E-mail can take cues from search analytics
Direct marketers looking to bring greater efficacy to their e-mail campaigns should take a close look at recent developments in paid search, the darling of online advertising.
There, companies are increasingly turning to sophisticated analytics to improve their return on investment. In other words, they're trying to find out what happens after the click.
Reaching your target audience is difficult enough as you've read: spam filters and an overwhelming amount of untargeted and off-base messages conspire to generate a feeling of apprehension. But the battle doesn't end when an e-mail is opened. That first click could be the last.
So what are smart companies doing to solve the problem? Market leader Google has invested in software that allows marketers to do online analysis of visitor behavior.
Google understands that its advertisers want to know how their search buys increase revenue and provide ROI. Using analytics, they can get in-depth information about their users' behavior and adjust their campaigns to maximize results.
As direct marketers, we should take these best practices from Web analytics and apply them to e-mail marketing. To do this, we need to realize that traditional measures of success, such as click-through rates, are not enough. Instead, we should apply sophisticated metrics to gain useful information.
Below are three examples of how we can gain greater insight into our customers' behavior.
Segmentation: Segment the traffic of people coming from an e-mail campaign - not just on the initial visit, but for every visit. This process will help determine if letter receivers are higher value customers or if mailing lists need adjustment.
Intelligent comparison: Compare the results of traffic coming from an e-mail campaign to a site's general audience. Do the e-mail visitors visit more pages or generate more revenue per order?
Behavior tracking and metrics: Measure not only the clicks an e-mail generates, but also the behavior of users once they reach the site. Examples of desired behavior and corresponding metrics include:
Metrics: Average order value and visit to purchase-conversion rates
Metrics: Visits to registration and registered users to purchase-conversion rates
Behavior: Web self-service
Metrics: Customer satisfaction surveys and offline call-center cost-deflection rates.
Sophisticated metrics like these can deliver a deep understanding of customer desire and motivation. By tracking them closely, you will be able to better serve customers and drive your company's agenda - both before and after the click.