Google Ads Lessons: Effective Ad Personalization and Customization

Ad personalization in digital advertising is being discussed increasingly, and in this article, we will share our perspective on why it is important. But before we discuss its importance, we should first explain what ad personalization is and how it differs from general ad adjustment.

Ad personalization vs. ad adjustment

Ad personalization and ad adjustment are related terms. However, they differ in focus and application.

Ad adjustment

Have you ever been in a situation where you’ve written an ad and thought it was the best one, with all the unique selling points (USPs) that will achieve all the KPIs we set out at the beginning? However, after running your ads, you realize the reality is quite the opposite. And why is that so? Most users differ in many ways, such as age, gender, local culture, the platform and devices they use, and the impact of external factors such as current events, holidays, and the season they are in. With this in mind, ads should be tweaked to be more appropriate and relevant to our target audience and the platform used to advertise. In this case, we are talking about ad adjustment.

To ensure you do the quality ad adjustment, here is our checklist that we use as guidance:

• Are the ad format and technical specifications appropriate for each platform on which the ad will appear?
• Is the content localized and culturally appropriate?
• Is the ad aligned with trends and seasons?
• Is the tone of voice appropriate for the target audience?
• Are the visuals and copy optimized for the viewing device?
• Has the ad been tested and refined for maximum impact?

Ad personalisation

While ad adjustment focuses on preparing ads to be appropriate and localized for target audiences across platforms in terms of content, visuals, and tone of voice, ad personalization focuses on delivering ads tailored to individual users. What is underneath the veil of tailored ads? Tailored ads are based on user preferences, behaviors, and demographics and aim to make ads more relevant and engaging by utilizing the data collected from users’ online and offline activities. With ad personalization, advertising tries to provide the best solution for the user by understanding their needs. In addition to trying to meet their needs, by serving ads closer to their interests, users will be more likely to engage with them because they will identify with them.

Types of personalization

Ad personalization can take several forms, each targeting specific user attributes or behaviors to deliver relevant ads. It’s important to emphasize that the key is to rely on data, not gut instinct. By analyzing user attributes, we can create an idea of our buyer persona, and based on that, we can create our strategy for pivoting our ad personalization in terms of demographic, geographic, behavioral, contextual, predictive, and retargeting targeting.

Visual representation of types of personalization where it’s shown five different subtypes

Demographic Personalisation
Tailoring ads based on user demographics such as age, gender, household income, education level, marital status, home ownership status, employment status, and parental status. This personalization creates a special connection with the customer, building more substantial and loyal relationships with a better overall customer experience. By analyzing demographic data, specific groups within your target market can be identified and used to create tailored marketing campaigns that respond to the unique characteristics of each segment. This level of customization often results in increased brand loyalty and customer satisfaction, driving long-term business success.
How is the data collected? It is often collected using CRM, marketing analytics software, surveys, customer feedback, social media insights, or third-party data sources.
Let’s talk about an example. Imagine you own a beauty brand that sells a wide range of beauty products. Each product is designed to meet different needs based on age, gender, skin type, and budget. In this case, the ad would not be as effective if you showed the men’s beard care product to women. Or, if you show a luxury skincare product to people in a lower financial bracket, your audience is not well-targeted, and engagement is likely to be lower. If the men’s grooming product is advertised to married men or women with the expectation that they will buy it for their husbands, the likelihood and expectation of engagement with the ad increases. Similarly, if a luxury product is advertised to a wealthy individual, the possibility of engagement with the ad increases.

Geographic personalisation

Tailoring ads based on a user’s geolocation (city, state, country) can be a much more effective way to reach potential customers in a specific area, as it is more likely to resonate with the user on a personal level. Geographic personalization allows businesses to ensure that their content is relevant to the user’s current location, and this type of advertising is more effective than generic advertising.
Data is collected using GPS, IP addresses, or geofencing technology in this case.
But how does this all work in practice? Let’s say our beauty brand has a store in London. Geo-personalization will focus on geo-targeting those users in London and trying to show them the special offers and promotions they currently have. This would ensure the user sees relevant content specific to their current location. Another way of geo-targeting would be to try to reach users in an area where our competitors are. In this case, our beauty brand could target users currently in Paris and try to build brand awareness among them.

