Artificial Intelligence in Sales: B2B Algorithmic Management
Modern data-driven management in B2B sales is where Big Data meets Artificial Intelligence. Using AI for sales efficiency.
Although algorithmic management boasts a fancy, new name, managing a workforce using data is not necessarily a new postulate. Just remember that “The Principles of Scientific Management” were published by Frederick Taylor in 1911 and soon became a culprit of the data-driven management.
Algorithmic management is Taylorism in times of big data and artificial intelligence. It uses machine learning to manage and control workforces. Millions of people employ algorithmic management when ordering food, buying online or taking a cab. Millions of workers respond to algorithms. For some, the future of management, for others a depressing picture.
Successful executives prefer to see “algo management” as an autopilot. And as with autopilots for long-haul flights, companies not using AI-based sales management are missing out on a huge opportunity and a critical competitive advantage.
Algorithmic management is critical in situations and industries where sales talent and personal are hard to find. Companies using AI-based algorithms for sales efficiency quickly gain an advantage over competitors not using them.
Sales executives can use algorithmic management in a variety of ways: for general AI-based sales management, in the form of churn management software, or to timely find cross-selling potential.
Let’s begin by discussing what algorithmic management is.
What is algorithmic management?
Have you heard the term “algo management”? Or “data-driven management” (not the same)? Or the older cousin “scientific management”?
If you have ridden an Uber or a Lyft car, watched a film in Netflix, ordered food via Deliveroo, hired a freelancer via TaskRabbit, or bought a present in Amazon, you have required an algorithm to make someone work for you.
Around a century ago, a rich Harvard drop-out called Frederick W. Taylor aimed to replace a “rule of thumb” management method with “the establishment of many rules, laws and formulae which replace the judgment of the individual workman”. Taylor saw a chaotic workplace where employees worked as sluggishly as they could get away with while their bosses paid them as little as possible. Travel 100 years in the future of sales and you will find Taylor has become a robot.
Algorithmic management is not exactly new.
Frederick Taylor published “The Principles of Scientific Management” in 1911. His theory became mainstream through the factories of America and evolved in what we later defined as “data-driven management”.
Thanks to faster communication infrastructure and increasing amounts of data, artificial intelligence can now automate tasks previously reserved to middle or upper management, including the supervision of a sales team.
Alexandra Mateescu is a researcher in Social Instabilities and the impact of new technologies. Mateescu has researched the experiences of domestic workers using online labour platforms to find work, and the ways that the usage of AI-based algorithms continue to shape historically entrenched inequalities in the industry. Together with Aiha Nguyen, leader of the Labor Futures Initiative at Data & Society, an independent non-profit research organization, Mateescu and Nguyen have established the term “Algorithmic Management in the Workplace”.
Nguyen and Mateescu argue that this innovation makes the so-called “gig economy” possible, where workers are managed not by other workers, but by algorithms. Think again of Uber, whose business model depends on managing and controlling crowds of self-employed workers.
How does algorithmic management affect workers and salespeople?
For Jeremias Prassl, a law professor at Oxford University, algorithmic management in companies such as Deliveroo and Uber are Taylorism 2.0. “Algorithms are providing a degree of control and oversight that even the most hardened Taylorists could never have dreamt of,” he argues. His warning is bad news for workers’ rights and autonomy.
In summary, building with technology on the early concept of Scientific Management, Algorithmic Management employs a varied set of technological tools to structure the conditions of manage workforces.
Cast aside for a moment the discussion regarding the ethical implications of surveillance and workers’ right, something the researchers Mateescu and Nguyen discuss in length (more in the link below). Let’s focus on the impact of AI on the data-driven management of a sales force.
How can you use algorithmic management for AI-based sales?
Think of algorithmic management in B2B sales as an autopilot, a more positive picture. Autopilot is an almost-autonomous flying system that makes possible modern aviation, ensures the highest levels of safety and is employed to make expensive workers (pilots) more efficient.
Sales managers can employ algorithmic management to improve the effectiveness of their sales force, to reduce their levels of stress and to make customers happier. Recent findings in the field of sales controlling underscore the importance of effective management for sales managers across planning and analysis.
Managing B2B sales teams using AI can have a significant impact on revenues and margins. For example, a churn management software alone can increase EBIT by 3,5 %.
Pricing optimization is another new use of sales AI technology. Considering that the price is the variable with the highest impact on earnings, knowing the price levels most likely to be accepted can make a huge difference. Today, for example, an AI-based algorithm can tell the Key Account Manager what the ideal discount rate is for a product or sales proposal.
What other applications of algorithmic management are companies missing out today?
Forecasting individual customer lifetime presents a clear case for the usage of algorithmic management. Due to the abundance of internal and external data and the number of different customers, in industries such as manufacturing and industrial distribution, no sales executive can compete with artificial intelligence.
Another relevant example of AI in sales is Upselling and Cross-Selling. Since one of the most economical ways to grow a top-line revenue is to sell more to an existing client base, algorithms that can predict cross- and up-selling can make a significant difference in sales performance. Quickly finding new sales opportunities with existing customers is a competitive advantage that B2B companies can only ignore at their peril.
Lastly, to maximize customer lifetime, churn management software should also accurately predict “soft” churners. Soft churners are harder to detect, for they are customers slowly reducing consumption or order levels, compared to similar customers. To effectively tackle a customer churn, AI-based algo management must consider soft churners as well.
Artificial Intelligence in Sales: B2B Algorithmic Management – Summary.
Algorithmic management is Taylorism on steroids. It is the cousin of the AI-based recommendation algorithm predicting your next Netflix film or Amazon product.
Algorithmic management is the application of AI-based data mining methods on sales data. Successful sales executives see AI-based management as an autopilot – a software we are willing to bet our lives to.
Since B2B companies are employing nowadays AI to outcompete opponents, sell more and innovate, not considering algorithmic management for sales means losing revenues or a competitive position.
There are several applications of AI in B2B, where sales executives use algorithms to discover pricing opportunities, cross-selling and churn risk.
How relevant is the topic Algorithmic Management to you? Comment below!
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