Webinar Summary: How Shopping Cart Analysis Can Reduce Cart Abandonment
We made a recording of the How Shopping Cart Analysis Can Reduce Cart Abandonment webinar (which is now available online).
Our business consultants Karel Žitný and Jan Rážek, whom you might already know from our first webinar How to Enable Data for Personalization Opportunities in Your E-shop, have this time focused on introducing the most efficient strategies to prevent your visitors from abandoning their shopping carts. In addition, they talked about methods for predicting abandonment intent and trigger actions for e-cart recovery.
What is shopping cart abandonment?
Cart abandonment has two definitions. One is the cart abandonment itself, which is when customers are leaving the e-commerce site while still having products in their shopping cart. The second is when customers are leaving the e-commerce site specifically during the checkout process.
We can take certain actions to lessen cart abandonment and we can divide these into three stages:
Prevention – focusing on prevention, we mainly talk about the analysis of historical data and finding patterns between behavioral data and data that indicates the shopping cart abandonment. You can monitor different user behavior of your customers who have made some action in the initial stage of their customer journey e.g., on the home page, product detail page, or category page, that eventually lead to the cart abandonment.
Intervention – second stage, intervention, can be largely influenced by real-time campaigns, which would be triggered during the checkout process itself, so that the customers do not leave the cart, and they continue to purchase.
Recovery – in this stage we focus on the scenario where a customer leaves the shopping cart. It does not necessarily mean that everything is lost, because you are entering a time window when you can still bring them back to the site and convince them to make a purchase.
You might ask, is shopping cart abandonment such a big deal?
In fact, it is, very much so. The average abandonment rate worldwide reached 79.8% in March 2021. This means that you could have five customers and out of those five customers only one makes a purchase, whereas the other four leave and never buy what they put in their shopping cart. Abandonment rate differs a lot amongst industries. For example, in Automotive, Airlines, Fashion or Luxury, the abandonment rate reaches almost 90%. The reason behind this is usually a longer decision-making process. When you are buying something luxurious or expensive it usually takes you longer to decide if you actually need the product, and so you conduct research on other e-shops to make sure you are getting the best price available.
On the other hand, when you look at industries such as Groceries, Consumer Electronics, Pharmaceutical, or Cosmetics, their cart abandonment rate is much lower as the decision-making process is not that complex.
Why do users abandon their shopping carts?
According to research conducted in the UK in 2018, 39% of users leave the shopping cart because of the delivery charges. Either customers prefer a free delivery, or they might be willing to pay the delivery fee, but they found out about it too late into their customer journey, got surprised, and decided to leave your e-shop. 37% of customers leave because they were just browsing and 27% of shopping cart abandonment happened due to the item not being in stock, which is an issue that can be solved by improving data quality or data integrations.
The situation with cart abandonment in the US (2021) shows that 49% of customers abandoned their shopping carts due to extra costs being too high. This is similar to the UK customers but can also include extra taxes and fees such as Sales Tax. 24% of customers left because the site demanded them to register and 19% abandoned their cart due to slow delivery times.
How to influence cart abandonment?
In our webinar we looked at three stages of cart abandonment and how to positively influence each of them. First, we focused on the E-shop Customer Experience, which is everything leading up to the checkout process, to see what could be influenced so that the customers do not leave or abandon their shopping cart. The second stage is the E-cart, where we talked about what real-time campaigns can be triggered so that the customers complete their purchase. The last stage is the E-cart Recovery, which focuses on what campaigns can be triggered so that they efficiently bring the customer back.
1. E-shop Customer Experience (CX)
The first use case scenario our business consultant Karel picked was a first-time user landing on a home page. In this scenario we can leverage the campaign specific third party data, or insights from transactional data such as top products, discounts, or seasonal products, for improving the cart abandonment.
The second use case is focused on already known users landing on a home page. Leveraged insights in this scenario are coming from behavioral data (recently viewed items), transactional data (product recommendations), and customer segmentation.
With known users, where we have already collected some data from their browsing, we can easily offer them recently viewed products for cross-sell or similar, or complementary products for upsell. To enhance the possibility of your customer to make a purchase you could try, for example, time pressure offers (countdown offers, limited availability/last pieces in stock). When the customer lands on a product detail page we can also use an exit intent survey and improve the customer journey based on these answers.
It is crucial to have a single point of truth for your data, segment your customers thoroughly, and work on the e-commerce tools’ integration efficiency. Then, it is very important to use engaging call to action buttons, therefore your e-shop must have a good user experience (UX) and user interface (UI), and finally, you must A/B test your campaigns.
2. E-cart
When the checkout process begins, the first thing you need to do is to predict the cart abandonment intent. There are two primary ways how to do this. You can either do it via real-time intent detection, or you can try and use artificial intelligence and machine learning.
