Advertising is a crucial aspect of any business that aims to reach its target audience and increase revenue. However, creating effective ad creatives is not always an easy task. While testing different ad variations is essential to find the most effective designs and messaging, it can also be a time-consuming and expensive process. This is where predicting ad creative performance prior to testing comes in. By leveraging historical data and machine learning algorithms, marketers can make accurate predictions about how new ad creatives are likely to perform, without having to go through the process of testing each variation.
In this article, we will explore the methods and data needed to predict ad creative performance prior to testing and how marketers can leverage AI-powered tools to optimize their ad campaigns and allocate their advertising budget more effectively.
The problem that we are trying to solve is to identify which ad creative will perform the best before actually launching it. This is important because testing ad creatives can be time-consuming and expensive. If we can predict which ad creative will perform the best beforehand, we can save time and money by only launching the top-performing ad.
Predicting ad creative performance prior to testing involves analyzing data and using machine learning algorithms to make predictions based on historical performance data, audience demographics, and other relevant factors. By doing so, advertisers can make informed decisions about which ad creative to launch, optimizing their ad spend and maximizing their return on investment.
In the next few sections we talk about the data we need to assemble for the AI to use to learn the patterns and predict the ad performance.
Historical performance data from past ad campaigns can be an incredibly valuable resource for marketers looking to predict the performance of new ad creatives prior to testing. By analyzing historical data, AI can gain insights into what types of ad creatives have performed well in the past and use this information to predict the performance of the future ad campaigns.
The most important part of the process is to assemble the data for thousands of ads and their performance numbers. This is no easy task. Some of the performance data can be generic data which involves data for the ads that had the best performance from various industries for wide ranging audiences. The second is more specific performance data for the ads in the same industry the new creative ad is being targeted for.
In addition to the generic data above, getting access to the performance data for the ad creatives from previous campaigns from the company is very important. These ad creatives are specifically targeted towards a specific demographics segment, and this allows the AI to learn more about the user segmentation patterns.
One of the primary ways that historical data can be used to inform predictions for new ad creatives is by analyzing key performance indicators (KPIs). KPIs are metrics that are used to measure the success of an ad campaign, and include metrics such as click-through rates, conversion rates, and engagement rates. By examining how different ad variations performed in the past in terms of these KPIs, marketers can identify trends and patterns that can inform their predictions for future ad creatives.
Several key data points in combination could make an Ad click. First, the message of the ad should be crystal clear and the design should be visually appealing & engaging. Ad needs to be relevant to the intended audience and cater to their specific interests and needs. The format of the ad, whether it’s static or video, can also have a significant impact on its effectiveness. The device, the platform and the time of the day can greatly affect its performance. Finally, the language and call-to-action used in the ad can play a crucial role in driving conversions. All these key data factors need to be assembled.
Data to be captured includes
- Type of the creative
– Text or image or video
- Data about the Ad Creative
– This includes logo, colors, size, text, any images used
- Who was it sent to?
- What time was it sent?
- Which device was used?
- What social media channel was used?
We will cover each of this in this few sections.
Users may have preferences on which type of creative they want to engage with. It can be image or text or audio. This preference also depends on the device they connect from as well as the time of the data.
For example, if a marketer is looking to create a new ad creative for a product that they have advertised in the past, capturing historical data on which types of ad creatives performed the best in terms of conversion rates will be a good input to the AI model. If the ad creatives featuring videos performed better than ad creatives featuring static images, AI will use this information to learn how to score for the ad creative.
The industry stats are more aligned with video. 80% of people recall a video ad they saw within the past month. 64% are more likely to buy a product after watching a video and more people are inclined to share a video than image or text by an order of magnitude. A video ad may be more effective for a product that requires demonstration or explanation, while a carousel ad may be better suited for showcasing multiple products or features.
Ad format refers to the specific layout and design of an ad creative, such as the size and placement of images, text, and other visual elements. Ad format is a crucial factor in predicting ad creative performance, as different ad formats can have a significant impact on engagement, click-through rates, and other key performance indicators.
In addition to the specific format of the ad creative, the visual elements that will be included in the ad, such as images, text, and logos plays a significant role. For example, studies have shown that ads with bright colors and contrasting hues are more likely to be noticed and clicked on, while ads with cluttered or confusing visuals are less effective.
