Lingerie e-commerce store conversion rate optimization case study on +28.8% conversion rate growth

About the client

 
Mariemur is a lingerie brand that’s marketed only directly to consumers via an e-commerce store. The monthly traffic during our contract was 400,000+ users per month mostly paid Facebook and Instagram traffic.

Mariemur reached out to us, as they had reached a plateau in scaling paid traffic because a further increase in customer acquisition cost that was needed to increase traffic was no longer profitable. Also, the company couldn’t fully dedicate their in-house team to optimizing e-commerce store conversion and needed an experienced CRO agency.

Research

Based on the funnel and quantitative user journeys analytics, we discovered that the main funnel bottleneck was 84% cart abandonment. So we started investigating this issue, but event correlations, ABC analysis, and comparison of segments of buyers vs. cart abandoners didn’t give us representative data on the reasons behind cart abandonment.

We therefore conducted an online poll on users who were about to abandon the cart, with an exit-intent targeting. As a result, we found out that 14% of users weren’t buying due to a lack of confidence in choosing the right size, and 82% of users from this segment were not aware that Mariemur has a free returns policy for the US market if the size doesn’t match.
 

 

Combined segments of size issues were the second most common problem after the price rejections (59%) that was not the area of our optimization efforts as discussed with the client.

Hypothesis

We proposed to overcome size rejections by adding “14-day money-back guarantee. Free returns if size doesn’t match” for the US market and “14-day money-back guarantee. Hassle-free returns if size doesn’t match” for other countries, as Mariemur had a free returns policy only for the US market.

Validating the hypothesis

To validate this hypothesis, we conducted an A/B test with personalized alternative versions based on user countries. The experiment measured the impact of the new copy on the conversion to a transaction, cart abandonment, AOV, and ultimately ARPU.

The experiment had the following characteristics:

Experiment type: A/B test

Traffic split: 50/50

Device: desktop

Primary metric: ARPU

Secondary metrics: AOV and CR

Version A – 2,296 users, 93 transactions, 4.05% conversion to transaction
Version B – 2,376 users, 124 transactions, 5.22% conversion to transaction