How will AI redefine customer engagement?

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Customer experience is the next competitive battleground. It’s where businesses are built or lost.

– Tom Knighton

Just a few years back, customer interactions were slow. Customers sent their queries through e-mail and spent weeks waiting for a response that might never arrive. All of that has changed dramatically now. In the last decade, competition has increased rapidly. This has provided customers with a plethora of options to choose from. Personalization is no longer the best bet companies can put forward – an excellent experience is defined by relevance!

Responsiveness for a handful of customers is not that big a deal. But, what happens when the number reaches hundreds and then thousands with individual points of contact for different stakeholders? Or what happens when a company wants to predict the market demand of its product that is still in the R&D phase?



If you’ve been staying updated with the latest buzzwords – you’d say AI is the solution – solution to a better future, solution to more growth, and solution to making customers happy. Well, let’s delve a little deep into it. AI is the umbrella term that encompasses various technologies inspired by human biological networks. In fact, AI had been around for a long time. Starting from ‘The Logic Theorist’ in 1955 to 1 billion-plus interconnected devices, computers are growing exponentially in terms of processing power and speed but shrinking in terms of size.

Let’s have a look at how the environment is set to mark the success of AI and how this will impact customer experience.

Availability of data

Artificial intelligence is about replicating the human way as much as possible and it does that in forms of learning. Artificial networks are trained with huge amounts of data sets, millions and millions of them, before they can become competent in a particular task.

For example, the precision rate at which Google delivers the normal search and image search results depends directly on the volumes of data fed into Google AI algorithms. With big data in the picture, businesses are not only able to collect, analyze, and filter specific information at an amazing speed but also able to recognize patterns that drive decision-making policies based on this analysis.

How will it affect CX?

Availability of customer data collected through websites, mobile applications, and cookies enables companies to employ customer analytics to provide personalization at scale by continually fine-tuning every aspect of the marketing mix for every customer audience or persona in real-time.

Better Algorithms

Algorithms are nothing but a set of mathematical instructions that execute according to a common pattern. Their use in computers can be traced back to one of the giants in computational theory Alan Turing, who became famous during the Second World War for breaking the Enigma Code. Algorithms form the base of an Artificial Neural Network (ANN) to determine whether to fire a neuron, which prompts the subsequent neurons to react in a particular way.

How will it affect CX?

Most of the algorithms today are supervised. However, with more sophisticated algorithms, there can be unsupervised learning, which can even derive meaning out of unstructured data. Along with understanding customer data through Machine Learning, customer behaviour also plays a vital role in bringing an enriching CX. This behaviour entails all the psychological aspects of buying interests in a customer. The generated data could act as a reference to the customers’ interest patterns for a product or a service.

Cheaper and faster hardware

Traditional computing can only be useful to a certain point as it cannot process data independently whereas many aspects of human intelligence are more like a parallel structure. CPUs, with few cores of cache memories, are not enough for processing these tasks with efficiency. For implementing AI, Tensor Processing Units (TPUs) are used. A TPU is a custom-built chip developed specifically for machine learning and tailored for TensorFlow, Google’s open-source machine learning framework. A TPU can process over 100 million images a day, paving new horizons for the implementation of smart AI systems.



As already mentioned, AI is like an umbrella. Under this umbrella, there are a variety of software and algorithm-driven approaches to perform human cognitive functions.

Let’s have a look at how some of the more pervasive approaches of AI being applied in business today.

Machine learning in chatbots – Natural language processing

One of the most powerful applications of machine learning is Chatbots. Chatbots are revolutionizing the way major brands interact with their customers. When a chatbot is well-designed, the results are positive and have a major impact on improving customer interactions and saving operational costs.

Following is an example of the National Geographic Channel using its chatbot, Genius, which revolves around a charismatic and intriguing character.

Now, who possibly would not want to have a casual conversation with the Physics guru who is incredibly chatty, and doesn’t mind dropping bits of wisdom?

What is in it for your organization?

Most companies are using chatbots to troubleshoot their customer complaints or answer simple queries. Since the interface is such that the customer is speaking to an employee itself, the learning curve for the customer is almost zero. Also, most of the chatbots can be easily integrated into the website or application, therefore, the development costs are comparatively lower. For example, for addressing user queries, companies are using the Facebook live messenger as their chatbot platform and it’s gaining popularity across industries.

Computer Vision enhancing CX

In the retail sector, computer vision is significantly affecting the overall customer experience. For instance, density is a traffic analysis solution used in retail stores. It has a characterization module to recognize the demographics of the visitors as well as count their numbers. A behaviour module tracks the heatmaps to provide information about information flow and an analysis module portrays the data in a user-friendly platform.

Thus, it helps retailers to collect data and thereafter link them to their sales data using big data algorithms to derive trends and insights to modify and improve their customer experience.

Pinterest has recently launched a tool called lens, which enables visitors to point their camera at a particular object and perform a Pinterest search. This will direct customers to a particular product or image.

Deep learning optimizing customer experience

More and more companies are focusing on the most crucial part of a buyer’s journey – the awareness phase. As we all know, content strategy helps products win the game here. Nothing can be more disappointing than showing customers an article that they don’t find relevant and such an experience further deteriorates the customer journey. Fortunately, deep learning models can pick up words and cues from the issues reported in customer help, and support the creation and actualization of better-tailored content that is user specific.



Another use of deep learning models is just in time engagement. According to research by Forrester, 77% of consumers in the United States consider valuing their time as the most important part of their interaction with a brand. By understanding the user context and intent, machine learning allows brands to serve personalized actions at the precise time the customer expects. This creates a distinct experience that values the customer’s time, increasing their loyalty and engagement.

AI-driven technologies not only help brands collect data from multiple sources but also enable them to map each step of the customer journey and initiate meaningful engagement opportunities with them. Starting from extracting relevant insights to taking necessary efforts in retention, AI is certainly all set to redefine customer engagement.

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