To win any ad campaign, you must continuously test. Multivariate testing creates the perfect feedback loop that informs and educates your team, in figuring out your best-performing ad creatives.
Your ad creative is the first thing your prospect associates your brand with. It’s like an introduction to your brand and what it can do for her.
It also determines how people think, feel, interact, click, visit, and finally make a purchase on your website.
Therefore, advertisement testing is very important for the growth of brands and businesses.
If a certain color, image, or call to action was more likely to lead to a purchase, would you like to know?
Let's look at the importance of ad testing, the pros and cons of the two most common testing methods, and best practices for large-scale ad testing.
Why is it necessary for brands to test their Ad Creatives?
- Ads testing helps you continuously improve your ad performance and get the most out of your ad spend.
- You can learn what works best for your target audience in terms of engagement, clicks, and purchases.
More than ever, it's important for brands to check their ads regularly. Before that, it is possible to throw a few creatives, find the winner and launch those. The advent of user security measures - third-party cookie dissolution, iOS 14 ATT, GDPR, CCPA - has made traditional audience targeting increasingly unreliable.
It also means it will take longer to figure out which ads are running on platforms like Facebook because they receive and deliver significantly less data than before.
The less reliable your targeting and the longer it takes to get a response, the more important it is than ever to be creative. Here's why:
- You can no longer assume that the best potential customers will see your ad.
- Along with a broader audience, a lot of people don't care about your brand or what it has to offer.
- Impressions become an almost irrelevant metric because so many of them are potentially wasted.
- Getting there quickly is important. The longer it takes for your ad to reach the subset you want to target, the slower your conversion will be.
- Growth stagnated when conversions were delayed.
- To reach a wide audience, you need super-resonant ads.
- The key is to find an ad that fits your demographic as quickly as possible within your budget.
How can you be sure your creatives will work?
In the past, you could try to achieve the same results by releasing several different versions. However, right now with the advancements in machine learning and AI, it has become pretty straightforward to make a solid prediction based on a lot of data points from thousands of users.
Adcreative.ai has built its proprietary ML algorithm that predicts which ad creatives would seemingly do well and ranks them accordingly.
The one thing that one must note is that even though the AI engine may be very strong, however, there still should be tested at various stages, as results vary from brand to brand.
So one must test as many ads as possible - the faster, the better - to find the ones that work best. In other words, go from searching for "winning ads" to searching for "winning properties" that can be combined to create the best ads.
This allows your team to make advertising decisions based on data, not prejudice or "creative intuition".
You can then use this data to improve ad performance by combining multiple conversion metrics into a single ad and scale.
For this reason, as well, the team at Adcreative.ai built something called “Creative Insights” integrated on Adcreative.ai, which tells you which creatives are performing the best for you. All you need to do is just enable your FB/Google Ad accounts from the platform, and you can check all the data in one place.
Let's talk about the kind of testing required to get such detailed creative data, as it might be different from what you're used to.
Multivariate Test and A/B test
There are two main methods of advertising testing: multivariate testing (MVT) and A/B testing. Both are effective ways to learn about ads. Both
Compare an ad with another
Measure ad performance for an intent (conversions, engagement, etc.)
The difference between multivariate testing and A/B testing is as follows:
A/B testing measures the effectiveness of two or more different creative concepts.
It has long been a standard in creative testing. This is the historical way that brands decide which ads or ad campaigns to use against media buying. Today, many brands run their ad creations under some form of A/B testing before going live.
The biggest downside of A/B testing, however, is the sheer number of uncontrollable variables - each advertised concept in an experiment is often very different from the rest. So, A/B testing can tell which ad concept people prefer, but can't say why.
Do they like the title? Do they like the photo? You never know with A/B testing. However, testing on multiple variants can reveal these details. As a result, A/B testing alone is no longer enough to achieve peak performance.
The Super Power of Multivariate Testing by AI
Multivariate testing measures the effect of every possible combination of creative variables. As humans, we may miss some of the parameters due to biases, however, machines do not suffer from human biases. Hence, an AI can make informed decisions that work.
Creative Insights is one such feature built by the team at Adcreative.ai.
So what are these Creative Variables?
Variables are any single ad element - images, headlines, logo variations, calls to action, and more.
Since you can measure the performance of each variable against any other, you can not only understand which ads are most liked and disliked but also which variables they like or dislike the most.
Instead of testing the performance of these variables individually, you can see their performance regardless of what they're linked to in the ad.
You can use the micro level of this ad data to optimize your ads for maximum performance.
