How to Improve Customer Retention Using Machine Learning?
4 min read | By Postpublisher P | 22 February 2023 | Technology
A business thrives depending on how customers stay with them for a given period of time. This is called customer retention (the opposite is churn rate) which is a critical success metric for all B2B and B2C businesses.
A company maintaining long-term relationships with customers is essential to improve customer retention. But is it easy today? The intense competition for customers, changing customer preferences, lack of personalization, delays in customer service, and other reasons make it challenging for businesses to achieve a good customer retention rate.
If your business is dealing with unhappy customers and seeing an increase in the churn rate, it’s time for you to do something that stops your loyal customers from ending their relationship with your brand. Always remember it is more expensive to acquire new customers when compared to retaining existing ones. The good news is that machine learning advancements can help you with customer retention.
Machine Learning to Improve Customer Retention
Machine learning (ML) is the branch of artificial intelligence that makes accurate predictions (output) using historical data (input). This allows software applications to increase their accuracy.
How can this technology help to improve customer retention?
With machine learning, businesses can analyze large amounts of customer data and gain insights into customer behavior, preferences, and needs. The algorithm helps to identify patterns in the data to personalize the customer experience, understand pain points, deliver target messages, and more that provide value or resonate with the customers. In this article, let’s have an in-depth look at how to improve customer retention using machine learning.Understand Customer Behavior Using Machine Learning
Machine learning helps you master the art of sending the right message to the right audience through the right medium. This is made possible by proper customer segmentation based on their behavior, preferences, etc., and tailoring the products to meet their needs. It increases your ROI by delivering the product your customer is looking for. With a segment generated with customer preferences, it is now easy to provide personalized product recommendations, targeted marketing messages, and customized pricing. You can also predict future customer behavior and further analyze the feedback, online reviews, support interactions & social media posts to identify areas for improvement and address the issues before they turn into problems.Customer Data Sources That Can Be Analyzed With ML
- Demographic data: Analyze age, gender, income, education, and occupation. Helpful to create targeted marketing campaigns.
- Purchase history: Analyze what products a customer has purchased, how often they purchase, and how much they spend. Helpful to create personalized product recommendations.
- Social media activity: Analyze what social media platforms a customer uses, what they post and share, and how they engage with brands. Helpful to identify user-generated content, learning feedback, and target marketing campaigns.
- Web browsing behavior: Analyze what websites a customer visits, what pages they view, and their session duration. Helpful to create personalized website experiences and promotions.
- Customer support interactions: Analyze what issues a customer had, how they were resolved, and how satisfied they were. Helpful to identify areas for improvement in customer support.
- Email and marketing campaign interactions: Analyze what emails a customer open, what links they click on, and what promotions they respond to. Helpful for creating more effective email and marketing campaigns.
Predicting Churn Using Machine Learning
How good it will be to identify the customers who are at risk of leaving to take proactive steps to retain them? Yes, that’s possible with Machine learning to improve customer retention with these common approaches listed below.- Decision Trees: It is a supervised machine learning algorithm helpful to visualize the data and classify it according to the conditions. The data will be classified into smaller subsets making it easy for you to interpret.
- Random Forest: A single tree makes no forest. Similar to that idiom, random forest is a machine learning algorithm that integrates multiple decision trees output to reach a single result with better accuracy.
- Logistic Regression: It is helpful in predicting customer retention by estimating the probability using a binary outcome (yes or no) based on various inputs or prior observations of a data set.
- Artificial Neural Network: A deep learning algorithm named after human neurons, these networks are helpful to predict large, complex datasets with many different features.
Characteristics to Predict Customer Retention
Did you wonder what factors the Machine learning algorithm takes into consideration to segregate the customer? If yes, this is for you. Every customer has specific characteristics that allow businesses to identify where they stand and the possibility of them leaving. Machine learning uses the below (but not limited to) characteristics to give results that improve customer retention.- Purchase frequency: People who make frequent purchases are less likely to churn compared to those who make infrequent purchases.
- Complaint history: Customers with a history of complaints or issues with a business (product/service) are more likely to churn.
- Customer engagement metrics: Metrics such as time spent on a website or app, and interactions with support staff are also used to predict churn. With higher engagement, there is less probability to churn.
- Demographic information: Factors such as age, gender, and location also play a role in improving customer retention. Depending on your business, men may be more likely to churn than women or vice versa. The same applies to location (nearby/far) and age groups (young/old).
- Product usage: Customers who have not used your product in a long time may be more likely to churn.
- Customer loyalty programs: If your business has a loyalty program and customers have a history of redeeming rewards, they are less likely to churn.
Machine Learning Automation To Improve Customer Retention
As a final section of this blog, let’s have a look at how repetitive tasks can be automated with the help of machine learning. This will be helpful to provide a good user experience and thereby improve customer retention.- Chatbots: How about robots taking care of the initial communication with your customers 24/7? It can free up time for humans to take care of the important tasks and your customers feel heard too. These chatbots can be trained using machine learning to understand customer questions and respond with appropriate answers.
- Virtual assistants: Need your bots to do some big tasks? Opt for a Virtual assistant which is similar to chatbots, but can automate more complex tasks. These virtual assistants can use natural language processing to schedule appointments, process orders, resolve complaints, etc.
- Sentiment analysis: It helps you to identify the emotional tone behind customer communication like feedback, reviews, and social media posts. This can be used to understand the sentiment of customers and identify how well your business is performing.
- Personalized Recommendations: Helpful to analyze a huge set of customer data and provide personalized recommendations individually for products or services to meet specific needs and preferences.
To Wrap Up
Machine learning is proving to be a powerful tool for improving customer retention. Its ability to enable businesses to better understand customer behavior and preferences has to be leveraged by all businesses. If you are looking to integrate machine learning into your business, we can help. Our machine-learning development services have benefited many enterprises to attain growth. You can book a free consultation call and speak to our experts to know what can be done exclusively for your business. Together let’s gain a competitive advantage in today’s fast-paced and ever-changing market.The latest from our editors
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