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The failed promise of A/B testing for messaging in the pharma industry.

Is it really helping marketers bring the best messages to market?
Newristics 18 July 2022

Pros and Cons of A/B Testing: Ditch A/B Testing Marketing, Test 100s of Ideas, & Find a Winning Message

What is A/B testing in marketing?

Message testing analyzes what types of marketing messages create the most impact on target audiences. Historically, messages have been tested in primary market research with a representative sample of a few hundred customers. This has serious limitations because respondents artificially pay attention to messages, and it does not produce real-world responses.

With the growth of digital advertising, the concept of campaign message testing has gained traction. Brands can now test messages while they are being executed through messaging campaigns using techniques like A/B testing marketing.

So what is A/B testing in marketing? A/B testing marketing, also known as split-testing, is a method to compare 2 versions of a brand message or communication to see which message performs better. Although it is most widely discussed in the age of digital advertising, the method is nearly 100 years old. The basic idea of A/B message testing is the marketing team figures out what they want to test and determines how they will evaluate the data. To run the test, the team shows 2 groups 2 different versions of a message, such as emails or ads with different headers, subject lines, first lines, or CTAs. Then, the A/B message testing software interprets the results of the A/B message test.

Pros and cons of A/B testing in marketing

There are pros and cons of A/B testing. A/B message testing software and data analysis tools that are powered by artificial intelligence help brands test messages continuously through in-market messaging campaigns. This gives researchers insight into actual in-market responses and is more reliable than primary market research. Based on this analysis, businesses can determine what type of content, offers, and marketing messages to their target audience will create the most engagement.

However, there are significant drawbacks to A/B testing marketing and it is not the best method for most brands:

Can’t test a lot of messages in 1 study
Unless you’re a big brand, think the size of Amazon or Google, A/B testing has constraints on how many ideas you can test. You need to test 100s of messages to find winning campaigns, but an A/B testing study can only test 4-6 messages!

Too much time to get to statistical significance
Your website needs lots of traffic and fast or it takes months. The click-through rate is so low (less than 1%), that it takes time and a large sample size to get statistically significant numbers.

Testing is abandoned
More than 80% of A/B testing marketing is never completed. Marketers watch these tests run in real time and don’t want to wait for them to run their course. Instead, A/B testing is abandoned then marketers use their own judgment anyway!

For most brands, A/B message testing is a waste of time and money. Brands would see the most benefit from testing ALL ideas BEFORE launching their campaign and improving the likelihood of creating winning campaigns.

Pros and cons of A/B testing for the pharma industry

In the pharma industry, messaging decisions influence billions of dollars in revenue. Marketers must make decisions based on scientific principles, not gut and instinct, while constrained by heavy regulations and legal requirements.

In the last decade, the pharma industry has gone through significant changes in how it messages to physicians, patients, and payers. The processes and tools pharma brands use to develop messages have changed a lot over the years as marketers are being forced to develop more persuasive, behavior-change messaging for their brands.

Similarly, the execution of messages in the market has also transformed with the introduction of new channels of communication, new technologies that help optimize the delivery of messages, etc. But what hasn’t improved much is message testing.

A/B testing marketing in the pharma industry must go through extensive legal reviews and heavy scrutiny. This limits the usability of A/B message testing even further, narrowing the number of messages that can be tested and forcing brands to make tough decisions on what to test. This creates a cascading effect: bad solutions for messaging because not enough options were explored.

AI can improve the way research data is used to create winning messaging campaigns

In A/B testing marketing studies, data is loaded into statistical software systems like SPSS and the output is standard message hierarchies and/or a TURF analysis. Deploying artificial intelligence on data collected from message testing software helps in the quicker, easier, and hassle-free understanding of the message performance. It saves a lot of manual labor, hours spent, and marketing budget spent by the business on a message testing survey.
A message testing software includes prediction, exploration, and optimization aiming to improve the messaging performance with the campaign message testing process. Artificial intelligence can transform how data from message testing surveys is used to drive campaigns:


Be more campaign-ready after research by identifying optimal message bundles/story flow for every channel so that brands can be more agile and accelerate time to the market.


Create personalized campaigns for each customer segment based on their decision heuristics so that brands improve marketing efficiency and reduce marketing waste.


Train predictive models so that brands can refresh campaigns more often without more research.

Tests 100s of messages for a campaign-ready, omnichannel deliverable

After learning exactly what is A/B testing in marketing and the pros and cons of A/B testing, it is clear that there is a need gap between A/B testing marketing deliverables and what brands need to succeed. In today’s omnichannel marketing, a message hierarchy or TURF analysis is not enough. This is an incomplete deliverable and brands are not campaign-ready. What brands need is a messaging playbook for each channel and each segment. Innovation in decision heuristics science and AI provides a better way to test messages and create winning campaigns. Decision heuristics science is a better way to test messages in market research. Heuristic-based messaging enables testing of 100s of messages so that brands can find better message bundles out of billions of possibilities and improve their chances of finding winning messaging campaigns. This innovation provides better data to create even more successful messaging campaigns in the future. AI is a better way to use research data to design campaigns. Algorithms can use research learned to produce the best bundles for each segment by channel-turning research into segment-based, omnichannel campaigns.