these areas ripe for improvement, not all optimization opportunities are created equal. Some actions may greatly move the performance needle but would require a significant investment in time and effort. Other opportunities may be quick to implement, but have a lower impact on performance.

Interpreting collected data appropriately will reveal where to invest time and effort. For example, let’s say that a keyword is used to serve ads on Google search result pages and has an exceptionally high response rate where 50% of 100,000 ad impressions result in a click. Searchers are interested in the product or service the ad communicates. Otherwise, they wouldn’t click on the ad. However, let’s also say 0% of these visitors convert. A marketer would want to prioritize fixing the landing page experience, versus optimizing the ad to generate additional traffic.


While it may be tempting to measure the success of your marketing initiatives by the number of campaigns in-market, it’s often better to take a methodical and measured approach. Consider setting aside dedicated time for weekly campaign optimizations to identify actionable insights that improve your campaigns. 

From ad copy and landing pages to audience targeting, it can feel overwhelming to think about prioritizing optimizations, especially if you’re also responsible for upcoming campaigns. You’re not in it alone, though! From crafting bespoke full-funnel ABM experiences to developing and designing future-proofed websites for hyper-growth companies, ROI·DNA focuses on driving radically successful outcomes for our B2B clients. Want to learn more about how we plan campaigns that convert? Drop us a line and say hey!

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