As promised, we’re back to pick up where we left off on best practices for multivariate testing. Last time we introduced the growing importance of “Big Data” and how it influences successful cross-channel marketing campaigns through deeper insight into consumer preferences. We also stressed the increasing role of technology in data-driven direct mail campaigns.
Automated marketing tools don’t just add to message flexibility, they make multivariate testing and analysis possible. Now direct marketers can do more than just determine which campaigns performed better than others – they can analyze which elements (offer, headline, images, colors, etc.) influenced the improved performance. We referenced this statistic on Wednesday, but it’s important to reinforce: Marketers who engage in ongoing testing average 60% better performance of their campaigns than those who don’t.
Back to the best practices. Here is the continued list of tips shared by Rab Govil, CEO of Naehas, during our recent webinar on multivariate testing:
- Look for hot zones in your multivariate testing. After compiling multivariate data sets, look for stand-out numbers. These hot zones will give you much more guidance on how to refine your testing. For example, maybe the response of one demographic was significantly higher to a specific direct mail package.
- Testing is not an event, but a process. While advancements in technology have made learning from multivariate testing faster, it’s still an ongoing process to learn what resonates best – and that makes multivariate testing a marathon, not a sprint.
- The magic rule of 100: In order to reach statistical significance, you must avoid Type I (false positive) and Type II (false negative) errors – in other words believing something to be true when reality says it’s false and vice versa. The size of the cells in your multivariate data should generate 100 events to achieve 95% probability of a valid result.
Multivariate testing has come a long way with advancements in technology. It’s now more efficient than ever to not only gather data sets, but test, analyze and optimize them to drive better direct mail performance. If you’re in the process of reviewing your testing strategy, please contact me to learn more.
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