There are many combinations of potential messages that can contribute to messaging. Measuring interactions between these messages is critical in building effective strategies and individual messages. However, there is a problem - individual messages interact and can mis-inform customer research. Three common examples of interactions follow.

• The top-rated messages may all make the same point (be in the same 'category', e.g., efficacy) - minor wording changes usually result in similarly rated messages that add little in combination.
• By contrast, some combinations of messages may reinforce each other, providing more impact together than alone, yet this synergy is not uncovered by traditional techniques.
• Some messages may not rate highly in isolation, but their inclusion in the 'message bundle' may be critical to provide credibility and impact. A 'cost of entry message', for instance, confirming that side effects are equal to current products, may have to be included in the message bundle, even though it has little impact by itself.

Adelphi has researched this problem and developed an approach to identify, measure and resolve these interactions. This approach (1) measures 'interactions' between messages/sets to determine optimal message bundles, and (2) predicts relative impact of each message, and message bundles, on likelihood to prescribe. Output is provided via an MS Excel-based user-friendly simulator.

Click here to read the article from PBIRG Perspectives, Vol 9, Issue 1, Spring 2007, pages 9-10.

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