An ecommerce client studied the impact of product page attributes towards conversions.
The ecommerce client was going up the Alexa ranking and figured consistently in the first page of significant number of search results. However, they were not seeing similar conversions rates across all their products. They wanted to understand the impact of multiple attributes - promotions, the product title, image size and the number of images were having on conversion rates – and if they could come up with best practices.
What influences visitors’ actions?
The client was observing different conversion rates across their product pages and wanted to understand the attribute features that drive conversions better.
- Price discounts have 12% higher conversions than other promotions
- Products with 3 or 4 images, of half page size each have 7% more conversions
- Listings with 30+ 5 star reviews has 8% higher conversion rates
The analysis helped the client make small, incremental changes to improve the shopping experience and increase the overall conversion rate by about 1.4%
Great listings. But the right way to list?
The ecommerce platform was spending consistently on digital marketing and they were getting more visits by the day. Some of the product listings were having a very healthy conversion rates of about 2% and higher. While a few other products were not even getting a conversion of 0.2% despite having similar visitor counts. They wanted to dig deeper into the problem.
They had already used A/B Testing and few optimization techniques, like single click check out on all pages, consistent product description and page structure across all products.
What they needed was deeper – an understanding of how the promotions on products impacted sales differently, the role of product images, product reviews, number of reviews,overall product rating, the title (and searchability) of the listing and similar other product attributes.
Analysing the difference between a sale and a bounce
The conversion rate analysis helped the ecommerce client bridge the gap between what the customers are looking for, what information they need to make a purchase decision, the product details (including images), on-site language, content and customer reviews.
It also helped them identify the reasons why certain product listings, even with similar visitor counts had a very low conversion rate compared to the ones that were popular with the customers.
We used mixed modeling technique, with page attributes as features to measure the impact of each attribute on conversion. Bounce rate metric shares a lot of similarities with conversion rate but tell completely different attributes of the platform.