June, 2020

UX at mobile.de, Ebay, Lead Generation and how we optimize Conversion Rates

UX Design, Lead Generation, Conversion Rate Optimization

Intro

When it comes to e-commerce, conversion is quite important. A better conversion means that more potential visitors buy the products, register accounts, or enter personal data in order to convert from visitors to leads. There is a huge set of measures and best practices to improve conversion rates. Usually you pick some promising ideas that might work in your context, prioritize them and then test the ideas (using the A/B method) in order to validate your hypothesis. This is precisely how teams at Ebay proceed.

My current client is mobile.de, a subsidiary of Ebay, where I work as a freelancer to support the UX team. Here, we create concepts and UI designs of new features and ideas in order to improve the user experience and conversion rate – so called conversion rate optimization, or CRO. In this post, I briefly describe the role of UX at mobile.de and then show some concrete examples of what we tested and how well these tests really worked out.

UX at mobile.de, Ebay

Mobile.de is Germany’s largest platform to buy and sell cars. With around 300 employees, it is quite small. But the UX team is pretty effective. Most members, besides special functions like UX research, are dedicated to business units, like the dealer area, private selling, or financing. They all work closely together with the product owners of the unit or squad. But once every week, the UX team meets to exchange learnings and feedback on new concepts and designs.

The whole company is numbers-driven. Before new ideas are rolled out, they get tested. As a tool, we use Optimizely, where you can easily set up A/B tests. Afterwards you know the impact of the change in regards to the defined set of metrics. For example, one can discern how many more users perform a certain action and which side effects occur. 

Consulting the car financing business unit

Within the UX team, I  currently consult the financing business unit, and help prospective customers to finance cars directly in the mobile.de ecosystem, either with an online credit or on location at the dealership.

Topics: 

During the last months, we dedicated a big portion of our attention into improving our funnel. In such a funnel, you usually lose potential customers at every touch point. A touch point is e.g. one step of the application form. So every improvement of the funnel in order to push more customers through means an increase in leads and ultimately sales, as defined by the financing North Star Metric. 

Therefore we performed quite a lot of A/B tests in order to validate our ideas. Of course, many tests did not work out as expected. But some of them were successful,  and I want to provide some examples here.

A/B tests

Blind offer

In the current version, we showed a vague range of possible monthly rates at the beginning of the user journey, focusing primarily on the landing page of the financing funnel. Hypothesis: For many users, this range is already sufficient and they will not continue to get a concrete rate and a personal credit decision based on their personal data. So if we don’t show this estimate at the beginning then more users will take the effort to enter their personal data in order to get the exact monthly rate.

Uplift: More than 100% leads could be generated

The carrot

In the previous “blind offer” test, we removed the range of the possible monthly rate. User research shows that receiving the monthly rate for the car is the users’ main motivation, when going through the flow. Now our hypothesis was that users are more willing to provide their data and create a lead, if we remind them, what they get at the end – the carrot. We used an “empty” rate and a photo of the car during all steps.

Uplift: More than 30% leads

Ranges

Both user research and data analysis show that users feel uncomfortable entering personal financial information such as net income or monthly expenses. Our hypothesis was that by asking for ranges instead of exact numbers, users are more willing to provide this information, because the form will be perceived as more convenient, anonymous, reasonable, and less prone to error. Notice: This sounds so obvious – but here the challenge was to check with the banks and find out that they also base their credit decisions on ranges.

Uplift: More than 25% leads

Wizard

Insights from behavioral psychology studies show that users are more motivated to provide information if asked through a conversational interface. 

Our hypothesis was that if we ask users simple questions instead of blocks of content, more users will convert, because the process appears to be easier and convenient. Additionally, we use underlying psychological patterns such as Gamification, Flow, and Completion to emphasize this effect.

Uplift: More than 5% leads

Plain form

We believe that if we remove irrelevant information from the page, users are less distracted and more focused, and therefore more users will fill out the form. 

In particular, we wanted to understand if the blue boxes, which are giving a bit more context and try to motivate the users, have an impact on the conversion rate. That’s why we tested the effect of removing the boxes.

Uplift: More than 10% leads

David Bühn