At the intersection of pharmaceutical sales and digital transformation stands Kunal Girotra, whose innovative work is reshaping how modern sales teams engage with customers. With over a decade of experience in commercial operations and business analytics, Kunal has been instrumental in modernizing traditional sales approaches through data-driven strategies and innovative technology solutions.
As organizations across industries are faced with the challenge of balancing technological efficiency with human connection, Kunal’s experience in leading contact center-based sales teams and implementing self-service analytics tools offers valuable insights into successful digital transformation. His work has not only earned recognition—including the 2024 Customer Engagement Champion award—but has also demonstrated how organizations can effectively leverage data and AI while maintaining meaningful customer relationships.
In this in-depth discussion, Kunal shares his perspective on the technological and organizational challenges of modernizing legacy sales systems, the principles of successful change management, and the future of AI in enterprise sales operations. His insights are particularly relevant as businesses increasingly seek to transform their customer engagement strategies for the digital age while maintaining the human element.
Kunal, you have led the transformation of traditional sales operations into a data-driven contact center model. What are the key technological and organizational challenges in modernizing legacy sales systems, and how did you overcome them?
The human element is frequently underestimated in business transformations. We generally have a tendency to be risk-averse, and the idea of overhauling a key business component, coupled with managing the associated changes, represents the biggest challenge in any transformation project. Modernizing sales systems is no different. It involves convincing all levels, from the C-suite to the sales representatives, of every facet of the project. Overcoming this challenge calls for demonstrating victories, quantifying the impact on KPIs, and building waves of support while remaining agile and integrating feedback from all stakeholders.
Coming to technology, there’s always a “buy or build” question that needs to be addressed in transformation projects. Buying a new technology stack might be the more efficient option but will always come with the need to customize it to your specific business, and often with limitations on the final product. Building inhouse opens up the potential to tailor make the technology to suit your business but requires expertise and a much longer timeframe. The answer is different for everyone and depends on the organization’s current capabilities and 5 year plans.
You’ve also successfully driven adoption of self-service analytics tools across large sales organizations. What principles of change management and user experience design have you found most effective in getting sales teams to embrace new technology?
I always treat adoption as a selling exercise. A good adoption strategy includes a compelling pitch to all members of the team highlighting the benefits of transitioning to the new tool. A great adoption strategy takes it a step further by incorporating an adoption tracker and proactively nudging users to the new tool when the lift in adoption is slowing down. Recognizing and rewarding power users has also proven to be an effective strategy to encourage adoption. This creates champions within the teams and organically builds confidence in the new tools.
Change management is inevitable when launching new tools to any team. A closed feedback loop is a vital part of the adoption process as it enhances the tool’s effectiveness, with power users often providing the most valuable input.
How do you approach building a unified view of customer interactions, and what technologies have you found most effective for deriving actionable insights?
There are two essential components at play here. The first component involved gaining a thorough understanding of the needs of all customer personas at various points in their journey. Whether it’s purchasing a product from an app or negotiating a new contract in a B2B setting, customer needs vary based on their position in the sales process.
Second, it’s imperative to gather and integrate data together to create a consolidated customer view. This may involve purchasing third party data or implementing systems like POS or mobile apps to collect information on customer behavior and preference. A robust CRM with automated workflows can streamline such data processing, allowing businesses to anticipate customer needs and proactively offer products or services at the right time.
Many organizations struggle to translate data insights into field execution. Could you share your framework for converting analytics into actionable recommendations that drive measurable business outcomes?
I consider gaining alignment on KPIs from all stakeholders as a prerequisite. Recommendations geared towards reducing customer acquisition cost will be vastly different from those aimed at improving employee retention.
Robust data is the backbone of any insight generation framework. Top organizations utilize a mix of internal data and external market research from reliable sources. An integrated data strategy backed by AI that combines historical trends with customer preferences and market dynamics can help uncover key insights, forecast upcoming trends, and highlight untapped opportunities. Effective dashboards can further enhance visibility of such opportunities. It’s also important to run these opportunities by industry veterans and domain experts to “sniff test” the data and subsequent recommendations.
The importance of data-driven culture in an organization shouldn’t be underestimated. Top organizations have the expertise to manage their data assets over time and the infrastructure to process their data effectively to enable data-driven decision making.
Looking ahead, how do you see AI and advanced analytics reshaping enterprise sales operations? What should technology leaders be thinking about as they plan their sales tech stack for the future?
AI will have a more prominent role in the sales process. B2B sales teams will see a rise in CRMs with integrated AI companions designed to provide tailored recommendations to sales representatives at various stages of the sales cycle, from identifying new leads to renewing contracts. Additionally, AI will take on more responsibility in automating administrative tasks, allowing sales representatives to spend more time building strong relationships with their customers.
A robust integrated data strategy and infrastructure will be crucial going forward. The effectiveness of AI will depend on the availability of high quality multi-dimensional data tailored to the organization’s needs. Progressive leaders must ensure their organizations have a solid foundation to allow AI to effectively support their sales teams.
