In recent blogs posts, we highlighted the benefits of using a free tool such as Google Analytics and Advanced Segments to dig up actionable insights for websites powered by cheap, Linux hosting platforms.
These same methods can be applied to shopping carts to analyze abandonment rates and implement new pricing structures to increase conversions. A recent case study by Google showed how an online food retailer cut shopping cart abandonment rates by 70% in key regions.
The retailer was already generating good online revenue sales, but one particular product lagged, most likely due to higher shipping rates; but nobody had proof this was the reason until they grouped visitors into two regions:
- Region A visitors were close enough to the warehouse to always get reasonable shipping costs.
- Region B visitors were everywhere else, and had to use a more expensive shipping method for the key product category.
They then measured the impact on sales whenever one of the key products was placed in the cart by installing Event Tracking to the “Add To Cart” buttons on every one of their product pages.
The magic sauce was implementing Advanced Segments and Custom Reports to separate visitors in Region A from Region B, and then drilling down to view performance by product category.
“Sure enough, visitors from Region B were found to be 48% less likely to purchase if they placed an item from the key product category in their cart, which raised total shipping costs,” said Google.
So, know they now knew for sure a new pricing model was needed if they wanted to elevate sales in Region B.
The solution was a less expensive flat-rate shipping model in Region B, followed by closely monitoring the results, which turned out to be 70% higher sales than before.
“Just to be sure, they checked to see if there was a similar increase in conversion rate for Region A visitors, and found that it did not fluctuate more than 3.4% over the same time period,” said Google. “The analysis confirmed that product shipping rates greatly impacted shopping cart behavior, and used data to measure the results of a key business decision.”