Why AI Means Marketers Must Stop Testing and Start Experimenting

In the artificial intelligence era, marketers are experiencing a compounding curve of change. New channels emerge, and platform owners like Apple or Google change the game with their latest release. At the same time, new legislation and evolving regulations require greater oversight. The result is that tactics marketers may have counted on last year or last month no longer work, while sometimes it seems that the only thing that stays constant is the demands on marketing teams to continue to increase year-over-year performance metrics.

As marketers seek to keep pace with change and deliver results in this challenging environment, the inclination is often to “run tests.” However, in the AI era, marketers cannot test their way to success. Rather, marketers must stop testing and start experimenting. 

Here’s why.

Experiment Rather Than Test

Tests are about pass or fail. Running an A/B test sets out to prove that something works or not, and often is designed to optimize what's already known to be true.

The problem is, however, that as we move forward we can no longer rely on the foundation of what we know to be “true” in marketing tactics.

That’s why experimentation works better than “testing.” Experimentation, which is about generating insights and guiding strategic choices, brings forth learnings rather than pass/fail results.

Here’s an example. A “testing” approach would seek to compare a brand’s broadcast email marketing program against its broadcast SMS marketing program, and then determine one of these channels has a black-and-white higher return on investment based on the inputs that the marketing team controls, in turn using this data to then determine how much budget to allocate.  In contrast, taking an experimentation approach would explore how a true cross-channel campaign can improve overall marketing metrics with AI-determined inputs across many channels and looking at those leading metrics to guide overall decision-making. 

How to Experiment — Use Vibe Marketing

Every experiment has four parts: design, generate hypothesis, test it, and analyze the results. Today, vibe marketing allows your experiments to be even more ambitious.

1. The design of an experiment includes the problem you're seeking to validate, proposed solution to the problem, and how you will measure the experiment. The measurement may be quantitative or qualitative results. You can leverage AI to mine your data or run analysis on previous campaigns and propose areas of interest.
2. The hypothesis defines the impact and boundaries of the experiments. There are many hypotheses outlines available so you can choose a standard outline such as, "We believe [the proposed solution] will produce [expected impact] for [who/what is impacted] by [how much] during/over [how long]."
3. The test plan can include using A/B testing tools, qualitative surveys, or micro-campaigns. With today’s technology for segmentation and cross-channel reach along with generative AI to generate on-brand content, experiments can be multimodal and dynamic without needing a team to create and deploy. These should be cheap and low-risk. Be sure to be able to clearly measure your success criteria with your results.
4. The analysis of the test results is the most important. Did the test validate your hypothesis and what did you learn? What, if any, results were unexpected? What do you know now that you didn’t know before? What subsequent tests might you desire to run now? 

The learnings from the analysis are the most important part of the experiment, and should be summarized and shared with stakeholders and teammates so everyone can learn from them. It's often correlations of learning across experiments that lead to the largest impact.

Rethinking the Approval Process Based on Experimentation

Most marketers have experienced the CEO or executive seeing an email or campaign and providing negative feedback. Once that happens, the “C-Y-A” process is put in place and every piece of content has a multistep approval process that by the time leaders have their input and return rounds of feedback, the content no longer meets the original campaign goals. What’s more, this type of approval process isn't feasible in the one-to-one personalized marketing that today’s consumers expect. With AI, technology can now deliver one-to-one messaging that's dynamically created and delivered, which means marketers are no longer limited by technology but rather by their internal processes. 

To leverage technology today, the approval process has to change. Leaders can focus on the results of the experiments and approve the learnings of the experiment rather than the content. Therefore, the next time an executive has feedback on a specific email, you can share with them the data as to why it was sent rather than debating the subjective nuance of the copy and visuals. 

Experiment to Stay Agile With AI in Marketing

With the constant pace of change, marketing must move beyond a “pass/fail” testing paradigm and embrace experimentation to achieve lasting success. Adopting an experimentation approach, and sharing learnings with executives and stakeholders, will ensure a brand’s readiness to adapt, evolve and sustain growth in the years ahead.

Deanna Ballew is the chief product officer at Listrak, a cross-channel marketing automation platform for retailers that unifies data, identity and messaging across email, SMS, push, and web.