Marketers should know which ad works well. So they can perform better on future campaigns. But doing so isn’t easy. With many options and so little time, the questions are filing up. Luckily, Facebook introduces “Experiments.” It’s a simple solution that won’t slow marketers down. Experiments combine “Test & Learn” and “Split Testing” into an A/B test. It allows testing ad versions and measures conversion in one interface. To measure total conversion, marketers can also use a holdout test and brand survey.
Introducing Experiments
On 30 March 2020, Facebook is rolling out Experiments on its platform. In the Ads Manager, it’s the second category under Measure & Report. The page allows one to do an A/B test, holdout test, brand survey, and campaign budget optimization. Before testing, it’s important to assess the factors influencing consumer behaviors. These affect how the audience interacts with advertising. From this input, one can determine the best question that fits a business goal. It’s best to use in the test and learn phase.
Tests Available in Experiments
Testing multiple iterations of an ad helps one determine what works and what doesn’t. This can be done through an A/B Test or split testing. Such a test is created by showing 2 or more versions of a Facebook ad. The audience is then divided into groups. As they see the ads, their actions will be measured. Users can see all the results in the test and learn. The ad providing the highest conversion is the winning ad. It optimizes traffic on a Facebook page. An A/B test works best in comparing two strategies or measuring changes in advertising.
A Holdout Test measures the incremental conversion lift of an ad campaign. The audience will be randomized and divided into two groups. The first group consists of people who saw the ads. The second group or holdout group are those withheld from seeing the ads. In comparing the results between these two groups, marketers can measure the total conversion of the ads. They can better understand the impact of the ads on Facebook.
Brand Surveys poll the two segments in your A/B & Holdout tests. Poll topics may include ad recall, message association, and brand awareness. The brand lift then calculates the performance difference in both groups. Such gives a clear view if marketers are investing in the right strategy.
Experiments also offer a Campaign Budget Optimization Test. Here, marketers can see how a campaign budget affects the cost per result performance, First, one needs to choose an existing ad campaign. It will then be duplicated with the campaign budget optimization turned on. The budget will be split evenly between each new campaign. The test will determine which performs better by randomizing all ads.
Conclusion
Facebook Experiments is the simplest solution to test ad campaigns. It helps users make strategic decisions through automated testing.
Implications for Brand Marketers
Brand marketers can get new insights into the Experiments feature. It’s best to do A/B testing on advertisements to see how ads perform and learn about conversion rates and analytics.
Reference: https://www.facebook.com/business/news/evolving-the-way-businesses-test-and-learn-with-experiments/
Facebook delivers ads from millions of brands to billions of users daily. It aims to show users the right ad for the right product. This is how they give value to both the users and brands. For users, it gives them a better experience through personalization. For brands, it helps them create affordable ads to grow and create more jobs. To explain how the platform decides which ads to show people, it posts an update entitled “Good Questions, Real Answers” this 11 June 2020. Such a post is about Facebook’s new overview of how ads work.
Facebook’s new overview of how ads work listed two factors that affect ad decisions:
- the advertiser’s actions to place an ad;
- the use of machine learning for ads review.
The 2 Factors Affecting Facebook Ads Decision
First, the advertiser makes 3 important decisions when placing Facebook ads. These are the target audience, their business objectives, and the bid price. Brands can choose their target audience through the user’s interest, age, and gender. They can also provide info of their old customers that they wish to include. Then, they’ll choose from different objectives which commonly are building awareness, increasing traffic, and driving purchases. Afterward, they’ll place a bid. This is the amount their willing to pay for someone who sees their ad to complete their chosen objectives.
Second, on the ad review, Facebook uses machine learning to estimate how users will complete the advertiser’s objectives. When a user searches for an ad or feed, Facebook initially gathers the ads that include the user as a target audience. It then proceeds to the auction stage. This is where the platform chooses which ad to show the highest total value based on the user’s interest. Such a step is done by adding the advertiser’s bid, estimated action rate, and ad quality. The system assigned high-estimated action rates based on the user’s activity on their news feeds, off-Facebook actions, related actions of other users, and even the time of posting. For ad quality, Facebook rating depends on their quality guidance scoring and feedback from users who’ve already seen the ads.
As such, a higher bid doesn’t guarantee an ad placement to win. What weighs more is its action rate and quality. This new overview of how ads work highlights that machine learning makes bid decision objectively. It also shows that Facebook does not sell user’s data to advertisers. They provide brands with general data and not individual ones.
Implications for Marketers:
Facebook’s new overview of how ads work showed that the platform’s ads are highly targeted. With the use of very large demographics, its a powerful marketing tool. Also, it’s the most affordable. Marketers can target their audience based on demographics and interests. Plus, they can set their objectives and budget based on their preference.
As action rate and ad quality weigh more on ad bid reviews, marketers should take advantage of A/B testings. This is to create powerful messages that do more than presenting a product or service. They can also experiment with narrowing their target audience to meet business objectives.