Can innovation in market research lead to winning messaging campaigns?

Lack of innovation in message testing research

Despite all the innovation seen in market research over the past decade, one type of market research that is still operating in the old model is message testing research. There has been little methodological innovation in message testing and standard survey methodologies conventionally used for message testing still don’t meet some basic needs of brand teams:

• Can’t test a lot - Researchers still cannot test a lot of messages in one survey. It limits the ability of brand teams from exploring a wide range of messaging ideas and platforms or researchers have to conduct rounds of iterative research to test a large number of messages.
• Sea of sameness – Conventional message testing methodologies often produce similar scores across all messages because they are not sensitive enough to pick up nuanced differences in messages. Marketing teams struggle to make campaign decisions if there isn’t enough separation between Good/Better/Best message scores from surveys and end up making decisions based on their judgment more than research.
• Message drivers of appeal – There is still no way for researchers to understand the driver of appeal for a message without asking stated diagnostic questions like, “What do you like/not like about this message?”
• Campaign readiness – The main deliverable of most conventional message testing methodologies is a rank order/hierarchy of messages, followed by a TURF type analysis to estimate the optimal number of messages to use. In order to be campaign ready, marketing teams need message testing research to ideally deliver a segment-level, channel specific message map, which is not what they are getting from research today.

With increasing use of digital marketing channels, message testing has also been shifting out of market research/insights teams to marketing teams because they are conducting in-market A/B testing as part of their marketing campaigns. While A/B testing is easy to conduct and uses real- world results, data shows that 80% of A/B tests don't produce any results and are canceled before they reach statistical significance.

A/B testing has many of the same constraints that market research-based message testing has. Most brand teams can only afford (time and money) to test a few (6-8) messages in A/B tests. A/B tests don’t explain why one option is better than the other and attribution analysis is difficult if the A/B tests are run on bundles of messages instead of single messages like email subject lines.