Improving Customer Experience with Machine Learning

Improving Customer Experience with Machine Learning

In this digital age, we're witnessing an exponential growth in Big Data which is delivering a gigantic amount of customer data to companies. Hence, there is an intrinsic need to use this data for business growth by delivering superior customer experience.

According to Forrester Research, companies which are mining insights and using those to drive their business will witness 27% annual growth in revenues from 2015 to 2020, touching $1.2 trillion in total revenue, and Machine Learning technologies are projected to evolve into a $100 billion market by 2025. Essentially, businesses learn from historical customer interactions via ML techniques and fine-tune the customer experience in a holistic manner by delivering the right content at the right time via the right channel for the complete customer journey — from awareness and conversion to retention and evangelism.

Read on to understand how you can apply Machine Learning in your business to provide a robust customer experience.

What is Machine Learning?

Machine Learning is an application of artificial intelligence (AI) that provides computing systems the capability to automatically learn from previous experiences (i.e., data) and improve without being explicitly programmed.

ML systems are designed to process large datasets and several combinations of variables to uncover hidden relationships and provide predictive insights. This can be effectively used by customer-centric companies to gain and retain customers while delivering impeccable loyalty plans.

Intricate understanding of customer

It is necessary for businesses to gather customer data for relationship management. However, to improve customer relationships, companies need to deploy ML systems that can process Big Data encompassing enormous amounts of previous customer data for laser-sharp analytics. This will shed light on the customer touch points and the complete buyer journey. Using this historical data, the ML system should be able to predict customer behaviour and match the same with actual customer action to further improve the predictive engine.

One important factor in the relationship management and growing revenue for existing customers arises from churn management. Companies that can deploy ML capabilities effectively would be able to handle customer expectations, unveil the primary causes of account cancellations, detect early signs of risks that push customers to churn. This will help the company take corrective measures wherever applicable to improve customer retention.

Delivering an outstanding customer experience

Once businesses have set up the infrastructure for customer data and analytics, the next step should be focused on customer experience optimization. Let's explore more in the following subsections.

Better customer support

Customer retention takes a hit when businesses take more than the required time to solve queries and issues. However, there are limited resources in terms of personnel — hence, Natural Language Processing (NLP), a subfield of ML that allows computing systems comprehend written and spoken human language, can deliver a better experience by allowing customers to explain their issue using their own thoughts. ML algorithms would be able to predict the root cause behind the customer's support query initiation and transform the content into an actionable message for the customer support team.

Note that building systems for NLP have become relatively easier as there are a plethora of large datasets for training Machine Learning algorithms.

Facilitating customer self-service

There are certain cases in which support agents are not required. Mostly when the prospect is beginning the buyer journey, chatbots, which are NLP-based messaging applications can be used to communicate with users. In the present scenario, simple conversations can be handled and the right solution can be suggested to the buyers without human involvement.

User interfaces based on NLP can further improve self-service as they can be more intuitive based on customer's need and behaviour. This allows customers to become more engaged as they can access the right content exactly as per their need.

Managing and driving brand strategy

The web contains a significant chunk of alternate data and this web data can be a valuable asset for any company's data strategy, it is ever-growing and perpetual. For instance, businesses from any domain can extract data via web scraping service to collect customer reviews, tweets, YouTube comments, product catalogues available on various sites for both own products or competitors' products to uncover trending topics, pricing insights, customer sentiment, handle brand reputation and much more. This requires applications of various NLP techniques and ML-based analytics.

Also, by predicting which customers will be having an issue, and at what stage, companies can deploy pre-emptive measures and approach customers early to offer the right solution. This can deliver a superior experience which can potentially convert a customer into your solution advocate and take care of retention.


Machine Learning can help businesses build a robust personalized customer experience via ML-based go-to-market strategy which will delight buyers in every touch point and eventually turn them into evangelists.

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