Behavioural personalization

Customise ads based on past user actions such as browsing history, search queries, purchase patterns, and app usage.These insights can then be used to create targeted content that is more likely to resonate with the individual. This can improve the customer experience, increase conversion rates, and boost engagement.
To do behavioral ad personalization, the user’s web browsing habits must be gathered. This data can be collected through web analytics tools, cookies, or user surveys.
When done correctly and by respecting the privacy of those who do not wish to be tracked, behavioral personalization can create a more seamless and engaging experience for the user.
A user can browse several products from different categories on the site for beauty brands. If the user enables data collection, the advertiser can create a special offer that can be sent to them via email, Google Network, social media, etc. This personalized offer increases the chances of a sale and ensures the customer is happy with their purchase. For example, if a user has added items to their shopping cart but hasn’t completed a purchase, they’re likely interested in these items, so creating a special offer that gives them free delivery or 10% off will increase the chances of a sale and ensure they’re happy with their purchase.

Contextual personalization

Serving ads based on the immediate context in which the user is interacting with content, such as the type of website, time of day, or current weather, provides true personalization and a better experience for each audience segment by relying on AI and machine learning rather than humans in a decision-making process. With this type of customization, there is no need to wait for enough data to decide.
In the case of our beauty brand, let’s say that 60% of customers are interested in discounts and 40% are interested in free delivery. Even if most OD customers are interested in sales offers, you can’t forget about the other 40% of customers by showing them something they weren’t interested in.
This is where contextual personalization comes in. Based on all the different contexts, it shows the best-fitting variant for each customer.

Predictive personalization

Predictive personalization is important for providing the best possible experience for customers and staying ahead of the competition because it allows companies to anticipate their customers’ needs and wants.
It is powered by large amounts of data about past user behavior to make accurate predictions. Data is collected using website clickstream data, user surveys, and social media data.
Predictive personalization, in the case of our beauty brand, would be to recommend products that users had bought before, and now it would be time to repurchase them. Another scenario would be to show complementary products that customers might be interested in buying based on previous purchases.
Retargeting Personalisation
Retargeting users is based on reaching those who have previously interacted with a brand but haven’t completed a desired action. Advertisers must use data from people who have previously visited a website to personalize retargeting ads.
These ads are specifically tailored to the user’s behavior, with the primary goal of reactivating their interest in a brand and its products or services. By showing them relevant products they have previously viewed and highlighting specific offers, users are more likely to continue browsing, purchase additional items that increase the value of their basket, and, most importantly, complete the purchase.
Let’s say our beauty brand wants to do retargeting personalization. They have a seasonal sale of men’s grooming products and want to reach a relevant audience. In this case, the target audience would be those who have visited and viewed these items, as these users may be the most likely to make a purchase.

How to personalize ads

As mentioned, personalized advertising is a powerful tool for attracting and retaining customers, increasing conversion rates, and building brand loyalty. We also noted different types of ad personalization, but how can the personalization be used and implemented effectively?
First and most important is data and its collection and analysis. Using data to create personalized ads is necessary and will help you improve your digital marketing strategy and achieve better results. Web analytics tools, such as Google Analytics and Meta Pixel, focus on tracking and analyzing website or app traffic data, such as the number of visitors, demographic reports, geo-location, and on-site behavior. Customer data platforms (CDPs), such as Salesforce, focus on collecting, storing, and managing customer data from their interactions with a brand across online and offline channels, including website activity, purchase history, and email. Third-party data providers collect information about consumers from multiple sources across the web. This gives them a complete picture of user behavior and interests. Surveys and forms are valuable data that provide insight into customer interests, demographics, and behaviors. Social media platforms collect rich user data, including interests, behaviors, and demographics.
Segmentation is the next and equally important element of ad personalization. Living in a world of big data means nothing if the data is not segmented meaningfully and effectively. It helps you understand your audience. Once customer data has been collected, the next step is to segment the audience into buying personas based on demographics, behavior, and interests. Using these segmented groups in advertising campaigns allows for a more relevant combination of content and offers that resonate with each specific group.
After segmenting our audience into buying personas, part of ad targeting is using dynamic ad customization like ad customizers, which helps us match the best communication about UPS to the terms people are searching for online.
Finally, but equally important, is testing and optimization. A/B testing to evaluate which type of personalization delivers the best results for each audience is key.

