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Is A/B Testing Still an Effective Tool for Marketers?
By Jonathan Crowl on September 17, 2018
By now, marketers have got A/B testing down to a science-because that's exactly what it is. Any time a new marketing channel has presented untapped opportunities without a road map to driving results, A/B testing has been there to separate the successes from the failures, discover a path forward, and optimize your marketing efforts.
A/B testing is a tried-and-true approach for digital marketers, but today's marketing landscape has more paths than ever before as brands take multichannel approaches to storytelling, incorporate user-generated content into their strategies, and leverage cutting-edge technology like VR and AR to enrich the larger customer experience. When every piece of content you're producing or campaign you're evaluating contains a multitude of variables that could impact results, how can you find the time to judge each item on an individual basis?
The problem isn't that A/B testing no longer gets results, or even that there was a better alternative to marketers in the past. It's that marketing techniques have evolved so rapidly in recent years that A/B testing is no longer the most efficient option for evaluating current performance. According to a survey from eMarketer, most marketers report that 80 percent of A/B tests don't produce significant results. On one hand, that's not a surprise: A/B testing is a process of trial and error, which means businesses should expect there to be more misses than hits.
But on the other hand, that lack of efficiency stands out in an industry obsessed with optimizing, fine-tuning, and incrementally improving every aspect of their marketing machine. In this progressive environment, the shortcomings of A/B testing have been thrown into relief by the development of enhanced approaches to the same marketing problems, offering a number of upgrades to this time-honored testing strategy.
Should you give up on A/B testing today? No way. In spite of its limitations, for many organizations it's still the best way to refine a campaign strategy over time. What you're better off doing is understanding what technological improvements-most notably the use of automation and artificial intelligence-are poised to make your content marketing decisions as impactful and productive as possible.
Image attribution: Bruce Dixon
Manual A/B Testing Gives Way to Automation
A/B testing marketing campaigns is a reliable process, but it's a slow one. To track results, marketers can only test one variable at a given time. When it comes to testing social media elements-such as the impact of one photo over another, or the engagement rates driven by two headlines written for the same piece of content-the results are pretty straightforward.
Problems start to crop up when you want to test a lot of different variables, and especially when you want to try as many combinations of those variables as possible. As content marketers continue to take more nuanced, layered approaches to brand storytelling, there are many more elements in play, all contributing to the success of a single piece of content. Given the time it takes to test the performance of each pairing, it becomes impractical-if not impossible-to be comprehensive in your testing efforts.
Marketing technology developers understand that, and they're eager to deliver tools that trade out some of this manual testing in favor of automated solutions. One such solution getting a lot of attention right now is Google Responsive Search Ads. This new ad product allows marketers to write up to 15 different headlines and four ad body text descriptions to be displayed in a responsive search ad. Google then uses contextual information to automatically select the best headline and description for any given scenario.
Consider the implications across other marketing channels. Social media posts can be automatically scheduled to send at an optimal time. So can emails. For content marketers, headlines and preview text can be optimized on a case-by-case basis without demanding any additional effort. These advancements in technology make it simpler and faster for content marketers to offer people a wider range of personalized experiences beyond two slightly altered test versions. The applications of automation are endless.
Still, concerns about quality and optimization are valid, especially when these automation solutions don't have a proven track record. But even these concerns may fall to the wayside once artificial intelligence is added to the mix.
The Future of Performance Testing Has Multiple Variables
Image attribution: Pratham Gupta
Artificial intelligence is already being used to support more intuitive and responsive marketing automation tools, and it's only a matter of time before it turns A/B testing upside down. According to ClickZ, that change will be most dramatic in AI's ability to expand testing beyond one variable at any given time.
In fact, AI solutions could effectively test dozens of different variables during a single A/B test, generating insights and results that drive AI conversions for A/B tested marketing campaigns. By testing multiple variables at once, marketers are able to get faster results, and the increased body of data supports better accuracy. This data could help marketers not only identify what's working and what needs improvement to drive more conversions, but also whether those needed changes concern the marketing and campaign targeting or the creative work behind the branded content.
In the not-so-distant future, the role of AI in A/B testing may go way beyond search ad campaigns and social content and play an integral role in the content creation process. Creators could use AI to brainstorm storytelling strategies, brand narratives, and even synopses of branded content with the goal of creating more engaging stories that resonate with an audience. Such an innovation wouldn't replace human creators with their computer counterparts, but it would give them a valuable tool for sparking creativity and exploring other angles of telling a branded story.
Over time, A/B testing becomes more effective and valuable as new insights are built on top of old ones. But when you're only testing one variable at a time, that accumulation of insights is slow. Automation and AI are poised to accelerate this process and deliver better, smarter, faster insights while reducing the workload of the individual marketing professional. It may look like the A/B testing we're used to, but the difference in results will be unrecognizable.
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Featured image attribution: Julien Meriot