Opinion | Neuroeconomics to the aid of corporate sales performance4 min read . Updated: 24 Dec 2019, 12:00 AM IST
Created by the convergence of behavioural economics and computer science, the field seeks to map how decisions are made
Some 30-odd years ago, when I was a postgraduate student, the top practitioners in economics did not give much credence to behavioural economists. It was thought that they could not provide sufficient replicable empirical mathematical evidence, and so they were relegated to less prominent schools and universities.
A lot has changed. The modelling of decisions made by a typical “rational" human being, which was the cornerstone of most empirical schools, was but a contrivance used to arrive at mathematical proofs that were presented in top journals such as the Journal Of Financial Economics. Rare is the student of economics who has never considered that this “rational" human being is, in fact, just a myth. Such scepticism has been proven right over the last few years. The field of behavioural economics is now well known and highly regarded, and has even produced Nobel Prize winners.
Even so, the older empirical models yielded several significant advances in the theory of economics and finance. The beauty of any seminal thought in economics lies in its ability to engender a reaction in the reader that goes along the lines of, “Yes, of course, that’s true. I knew that!" Well, you may have known it, but it is always the man or woman who first gives voice to the thought who goes on to win a Nobel Prize.
One such was the theory of agency costs, first promulgated by William Meckling and Michael Jensen, who were both professors at the University of Rochester at the time. Agency costs laid out a simple economic truth that an owner’s agents were less likely than owners of the business to run the business efficiently. In other words, managers (agents) needed to be compensated in much the same way as owners (shareholders). This led to the liberal use of stock options and grants in the compensation schemes for senior managers of a firm, thereby strongly linking executive pay with stock-price performance. This compensation technique is now widely used the world over.
However, while stock ownership works well for senior managers, its efficacy begins to go down significantly when it is used with relatively junior employees, who have much less to do with the overall stewardship of a firm and also get very few stock grants in comparison with top executives. Grants are only symbolic at these levels. Compensation among the rank and file has both a fixed component and a variable one that is more directly tied to the employee’s performance.
Early in my career, I was part of a very small team in charge of setting sales incentive compensations for Xerox, which had more than 16,000 sales representatives in North America. This was more art than science, but nonetheless tied to several key empirical factors that allowed for attaining the manufacturer’s price without steep discounts, while fulfilling large sales quotas carried by sales representatives. The assumption was that adjusting the size of the sales person’s commission was the best way to drive better sales performance. We assumed that every salesperson was similarly motivated, and, as a rational human being, would try to maximize commission earnings.
The emergence of behavioural economics has shown that reliance on a single measure, such as a commission, is naive and that the average rational human being is a myth. Lately, the application of computer science to this field has given rise to a new field called “neuroeconomics", which tries to model and instigate human action. We have seen some of these models being used to drive political action, especially through the subversion of social media platforms, but these models are also equally applicable to the management of large businesses.
Software giants such as SalesForceDotCom (SFDC) have been managing the lead generation and sales process space and providing consistent, streamlined methods to push the sales process along. These are template driven, but can be tweaked with the help of neuroeconomics to deliver much more effective results.
Not all human beings are externally motivated. Not all can be led to compete for, say, the honour of being the top commission earner or winning a “President’s Club" cruise in the Bahamas. Some of us are “internally referencing", where we prefer to compare our performance with our self-set goals. This insight has been seized upon by several startups that seek to help corporations enhance their sales performance by helping sales agents along in their “call to close" process. These include firms such as Cognitive Scale, Maven, Penny, Vymo, and Worxogo.
The availability of information to salespeople is known to spark a strong “intent to act". However, this in itself may not result in action. Information overload and indecision often go hand in hand. This leaves sales managers frustrated over their failure to achieve “desirable" outcomes. I spoke recently with the founders of a startup who claim to have built an engine that can incorporate an understanding of high-performing and poor-performing behaviours and deliver “cognitive behavioural nudges" via an easy-to-use application that drives the intent outward by helping sales personnel choose the best performance action each time.
Confronting this last-mile problem involves the embedding of algorithmic behaviour design to ensure that sales personnel consistently follow through on targets. Getting an individual employee to act requires recognizing both the blocks that inhibit action and the motivations that initiate it. The founders I spoke to claim their engine is designed to prompt appropriate actions among sales personnel that are aligned with their unique behavioural traits, motivations and challenges. Yet, we don’t know whether using algorithms to predict human behaviour is worthwhile. A mind, after all, is difficult to map.
Siddharth Pai is founder of Siana capital , a venture fund management company focussed on deep science and tech in India