The specific data to collect here depends on the type of ad creative (text or image or video). In general, an Ad creative will have information about the company in the form of a name or logo, company’s brand colors, depending on the target platform the ad creative can come in different sizes, messages, and call to action present in the ad, any specific engaging image and the background images. All these are important characteristics of an Ad creative. These are the feature sets AI (computer vision) can look at the Ad and come up with specific features to extract. Remember these needs to be evaluated along with other factors such as audience, time of the data, device and social media platform.
Computer Vision is a new technology that exploits the power of artificial intelligence to analyze images. With computer vision, computers are trained to recognize and analyze whole images, not just individual parts and components. This is a much more comprehensive way to approach image and video analysis to find patterns. Computer vision can review 1000s of such visuals and learn the patterns in the historical data on why an Ad creative worked and look for these positive patterns in future unseen ad creatives.
Audience data is another critical component of predicting ad creative performance. Audience data refers to information about the target audience for a particular ad campaign, including demographics, interests, behaviors, and other characteristics.
One key source of audience data is social media platforms, such as Facebook and Instagram, which collect a wealth of information about their users such as age, gender, location, interests, and behaviors.
In addition to social media platforms, there are also many third-party data providers that offer audience data for ad targeting. These data providers may collect information from a variety of sources, such as public records, purchase histories, and online behavior.
By analyzing the characteristics of these custom audiences, AI model can gain insights into what types of ad creatives are likely to be most effective with different segments of their target audience.
Device, platform and time of the data
Additional data to capture include
- Capture the time of day or day of the week when ads were most effective.
- Capturing which ads are effective in which device
- Capturing which ads are effective in which Social media platform
By analyzing the characteristics of where, when, and how they open the ad, AI model can gain insights into how time of the day or the devices or the social media platform impacts the performance of the ad creatives for the target audience.
It is important to note that not all KPIs are created equal, and different KPIs may be more relevant for different types of ad campaigns. For example, engagement rates may be more important for ad campaigns that are focused on building brand awareness, while conversion rates may be more important for ad campaigns that are focused on driving sales.
So far we focused on assembling the data which is the most important and most difficult part of the process. In the next section we will talk about using this data for training.
Model training and performance prediction are critical steps in using AI-powered tools to predict ad creative performance prior to testing. To train a model, all the historical data including audience data, information about the ad format and placement data as described in the previous section are collected and prepared. This data is then used to train the model, which can make predictions about the performance of new ad creatives based on the input data. The exact process for doing the training is beyond the scope of the current article but will be a potential topic for a future article.
All the data mentioned in the previous section is fed to the AI model to learn how to score an ad. It analyzes the demographics data, the content of the ad (computer vision), time of the day, device and the social media it was opened from and the effectiveness of the ad.
Its very important to keep an eye on the metrics for evaluating the efficacy of the accuracy metrics. You can read about that here. You can read more about the other business factors to keep in mind when deploying predictive model.
The performance prediction step involves feeding new ad creatives into the trained model and receiving predictions about their expected performance. By using AI-powered tools for model training and performance prediction, marketers can make more informed decisions about their ad creatives and optimize their ad campaigns for success. However, it is important to note that these tools are not a substitute for actual testing, and marketers should still conduct testing to validate their predictions and adjust as needed. These adjustments made need to be fed back to the model so it continues to learn.
AI can take many Ad creatives as input and score them at scale. The following is an example of multiple creatives the model has examined and provided a score. This helps the marketers to try 100s of ad creative ideas and allow the AI engine to score which ones will get the most traction. They can take may be the top 2 or 3 and further do experimentation to pick the ones they want to roll out to their audience.
In conclusion, predicting ad creative performance prior to testing is a critical step in developing successful advertising campaigns. By using historical performance data, audience data, ad format considerations, and AI-powered tools, marketers can gain valuable insights into how their ads are likely to perform before launching them. This approach can save time and resources by allowing marketers to focus on the most promising ad creatives and avoid investing in ads that are likely to perform poorly.
It is also important to continue tracking and analyzing ad performance data once the ads are live. Every decision marketer makes with the help of the AI needs to be fed back to the AI system so it can continue to learn from the human in the loop. This can help marketers optimize their campaigns in real-time, making adjustments as necessary to maximize ROI and reach their target audience effectively.