Das Narayandas

Why are some firms better and faster than others at adapting their use of customer data to respond to changing or uncertain marketing conditions? A common thread across faster-acting firms is the use of AI models to predict outcomes at various stages of the customer journey. These firms are using AI to predict which customers are likely to churn, while their competitors react after the customers have already left. And when their predictions go off track because of external changes or market conditions, they use that feedback to quickly reorient and redirect their marketing and sales efforts. Using AI models to predict customer response has translated, in effect, to designing and running a large number of digital experiments that helped these firms respond to market changes faster than firms not using those tools. And while AI tools are far from infallible, they could reshape how we make decisions in functions such as marketing and sales and maintain a competitive advantage.

Advanced analytics can help companies solve a host of management problems, including those related to marketing, sales, and supply-chain operations, which can lead to a sustainable competitive advantage. But as more data becomes available and advanced analytics are further refined, managers may struggle with when, where, and how much to incorporate machines into their business analytics, and to what extent they should bring their own judgment to bear when making data-driven decisions.

In general, humans are better at decisions involving intuition and ambiguity resolution. Machines are far superior at decisions requiring deduction, granularity, and scalability. How can you find the right balance?

There are three common approaches to analytics: descriptive, where decisions are made mainly by humans; predictive, which combines aspects of the other two; and prescriptive, which usually means autonomous management by machines. This article describes when and how to use each approach and examines the trade-offs and limitations. Although the focus is on marketing and sales, the principles may be applied more broadly.

The need for leadership development has never been more urgent. Companies of all sorts realize that to survive in today’s volatile, uncertain, complex, and ambiguous environment, they need different leadership skills and organizational capabilities from those that helped them succeed in the past at all levels of the firm. But the leadership development industry is in a state of upheaval. The number of players offering courses to impart the hard and soft skills required of corporate managers has expanded well beyond traditional business schools, corporate universities, and dedicated consultancies. And yet organizations that spend billions of dollars annually to train current and future executives are growing frustrated with the results. 

Even as the demand for managerial skills continues to grow, executive education worldwide has entered a period of disruption caused by the digitalization of content, connectivity, and communication. The current offerings of many executive education program providers fall short of creating new skills in executives and developing fresh capabilities for organizations. Based on a study of all the programs offered by the business schools, consultancies, corporate universities, and online education providers, we analyze the advantages, and the constraints, of the existing programs. We also map the vehicles for skill development—such as case discussions, lectures, simulations, coaching sessions, live projects, etc.,—in terms of their potential to develop executives for the future. We then examine the impact of the forces of digital disruption—the disaggregation and disintermediation of activity chains and the decoupling of the sources of value in education programs—on the future of executive education.

Incentives versus Reciprocity: Insights from a Field Experiment

Das Narayandas is engaged in ongoing research on vendor firms' management of long-term customer relationships. The initial phase of his research involved identifying vendors that stood to benefit from long-term relationships with select sets of customers and the sources of those benefits. Narayandas is currently examining issues associated with (1) selecting an optimal set of customers, (2) managing transactions and relationships with customers to maximize mutual benefits over time, (3) measuring and monitoring the health of customer relationships, and (4) customer profitability.