Marketing Automation Helps Tower Improve Email Prospecting

Posted on by Beth Negus Viveiros
Tower has 12 branches and over 184,000 members.

Automating its email marketing processes has helped Tower Federal Credit Union improve targeting to reach prospects for new loans.

With more than $3 billion in assets, Tower is the largest federal credit union in Maryland, with 12 branches and over 184,000 members. The company works with more than 500 employees groups, including government agencies and local businesses and has a 10 person marketing team that acts as an in-house agency.

In the past, Tower used Constant Contact to send basic email campaigns, but as their marketing needs evolved they wanted to move to more automation, says Marc Wilensky, vp-communications and brand marketing at Tower FCU.

“We wanted to get a better sense of what people were doing, and track the pages they were going to when they opened an email to build profiles for our sales funnel,” he says.

Tower implemented Act-On to institute a lead scoring system and automate email campaigns based on member behavior. The end of the year is a busy season for auto lending, so the system was used to power November to early December campaigns targeting potential new car buyers.


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“The initial email went to a large list—it’s hard to predict when someone is in the market for a car, and we wanted to get them in the sales funnel,” says Wilensky, noting that a large number of cars are sold between Christmas and Jan. 1. This meant it was optimal to reach prospective new vehicle buyers 30 days out from their purchase.

Internal content that had previously performed well was repurposed for enewsletters, to address people potentially in the car buying process. If someone filled out a form after reading content or watching a video, they received a follow up email. Open rates were two to three times better than average.

A similar approach is being taken with mortgages, which have a longer buying cycle. For wealth management, buyer personas will identify good prospects by both their age and savings history. A credit card is more of an immediate yes or no decision, so automation wasn’t as essential for that type of product.

ROI will be judged based on the number of loans, as well as what triggered someone to take out a loan. For email, open rates and clickthroughs will be analyzed.

“At the end of the day, we want to see how many loans were booked from a campaign, and year over year, what were the lifecycles of a loan product,” says Wilensky.

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