Unveiling the transformative landscape of customer experience in digital age: Aatish Rathi, CK Birla Group

In the digital age, the landscape of customer experience is undergoing a profound transformation. From the integration of AI-driven insights to the meticulous balance between personalization and data privacy concerns, the evolution is evident. Companies are exploring the vast potential of big data, machine learning, and data analytics, not merely seeking new information but endeavoring to harness the power of existing data. This shift emphasizes the crucial need to reassess the overlooked wealth of historical data, aiming to adapt and reframe the algorithms for extracting relevant insights. The fusion of technology and consumer-centric practices has led to an innovative era where the focus isn't solely on acquiring new data but on refining strategies for deriving value from the wealth of information already in possession. This transformative landscape signifies a pivotal shift in understanding, emphasizing that progress lies not just in accumulating data but in effectively utilizing and analyzing the available resources for a more impactful and improved customer experience. Aatish Rathi, National Sales Manager, Birla Fertility & IVF at CK Birla Group spoke to Dataquest and shed more light on the transformative landscape of customer experience.

DQ: How has the digital revolution influenced and elevated customer expectations? What are some of the things you have been witnessing across verticals?

Aatish Rathi: There are numerous changes, such as the evolution in the process of collecting feedback. Previously, it involved a manual form where customers would provide feedback, requiring a few individuals to read and analyze these responses. Now, the feedback mechanism has shifted towards automation, just one example among many. This transformation extends across various areas where customer feedback collection and analysis have been elevated to a higher level, facilitating quicker decision-making. The advent of AI has significantly contributed to this shift. I believe that judicious use of AI involves understanding where AI can effectively operate, learning its role and necessity within a company, and finding the right balance. This balance between human input and AI integration can potentially create a substantial impact in the future.

DQ: What are some of the most significant barriers or roadblocks organizations encounter while delivering enhanced customer experiences? How can organisations overcome these challenges? 

Aatish Rathi: There are a couple of significant roadblocks that I see. One crucial aspect that often goes unnoticed is the happiness quotient of a company's own employees. When these employees engage with customers, their level of contentment significantly impacts the customer experience. If the employees are unhappy, it becomes easily discernible to the customers. Therefore, in order to truly enhance the customer experience, companies need to prioritize and take care of their employees' experience. This relationship between employee satisfaction and customer experience is directly correlated and should not be overlooked. Another roadblock is encountered when companies rapidly expand their customer base, especially with the quick growth of startups. While the customer base expands, maintaining the same level of customer support and experience becomes challenging. Although companies understand how to scale up their business, often the same attention and pace are not applied to scaling up the customer experience. This mismatch in priorities can hinder the overall success. It's crucial to include plans for scaling up the customer experience alongside business expansion. Therefore, the key roadblocks I identify are the lack of planning in scaling up and the oversight of employee happiness within the customer experience strategy.

DQ: How does the integration of data analytics and AI-driven insights contribute to improving CX? What potential benefits and opportunities do you see in this area? 

Aatish Rathi: When contacting most other airlines, the process can be quite arduous—dialing through various options and waiting for what seems like an eternity before finally reaching a customer service representative. However, some airlines have replaced this with a voice search system. With this improvement, customers simply articulate their needs, and the system navigates accordingly. This transformation results in an incredibly positive experience for the customer, saving time and effort. What's particularly remarkable is its user-friendliness—so straightforward that even my father and grandfather can effortlessly use it, making it accessible to those who might not be digitally savvy. It's crucial that technological advancements focus on simplicity, as demonstrated by Indigo Airlines. Additionally, AI can significantly enhance customer experiences. For instance, in the realm of food delivery, AI could be employed to automatically process quality-related complaints without necessitating a conversation with a customer service agent. By analyzing images of the delivered food, AI could determine issues with packaging or food quality and facilitate refunds. Although not infallible, such a system could be accurate most of the time, showcasing how AI can elevate customer experiences while maintaining simplicity—a key element that must not be overlooked in technological advancements.

DQ: How do you balance the need for personalization and customization with data privacy concerns and regulations? What strategies must be implemented to ensure a secure and trustworthy digital customer experience? 

Aatish Rathi:If we consider the necessity behind this law, it's essential to acknowledge that it was us, the people, who compelled the government to enact it. Personally, I've reached a point where I've stopped answering calls from unknown numbers. This action leads to missing crucial calls, but it's a necessity due to the overwhelming volume of spam calls—about 10 out of 15 are typically spam. This situation pressured the government into implementing this law. Currently, in India, around 70 to 80 percent of individuals show disregard for their data and its usage. However, the remaining 20 to 25 percent, who value their data, experienced significant misuse, prompting the need for regulation. Compliance becomes the only choice once a law is established. Indians are adept at finding a balance swiftly. When the ART law was introduced, many saw it as a threat to business. But I perceive it differently. This law aims to ensure ethical business practices. Those conducting business ethically can enhance their operations, while those not adhering to ethical standards will likely face a decline. It's crucial to view the law as a means to seek consent. Many individuals won't mind providing consent for receiving offers or customizations. If consent isn't given, it must be respected. Indian ingenuity will likely discover lawful methods to market products without disturbing customers while delivering personalized services.

DQ: Looking ahead, what emerging trends or technologies do you believe will have the greatest impact on the future of digital customer experience? How can organisations stay ahead of these trends and continuously improve their CX strategy?

Aatish Rathi: Yes, we frequently discuss AI, but beyond AI, there are numerous other significant elements such as big data, data analysis, and machine learning. There exists an abundance of available data, yet the common perception is that we always lack enough data. Companies often engage the big four and invest in new surveys to acquire additional data, constantly seeking new information. However, it's crucial to redirect this same energy towards analyzing and mining the existing data. Unfortunately, data mining and similar practices have taken a backseat. It's imperative to pause and consider the wealth of information within the data we already possess. Data does not become obsolete; the key lies in adapting the analysis of older data to the present. The focus should be on altering the analytical algorithms to extract relevant insights even from data that's a decade or two old. We tend to overlook the significance of historical data. Therefore, developing algorithms that can make older data pertinent to the current context is essential. These considerations are pivotal for swift progress without incessantly seeking additional data.