Measuring, evaluating, and predicting a customer’s purchase intent isn’t the only use for Deep learning in the retail industry. Other deep learning applications include:
1. Predicting churn rate
Churn — AKA attrition — is a term used for subscription businesses to measure the number of people who unsubscribe from and stop using a service. You may have also seen churn used to describe the rate of employees leaving their jobs at a given company. This concept is the opposite of a growth rate. For a company to succeed, its growth rate should be higher than its churn rate. As retailers predict purchase intent, which is often used as a proxy for growth rate, they also will want to forecast the churn rate.
2. Recommendation algorithms
Sites like Netflix, Amazon Prime, and, even Udemy are great at acquiring new customers. In order to maintain a low churn rate, these companies must understand how to retain customers by keeping them satisfied and engaged with the product. Relevant recommendations have become an essential tool to keep customers engaged. These deep learning recommendation algorithms use data from the customer’s habits on the site and with product use to recommend shows, products, or courses.
3. Fraud prevention
While financial fraud may attract the most headlines, tiny fraudulent activity happens all the time and can disrupt your customers’ interactions with your brand. There are fake likes on social media platforms, fake emails sent by lookalike companies, fake reviews to boost a product’s profile, and fake social media profiles to make a community look more popular than it is. All of these types of fraud can be identified or prevented by leveraging deep learning algorithms. Companies that employ deep learning to prevent fraud of all kinds do so in order to improve or maintain a customer’s positive associations with their brand.
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