How Artificial Intelligence Is Enabling the Future of Customer Experience

Good customer experience has enormous power in drawing and retaining customers. Studies have proven that customer experience is a bigger differentiator than pricing. Modern-day customers are ready to dole out at least 16% higher prices for a slightly better customer experience. Also, 59% of customers would stop dealing with a brand after one or more instances of bad customer experiences.

How can a business create a customer experience that their customers love and not hate? Turns out Artificial Intelligence can help finding that secret ingredient to great customer experience.

AI adoption in recent years has also skyrocketed. At least 1,500 companies, both big and small, are estimated to be working neck-deep in developing AI programs. Microsoft, IBM, Google, and Amazon are some of the bigwigs that are leading the AI investment charts.

Studies by CB Insights show that investment in AI startups has increased by 4.6 times since 2012. An estimated 698 Million USD was pumped into AI startups in 2016 alone.  

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Role of Artificial Intelligence in Rewiring Customer Experience

Apple Siri. Google Personal Assistant. Amazon Alexa/Echo. We already have a range of AI-enabled voice and virtual assistants as permanent fixtures in our living rooms. From finding the local business listings to checking the weather, these AI-enabled devices help with many things in our daily lives.

Can they help businesses too in enhancing customer experience? Turns out they can. Here are some possibilities. 


Optimizing Customer Journeys

The journey of a lead becoming a deal is one that is punctuated with several twists and turns. Sales agents and customer success managers often have to personalize customer journeys on a one-to-one basis to deliver the best customer experience possible.

Needless to say, it is an ineffective and unproductive way of running a business. It is not only time-consuming but can also lead to inconsistency in customer experiences.


Sentiment Analysis of Customer Complaints

One of the key capabilities of AI is its ability to sift through text and arrive at a conclusion about its sentiments. Natural Language Processing (NLP), which is a subset of AI, is helping businesses analyze textual data contained in customer emails, survey feedback, social media messages, and even in WhatsApp messages.

Such an analysis of customer conversations can help businesses unearth the root causes of customer dissatisfaction. Armed with such data, they can implement corrective measures that will remove pain points and enhance customer experience.

The Royal Bank of Scotland is a real-world example of how Natural Language Processing can be used to enhance customer experience. The two-centuries old banking institution recently digitally transformed its customer service. As part of the exercise, the bank unlocked the 'Voice of the Customer' hidden in 250,000 web chat conversations per month.


Enhancing Customer Support with Chatbots

IBM estimates that chatbots can free answer 80% of routine questions. By automating responses to basic customer support questions, chatbots help in freeing up customer support agents time for more challenging work. In fact, the freed up time can be effectively used to attend to high-priority and complex customer support tickets.

Does the utility of chatbots end with customer support? Not at all. They can further assist a business in swiftly scaling their sales operations. Sales have long since been stigmatized as a one-to-one transaction which cannot be carried out without human intervention. Chatbots are all set to rewrite that social stigma.  

There are AI-enabled sales bots, like the Acquire Salesbot, that can help businesses sell to customers at scale. These chabots are trained using Machine Learning and Deep Learning technologies to serve the right product suggestion or response to customer queries.

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These chatbots work by offering product suggestions to customers in the form of clickable links or buttons. The suggestions are populated based on text analysis of customer requirements. In some cases, the chatbot offers a list of suggestions from which customers can pick their options. 


Predictive Personalization in e-Commerce

Imagine an online store that can give accurate product recommendations based on your past purchase history or wishlist? Well, Amazon must have already surprised you with its product suggestions. Don’t be surprised if we tell you that it is Artificial Intelligence that is working behind the scenes to make it happen.

Artificial Intelligence systems are basically data-driven. They collect data like pages on which customers spend maximum time, the kind of products that sell the most, the price band of best sellers, demographics of customers, and much more. Based on this data, the system curates a list of product suggestions that closely relates to the customer’s preferences. As a result, the average order value and conversion rate also increases positively.

It is no surprise that almost 72% of retailers are planning to invest in AI technologies by 2021.

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AI-Enabled Analytics for Accelerating Decision-Making

Tapping into the data-crunching ability of analytics is a top priority for every business that is keen on growth. But, ordinary analytical tools often do not answer the questions that business leaders have. It only gives answers to a package of questions that dissect and find out what happened in the past.  

Artificial Intelligence, on the other hand, can help unlock the true power of analytics. If you look deep enough, AI is basically data analytics at its best. AI uses a series of data analysis, matching, and interpretation techniques to arrive at conclusions about a specific business aspect. In addition to understanding what happened in the past, it can also help hint what could happen in the future and the variable factors that could lead to the event.


Taking the Leap: How Organizations Can Implement Artificial Intelligence

Implementing AI in an organization is like developing and implementing any other software product. The value expected from AI and the problems it will solve will differ from organization to organization.

Here are some steps that organizations can take to implement Artificial intelligence the right way.


Decide Which Problems to Choose

Before deep diving into AI, understand that it cannot all business problems. There are specific areas where AI can make a difference.

