A/B testing - a long-standing research practice that is undergoing today to include an accelerator (Photo: Pexels.com)
According to data from Business Of Apps, today 20% of advertising budgets on social networks are thrown in the trash on 80% of creatives who fail tests, and this without considering the waste of working hours and manpower of copywriters and designers who created the tested products.
The Evolution of A/B Testing
A/B testing is a method of comparing two versions of an advertisement or marketing campaign to determine which version is more effective.
The practice of A/B testing has been used in the advertising and marketing industry for decades, and has become increasingly popular in recent years as it has become fully automated in the age of social media.
Back in the 1920s, huge corporations used this practice in the written press.
G&P thus tested the effect of their shampoo advertisements in newspapers.
The company published two different ads and tracked which one performed better at the box office.
After the sample was made, they made decisions about the continuation of the advertising campaign, the target audience and its scope.
Along with the technological changes and the introduction of television into our lives in the 1950s, A/B testing was used in this medium as well.
Companies broadcast different types of advertisements during the same program and analyzed the viewing data during the broadcast hours.
This is how they reached marketing conclusions regarding the effectiveness of the advertisements, divided into strips and broadcast hours.
In the age of social networks, this process has become an essential automated step in any advertising campaign, allowing advertisers to make engagement-based decisions and optimize their return on investment.
But as mentioned, it is still a wasteful and long process, estimated at hundreds of millions of dollars a year, without it providing quality data for long-term use.
We know what works better and what works less than the tested options, but we don't know why, what the reason is, and what the parameters were that led to success.
This process has been refined into what is called Multivariate Testing.
Although it is more in-depth and includes more variables, advertisers are not in a hurry to use this practice because it requires a lot of resources, budget and time to create the campaigns in question.
Even in this case, no data is collected that will optimize the process in the future.
Artificial intelligence testing
The traditional test, which has accompanied us with the development of the media for decades, will not disappear from the world and will continue to be an essential part of the role of the advertising people, but the advancement of technology and artificial intelligence also brings with it the new generation of advertisers and advertising agencies.
AI (artificial intelligence) streamlines processes, improves decision-making processes through deep data analysis, identifying data-based patterns and trends, and creating campaigns with maximum impact and engagement.
The AI knows how to break down any 30-second creative video into more than 25,000 parameters such as color, text, sound and facial expressions, and cross them in different combinations, check the performance, and reach the optimal version in a completely automatic way.
In this way, you can perform extensive competitor research and produce complete graphics, write texts, and all in just a few seconds.
The machine leaves us dust.
All these and the advertising industry's constant need for immediacy and accuracy, led the industry to adopt AI for various practices.
Artificial intelligence is a solution to huge budgets that are thrown in the trash for creating failed products and many working hours spent on unnecessary tests, and designing ads whose entire purpose is to fail the test.
Artificial intelligence provides an answer to very painful problems in the industry.
While it cannot replace the professionals, it is able to provide them with data-based insights in real time, to refine their work, maximize profit and optimize business and marketing processes, until the creation of the winning campaign.
The author is Assaf Yanai, founder and CEO of Alison.AI, which specializes in AI-based creative analysis.
Marketing and digital
MarTech
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