Email marketers are no different from people who work in other channels: They need data and analytics to drive decisions and targeting.
How good are these folks at achieving those goals? Better than you might think, according to Marketing & Technology Strategy, a study released on Wednesday by Ascend2.
Of 233 respondents, 81% say they are successful in using data and technology to achieve their priorities —and 29% say they are very successful. Only 16% say they are somewhat unsuccessful, and 3% rate themselves as being in the basement.
Those polled are also largely satisfied with the data they are getting, with 89% of marketing influencers saying that its effectiveness is increasing. Again, 29% say the increase is significant. And only 11% are disgruntled. But many have trouble obtaining social network and public data.
Their strategy priorities will be familiar to anyone in the online marketing business:
“These priorities are a strategic fit, as the quality of your data will impact (positively or negatively) the accuracy of the decisions you make,” says Todd W. Lebo partner/chief marketing officer, Ascend2.
But there are obstacles, including challenges in reaching some of the very goals listed above, according to the survey. Here are the hurdles:
“Integrating data across marketing technologies has a long way to go,” Lebo says. “For marketers to provide a seamless experience for customers, regardless of channel or device, they will need to do more than just talk about how to integrate data across technologies.”
Email is bundled in with internal sales customer service and internal marketing programs, Lebo confirms
As Coherent Path notes in a report, you also need models to predict the evolving taste and the moods and behaviors of customers you don't see very often. Of course, that takes accurate data. Here is the list of data sources:
If there’s a lesson here, it’s that you need data and the analytics capability to make sense of it. Lebo concludes: “Marketers can sleep well at night if they are making their decisions based on the quality of data. Poor-quality data may result in some sleepless nights!”