For example, using text analysis and sentiment analysis to read customer messages could be a starting point if you are planning to streamline customer support. Alternatively, you can look at image recognition and optical character reading technologies to automate manual processes. In short, Identifying what problems to choose should be the first step to implementing AI.


Start Small, Scale Big

There is a common misconception about AI. Most business leaders treat it to be a business wand that can solve all business problems in one stroke. As a result, they take up massive projects that are aimed at radically transforming the business process. Quite often, core business processes are also made part of this transformation journey.

Unfortunately, not all of them end in success. Like startups, a majority of Artificial Intelligence initiatives will fail if they are not planned and implemented properly. So, like with MVP development for business idea validation, even with AI implementation, it is recommended to start small and scale big.

Pick up a small process that can be automated or streamlined with AI. Once its feasibility is established and success ratio is proven to be healthy, scale it big. Subsequent this pilot project, other business areas can also be selected.


Consider AI as a Complementary Tool and Not as a Replacement

AI will eliminate humans from customer support jobs,” read the editorial piece in a reputed magazine. This is far from the reality. Artificial intelligence has not reached the level of maturity where it can replace humans entirely from a business function.

Consider AI as a complementary tool that can augment the productivity and efficiency of a business function. For instance, it helps customer support agents focus on complicated tickets. It spares them from bogged-down routines and fundamental questions that can be handled by an AI system (like a chatbot).


Work with a Specialist Team

AI is a new-age technology. Although the history of Artificial Intelligence dates back to the 1950s, it is only in recent years that it has matured into a technology that can be adopted on a massive scale. As a result, the talent to implement AI programs are also rare to come by. And you cannot treat it like a DIY project. 

There is a pressing need to bring in and work with a specialist team who knows the business logic along with AI and its unique nature and who can help integrate it the right way.


How Artificial Intelligence and Humans Can Collaborate to Augment Customer Experience

Artificial intelligence cannot work on its own. It needs human intelligence to create maximum impact. In fact, enterprises can reap maximum benefits only if there is a successful collaboration between humans and artificial intelligence.  

According to Harvard Business Review, successful collaboration between humans and AI can be based on five major principles:

  1. Reimagine business processes.
  2. Embrace experimentation/employee involvement.
  3. Actively direct AI strategy.
  4. Responsibly collect data.
  5. Cultivate related employee skills.

When the collaboration is based on these five principles, the AI initiatives perform better in terms of cost-savings, revenue and productivity enhancement. A survey conducted hinted that the performance improvement can climb from 2 times to as much as 6.5 times if all the principles are adopted. 

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How Can Humans Collaborate with Artificial Intelligence

There are three major ways how humans can assist AI systems to thrive. They are: 

  1. Training
  2. Explaining
  3. Sustaining



AI systems are computer programs. They need to be trained to act and react to given scenarios. They should be fed with enormous amounts of training data and live data to learn how to perform rule-based analysis and yield output. AI systems need training staff who can code and instruct the system to perform the ideal way.

Take, for instance, Apple Siri. Siri has a reputation for being an intelligent assistant that brims with intelligence, and even humor. How can a computer program be trained to be funny? Only by humans. They are trained to develop personalities that will interact in an ideal manner with humans. For instance, Siri was trained not to be dominating, but to be co-operative. It was trained to have a serving attitude without pointing out the user’s ignorance. At the same time, it was also trained to be compassionate when the user gave inputs about negative instances.

In fact, Siri’s interaction with the user begins with it being trained to recognize the user’s voice.

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AI is a sophisticated technology. However, it is used by non-expert users who have little idea of how the system works. In fact, in AI and data science parlance, it is referred to as the black-box problem. Sometimes it is not possible for users to understand why the suggestions were given in a scenario. This can prove to be a challenge when AI is used in sensitive domains like healthcare, law, finance, etc.  

There arises the need for professionals who can break down the working of the AI system and make it easier to understand why the system worked the way it did. They are often referred to as ‘explainers’. They are humans who can get under the hood of the AI system to decrypt its thinking pattern, which data points it matched to, and how the suggestions or results were created.  



The unique nature about Artificial Intelligence and all its subsets is that they are all continuously learning. They do not stagnate with a single dataset. They need to be continuously taught with latest data sets, scenarios, and even mannerisms to stay up-to-date. In other words, their performance has to be sustained.

Then comes the need for sustainers. Human workers who can continue to teach the AI system on the latest changes in data and real-world scenarios.  

An ideal example is a self-driven car like Tesla. Tesla has to program and update the algorithm on a regular basis, through cloud servers, so that cars on the road can follow traffic instructions and carry their passengers safely.  


Final Thoughts 

When Alan Turing set out to design an AI system in the 1950s, little did he have imagined that it would become a customer-facing technology in the 21st century. Yet, here we are, dabbling in the endless possibilities of AI and its ability to transform customer experience.

With every passing day, AI is growing in power and capabilities. Its ability to understand customers and to predict their wants is simply world-changing. Considering the examples discussed above, we can safely assume that the future of customer experience would be led by Artificial Intelligence.