I love split testing - Visual Website Optimizer Blog
I love split testing - Visual Website Optimizer Blog |
How ZALORA’s Product Page Optimization Grew Checkouts By 12.3% Posted: 14 Mar 2016 02:59 AM PDT About ZALORAFounded in 2012 by Rocket Internet, ZALORA is the fastest growing online fashion retailer in Asia, currently operating in 10 countries in the Asia Pacific (APAC) region – Singapore, Hong Kong, Malaysia, Philippines, Indonesia, Thailand, Taiwan, Vietnam as well as Australia and New Zealand under the name THE ICONIC. In its four years of existence, ZALORA has raised $238 million in four rounds of funding from the likes of JP Morgan, Access Industries and Rocket Internet. Competing in the exciting APAC online fashion market with more than 600 million people and worth approximately US$63.5 billion, ZALORA's mission is to become the region's leading online fashion destination. We got in touch with Tan Liang Jian and Yong Wai Teng who are part of the Regional Onsite Marketing team at ZALORA to learn all about their successful checkout optimization test. Why Conversion Rate Optimization?As a firm that has always taken pride in being data-driven, ZALORA was convinced of the need to optimize its conversion funnels. Liang Jian elaborates:
The Process of OptimizationThe ZALORA Experience Revolution Loop (ZERL) framework drives the process of on-site optimization. The first step of optimization is research. The team conducts quantitative and qualitative research making sure to include feedback from other departments such as Customer Service, Online Marketing and local teams. Based on this research, the team constructs hypotheses for testing. Prioritization of test ideas come next based on parameters like potential business impact and ease of execution. Testing ensues, followed by analysis of results. Finally, the team loops in the Product Management team to make final changes to the page(s). Details of the A/B testTo give us a better idea of how the process works in practice, Wai Teng, the A/B testing specialist within the team, takes us through the particular test and consequent change that resulted in a 12.3% improvement in checkouts.
This is how the original product page looked:
The team proposed two changes to improve the visibility of free returns.
The test was conducted across four variations:
Results and LearningVariation 1 emerged the winner improving checkouts by 12.3%. Wai Teng attributes the success of this variation to two factors:
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