A recent study by Adobe and E-Consultancy has asked a number of financial marketing services that their were priorities for 2016. One-third of respondents said that the growth of their customers was their first priority. New account acquisition of financial services can be a challenge because it is a highly regulated environment with many legal restrictions. CreditCards.com, however, is a company that meets these challenges.
The challenges of CreditCards.com
CreditCards.com – the largest online marketplace in the world for credit cards – have recently discovered how they could take advantage of Adobe Media Optimizer to predict the amount of revenue generated by their tracks. This was particularly useful because it normally takes several months to get that revenue.
Lead generation to financial services can also be very difficult – especially with the growing importance of cross-device and cross-channel attribution – but for CreditCards.com in particular (such as a digital marketer ), every problem the company faces is exacerbated by (CPA) relations cost per acquisition with issuers with which it works. This means that the company generates revenue if the son he sent to various banks actually convert users that are approved for credit cards.
Because the company was unable to track data after a customer left the site, they began to focus on what they can track, measure and optimize their own website then the client was still there. The data consisted of the conversion of people leaving their website and go to the site of the credit card issuer. Somehow, CreditCards.com needed to understand how to optimize the lead instead of by approval.
Cartography CreditCards.com Road to Revenue Per Lead
They decided to start by identifying how much they spent per lead. They sat 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 map on each page, and acts as a stopgap. With this system, CreditCards.com can estimate the revenue generated by their son. Then when they actually receive the number of banks business three months later, they are able to determine the precise areas that were against those who were inaccurate and pivot from there.
They created tracking ID – for each page, and each card offered – to track where conversions occurred in the exact page and a credit card and identify trends. An example of this is when people see an ad for CreditCards.com on a major search engine and clicked. Maybe then, they sail to the travel reward-page where they convert on one of the available cards. The monitoring provided by Adobe CreditCards.com will then be triggered to let Adobe know that a conversion happened on this page.
Determine how many conversions are worth is simple in theory but difficult in practice because it is difficult to be precise. He returns to determine the probability that a combination of drivers maneuvering through a certain page and credit card will result in approvals that will generate income. To break it, imagine that you are working with a credit card issuer that will give you $ 100 in revenue each time someone is approved for a card. You can then locate the combination of the page ID and card ID associated with this card offer.
Your sales-analytics team could look like this combination of page ID and card ID and determine that people who become tracks here have a chance to convert 10 percent of approvals. $ 100 multiplied by 10 percent means your average cost per head of this combination of page- and an imaginary-ID card is $ 10. You can then apply to all possible combinations of the page and ID card that is on your website.
This process resulted CreditCards.com income per head that they were looking for. In addition, they have something to optimize their website they know correlates with success on the backend when revenue is available in three months. Instead of waiting three months for all data revenue at all, they receive a daily overview of how their accounts and search engine marketing campaigns are performing. They can see exactly what is the engine conversions and profitability and adapt to areas that do not work as well.