According to DiClemente and Hantula (2003), theoretical orientations in the study of consumer behaviour have emerged as a diverse stream. The principles in behaviour analysis are applied in consumer behavior with factual accuracy where somehow the consistency in theoretical approach lacks and the focus is to generate reflexive conditioning exposed to advertising stimuli in consumer or modifying the distinct consumer choices.
John B. Watson, a notable American Psychologist, published his work in 1913 about the human behavior analyzing through “observable activity” (“Psychologists in Marketing”, 1958, p.1), and the information was focused on predicting and controlling the responses as per Buckley (1982, p.208).
According to Benjamin (1988, p.396), Watson was credited hugely for his behaviourism’s application to psychology which is considered as the early manifesto for behaviour study whereas according to Bartholomew, A. (2013), the core theory of Watson was based on mimicking the evolution study where different species including human beings were observed and experimented to use the observable traits and behaviour and explain the subject’s action. This led to the conclusion by Watson that studying the observable behavior and using the information could lead to predicting the actions which could apply to all the species.
According to Baum (1994, p.10), “all behaviourists agreed with Watson’s basic premise that there can be a natural science of behaviour and that psychology can be that science” but B.F. Skinner had a contrasting thought about it explained in the next chapter.
The next chapter describes the thoughts by the behaviorist for intellectuals who are not so familiar with behaviorisms theory followed by the theoretical analysis of modern marketing and the importance of predictive analytics in analyzing behaviors of consumers and the impact it in modern marketing. The study is also supported by some statistical results.
2. Origin of behaviourism theory
In this chapter, we study the origin of behaviorism as a mode of study which later became a crucial source impacting modern marketing. According to Foxall (1987), in the early twentieth century, behaviorism was developed as an approach to psychology. The subject which was appropriate for scientific psychological investigation was observable and behavior that can be measurable.
As per Rothschild & Gaidis (1981), the behaviour which was measured were the result of its consequences and environment and Bales (2009) stated that, throughout history, a significant amount of behaviourist thought had been developed and it is mainly characterized to John B. Watson, the American psychologist who has been the father of the particular discipline.
According to Reber, Allen & Reber (2009) “Watson’s approach was ‘sentimentalist to the extreme’, ‘embodied a strict environmentalism’ and relied on only that which is publicly verifiable in attempts to theorize about, explain and predict behaviour”.
Wells (2014, p.1120) stated that a good amount of debate was surrounded by the classification of behaviourisms. According to Skinner (1974), two types of behaviourism are distinguished: One is based on the development of Watson’s work, i.e. methodological and the other one attempts to understand its environmental context and analyze the behaviour called as radical by Blackman (1985), whereas as per O’Donohue and Kitchener (1999), around 15 different behaviourisms are based on influential behaviourists such as Watsonian behaviourisms, Tolman’s purposive behaviourism and broader categorizations such as radical and theoretical behaviourism are researched and some of them have been used in marketing and consumer behaviour.
The process of attempting behavioural psychology to marketing and consumer behaviour came in the 1950s and 1960s by Wells, V.K. (2014, p.1120). As per Goldsmith (2014, p.13), the key reason that stimulated behaviourist approaches that are relevant to consumer behaviour is, “[H]humans are animals that have evolved over long periods of time. A such, humans behave much like other animals because they learn and adapt due to their interactions with the environment, and their learned behaviour is analogous to animal behaviour so that it can be modeled (described) mathematically as patterns of responses to environmental stimuli”.
As per Nord & Peter (1980), the method of conducting a behaviour analysis involves arranging the conditions for example “by positively reinforcing successive approximations of the desired behaviour”. Along with that the reinforcement on schedules can be arranged which can be in the form of continuous (the behaviour is reinforced every time) or intermittent (the behaviour is not reinforced every time)
According to Foxall (1990), the primary reinforcers which include food and water, and the secondary reinforcers, for example, those that are learned or conditioned and can include money or tokens can be utilized in marketing. Whereas Peter and Nord (1982) stated that food and other primary reinforcers can be easily exchanged by money in some circumstances and for marketing strategies can be more flexible and useful.
Though the above theories are the foundation of today’s consumer psychology and analyzing behavior in modern marketing, some concepts are not so well known and respond well to marketing and consumer behavior analysis.
