Crafting Impactful Community Messages: The Science of Message Testing

Originally published on LinkedIn on August 14, 2024

In our madly connected world, how do you ensure your organization’s message resonates with diverse audiences? For health-focused organizations, such as health systems, HMOs, academic medical centers, where trust and clarity are critical, getting your community engagement communication right can make all the difference. This is where message testing comes into play – a strategic tool that offers data-driven insights to refine your communication efforts.

At Bazis Americas, we use trade-off methodologies, based on the principles of experimental design, to uncover the most compelling messages for our clients. End goal is to help clients refine communication strategy is to meet the needs of the communities they serve.

Understanding Your Audience: Demographic and Behavioral Variables

Effective message testing starts with understanding your audience. Covering a broad demographic spectrum provides a comprehensive view of community perceptions and priorities. While factors such as age, language, and income are used as a traditional baseline for segmentation, other, behavioral factors, such as healthcare decision-making roles, trust in medical services, health literacy, often explain the crucial differences in the segments. Inclusivity ensures your messages resonate across various segments of the population.

Trade-off Techniques to Tap into Intrinsic Motivation

Imagine asking your audience to choose between options not just once, but multiple times in different combinations. Which phrase, message, visual do they find most compelling? Which one falls flat? This is the essence of MaxDiff (Maximum Difference) analysis, a powerful technique in message testing. MaxDiff is one example of trade-off methodologies.

When a trade-off research is carried out, respondents go through multiple sets of profiles, each containing a subset of the total profiles being tested. Respondents pick the most and least appealing options from each set, helping us build a ranking of message appeal. This method effectively identifies the relative importance and preference of different messages, providing a clear hierarchy essential for strategy development.

Case Study: Community Values Messaging

Let’s take a look at messaging options for a large health system in Illinois. The goal? To identify which the client company’s values resonate most with the community.

Methodology and Participant Profile

We deployed a quantitative online survey incorporating MaxDiff analysis. The sample reflected the demographic distribution of the family health decision makers, including considerations for age, education, income, and healthcare roles. The survey was available in multiple languages to ensure inclusivity.

Advanced Approach for Deeper Insights

To delve deeper, we didn’t stop at the initial message preferences. Based on individual responses, we identified the most compelling messages (on the fly) and presented them back to respondents for further probing. This step brought nuanced insights into what drives their preferences.

So, what did we discover? Here are some highlights:

1. Messages that emphasized the empathy and attentiveness of doctors during patient interactions resonated strongly. These messages underscored the importance of personalized care and strong doctor-patient relationships.
2. Messages promoting a streamlined and hassle-free appointment process were among the top preferences. The community values quick, efficient scheduling and minimal waiting times.
3. Messages that showcased the availability of robust social support networks within the hospital were highly valued. These messages emphasize the holistic approach of the healthcare organization, catering to not just the medical but also the emotional and social needs of patients.

Gen Z Prefers Digital Access, While High-Income Value Exclusive Services

We also identified variations in message preferences across different demographic segments:

• Younger residents preferred messages about digital accessibility and efficient appointment processes, while older residents prioritized messages about doctor-patient interactions and access to social support networks.
• Respondents not fluent in English responded positively to messages highlighting accessible healthcare information in different languages.
• Higher-income respondents preferred messages that emphasized exclusive services and high-end healthcare facilities, as well as messages that highlighted advanced medical treatments.

Recommendations for Effective Message Testing

How can you make your message testing more effective? Based on our experience and findings, Bazis Americas offers these recommendations:

1. Diverse Audience Coverage: Ensure that the survey sample is representative of the target population, including different age groups, languages, healthcare roles, and other. We do this through qualitative pre-pilot with potential respondents.
2. Optimal Sample Size: Aim for at least 300 respondents to ensure reliable data, with larger samples for segmented analyses (minimum N=150 per segment).
3. Utilize Conjoint Analysis: We can guide you to choose the right technique from a group of trade-off methods (conjoint, MaxDiff, or discrete choice). These techniques rely on experimental design approach, where we get a glimpse of intrinsic, unconscious preferences, as opposed to a more traditional research tactics of a stated preference approach. 

The Way Forward: Innovating Message Testing

At Bazis Americas, we LOVE to imagine what the future brings, and how our research approaches can evolve. After one project finishes, we start thinking about future improvements. Right now, we’re diving into the exciting world of AI-driven open-ended probing techniques. Imagine this: the classic depth of qualitative laddering, now supercharged with the speed and precision of digital probing. The result? Faster, sharper, and more nuanced insights that keep us—and our clients—ahead of the curve.