Mastering the Art of Customer Engagement: Expert Insights from Arunkumar Pillai

Customer engagement software technology is revolutionising the way businesses interact with their clients by creating meaningful connections, fostering loyalty, and driving growth. As the digital landscape continues to evolve, companies are leveraging advanced tools and platforms to efficiently manage customer interactions, track their preferences, and personalise communication across multiple channels. This innovative technology streamlines various touchpoints, including social media, email, live chat, and customer relationship management systems, enabling organisations to deliver seamless, data-driven experiences that exceed customer expectations and promote long-lasting relationships.

Software engineers and costumer engagement specialists have become some of the most in-demand career candidates in today's world of quickly changing technology. Every company needs to invest a significant focus on both customer acquisition and growth. Arun Pillai is a distinguished professional who has made a significant impact in both of these fields. With over two decades of experience in software development, Arun has worked for some of the world's most reputable companies, such as Amazon and Coinbase.

Currently serving as an Engineering Manager at Coinbase, Arun is leading two engineering organizations focusing on core customer acquisition and engagement functions for the global Coinbase platform. In addition, Arun is responsible for a 12-member engineering team that has delivered a new machine-learning platform for the notification system, resulting in a staggering $40 million incremental revenue for the company.
Arun's accomplishments have earned him a reputation as a distinguished software engineering and digital engagement professional. His success is a testament to his strong work ethic, leadership skills, and ability to consistently deliver results. He is a member of numerous prestigious & exclusive scientific and industry organizations, including Fellow of  Scholars Academic Scientific Society, Fellow of Royal society of Arts and Commerce,  Senior Member in IEEE, and Leaders Excellence at Harvard Square.

1. What initially drew you to specialize in digital customer engagement tech, and how has your passion for this field evolved over time?
I was initially drawn to specialize in customer engagement domain, during my stint at Amazon prime video. Prime video had just launched globally and was about to start a multiyear period of rapid growth. We realized the need for focusing on both customer acquisition and engagement. As a subscription service continued engagement and
educating customers about the best products and feature experience this platform has to offer was very critical. We learnt that building communication systems along with a
robust feedback loop was critical for sustained improvement of customer engagement.

2. In your opinion, what are some of the biggest challenges that companies face when it comes to customer engagement, and how can these be overcome?
One of the biggest challenges that companies face when it comes to customer engagement is building a product strategy for growth. Every company will enjoy a period
of rapid growth mostly aided by paid marketing and organic growth due to novelty and based on the product differentiation they enjoy. It is very important to identify a strong
framework for growth and engagement using the core product features and customer feedback for it. Growth an customer engagement is a separate domain and often not to
be confused with the core product. Assuming that growth will follow based on the merits of the core product offering is a fallacy. Customer engagement will need a very
robust experimentation framework but also need guard rails to prevent customer fatigue and over use of any communication channel.

3. How do you see the role of machine learning and artificial intelligence evolving in the field of customer engagement in the next few years?
In the next few years, I see machine learning and artificial intelligence playing an increasingly important role in customer engagement technologies. These technologies
can help to identify patterns in communication engagement such as email open rate, click thru, subsequent action on the product as well as fatigue driven actions such unsubscribe, opt out of channels. Machine learning models can effectively help to select the best channel or communication method , best product feature and the best
time to reach a customer to maximize engagement. A very niche machine learning model extensively used in customer engagement is Multi armed bandit models.

The multi-armed bandit model (MAB) is a powerful and efficient approach that can significantly enhance customer engagement strategies. By treating customer engagement as a decision-making problem, the MAB model optimizes resource allocation and personalizes content delivery to maximize engagement. Here are some ways the multi-armed bandit model can help in customer engagement:

Adaptive experimentation: MAB algorithms allow businesses to continuously test and refine their engagement strategies, adapting in real-time to customer preferences and
behavior. This helps improve the effectiveness of marketing campaigns, user interfaces, and content recommendations.

Personalization: By analyzing user data, MAB algorithms can identify patterns and preferences to deliver personalized experiences tailored to each customer's interests
and needs. This increases customer satisfaction and strengthens brand loyalty.

Balancing exploration and exploitation: The MAB model balances the trade-off between exploring new engagement strategies and exploiting known successful ones. This
ensures that businesses can continually discover new opportunities for engagement without sacrificing the benefits of tried-and-tested methods.

Resource optimization: MAB algorithms can efficiently allocate resources, such as marketing budget and staff time, to high-performing engagement strategies. This
improves the return on investment and overall effectiveness of customer engagement efforts.

Real-time decision making: The MAB model can dynamically adjust to changes in customer behavior, market trends, and other external factors. This enables businesses
to respond quickly and effectively to evolving customer needs, maintaining strong engagement even in rapidly changing environments.

For more discussions on Customer engagement or software engineering, Arun is available for a free mentoring or consultation session for anyone, and you can book his time
here.

Read More News :

Bharti Airtel logs Rs 8,346 crore PAT for FY23

MakeMyTrip logs $70.3 mn operating profit in FY23, highest ever in its history