Background

The filtering capability is one of the most critical components for the success of any e-commerce business. At De Bijenkorf we decided to revamp the existing filter, which was deemed outdated and underperforming.
I was tasked with the filter redesign, with the ambitious goal of increasing the conversion rate by 3%.

Identify & Prioritize Customer Problems

I identified the customer problems based on data, user feedback, Baymard & Nngroup research and our previous research insights. There are many of them, clearly, not all of these can be addressed at once. Therefore, I categorized these problems into three groups to create a well-defined prioritization plan and aligned this plan with stakeholders like the conversion, CRO, data, category team.

How to measure success?

Before I start to work on the task, I always defined with stakeholders about how we measure success, we decided together to use A/B testing and online survey to measure success from business perspective and user perspective :

What are the user needs/goals in using filter?

There are different needs based on the different scenarios in using filter, so I conducted a focus group discussion to uncover these questions below:

• What are the scenarios when users use filters？
• What are the User Goals in these scenarios？
• What are the User Needs in each scenario？
• What kind of filter interaction do they prefer based on these scenarios？

After this session, I found there are 2 main scenarios for filter usage and also discovered what kind of filter interaction do they prefer in different scenarios:

Design Decisions Based on Data

With varying user requirements across different scenarios, the challenge is to determine which requirements to prioritise into the design.



Based on the data, there are 66% users come with a specific purchase goal, which is the scenario 1 in the focus group discussion. Both the result of my focus group discussion & Nielsen Norman group research both indicate this type of users’ preference is batch filters ( users adjust multiple filters before the results of the compound query are returned ).



Furthermore, the "batch filters" are also used in the current mobile version, from the data that we know it performs better than the interactive filters (The results returned after each filter value) on desktop. Based on these findings, the decision was made to integrate "batch filters" into the redesign.

Design for Experiment

The Hypothesis:

By updating the filter design, users can find more relevant products, leading to an increase in the conversion rate.

The A/B testing scope

Test duration
2 weeks

PLP Views
Control:   913515
Variant:   947774

The set up

A/B distibution
50/50

Sessions in using filter
Control:    55097
Variant:    35319

Iteration and Outcome

The visibility of the filter button is low was identified as a potential issue. In the iteration, I integrated both batch and interactive filters, providing users with easy access to the most frequently used filters. But still keep the batch filtering for these users who prefer it.
This design still under the A/B test now. I will contiue update the following precess.