Design process

Stakeholders interviews

Most of Back Market activity is dued to its selling functionality. However, with the recent company growth, refurbishing partners factories need to get more and more to work on and keep pace with the demand. To help them with it, it became a priority for Back Market to develop their Buy Back activity.

While the Marketing team reaches a very good conversation rate while trying to bring traffic on the page, the dedicated Business team wants to improve the conversation rate of the estimation page, as well as the numer of actual devices sent to refurbishing partners. These two elements were my focus during this process.

Beginning of Back Market’s Buy Back user flow

Competitive analysis

Selling options for individuals aren’t lacking in France. While Facebook or Le Bon Coin offer standard peer-to-peer selling services, Remade, BuyBack, CashConverter, Volpy, HelloZack — among dozens of others — share with Back Market the rest of the refurbished products’ market.

While this analysis confirmed that the strength of Back Market lies on its SEO and advertising campaigns, it also showed that they haven’t a clearer advantage on the accuracy of its estimation system, the height of the Buy Back‘s offers and the device sending process.

Moreover, most of its competitors have a mobile-first design — when not a native app — which was at that moment not the of Back Market.

Google Analytics analysis

Interesting datas came out of Google Analytics regarding the traffic on the considered feature.

First, a large majority of the users appeared to be men, between 18 and 35 years old and living in big cities. Secondly, the most sold devices are iPhones and Samsung smartphones which have been released 3 to 4 years ago and which are, when in good shape, still valuable on the market today

Also, while the higher number of visits of the Buy Back page happens on mobile, the conversion rate is stronger on desktop.

Google Analytics datas overview (September 2018)

User interviews

Meeting actual users of Back Market’s Buy Back functionality — or competitors — helped me a lot to understand further why and how they use this service. Here are the most shared insights I gathered:

  1. People sell their phone because they’ve decided to buy a new one — the selling allows them to finance a large part of their buying.
  2. Many of them weren’t familiar with Back Market’s (or competitors’) Buy Back offers— they found out about it through advertising. Without it, they would rather use a peer-to-peer platform, as Facebook or LeBonCoin in France — which appeared here to be the direct competitors.
  3. What make them choose to use this kind of functionality is the convenience of the service — they don’t have to deal with countless negotiation offers, messages, potential payment insecurity — characteristic elements of peer-to-peer platform s— and can go through the process right away.

User tests of current user flow

I took advantage of the interviews to make the users test the current flow of the functionality.

Among a dozen of micro-irritations that were pointed out during the test, 3 patterns appeared to be major pain points:

  1. Support is lacking when it comes to describe the state of the phone — users answer but tell me that they don’t really know if their answer is accurate or not. Moreover, they feel frustrated not to understand the impact of their answers of the offered price.
Users feel they lack of support to be accurate while assessing the state of their device.

2. A first price estimation appears before the end of the estimation process — users understand that the offered amount is the maximum that they can get, so it is likely to get lower regarding the next elements they are going to provide. This, of course, brings anxiety to the process.

Users say it makes them anxious and frustrated to see the estimation price going down.

3. By the end of the process, bank account datas (and later on , a copy of their ID card) are requested. Even if they don’t have to send them right away, users say they feel surprised to have to provide them before even knowing exactly what is going to happen next. Also, if they had started the process of their mobile, they wish they could switch to desktop to fill in these informations without going through the whole estimation process again.

Users wish they have known before they would have to provide advanced personal datas and want to fill them on desktop rather than mobile.

User Journey

Both interviews and users tests of the current feature flow helped me to identify big similarities between the different users’ journeys. Indeed, most of them were more or less same as the following one:

Typical unsatisfied user journey


We can synthetize what we gathered by analysing Google Analytics datas and the goals and frustrations that came up the most during the interviews with the following persona:

Problem statement

Taking into account Theo’s motivations and frustrations, it is now time to find how to improve his experience so he doesn’t not feel the need to go to a competitors anymore. In other words:

My obsession for the rest of the mission!


Features prioritization

After a few ideation workshops (following the amazing Crazy 8’s and Round Robin methods), came the moment to prioritize all the features that could be implemented.

Let’s rock this feature!

Low-fidelity prototype

Conducting paper prototype tests is extremely helpful to get feedback on features without having any bias coming from graphic design considerations. Indeed, as users see a prototype looking like a draft of your work, their feedback tends to be more honest.

For this prototype, I decided to cut the user flow in two parts:

  1. The first one in mobile — from the Facebook Advertising to the landing page

2. The second one on desktop — from the beginning of the estimation process until the confirmation page — as many users told me during the interviews that they preferred do things dealing with advanced peronal datas (such as bank account informations) on their computer.

Tests & iterations

This new flow was globally much appreciated by the testers. Apart from a few details:

1. The switch between mobile and desktop was a bit too early: I should have made it at the end of the estimation process as users want to get a quick overview of the estimation outcome on their mobile before going further on desktop.

2. Presenting the estimation outcome through the price of a new device (instead of a plain offer) was welcomed with enthusiasm but the look and feel of the section on my paper prototype reminds the users too much of advertising. Also, they found it hard to find the Buy Back actual offer — information that they were still interested in.

Users felt excited by the concept but overwhelmed by the information architecture.

Mid-fidelity wireframes

In order to turn all the users’ feedbacks into iterations, I worked on the following mid-fidelity wireframes.

Main iterations that follow the low-fidelity prototype.


Now that the users were satisfied with the new feature flow, it was time to stregthen its look and feel!

To do so, I started by working on a moodboard that reflected a young, reliable and rewarding atmosphere, using both current Back Market’s communication material and new elements:

Young, reliable and rewarding atmosphere.

And translated it into the following Style Tile:

Current Back Market colors enriched with young accent colors.


High-fidelity wireframes

I worked then on high-fidelity wireframes and made the prototype alive thank to MarvelApp. Here is the result!

High-fidelity wireframes turned into a prototype on Marvel App.

Next steps

Before going further into UI design, desirability tests can be conducted on the prototype above to confirm that the new look and feel brings the expected emotions to users. A/B testing can be also used to assess the impact of each iteration proposal.

Thanks a lot to Back Market Design and Business teams for this opportunity!

Source link


Please enter your comment!
Please enter your name here