MassMutual, the financial-services provider, enjoyed success in moving beyond a one-size-fits-all communications model and tapping a slate of consumer "personas" based on particular mindsets among its target audience.

Greg Long, MassMutual’s head/client acquisition marketing, discussed this subject at KNect365’s Marketing, Analytics and Data Science (MADS) Conference.

Previously, he reported, the Massachusetts-based company used a “broadcast approach” when promoting its 401(k) retirement plans.

“Everybody got the same message the same amount of times,” he said. (For more, read WARC’s in-depth report: MassMutual peers into the minds of its customers to sell retirement.)

“There were some slight tweaks to the copy for segmentation, but it was a very basic segmentation. It was on age and gender, very demographic-based.”

As a result, “the content and the messaging wasn’t really relevant to the consumer. It didn’t speak enough to them and wasn’t distinct enough to move the needle."

A new strategy, however, divided its audience into three buckets – namely, “assured”, “aspiring” and “apprehensive” consumers – that were premised on factors such as confidence and self-perceived preparedness for retirement.

The goal of MassMutual’s activity here was to encourage people to save more in the retirement plans that were available through their employers.

And the initial campaign’s success led MassMutual to take a similar approach with later initiatives. “We continue to iterate and change the models and the segmentation,” Long said.

“We’re doing continuous improvement with the models, feeding [them] year after year data. Every time we run a campaign, we feed it back through the models and they continue to improve.”

Early returns spurred considerable enthusiasm among employers. “We keep telling our business partners, ‘Don’t expect this every year, because the results have been astounding,’” Long quipped.

A combination of research and data science that underpinned MassMutual’s work also served as an important internal proof point, too. “These results really brought home the power of data science to the entire organisation,” Long said.

Sourced from WARC