Message testing research has long been an ignored part of market research, with little methodological or technological innovation over the years. Even the most basic needs that market researchers have in message testing have still not been met:
• Still can’t test a lot of messages in one study
• Still can’t get good separation in scores between good/better/best messages
• Still have to ask respondents to explain likes/dislikes
• Still have no way of improving messages as they are tested
Current message testing solutions do not help marketing teams with campaign readiness and there exists an unaddressed gap between what marketing needs vs. what is handed over by market research. For example, while market research delivers top messages, what marketing really needs to execute a campaign is optimal message bundles and story flows for every channel and segment. Consequently, a significant amount of judgment is applied by marketing teams to get to translate market research results into omni-channel, personalized campaigns.
A pioneer in the use of decision heuristics science and AI to improve messaging, Newristics recognized that behavioral science and machine learning algorithms had the potential to transform message testing research. The idea led to the birth of CMO (Choose Message Optimizer), a message testing algorithm that is designed to deliver better campaign readiness out of market research. Built with 3 years of pure R&D, CMO significantly improves campaign readiness by providing the optimal message map for each channel and customer segment, ready for execution.
To demonstrate the power of CMO innovation, a large-scale message testing study was conducted using in-market messaging from a large financial brand (Chase Bank). The study tested over 400 messages across several attributes, with over 1,000 respondents. The messages were tested with consumers in an online survey using the CMO algorithm.
In-market messages from Chase Bank were tested against 100s of alternate articulations that were created using decision heuristics. Winning message bundles were then identified from the survey data using machine learning algorithms. Improvement in messaging from Chase in-market controls vs. new optimized message bundles was measured using preference share analysis. Message bundles for different channels and customer segments were predicted by CMO algorithms.
The results from the study were sensational, with the CMO-optimized message bundles outperforming current in-market messaging across ALL types of campaigns, with increase in preference share ranging between 30% – 50%. The findings of this study are especially powerful because the improvement in messaging campaigns was achieved with ZERO assistance from the Chase marketing team and simply relied on the power of decision heuristics science and AI to optimize messaging.
Learn more about the CMO (Choose Message Optimizer) here.
Newristics is famous for helping brands optimize messaging using a combination of behavioral science and machine learning algorithms. In the past 10 years, Newristics has optimized messaging for 100s of world-leading brands generating $100s billion in revenue every year.