Turning a browser into a buyer is a beautiful thing. It means that you have attracted the right shopper to your site, at the right time. You have presented your products in a way that made them want to buy from you—and actually buy—which is no small task.
But in instances where the shopper did not convert, what happened? In some cases, the shopper was not qualified (at least not today). In others, the shopper was interested, but something happened (or didn’t happen) along their path towards conversion which kept them from buying.
This path browsers take to complete a purchase is referred to as a conversion funnel. Conversion funnels mark the specific milestones that must be reached in the process of completing a purchase. By tracking the percentage of shoppers who reach each stage of your conversion funnel and analyzing their behavior, you can optimize for higher conversion rates from one stage to the next.
First, Understand E-Commerce Funnel Optimization Basics
An easy way to better understand your site’s conversion funnel is to evaluate its strengths and weaknesses. Find the points where most shoppers proceed to the next stage versus where they leave the buying path. This information will help focus your optimization efforts on stages which need the most help or have the largest impact on conversion rate.
For example, your product description pages might be really good at getting visitors to add items to their cart. This tells you that shoppers you drove to your site are interested in your products—win! But you also find that a significant percentage of these interested shoppers leave before visiting the cart page.
Once you have identified a point in which qualified shoppers exit the funnel, the next step is to figure out why these shoppers who seemed interested don’t progress to purchase. A common approach is to launch an A/B test or multivariate test to explore your theories and potential solutions.
Here are a few examples:
Your Theory: The Call to Action to visit cart is not prominent enough
Test: Alternate CTA styles and methods of presentation
Your Theory: Shoppers continue to browse other products on site but lose interest and forget to visit cart
Test: Variate on-screen reminders to visit the cart
Your Theory: Shoppers leave, planning to come back later but forget and/or are intercepted by another retailer
Test: Variate methods to increase the shopper’s sense of urgency to check out today
Your Theory: Shoppers are not ready to commit to the cost of items today
Test: Offer different discount levels to win the sale today
Then, Understand the Flaw of Basic Conversion Funnel Optimization
This is where it gets tricky. Some of your shoppers do well in the current experience. Some fall into each of the scenarios you identified as reasons for leaving. And some need help in ways you have yet to think of.
To make matters more complicated, the same shopper can change over time. Meaning an experience that worked for Shopper A last month might not be effective when they return this week. How can you give each shopper the experience they need to convert them today?
This is certainly not achieved by treating every customer the same in the top, middle, or bottom of your funnel.
Finally, Make Use of ALL Your Conversion Funnel Data
The good news is that e-commerce shoppers produce tons of valuable data while browsing your site. This data can be captured, analyzed, and used to develop new or better features. Similar to shoppers in a physical store, their digital body language can tell you whether they are serious buyers or just window shopping, whether they are well on their way to making a purchase or need some help, and more importantly, it can tell you how to help each shopper by revealing nuances in the way they navigate and interact with your site.
The key is learning to ‘listen’ to what your shoppers are saying by making sense of the behavioral data they produce. But how can you mine all of this great shopper data without overwhelming your team (and your shoppers) in the process?
At Granify, we use machine learning to analyze the firehose of data shoppers produce while on our partners’ sites. We make real time predictions about the best way to help each visitor convert today and tailor their site experience in their moment of need.
This means you can show Shopper A a special drop-down to help them find their cart AND create urgency for Shopper B by highlighting how much money they’re saving during this week’s sale. And you don’t have to show all messages to both shoppers—machine learning will select only what is appropriate for each visitor.
This approach allows our partners to move away from analyzing historical data every month to design a future change. They can analyze real time data instead and use it to help their shoppers convert now. So when Shopper A returns next month on their iPhone and is concerned that it's too hard to check out on a small screen, you can help them. Machine learning will be there to listen and let them know that they’re only a minute and 30 seconds away from completing their purchase.