According to O’Shaughnessy (1997, p.682) states that “silliness of assuming there is just one overall explanation of buying behaviour” and Foxall (2001, p.166-183) mentioned his work has never been “an attempt to reassert the importance of behavioural psychology to the exclusion of cognitive or other perspectives on consumer choice” and it has “never sought to pursue a behaviourist approach to the exclusion of other perspectives; indeed the coexistence and interaction of multiple theoretical viewpoints is central to its conception of intellectual development”.
According to Wells (2014, p. 1121-1122) states that most of the current approaches use a blend of both cognitive, e.g., attitudes and behavioral e.g., classical conditioning standpoints.
3. What is Modern Marketing?
According to Frederick & Webster (1984), the consumer in modern marketing management concept is not compelled by force but persuaded and convinced to buy their products. The consumer is kept with their interest in marketing management and the benefits of all the marketing efforts.
Kotler (2001) mentioned five concepts of modern marketing described as; production era, product era, selling era, marketing era, and societal marketing era.
According to Fullerton (1988), the period between 1750 and 1830 marked the beginning of universal stimulating attention and demand for meeting among all the society. As the period, marked the beginning of the Industrial Revolution in production which led to an aggressive attitude towards high capitalism in the life of business resulted in the large scale of potentiality in markets.
Plinke (2015, p.3) states that the evolution of the marketing concept has been steady growth.
According to the vision by Peter Drucker (1954, p.38-40),
“There is only one valid definition of business purpose: to create a satisfied customer. It is the customer who determines what the business is. Because it is its purpose to create a customer, any business enterprise has two—and only these two—basic functions: marketing and innovation. […] Actually, marketing is so basic that it is not just enough to have a strong sales force and to entrust marketing into it. Marketing is not only much broader than selling, it is not a specialized activity at all. It is the whole business seen from the point of view of its final result, that is, from the customer’s point of view.”
And as per Theodore Levitt (1960, p.50)
“Selling focuses on the needs of the seller; marketing on the needs of the buyer. Selling is preoccupied with the seller’s need to convert his product into cash; marketing with the idea of satisfying the needs of the customer using the product and the whole cluster of things associated with creating, delivering, and finally consuming (using) it.”
Even though the concepts are presented with valid arguments by the two main philosophers (Peter Drucker and Theodore Levitt) of modern marketing, it was not accepted by every firm mentioned by Plinke (2015, p.4).
The five main characteristics of modern marketing defined by Paul Christ (2012) are: –
- The marketers should have a basic business skill which includes analyzing problems and making decisions, written along with that good oral communication skills with basic quantitative skills and good team skills.
- The knowledge of how their decisions can impact other parts of the business. For example, before running a low-price sale for a particular product, the production house should be informed well in advance to avoid the shortage of supply.
- The marketers need to be Technology savvy. They should be able to use the latest technologies for daily activities and also able to spot the technology which is emerging and can provide a good potential business opportunity.
- Marketers need to be international sellers where they should have the skills to sell online.
- The marketers need to keep themselves dynamic with a steady-state of information.
The third point of the above statement can be validated by the statistics results shown in Figures 1 and 2. In figure 1, the survey result of the latest emerging technologies that got implemented has been shown, and in figure 2, the biggest impact of the technology trends on marketing companies has been fore caste for 2020.
Fig 1. Which of the following emerging technologies have you already implemented in your marketing organization?
In both figures, we can see that predictive analytics is playing an important role. In figure 1, predictive analytics is 43% concerning emerging technology and in the second figure as the latest trend that can have a big impact on marketing companies is 38%. The figures are translated from German to English version.
Fig2. Which technological trends will have the biggest impact on marketing companies in 2020?
As we can see from both the statistic results, predictive analytics plays a huge role in today’s modern marketing era. So, let’s explore in our second last chapter, the part of predictive analytics.
4. The purpose of Predictive analytics in Modern marketing
According to Watson (2013), what is going to happen in the future is analyzed by the predictive analytics method. This method is usually used for optimizing system performance. Regression analysis, factor analysis, and neural networks are used as an algorithm for predictive analytics.
