How about what it takes to get a qualified shopper to your site that has the intention of purchasing today? Based on the average e-commerce conversion rate, magnitudes more time, money, and effort. Let’s see how machine learning can make the most of your efforts to maximize revenue.
It’s Really Hard to Know WHY a Customer Abandons Cart
Getting shoppers to your site is half the battle. Next comes optimizing site layout, selecting lifestyle images, and making curated products discoverable.
Shoppers start adding items to their cart. Perfect. But wait, where are you going? For a wide range of reasons, shoppers may be abandoning items they intended to buy in their cart. For example, they add an item to the cart and continue to browse recommended items. But none of those products lead them back down the purchase path, so they get distracted and leave.
If you’re a clothing retailer, they could be concerned with fit or feel. They may not be familiar with the size options, or they may be wondering how difficult it will be to exchange or return the item. Is the shopper unaware that they qualify for free shipping and think they will have to pay? Are they questioning if the item they’re considering is mainstream fashionable and will be popular amongst their friends?
Imagine trying to put ALL the combinations of why a shopper abandons their cart into a set of rules. This is a task too big for even the greatest spreadsheet master among us. This is a job for machine learning.
Retargeting Should be Treated Only as a Last Resort Effort
Once a shopper leaves the site, retailers begin display, search, email, and social retargeting campaigns.
But what if the window of interest with that shopper closed? What if they already found a comparable product somewhere else to fulfill their need? Using machine learning while a shopper is still on your site reduces reliance, time, and money spent on retargeting.
Imagine a shopper walking into a physical store and loading up their arms with items they like. Then a store associate watches the shopper set down the items and walk out—without engaging the shopper. Unthinkable, right?
Machine Learning Reduces Cart Abandonment and is Actually More Cost Efficient
You are not alone. E-commerce cart abandonment happens more than 69% of the time. This means there’s a lot of room for growth, and even small improvements can have huge revenue gains. Shoppers abandon their cart for a variety of reasons and in different ways.
Using machine learning, Veltrod can predict when a shopper is exploring additional products vs losing focus. Without a timely message, the later shopper will wander away and lose interest in purchasing their cart today.
Based on the shopper’s digital body language, Veltrod can figure out why a customer may abandon cartand what message will help them stay on track. Identifying these digital objections can be challenging. The clues a physical store associate can easily notice are more-often undetectable in e-commerce analytics.
In these situations, Veltrod’s machine learning technology can interpret hundreds of data-points in real-time to take action. Show shoppers the information they need to see to move from consideration to purchase today before they leave your site.
If you wait until after the shopper leaves, you will have to invest more in retargeting. But if you can optimize for each customer in real-time, you can improve your customer acquisition cost. With the power of machine learning, it is possible to do this easily and at scale.
- Posted by Udit Agarwal
- November 2, 2017