E-commerce is booming – and retailers are facing more and more challenges. Due to the high number of online purchases, customers’ demands are also increasing. A seamless customer journey and a user-friendly user interface should be standard. Otherwise, customers will quickly switch providers. The task for online retailers: Strengthen the relationship with customers and build loyalty to survive against the competition.
While e-commerce accounted for 9.1 percent of retail sales in 2015, by 2021, it had risen to 18.3 percent. With more and more consumers choosing to shop online rather than offline, retailers are faced with how to build and maintain long-term relationships with their customers.
The essential prerequisite for a correspondingly loyal relationship is a fault-free technical process in the online shop. Advanced technologies such as machine learning (ML) and artificial intelligence (AI) can bring a decisive advantage for retailers. Many industries are using appropriate solutions every day to survive in the digitized “Industry 4.0” and the Internet of Things. However, both ML and AI can also help online retail and ensure that customers are more likely to bind to a brand. An example of a well-known use case in the area of payment processing: Here, ML helps many companies to carry out transactions correctly, and the number of credit card transactions that were canceled incorrectly,
With Machine Learning Against Erroneous Payment Refusals
Payment systems are incredibly complex. They include payment methods and reconciliation, billing, payment capture timing, dealing with recurring and non-recurring transactions, and billing optimization. But because payments are so central to long-term customer retention, organizations must address this complexity and the potential for error, which involves strategies such as dynamic transaction routing, retry logic, and machine learning. Because this increases authorization rates and increases customer satisfaction and loyalty – and, of course, sales.
An average of 10 percent of online orders are declined during payment authorization. Of these, up to 70 percent are “false positives” from customers who have the necessary liquidity to make the purchase. Therefore, it should be no surprise that around 40 percent of those affected no longer buy from this online retailer after payment was incorrectly rejected. And this is precisely where the intelligent IoT solutions come into play: companies can use statistical models and machine learning programs to route transactions and automatically determine which processor and acquirer a transaction needs to be sent to get the highest probability of success.
It all depends on the right time: After a failed payment, the respective payment provider will make another attempt to authorize it. But how and when this happens is critical to authorization success, especially for recurring transactions. This can be explained by payment cycles and how funds are processed. For example, a small credit union may have limited transaction processing capacity at certain times of the day.
Repetition logic is used to prevent false positives. They are designed to determine the best time and method to retry the transaction. Knowing the best time to submit a transaction can have a noticeable impact on authorization rates. By choosing the best moment, payment systems can automatically increase the rate without impacting front-end systems or the customer experience. This reduces the number of “false positives” while at the same time increasing customer satisfaction and growing buyer loyalty to the brand. This is especially important for subscription providers, who rely heavily on recurring revenue. The increase in subscriber churn is impacting business success because failed payment attempts affect sales and damage the reputation of a brand or an online shop in the long term.
AI & Intelligent Product Suggestions
Machine learning and artificial intelligence contribute to the optimization of processes in payment processing. Intelligent technologies can also make a significant contribution to improving the customer journey in supply chain management and product selection. In concrete terms, this can be shown using customer data as an example: People are creatures of habit – and above all, in their shopping habits. With the help of AI, customer data can be analyzed, based on which forecasts can then be made as to which products a customer would like to buy and when. The aim is for retailers to have the relevant products in stock at the time they need them and thus be able to offer shorter delivery times. This is intended to increase the willingness of potential customers to buy.
Another problem in e-commerce that can be addressed through intelligent data analysis is the high number of returns. Especially with fashion brands, the sizes and fits of the different brands often differ significantly, which can be seen in the high rate of returns. For this reason, various brands are currently trying to optimize an AI-based size advisor in their online shops with the help of large-scale data analyses. The analyzes take into account the personal preferences of the buyer based on previous purchasing behavior and general data on the respective age and population group. The AI uses this information to recommend the correct size to the buyer – because, depending on body type and average national height, the required fits differ from region to region. Based on the AI-based size advisor, the number of returns could decrease significantly, but customer satisfaction would also increase considerably in the long term. In addition, manufacturers can then adapt their new collections to current customer preferences, update sizes and continuously update the product description on the website.
Whether payment processing, supply chain, or size advice – innovative technologies support retailers in optimizing their offer and the customer journey and help customers get the most suitable products for them as quickly as possible. The stress factor should not be underestimated when it comes to returns. An optimized customer journey ensures customer satisfaction in the short term – it maintains the customer’s loyalty to the brand and increases the likelihood that it will be recommended to others. The result: long-term business success is secured.
Also Read: Machine Learning: The Next Step In Advanced Analytics