When it comes to making a purchase, customers have a plethora of options these days. Due to this abundance, the risk of customer retention has increased. 

Every business is aware that finding new customers is more costly as compared to retaining them. Hence, businesses are always looking for ways to keep customers and remain loyal to their company. A higher retention rate could potentially mean higher profitability for the business.

Machine Learning and Customer Retention

Machine learning can make a business highly successful, or at least responsive to customer behavior. Through the learning of data for providing predictions, computers perform remarkably. 

Data science consists of statistics and machine learning. The predictions and algorithms are useful if the data set used prudently to set parameters for predictive modeling. 

Machine learning allows for algorithms and predictive models to be designed and built, and thereby predictive analysis provides data scientists with reliable results. 

Machine Learning is beneficial for companies

It enables them to base marketing and product strategies based on predictive customer analytics instead of intuition or less reliable forecasts made manually. It has created a new window that companies can utilize to become more profitable. 

Customer retention analytics depends entirely on machine learning technology. It applies statistics to understand better customers and the period until they stay together before churn out.

Customer retention analysis offers more significant insights into customer behavior and identifies any customer retention trends to explore further. 

With customer retention analysis, a company can determine how long a customer would stick around, considering the seasonality impact. The factors also identify churned customers, which lead and ways to retain them better. 

How NextBee’s Customer Retention Model can be analyzed?

Past customer data can be successfully used with machine learning algorithms for successful customer retention analytics. It allows us to predict future customer behavior and big data makes it all possible. 

NextBee has given businesses great power to learn about their customers and to capture their behavior patterns. Data points to track to develop a customer retention model. 

The following metrics of this model allow companies to record and monitor:

• Phone call/ email history
• Purchase history
• Transactional history
• Loyalty rewards
• Membership rewards
• Customer demographics
• Other related data about the customer

Patterns and correlations reveal that using the model training process helps create the trained model that can efficiently predict customer retention. 

This data’s insights can effectively target such customers to ensure that they become more loyal and reduce churn risk. Using messaging, customer services, and other methods lower the churn rate. The model itself will provide the business with a useful set of actions.

Improve Customer Retention

To improve the cause of the reduction in retention has to be first understood for customer retention. Data science teams for predictive analytics create machine learning models.

It is important to note that specific issues make the development of machine learning processes difficult, and the deployment of models in various languages is problematic. 

Serverless microservice architecture by Artificial Intelligence solves such issues tremendously. The cost of time dedicated to managing, deploying, testing, training, and building the model also must be determined. 

Conclusion

NextBee is one of the most popular data-driven companies with a stellar team and a successful track record of over ten years. And we can certainly help your business reach its true potential. 

Machine learning is beneficial for improving retention, as it can analyze the customer retention rate for determining solutions and risks. Should you want your business to actualize the power of Predictive Analysis, feel free to contact NextBee’s experts.