The real value of multiple-choice testing lies in the depth of creative intelligence it provides and how it can be used in future promotional releases. If you know that a certain color, image, or call-to-action always drives more people to buy, you can use that knowledge to optimize your ads.
You may find that one headline performs better than the others, and an image doubles your click-through rate. You will use this headline and image in your future ads.
Tips for Testing Ads
Multivariate advertising testing is simple in theory - it's a scientific method used to get creative insights.
Creating and testing so many ads is no big deal for any marketing or advertising team. Versioning, data tracking...it can be overwhelming.
So it is a must to use tools like Adcreative.ai, which can do all this for you.
The following five best practices are important for providing multivariate testing to an individual.
Take the time to define your marketing goals
Before diving into multidimensional advertising testing, take a moment to understand what you want to achieve. What do you want to learn from the test you are about to take? Start your testing strategy with the results to more methodically plan and build your tests, resulting in more meaningful, creative data.
The first step is to answer this question-
What areas of the brand or company do we need to influence the most in creating the best advertising?
The answer largely depends on the maturity of your company, your overall growth goals, and your go-to-market strategy. Here are some examples:
Lead Generation
What works best for existing customers may not always work best for new customers.
Testing ads can help you find the best ones that will attract new people to your brand.
Retain existing customers
After making your first purchase, run an ad test to find one that can keep your customers coming back. Introduce new products and product lines: Prepare new products and product lines for success. Find compelling ads that encourage people to buy your latest offers. Design and test seasonal messaging: Find out which ads drive people best to your biggest holiday deals and sales of the year.
Start with a solid hypothesis
Without a hypothesis, you lack justification for which source you choose to test. This means that you cannot categorize your variables in a meaningful way. Your test will lack focus and your data won't be as robust and meaningful as it could be. Ask yourself, "What do I want to learn here?" is the first step to conducting effective multidimensional advertising testing. A well-designed hypothesis helps to communicate what variables - image, title, call to action, etc. - should be tested in advertising.
Decide and record what you want to know. Then, select and test creative sources that will help you find the answer.
A strong hypothesis leads to a more tightly controlled experiment, which produces more obvious results.
Large-scale testing with automation
Without some kind of automation, multivariate testing on any scale would be very difficult.
Manually designing and resizing each ad variation is tedious, can lead to burnout, and distracts attention from more strategic creative ideas and thinking. And can you manually test all these designs? Forget it. It's confusing and hard to follow. Not to mention, paid social platforms to negate manual testing because they spread the cost unevenly across all your instances. Scaling with automation is the key to successful multivariate testing. Multi-variant ad testing tools automatically do the work for you, from creating every possible ad variation to your test campaign's audience structure, budget, and placement, including controlling for the same spend across all ad variations.
In short, automation allows ads to repeat and optimize at breakneck speed. Cheering!
Constantly testing your creatives
Testing your ad with multiple variations should be an ongoing process, not a one-time event.
The best way to stay on top of what's working (and what's not) is to constantly test and refine your ads.
This doesn't mean you have to run loads of tests. You can and should focus your testing on the areas of your business that will have the greatest impact.
However, constant testing ensures that you are always learning and improving your ads to stay ahead of the competition.
Another benefit of continuous ad testing is to prevent ad fatigue - the point where your audience has seen your ad too many times, rendering your ad ineffective.
Consistent multi-dimensional ad testing keeps your ads up to date and reduces impressions per ad.
Measure your success
KPIs for brand advertising testing depend on your marketing goals, testing strategy, and hypothesis. Before running an experiment, decide on the primary and secondary KPIs you want to test. The best automated multivariate testing platforms let you choose the KPIs you want to measure before going live, including:
- Engagement
- Click-through Rate
- Products/services added to Cart
- Cost per Acquisition
- Cost per Action
- Churn
And much more.
Every time you find an ad or ads that meet or exceed your KPI goals, you'll need to scale them up.
How Adcreative.ai can help you test your ads
The modular approach to design
Modular design is a design approach that uses placeholders in the template to accommodate removable creative elements. This is the basis of large-scale advertising on Adcreative.ai that allows anyone to generate ad creatives in large volumes. The templates and every component of the design are not just auto-generated but refreshed according to the learning of the AI engine over time.
Drive your conversions with multivariate testing
Multivariate testing is an essential tool for creating effective ad campaigns at scale. Continuously testing and tweaking your ad creative ensures your ads are optimized for maximum conversion, despite today's landscape of diluted audience targeting.
Adcreative.ai automates the entire multivariate ad testing process, so you can iterate smarter and find winning ads faster than ever. If you're ready to test your way to top ad performance, we're here to help.
