I recently attended a webinar hosted by one of our partners, Salsify, where the focus was on what causes shoppers to convert when on product pages. I wanted to share some highlights with you in case you missed it.
Salsify surveyed 1,000 U.S. consumers who shopped online in 2017, and the results were fascinating. The survey focused on Amazon search engine results pages (SERP) and what product page elements contributed to creating higher conversion rates.
Personalization, images, ratings, and reviews are the key takeaways from the survey. With 78% of respondents saying personalization influenced their decision to make a purchase. While shopping 73% said they viewed three or more images. When it came to brand trust it was much more than having a previously good experience; 66% said “many good reviews” made them trust a brand.
For me, reviews are the big influencer when it comes to content that drives shoppers to make a purchase. As you start taking a more in-depth look at individual categories, reviews at the top of the consideration set with it comes to converting.
The graph below shows the responses for making a purchase decision in the electronics category. As you can see reviews are the number one factor.
Strong manufacture content and brand name aren’t enough to drive consumer conversion behavior. Further illustrated when respondents were asked why they would choose to purchase a higher priced option when considering similar products. You guessed it; reviews topped the list.
I kept asking myself, was there a way Indix data can help retailers and brands have visibility into the top reviewed products on Amazon? Why are Amazon’s top reviewed products so important? Results show 70% of shoppers (this is a combination of direct and Google) end their shopping journey with a purchase on Amazon. Based on this information I thought this would be a good signal for retailers and brands to leverage as they fight for the remaining 30% of shoppers.
Our data science team investigated and came up with something pretty cool. Through our accumulated data asset of 1.2 billion products, they were able to access the Amazon Top 100 sellers within a given category for the past 90 days. From there they filtered the data set down by eliminating products that had less than 100 reviews and an average star rating lower than four.
Applying these filters brought the product count from 1.2 million down to 40k and focused on products consumers are buying. The question we then asked ourselves is how could this be helpful for a retailer or brand.
The answer we came up with is two-fold. First, we analyzed the gaps in assortment between the Amazon data set and the catalogs for eBay, Walmart, and Sears. The results of this analysis were astounding, with Walmart only caring 7.8% of the products from the Amazon data set. We then went one step further and matched the catalogs of all three retailers to the Amazon data to pull a list of the exact products that were not being carried.
These insights could provide merchandising teams with an easy to use snapshot as to how their assortment compares to Amazons, utilizing the content consumers consider when making a purchase decision.
You can view the full version of this report here.