E-commerce operations: How to reduce the “shopping cart abandonment rate”?

Why would you give up placing an order? There are many reasons. Some people simply don't buy because they don't have the energy to figure out the complicated discount rules; some people think twice about their impulse purchases at the last minute and hesitate; some people even miss the ordering time purely because of the time difference.

These "shopping cart abandonment" behaviors are obviously not what e-commerce platforms want to see, because it means that all your previous efforts to encourage conversions have been in vain. This gave rise to a special metric to measure these "shopping cart abandonment" behaviors, which is the "shopping cart abandonment rate."

Shopping cart abandonment rate refers to the ratio of the number of abandoned shopping carts to the total number of shopping carts or completed transactions. Abandonment rate refers specifically to online virtual shopping carts. While consumers rarely abandon items they purchase in physical stores, abandoning items in virtual shopping carts is common. Typical online retailers have shopping cart abandonment rates of 60% to 80%.

How to reduce the “shopping cart abandonment rate”? How can we identify the factors and links that lead users to abandon their shopping carts and improve these links?

There is a practical and effective method that is widely used in the e-commerce industry to help major companies reduce abandonment rates through data analysis: Clickstream Analysis.

Clickstream reflects user behavior and experience

Improving user experience and satisfaction is an important part of achieving online business success. There are many analytical methods and professional audience research designed to help improve the relevance between e-commerce businesses and consumers, hoping that the products and information displayed online are attractive enough for consumers to place orders. The higher the relevance, the easier it is to lead to the user's final conversion.

Visitors to e-commerce sites often engage in a behavior we call “virtual window shopping,” which involves extensive exploration, clicking on products for detailed views, and comparing different products and prices. In many cases, even though users may spend up to 30 minutes browsing products on a website, only a small percentage of them actually convert to purchase.

People often add the products they browsed to their shopping carts one by one, but in the end they leave the website without buying anything. A report from form analytics platform Formisimo shows that Saturday and Sunday are typically the two days of the week with the highest shopping cart abandonment rates. During these two days, the shopping cart abandonment rate was almost as high as 90%.

A 2014 e-commerce survey of 1,000 consumers in the U.S. by visual website optimization platform VWO revealed several reasons why consumers abandon their shopping carts. The second biggest reason is requiring users to create an online account on the website before checking out. This is seen by many users as a forced behavior with a binding nature, which ultimately leads them to choose to give up the purchase.

According to a study by Baymard Institute on checkout usability, complex checkout processes account for 12% of the reasons why users abandon their checkouts. Therefore, in order to provide customers with a convenient, barrier-free and hassle-free e-commerce experience, e-commerce platforms must ensure that users have a smooth and convenient experience during the checkout process. In addition, some personalized offers and relevant product recommendations are also important factors in encouraging visitors to complete their purchases.

What exactly can clickstream analysis analyze?

To ensure that e-commerce websites have steadily increasing conversion rates and sales, e-commerce brands can use clickstream analysis to achieve this goal. Clickstream analysis solutions can be widely applied to e-commerce businesses of different sizes, and the operating costs are low. Marketing experts and data analysts can do this simply by looking at the website's clickstream data, customer historical purchase data, and product data.

Clickstream analysis can provide e-commerce businesses with valuable insights such as time spent on different product pages, page bounce rates, and other important metrics. Through this, e-commerce companies can know “what different products customers browsed” and “what product categories customers searched most frequently”.

By reviewing customers’ historical purchase data, e-commerce analysts can also assess information such as customers’ purchasing preferences, acceptable price ranges, and purchase incentives that they are not interested in. This data can provide e-commerce companies with important insights into customer preferences, and specific product data can also tell analysts which products are popular and best-selling.

Clickstream analysis can also explore how changing trends in prices and product ratings influence the purchasing decisions of website visitors. A simple A/B testing solution can help e-commerce companies gain valuable insights from clickstream analysis.

Clickstream data can also be combined with other e-commerce analysis factors (such as the shopping cart abandonment we mentioned at the beginning) to conduct a more comprehensive and effective in-depth analysis to find out the reasons for user churn.

Personalized recommendations boost e-commerce sales

After in-depth analysis of customer online shopping data and product data, the next step in promoting e-commerce sales strategy is often to analyze clickstream (Clickstream Analysis). As mentioned earlier, analysts can use this to discover the products that visitors viewed and the price range of these products. For example, the data might reveal that a visitor searched for high-end fashion items priced between $90-$130. By comparing with historical data, we can find that this customer often buys high-end fashion items worth around $100.

With the help of some analytical tools on the market, e-commerce analysts can associate customer behavior data with product databases based on customer IDs, and then establish customized customer segments based on the behavioral characteristics of customer visits. Due to different analysis angles, various customer classifications can be made according to various rules, such as lost users and retained users, new users and old users, single-purchase users and second-time purchase users. Analyze different customers' different preferences for products, identify which products better meet users' expectations, and then make personalized recommendations for them.

So when a customer visits an e-commerce website, e-commerce analysts can perform real-time click stream analysis based on the customer ID and display other related product lists to the customer based on historical purchase data and previous click data. It should be noted that segmentation is used for comparison. The purpose of comparison is to reflect differences and make adjustments and optimizations. The ultimate goal of segmentation is to guide operational decisions. This is the value of data analysis. Therefore, before doing customer segmentation, it is necessary to clarify business needs and determine the purpose of clickstream analysis.

The strategic combination of real-time click-through rate and historical data can help e-commerce websites conduct secondary marketing or cross-selling and quickly increase conversion rates. By combining clickstream analysis with historical data, retailers can discover how an existing customer combines their purchases and thus understand the customer's various needs. Therefore, e-commerce people can conduct cross-selling, promoting product B to customers who are browsing or have already purchased product A, helping e-commerce websites to make personalized recommendations for customers, while reducing the number of abandoned shopping carts and promoting sales.

In summary, in-depth analysis of clickstream data and customer browsing habits can not only increase conversion rates through cross-selling and up-selling, but also achieve other business results. E-commerce companies can use these insights to optimize digital marketing spend and various aspects of the consumer journey, as well as improve the overall online shopping experience.

By: MarTechCareer

Source: MarTechCareer