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5 Ways: How Big Data, AI and ML can Improve Customer Experience?

5 Ways: How Big Data, AI and ML can Improve Customer Experience?

Customer experience has become the new battleground for business. With businesses and customers both moving online, the competition has become fierce. Customers have access to a tremendous amount of information about products and their substitutes. Businesses, on the other hand, have access to a lot of customer data or at least they have means to collect any needed information. With so many similar alternative solutions, customers have made their buying experience as a parameter for deciding on purchases. The problem earlier was the limited ways to improve customer experience due to the lack of sophisticated analysis tools. Big data, artificial intelligence, and machine learning have significantly improved customer experience by allowing businesses to analyze their customer data. These technologies have come to the rescue of business to help them maximize the level of customer experience they offer. Here are 5 ways big data, AI & ML can be helpful to improve customer experience:

Read More:  How Internet of Things and AI can help improve customer satisfaction?

5 Ways in which Artificial Intelligence, Machine Learning and Big Data can improve customer experience:

Almost 60 percent of executives acknowledge the importance of big data in the success of their businesses, and 70 percent realize big data is vital to stay competitive. (Source)

1. Better matching of customer needs and products:

recommendation systems helping to improve customer experience

With the use of big data and machine learning, companies are able to offer products and services that consumers are most likely to purchase. Recommendation systems have a special role to play in this. These systems use machine learning and big data to suggest products to customers that they are likely to purchase. With the availability of extensive customer data like price range, preferences, buying cycle, etc. it is much easier to target them with specific offers.

Today customers can’t form a stable and clear preference as they lack the complete and accurate grasp of product information. At times, they are also given too much information to handle. As a result, customer’s choice is not fixed but fluctuates with the change of information in the buying process. Recommendation systems help bring comprehensive and personalized information to the customers. This results in changes in the customer’s preferences. With the use of proper recommendation systems and market basket analysis, companies can give information in accordance with the customer’s journey. This will enable better and more sophisticated communication leading to increased revenues and customer satisfaction.

2. Emphasis on smooth virtual experience:

While making purchases customers give equivalent weightage to the quality of the product and their experience of making the purchase. Customers not only purchase goods for necessities but also because of their emotional desire. Thus, they prefer brands who can resonate between perceptual product and self-psychological needs. By studying customer’s behavior in various stages of the buying cycle, big data analytics has enabled companies to improvise the customer experience they offer.

User experience and its usage to impact customer expereicne using big data, AI, ML

Read More: Why to measure your user behaviour using app analytics?

One way big data helps in improving customer experience is by analyzing their behavior on the site. To provide the best customer experience a website should answer customer queries in the order they are likely to be asked. This would save customers’ time in finding information and making purchases. By using behavioral analytics companies can analyze customer behavior, figure out their pain points and restructure the website to suit their needs. A smooth virtual experience is all that’s need to ignite a desire and stimulate purchases.

3. The role of credibility and reviews:

reviews - how artificial intelligence, big data and machine learning improve customer expereince

With virtual experiences, there exists a huge range of alternative solutions to a problem. Being stuck in the information overload, customers opt to choose the players with best reviews. Reviews have become the most important measure of credibility. Credibility has earned such massive importance that it now turns out to be a price advantage. Customers are likely to pay a higher price to brands with a better image than to risk purchasing from other low-quality alternatives.

Big data has made analyzing customer experience a lot easier. By analyzing data from various touch points like mobile,  in-store, helpdesk, social media, review platform etc. customers can know about any dissatisfaction of buyers and take remedial steps before the spread of word. For example, social media sentiment analysis can be used to predict emotion conveyed in a text. It is a big data analysis technique that can take inputs from social media sites and classify the text into positive, negative or neutral. Business can use such techniques to understand their customer’s psychology and take steps to improve reviews and ratings.

Read More: 5 Competitive Advantages of Using Big Data in Business

4. Importance of personalization in communication:

how artificial intelligence, machine learning, big data help in improving customer experience and what can you do about it

There is an increased need of personalized buying experience. Customers today can sense generic marketing blasts and have the tendency to ignore them completely. In order to achieve success with marketing communication, marketers need to personalize their messages to the greatest extent possible. 

Over 85% of mobile marketers report success with personalization — higher engagement, revenue, conversions. (Source - Wharton University) 

Read More: 7 Steps: How to Create a Successful Big Data Strategy?

Big data helps improve customer experience by examining customer data and predicting their needs and desires. Big data and artificial intelligence have brought many new personalization techniques like geofencing and loyalty programs to the table. With geofencing, it is now possible to provide location-based marketing messages. Loyalty programs aim to reward customer loyalty and in-turn intensifies customer advocacy. Both these programs aim to improve customer experience with the brand.

5. Automation decoyed as human conversation:

use of artificial intelligence, machine learning and big data for better customer experience

The demand for more information with limited efforts is always demanded by humans. In this age of information overload customers want direct solutions to their problems. Unfortunately, they have to go through to huge pile of content to find a solution to their problems. With so much unnecessary data customers require more need of human assistance for solving their problems. Big data, machine learning & artificial intelligence have given rise to a new customer experience enhancer i.e. chatbots.

Read More: 5 Benefits of Chatbots in Business

Chatbots are automated programs that can stimulate a human conversation. These bots use natural language processing to understand human communication in the right context and provide answers to queries. This way companies can address customers queries in bulk and reduce the need for human effort for managing communication. Chatbots can help improve customer experience by reducing the wait time and providing on-demand access to support.

The potential of these technologies is in no way limited to the above mentioned uses. From price optimization to after sale service, big data can improve the efficiency of business in every sphere. Not just marketing but these technologies can also be used in manufacturing, production, utilities etc. Not to forget that all these innovative technologies are still evolving. With time their capabilities will also improve. If you want to use Artificial intelligence, machine learning, and big data for your business then feel free to contact us:

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