Improve your Customer Analytics with Machine Learningby David Hunt Data Geek and Analytics Fanatic
With the growth and expansion of marketing in different industries, there is an enormous amount of data generated. Analysis of the data is crucial in understanding how the marketing has fared and more importantly deducing ways to improve it further. However, tackling such data manually is a near impossible feat. Today, we employ computers to do most of that analysis.
Machine learning has derived itself from the advent of big data and it is now seen as an important tool and an alternative to traditional analytics. Machine learning is teaching computers to learn from previously stored data and predict future outcomes. The systems learn from each cycle of application and refine themselves with each use. It’s defined as the study and construction of algorithms that can learn from and make predictions on data.
Machine learning has applications in all industries – the key feature being that it is a self-learning technology. Modern day call centers have machine learning enabled to handle simpler customer queries or concerns without intervention of humans – thus saving a great deal of overhead. Beyond these obvious utilities, machine learning is now deemed as the future – the backbone of self-driving cars, multi-lingual analytics tools. These more complex activities are made possible via deep learning and multi-layered neural networks.
The importance of machine learning stems out from the fact that machine learning when implemented correctly can make customer interactions much smarter. The key objective is to offer an exceptional experience to the users to turn them from visitors to customers.
Customer retention is the art of retaining your existing users by offering them a superior experience in your platform. Owing to the plethora of options the user has in the market today, it is natural for them to be dissatisfied easily and moving over to other competitors. Acquiring new customers is costly, retaining them is cheap.
Customer retention analytics with machine learning
Customer retention analytics is now possible with machine learning. Companies can base their offerings on predictive customer analytics rather than speculations.
Customer analytics for retention can help a company understand which personas are more prone to churning and which to retention. This provides actionable insights that can help make more effective product and marketing decisions.
Customer retention analytics using machine learning can predict future customer behaviour using past customer interactions and data. Enterprises can analyse and track various data points like demographics, transaction history, call history, website analytics etc. create customer personas and models.
During the model training process, this data will be used to find correlations and patterns to create the final trained model to predict customer retention using predictive analytics. This can determine the churn patterns to the most granular level, telling you which exact account are more like to churn. This can help the company tailor customized messaging and offering to reduce the chances of churn. Customer service can be improved for those accounts which can convince them to retain their associations.
However, customer analytics with machine learning is not entirely devoid of challenges. Deploying models written in different languages is not easy. Another challenge is overcoming the cost of time lost to building, training, testing, deploying, and managing a model, let alone multiple in a machine learning program.
Created on May 24th 2020 08:57. Viewed 219 times.