The real-time intent detection usually manifests itself either via an aggressive mouse movement to x-out of the page, being idle for too long, or highlighting the product name. The third action usually means your customer is copy-pasting the name of your product and entering it into Google search or an e-shop of your competitor. Once you have detected a user’s exit intent via the exit intent scenarios, you can trigger real-time marketing campaigns with an aim to keep the user in the checkout process.
The more complex way of finding out user’s intent to abandon the shopping cart is with the use of machine learning algorithms. We can produce algorithms analyzing either products and their likelihood of being abandoned in the shopping cart or algorithms that analyze user actions e.g., clicking the help button, that lead to shopping cart abandonment. These algorithms can also be combined to achieve the best possible outcomes.
The best way to keep your customers from leaving the checkout process is to improve the overall user experience (we cover this in a little more detail later in the webinar - see below).
3. E-cart Recovery
One of the traditional ways to handle e-cart recovery is email. With email it is crucial that the timing is adequate, and the data integrations / technical set ups are correct, so that the email will not end up in your customer’s spam folder. Remarketing ads can also be leveraged for e-cart recovery. With remarketing ads, it is important to work with correct data otherwise you might end up remarketing to your customer with a product they have already bought. To leverage overlays it is needed to find out what the customers might have missed during their session. You can achieve this via chat or via overlays in the form of surveys.
How can I improve the cart abandonment rate?
UX Improvements – according to the studies conducted in the UK and US the major reason users are leaving the cart are delivery fees. Therefore, it is crucial to present the delivery fees to your customer as soon as possible in their checkout process, or you can set the delivery fee as zero. It does not necessarily mean, the delivery will be free, you just increase the pricing of your product accordingly. The second thing is to be transparent about the prices of your products. You need to have all the fees and taxes included available for the customer from the very beginning, which helps to avoid unpleasant surprises at the end of the checkout. The whole checkout process must be as easy and simple as possible. Try avoiding too many steps in the checkout process – three or four steps are ideal – and it is important to always show your customer in which step they currently are.
Overlays – once you predict the customer is about to leave, you can trigger these overlays and you can show different things to the customer – such as time-limited offers, free shipping, or a money back guarantee.
Chat – Through customer segmentation you can find out if you have customers who require a little bit more attention and want to be helped through the checkout process. If this customer segment shows any exit intention, you can trigger this functionality and ask them if they need any additional help.
What can be done with data?
When trying to improve the user experience, you can leverage the insights from heat maps and web analytics. With both we can look at call to actions (CTAs) that have been most engaging as well as landing pages that most lead to the customer checking out.
With overlays it is crucial to have the integrations set up as best as possible. Otherwise, the latency of the pop-up may be too long. It is also very useful to have at least some basic personalization in the overlays and chat. It is also important to have the data quality in order. If your data quality is not sufficient you might have several accounts for the same customer, each with different information, providing irrelevant discounts to them and eventually, losing you money and worst of all, losing the customer completely.
If all these three steps are conducted correctly, we can increase the conversion rate by at least 1%, which can lead to increase in the company’s net sales by almost 9%.
Conclusions of the data analysis used for the cart abandonment use cases
For most of the analysis approaches you need basic data about orders and products. For some of the approaches it is very good to have some information about the customers and their personal characteristics, for example, customer segmentation. For customer matching, we need data from more than one source otherwise the customer matching service does not make sense. Overall, the more data you have, the better the algorithms we can provide for you. It will allow us to achieve more detail and improve the predictions of ML algorithms.
Marketing departments can greatly benefit from the so-called single customer view. The single customer view (aka 360 customer view) means that you have one location for every customer you do business with that provides an overview of all the data you have collected on them. We are getting the inputs on one side from all channels that the customer might interact with and we are using this data in order to achieve something. It can allow us to achieve some of your business goals. For example, it can be used to improve the churn rate, increase the revenues, or decrease the cart abandonment, and it lets us drive business decisions. However, the single customer view must be built in a way that is easy to maintain, easy to enhance, has quality rules, and considers all types and formats of data.
At Simplity, we usually start with the data audit. If you would be interested in our services, we would start with some consultations, set KPIs and ROI expectations, and assess the credibility risks both for us and for your business. Next, we will look at the data itself, do some basic profiling, check the data quality, and try to understand most of your data environment. Based on that we will be able to adjust the goals of our project. Then we focus on data analysis, which is the part where most of our work is done. Lastly, we would implement the algorithms for your business and trigger some A/B testing so both of us know how beneficial our input was and how much the performance increased in the end.
Learn more about Simplity’s professional data services and how we can help your business. If you would like a free consultation regarding the business and data challenges in your company, we offer a two-hour consultation with one of our customer analytics consultants – get in touch with us.
