Accelerating New-Account Acquisition in Financial Services
A recent study conducted by Adobe and E-Consultancy asked a number of financial-services marketers what their top priorities were for 2016. One-third of the respondents said that growing their client base was their top priority. New-account acquisition in financial services can be a challenge because it’s a heavily regulated environment with many legal restrictions. CreditCards.com, however, is a company that is meeting those challenges.
The Challenges Faced by CreditCards.com
CreditCards.com — the world’s largest online marketplace for credit cards — recently discovered how they could leverage Adobe Media Optimizer to predict the amount of revenue generated by their leads. This was especially helpful given that it normally takes several months to obtain that revenue.
Lead generation for financial services can also be very challenging — especially with the increasing importance of cross-device and cross-channel attribution — but for CreditCards.com in particular (as a digital marketer), every problem the company faces is exacerbated by the cost-per-acquisition (CPA) relationships with the issuers it works with. This means that the company only generates revenue if the leads it sends to various banks actually convert to users that are approved for credit cards.
Because the company was unable to track data after a customer left their website, they began to focus on what they could track, measure, and optimize to on their own website while the customer was still there. This data consisted of the conversions of people who were leaving their website and going to a credit-card issuer’s website. Somehow, CreditCards.com needed to figure out how to optimize per lead instead of per approval.
Mapping CreditCards.com’s Road to Revenue Per Lead
They decided to begin by identifying how much they were spending per lead. They sat down with Adobe and created a system to predict and estimate the amount of revenue generated by each lead that leaves their website. The system exists for each card, on every single page of the website, and acts as a stopgap. With this system, CreditCards.com can estimate the revenue generated by their leads. Then, when they actually receive the revenue from the banks three months later, they’re able to determine the areas that were accurate vs. the ones that were inaccurate and pivot from there.
They created tracking IDs — for each page of the website as well as each card offered — to track where conversions were happening according to exact page and credit card and to identify trends. An example of this is when people see an ad for CreditCards.com on one of the major search engines and clicks on it. Maybe then, they navigate to the travel-rewards page where they convert on one of the cards offered. The tracking provided to CreditCards.com through Adobe will then be triggered to let Adobe know that a conversion has happened on this page.
Determining how much conversions are worth is simple in theory but difficult in practice, because it’s hard to be precise. It comes down to determining the likelihood that a combination of leads who maneuver through a certain page and credit card will lead to approvals, which will generate revenue. To break it down, imagine you’re working with a credit-card issuer that will give you $100 in revenue every time somebody is approved for a card. You then locate the combination of page IDs and card IDs associated with that card offer.
Your sales-analytics team might look at that combination of page IDs and card IDs and determine that people who become leads right there have a 10 percent chance of converting into approvals. $100 multiplied by 10 percent means your average cost per lead from this imaginary page- and card-ID combination is $10. You would then apply that to every possible combination of page and card ID that exists on your website.
This process has given CreditCards.com the revenue per lead they’ve been looking for. Additionally, they have something to optimize to on their website that they know correlates with success on the backend when revenue comes in three months later. Instead of waiting three months for any revenue data at all, they receive daily insights into how their accounts and search-engine marketing campaigns are performing. They can see exactly what is driving conversions and profitability and adapt to areas that aren’t performing as well.