AI in customer experience

How AI Can Drive Personalization of Customer Experience At Scale

What is customer sentiment and why is it important?

Today customers are vocal and expect a high-quality customer experience when interacting with a brand at all stages of the customer journey. Additionally, given the widespread penetration of smartphones and last-mile connectivity, it has become easier for brands to collect customer feedback via surveys, social media platforms, and call centers.

The technology exists for brands to interpret customer sentiment and use that data for honing their customer engagement strategy. To achieve meaningful benefits from this data, brands need to be able to analyze, interpret and make decisions in real-time, and at scale. Additionally, brands need sufficient data to be able to establish the data network effects at play in their domain and refine their position based on these dynamics.

While the conventional customer satisfaction metrics such as NPS & CSAT are instrumental in maintaining the bottom-line, there is much scope for a deeper study into customer behaviors & patterns to drive the top-line as well. Several mature multi-billion dollar firms commit substantial resources to track and understand the emotional responses of their customers. In uncertain times such as the pandemic, when most businesses witnessed unprecedented market forces, it became critical to understand how the customers feel about your brand.

Some of the key benefits of doing a deep dive into customer sentiment are – 

  • Train CX, sales & marketing teams on what is really relevant to customers
  • Identify common patterns in customer behavior
  • Fill the strategic knowledge gaps that conventional surveys are unable to fulfill by design.

Brands have traditionally segmented their customers based on age, demographics, gender, wealth, and other common parameters. However, now increasingly the shift is towards individualization over-segmentation when it comes to providing a remarkable customer experience. The main reason for this change is the personalized approach being adopted by many brands when comes to customer journeys. Instead of confusing customers with a blunt communications instrument designed for a customer segment, it is prudent to aim for a more relevant and customized interaction with each customer. 

The trend towards individualization is rooted in the fact that each customer is unique, and challenges the segmentation model. Segmenting a large number of customers together based on some common attributes has no bearing on the buying preferences and intent of each individual customer. Hence a large percentage of communications centered around the customer segmentation methodology often do not achieve their intended target.

Propensity Modelling 

In order to implement the approach of tailoring the customer experience as per each individual’s preferences, brands need to create an ecosystem of data management at an enterprise level that is scalable as well as real-time. Today advanced statistical modeling and adaptive algorithms can facilitate data-driven predictions about customer behavior. With advances in data science, efficient propensity modeling algorithms exist today to achieve this at scale. 

In essence, a propensity score or a probability can be associated with each customer action relevant to sales, marketing and CX functions. For example, independent propensity models can be designed for tracking and predicting specific customer actions such as – 

  • Churn – It is never good for a business to lose existing customers. The AI can point towards customers with a high propensity to churn and brands can customize the touchpoints in the customer’s journey depending on the historical patterns of the customer.
  • Sales –  It is paramount for brands to be able to predict the intent to purchase, and potentially trigger interventions such as discounts, coupons, etc. depending on the propensity score of each customer.
  • Marketing –  Marketing campaigns can be optimized depending on customer behavior, for example, if a customer has a high propensity to unsubscribe from e-mail marketing campaigns then the frequency of contacting the customer can be managed accordingly.

Role of  AI in customer experience and how to track it

One can reap the true benefits of individualization only when it is done at scale, in real-time and tailored for each customer irrespective of the size of the customer base. This can be achieved using advanced AI which tracks implicit signals within the raw data and can be used to train propensity models to predict customer intent. 

Customers are highly likely to share their opinions and feelings in comment boxes after surveys, and according to a recent HBR article, this data is a far better indicator of customer sentiment than surveys. Customer sentiment is mostly qualitative and since most surveys are quantitative, hence they are not instruments for measuring customer sentiment by design. Brands need the right AI tools to crunch the qualitative data and an omnichannel CRM platform to aggregate this data in real-time from customer support, field interactions, e-mail, chatbots, APPs etc. While firms can potentially leverage this data using AI for predicting customer behavior, though in practice many firms deploy half-baked AI tools that focus mostly on good or bad comments. 

It is important for the AI to be able to track both emotional (happiness, disappointment, extreme reactions)  and cognitive responses (opinions, issues, appreciation, etc.) of the customers to create a holistic propensity model for each customer. 


AI adoption to personalize CX can provide the competitive edge needed by businesses to stay relevant and ahead of the curve, as in the future customer experience is poised to become the key brand differentiator, superseding both price & product.

Talk to our product squad to see this integration live in action.

About the Author

Nimit Kumar
Ashish Mittal

VP of Customer Success

Ashish Mittal is the Vice President of Customer Success at Kapture CRM, Bangalore India. A seasoned professional with over 15 years of experience, he has worked for Silicon Valley startups as well as Fortune 500 companies.
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