Campaign message testing in market research helps marketers determine what their customers and potential customers are thinking. Businesses find it useful to undertake campaign message testing to understand what messages will resonate with their audience and cause them to take notice. When messages that are winners from the campaign message testing process are used, the chances of the success of the in-market campaign increase.
At best without campaign message testing businesses run the risk of missing the mark and losing out on potential customers, at worst repelling prospective and current customers. It is a necessary stage in the lifecycle of the advertisement campaign, but significant limitations are baked into the current system.
Campaign 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.
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.
Message testing software and data analysis tools that are powered by artificial intelligence help brands test large numbers of messages continuously through in-market messaging campaigns. Based on this analysis, businesses can determine what type of content, offers, and marketing messages to their target audience will create the most engagement.
Market research testing and in-market testing of messages are suitable for different use cases.
Market research-based campaign message testing is ideally suited to evaluate the potential of messages for new products that are yet to be launched and to finalize messages for campaigns of high strategic importance to brands or champions that will be supported by significant advertising spending.
When there is little room for failure in a messaging campaign, it is generally advisable to test messages with message testing software before the campaign is finalized using market research-based campaign message testing.
Data analysis and AI advancements created message testing software that allows brands to course correct in the midst of campaigns and avoid harming their reputation (and bottom line). However, pivoting during a campaign can waste time and quickly become cost-prohibitive. The insights and success of campaign message testing and which stage of development brands choose to test at can be vital to the campaign's overall success.
Given the impact campaign message testing has on the success of the campaign, there should be continual innovation and progress in the field of campaign message testing. Yet, 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 campaign message testing.
Over the last 30 years, there has been little methodological innovation in campaign message testing and standard message testing surveys conventionally used for message testing still don’t meet some basic needs of brand teams:
Testing is Limited
Researchers still cannot test a lot of messages in one survey. This 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 campaign 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.
Can’t Know Why
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?”
Not Campaign Ready
The main deliverable of most conventional campaign message testing 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 campaign message testing to ideally deliver a segment-level, channel-specific message map, which is not what they are getting from research today.
With the increasing use of digital marketing channels, campaign 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 campaign 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). In the end, marketers make messaging decisions based on gut reactions, not data-driven insights.
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 communications, new technologies that help optimize the delivery of messages, etc.
However, what hasn’t changed much is the testing of messages in market research prior to execution. It is still too common for brands to test messages in qualitative IDIs despite the known shortcomings of qualitative research. In fact, entire message campaigns can be decided based on 20-30 qualitative interviews, campaigns that are supported by $10-100 million.
In an industry defined by science, the fundamental approach used in most conventional message testing methodologies (qualitative or quantitative) is the most unscientific form of campaign message testing. An ideal campaign message testing software would use science and AI to test MORE messages in market research and produce deliverables that are CAMPAIGN READY.
Share this Blog posts to your social media handles.
Back
Blog posts
Newristics
18 July 2022
Share
Case Studies
Time : 15 Mins
Webinars