As per an article published in February 2015, the method is usually used for demand forecasting, customer segmentation analysis, and fraud detection. It has been cited that the value of the market in predictive analytics is expected to be $2.3 billion by 2019. Due to the popularity of mobile devices, the data created by smart devices are the reason for predictive analytics tools to become a crucial source.
The companies can maintain a continuous relationship with the consumer and can virtually reach them at any time through browse apps, social media posts, mail which encourages engagement through a massive volume of data has been generated.
The article also states that the scope of customer engagements has been broadened due to the addition of data sensors, transaction data, and data from IoT devices where the companies are getting the opportunity to study the patterns in large volumes and able to predict what is going to happen next.
According to an article published by Smith (2016), predictive analytics can help the company to give an insight that can help the CRM make or break. The following ways described by Smith (2016) can help us to function with predictive analytics: –
- Customer behaviour being forecasted– The traditional method is to use the notes provided by the customer for the salespeople, but often they seem to be unreliable, and it’s not possible by a CRM system to predict the customer’s behaviour. But if you are using predictive analytics software, it can learn and adapt the actions by the customers to make a calculative prediction based on the past, present, and future behaviours.
- Strengthen the Customer Relationships- To build up a potential customer relationship, we need to access and analyse the customer’s prior behaviour which is not possible by CRM system to do it automatically.
Predictive analytics can be considered a powerful tool to improve and increase customer relationships. Thereby enhancing your sales record by knowing your customer better. To make your communication better with your customer a highly refined algorithm can be used to reveal the behaviour of your customer like for instance if you move into your favorite cafeteria and you hear your name and the drink you want to order, it really makes you feel special.
To enhance the company’s sales effort, predictive analytics do play a significant role on a larger scale. By using predictive analytics, many direct marketers figure out which emails are considered junk, and the data is not needed to be big.
- ROI budget can be maximized- For most of the company with actual marketing budget plays an important role to know their customer that they do need what you are selling. With a normal CRM system to manage such function is not possible but with predictive analytics, investment on the return can be maximized with no matter whatever the budget is.
- Data-driven decisions are allowed- You cannot get the exact sales number broken down by each customer in a CRM over time with ease whereas a predictive analytics software can allow you to identify precisely where your money has been made and which areas they are lacking. Along with that, you can get a specific list of customers based on what you are asking which can help you to make future predictions more properly.
- Formulating offer Intelligence- As per customer’s spending habits, predictive analytics can help you to identify special offers which can help you to keep yourself engaged with repeat customers who don’t have the idea what they want.
A special intelligence where the predictive analytics platform learns customer behaviour along with analyzing the actions and habits. The software can tell you precisely which customer is clicking the new offer, who is opening a particular offer, which customer redeemed the offer and when they did that and the amount spent by the customer including any upsells. The data can be more precise with details about the customer’s redeemed date and day of that week.
Predictive analytics can be used in a more detailed way where the customers can read easily by their past behaviour, action can be offered with tailored made offers according to their needs thereby building up a stronger customer relationship.
But to get to that high proficient level, you need highly rich data about the spending habits of customers or the product and services they are buying.
The data can range from the amount they are spending and how much on average they are paying. If you manage to get the demographic information about the customer, then predictive analytics can track the customer’s action automatically from start to finish.
Even though with so many benefits the following survey shows that around 74% of the people from Germany don’t know the term Big Data. The study was done for 1003 respondents who were from age 14 years old and above. The figure has been translated from the German version.
Fig 3. Do you know the term “Big Data”?
5. Conclusion
In conclusion, the goal of this paper was to outline the wide range of theories presented by a different researcher in the origin of behaviourism and provides an introduction of how it was related to consumer psychology. The study shows the latest trends which are influencing the modern market.
The third chapter considers the section about emerging trends that are impacting the marketing organizations and the latest technological trends which can have a significant impact on marketing companies by 2020. But on the contrary in the fourth chapter, a survey conducted in 2016 by TNS infrared shows that the term Big data is entirely unknown to 74% of the population.
Even though the last section highlights the importance of predictive analytics in the modern marketing field but it is still quite unsure how many market companies are using this kind of tool. As the devices often come with a considerable price, affordable only for the big players like Mckinsey & company or KPMG, the unknown percentage of 74% also shows that it is still not a prominent tool with most of the small or medium-sized companies.