Ad customizers – Best practice

There are several types of ad customization, but most of them have been replaced by machine learning. A few years ago, you had to manually adjust the schedule, device, seasonality, keyword bids, language, micro-location, and so on. But today, most, if not all, of these elements are easily optimized by machine learning, which reads thousands of signals.
But one element that has stood out for us as an agency is the Google Ads Ad Customisers. Although Google Ads is increasingly focused on consolidating the campaign element so that machine learning has as much information as possible to learn from, an essential element of all of this is that the user should receive content relevant to them and their needs. Even though Google does this exceptionally well with the amount of data it receives in audiences, headlines, descriptions, images, and videos, Ad Customisers take it to a higher level.
By testing different USPs, we have highlighted some that have proven extremely important to users. Communicating the price, discount, and duration of the promotion proved to be an essential trigger for the primary conversion – purchase.
In a very short time, we saw significant improvements in ad visibility, traffic brought to the site, lower costs, and higher conversion rate and ROAS. Not only did the ads perform better in metrics, but the ad strength also increased, with 90% of the ads rated as excellent.

Potential problems & limitations

Setting up the Google Ads Customiser is not tricky, but before you do it, you need to have a clear idea of what you want to achieve with it and think long-term so that the initial setup, which takes a bit more time, can be reduced to simple and quick maintenance.
What is often foreseen are the very elements that are added. For example, let’s say we want to communicate the name of the promotion and add it to the specified field, but the next promotion has an additional text that we want to speak to, and now we need another field. Generally, this is not very demanding regarding setup, but it brings us back to the initial step of setting up, which takes a bit more time. For these reasons, it is necessary to have an in-depth knowledge of the business, its objectives, and its communication goals.
In addition to the above, it is necessary to ensure that the texts added by the customizers are linguistically adapted and that they make sense when inserted in headlines or descriptions so that they do not sound robotic or out of the spirit of the language itself.
Similarly, when communicating elements such as prices and discounts, the specified data is necessary. Given that the aim of the Ad Customiser is automation, manually retrieving such data daily is a big no. This brings us back to the beginning, where we need to stop and think about what is best and most efficient with the least possibility of error.
Once you have set up the data and elements you want to include with your ad customizers, the next step is to connect to your Google Ads account and enable ‘communication’ between Google Ads and the customizer sheet to ensure the most up-to-date and relevant data is used. This step can be mistakenly skipped, resulting in incorrect communication of ad elements such as price, discount, promotion name, and sale period.

Effects of personalization and customization

Throughout this article, we have highlighted the impact of ad personalization, which is essential for a specific audience to receive particular offers tailored to their desires, interests, and needs. As a result, we ensure greater user satisfaction because they have received a relevant offer of products and services, which means that the person is more engaged with the brand itself, with a higher chance of making a key conversion (purchase, booking an appointment, filling out a contact form, etc.).
It is important to note that data changes daily, and so do our buying personas. This means that once a buying persona is defined, it is not a done deal; changes need to be constantly monitored.
Similarly, in the world of big data, we need to filter the relevant data and record its conclusions so that we can make strategic decisions from it, both in the business itself and our digital advertising strategy. It is necessary to monitor what works for the user and what does not and find a pattern that could lead to the golden formula for growth in business results.