If you look at your cart abandonment rate, you’ll see that 50% or more of the people that add to cart or even start checkout abandon before completing the purchase.
50% abandon?!?!? Why so many?
You’re never going to get 100% of checkouts to purchase, but you can look at some patterns in user behavior in Google Analytics and chat conversations with visitors to figure out the top reasons why people abandon. This will give you what you need to save a big chunk of these abandons.
At HelpFlow, we provide 24/7 live chat teams to over 100 e-commerce stores. We have analyzed millions of chats for hundreds of e-commerce stores over the last six years and identified specific patterns that lead to abandons.
In this post, I’ll share the patterns that typically lead to abandons, along with a way to use Google Analytics to analyze your own user behaviors that lead to abandons.
Let’s get into it.
#1 Find Abandon A-ha Moments in Google Analytics
I will share the common questions that lead to abandons from our chat data below. First, I want to explain how we use Google Analytics to see precisely what behavior leads to abandons so we can engage with them on chat early.
You can use segments and other filters in Google Analytics to see browsing behavior only for visitors that ultimately end up abandoning checkout. You can filter this down to all cart abandons or abandons on specific steps of checkout, such as the shipping or payment page.
Once you’re filtered down to the abandon you want to analyze, work backward to get a clear understanding of other browsing behavior on the website.
– How many products do these people typically look at before starting checkout?
– What types of filtering or product searches do they use?
– Are there specific products or product lines that are most common with visitors that end up abandoning?
– Is there a pattern to their behavior over multiple site visits leverage, such as a specific traffic channel as first touch or last touch?
This is a bit technical and the outcome will vary based on your business model product offering, but the end result you want to figure out is the patterns that the majority of abandons seem to follow.
Once you have this data, you can focus on proactively engaging with visitors on chat that look like they will abandon and also implement site changes that will mitigate the reasons why they abandon.
#2 The Most Common Reasons Visitors Abandon Checkout
We provide 24/7 live chat teams it’s over 100 e-commerce stores. We’ve analyzed millions of chats over six years, and part of what we look for is questions that lead to abandons.
There are a few key reasons visitors abandon checkout:
Amazon prime has trained people to shop online without any worry about whether they would like the product or not. Free returns mean if they don’t like it, they can press a button and get a refund.
You don’t need to match Amazon directly, but you need to be clear with visitors on what their options are if they don’t like it. The closer you can get to the benchmark prime has set, the lower abandons will be. But the key thing here is just to make sure you are communicating it clearly. Give them assurance. Don’t make them wonder.
Uncertainty in Timing
Going back to the Amazon example again, most prime products arrive within a day or two maximum. This has train buyers to shop online rather than locally for so many things because the difference of today versus tomorrow to get a product is often not a big deal.
The problem with buying from stores off of Amazon is that sometimes shipping time can add up. To get the product quickly, you are probably up charging on shipping. And sometimes the delivery window is over a series of days, a week or two into the future, not precise like Amazon.
Again, you don’t need to meet the logistics machine of Amazon to get two-day shipping for free. But you do need to be clear on what delivery time they can expect down to the day. This gives them certainty in when the product will arrive and future paces them into having the product in their hands.
You can do this in a few ways, either with apps that produce an Amazon-like experience based on your inventory and logistic data or by communicating it directly on chat when people ask. And they will ask.
Abandons Mentally Start at Product Pages
We’ve seen in so many chats that abandon is often related to something the visitor was uncertain about directly on the product page. For example, there may be specifications about the product that they are unclear about or they may not be sure the product is the right fit for them.
Although they may abandon in checkout seemingly due to a tough returns process that you communicate, the real reason they abandon is that they’re not certain the product will work for them based on what they found on the product page.
By analyzing user behavior directly on product pages, such as scroll depth and back-and-forth navigation, you could spot a confused visitor and engage with them on chat to remove that confusion before they move into checkout. That will ultimately decrease abandons from that cohort.
What Now? How To Save Your Abandons
Cart and checkout recovery is an ongoing process, but below are a few action items to implement from this post:
– Callout delivery times on product pages and checkout to provide certainty.
– Clarify your (hopefully) easy returns process to eliminate buyer’s risk.
– And use live chat to engage proactively with visitors that look like they’re going to abandon so you can save many of them.
At HelpFlow, we provide 24/7 live chat teams to over 100 e-commerce stores. We’d be happy to dig into your store to help you identify ways to save checkout abandons and drive more sales. Even if we do not work together, you’ll get a ton of value from strategizing on the call together. Book a time that works for you by clicking below. Always happy to help!
Your Store Needs a 24/7 Live Chat Team
HelpFlow provides 24/7 live chat teams to 100+ eCommerce stores. See how we can help you save checkout abandons, increase conversion rate, and ultimately drive more sales while providing an awesome customer experience.