Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice
Introduction
We are undergoing a time of profound transformations powered by digitization, information and communications technology, machine learning, robotics and artificial intelligence (Gupta, Keen, Shah, & Verdier, 2017). Many commentators in the business and economic sphere suggest that this will usher in a new epoch—the Fourth Industrial Revolution (Marr, 2016). The fundamental shift in the fourth industrial revolution will be in the area of decision-making. Whereas traditional informational technology helped with processing of communications and data, the decision-making was human. The new shift will be evident in emerging technologies allowing computers to also make reliably appropriate decisions. This digitization shift has begun and will have profound implications for personal selling and sales management functions. The sales profession has always changed in response to changes in the larger macro-environment (e.g., technological, macro-economic, demographic, cultural) within which it operates. As an example, with the advent of advanced telephones and rapid transportation, the sales profession moved away from the stereotypical Willy Loman (from The Death of a Salesman) type of traveling salesman with defined routes to visits based on demand. Similarly, with the advent of the Internet and databases, information became more widely available and some of the ordering moved away from written orders to ordering on the internet.
We hypothesize that selling in future decades will be disruptive and discontinuous, owing primarily to shifts in technology. In other words, digitalization of sales functions with the addition of artificial intelligence and machine learning represent a discontinuous change compared to the non-digital era. For example, an emerging firm, Node, uses machine learning and artificial intelligence to harness large databases and match them with data available on the web to create prospect lists (Node, 2017). Their website promises to provide “strategic insight, tactical guidance and cutting edge technology to help anyone find the right person at the right business at the right time with the right message...” (Node, 2017). Analogous to changes as Europe transitioned from the Middle Ages to the Renaissance, we label this shift as the “Sales Renaissance” where the focus of sales management will transition from traditional sales functions to new functions that may involve bridging inter-organizational and intra-organizational boundaries.
In this paper, we explicate the anticipated technology and environmental changes, and discuss existing machine learning and artificial intelligence technologies and processes. We then discuss the implications of technologies on selling functions. Our focus is on the sales process as a critical element of research and practice, and we use the seven steps of selling to describe the basic selling process (Dubinsky, 1981). In order to provide a deeper focus, the paper does not address big data in-depth for two reasons. First, discussion of big data in sales would be a paper by itself. Second, the definition of big data will change with advances in computing such that data sets that are ‘big’ today will become normal in the future. We have therefore focused on the more fundamental shifts, which are digitalization driven by machine learning and AI, rather than the size and ‘quality’ of available data.
The next section presents emerging trends in technology and environment. In the section after that we will describe machine learning and artificial intelligence and emphasize certain tools. In subsequent sections, we will discuss the impact of these technologies and methodologies on the selling process and highlight areas where further attention is needed. The final section will summarize and discuss areas for further research.
Section snippets
The fourth industrial revolution
In this section, we discuss the emergence of the fourth industrial revolution with a historical perspective on the previous industrial revolutions. The first Industrial Revolution occurred when mechanization, water power and steam power multiplied the efficiency of productive technologies that had previously depended on human and animal labor. The second Industrial Revolution was ushered in by mass production and the assembly line style of production. This stage was facilitated by widespread
Impact of machine learning and artificial intelligence on the sales function
The sales function has already started observing some of the impacts of the Third Industrial Revolution through extensive use of computing technologies and automation. Personal selling and sales management has a number of routine tasks (e.g., order entry, new product announcement). Since these tasks take time and energy away from the primary task of salespeople in developing relationships, the advent of automation in routine tasks enhances the productivity of salespeople. Some examples of tasks
Classification scheme to study the impact of machine learning and artificial intelligence on the sales function
We needed a classification scheme to create a parsimonious framework that would allow us to focus on some key issues. An in-depth discussion on all possible areas of personal selling and sales management would require much more space than a single article. We examined two classification schemes—the selling process and the classification of sales research. Different industries and different companies within an industry have different sales processes depending on the complexity of the
Conclusions
So far, the greatest impact of automation and technology in sales has been, and continues to be, on all routine, standard and repeatable activities. In these cases, technology acts as a supporting role to make the selling functions more efficient. Going forward, perhaps the greatest impact of digitalization in sales will be in all the activities and efforts that go into understanding customer behavior in order to design and deliver highly customized offerings. Thus, in the future, technology
Acknowledgements
This article was an invited article and did not undergo the traditional IMM review process. The paper was blind reviewed by three reviewers.
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