Dr Myles Wakeham, Mr Carl Wakeham and Ms Maria Hamman
INTRODUCTION TO ARTIFICIAL INTELLIGENCE
Artificial Intelligence (AI) refers to the creation of human-like intelligence that can process, learn, reason, plan, and discern natural language. AI comes in three forms, namely, narrow AI, which we are involved with on a daily basis and which is designed to perform specific tasks within an area (technology with intelligence in a particular domain) and general AI which is not area-specific and can learn and perform tasks anywhere and finally strong AI, which is an artificial super intelligence. Thus far, we have only managed to master narrow AI.
The application of AI uses amongst other technologies natural language processing, speech recognition, robotics, machine learning (ML) and computer vision. An example of AI that you may already be engaging with is SIRI presently available on Apple iPhones who reacts to your voice on command. SIRI will in addition have the ability to “learn” from you as you request information in the future.
According to Carolyn Frantz (Microsoft’s Corporate Secretary), AI will have a major influence on business and will equally have a dramatic impact on jobs. Frantz asserts that in the future, AI will make as much as 75 million jobs disappear in the USA but will be replaced by 133 million more challenging and less repetitive roles. Besides its impact on HR, AI will also influence operations and production, inbound and outbound logistics, Supply Chain Management (SCM), finance and as importantly, marketing.
One of the ways that AI is influencing marketing is with AI marketing assistants like IBM Watson’s Lucy, which is a cognitive problem solver (in contrast with emotional), which acquires knowledge through a determined leaning process. Lucy can be used to determine market segments, develop products, conduct competitive or market analyses, media planning, providing the numeric marketing data needs in writing a marketing plan, assisting with salient information in developing a marketing strategy, creating structured marketing content through a process called Natural Language Generation and so on. According to IBM, Lucy is a powerful tool that marketers “…can use for conducting online research, segmentation and planning and it is so powerful that it can do more in a minute than an entire team of marketers can achieve in months”. Needless to say, the advantage of a marketing assistant like Lucy is that it can digest and analyse literally all the data a company possesses and once it has absorbed all of this data, marketing personnel, according to Watson can ask the following questions, when attempting to solve marketing problems:
- What are the personality characteristics and attributes of the organisation’s target audience based on a set of predetermined variables?
- Which segments, towns or regions should be targeted first in order to maximise sales?
- What content mix should be created for the target audience to maximise the attainment of the marketing and promotional mix objectives? and
- What is the current competitor activity and how can the organisation use such data to make better marketing decisions specifically within environments like retail channels?
The above are questions that companies need to answer in order to formulate marketing strategies that achieve the marketing goals as set by the enterprise. Lucy and similar AI marketing assistants can, therefore:
- Create viable segments of a company’s target audience so that it can develop highly personalised content that is designed to appeal to such an audience (target market);
- Assist in the planning of marketing strategies by interrogating the needs and wants of the target market and how best to maximise sales and profits because of such market intelligence through programmatic targeting as an example.
- Implement and control the different strategies so that the firm’s objectives may be realised based on data feedback loops put into place; and
- Create promotion content that is customer-specific so that the organisation’s strategy and promotional mix can be directed specifically at satiating customer and organisational needs and wants.
According to MIT’s Brian Bergstein’s article, which was published in the MIT Technology Review in February 2020, AI as it currently stands:
- Cannot question decisions so it is basically led by data which could be incorrect;
- Cannot explain the decisions it has made to qualify or quantify the decision;
- Cannot understand causation (why things happen following on from an occurrence);
- Cannot measure psychographic typologies;
- Cannot reason qualitatively, e.g. how people feel about a brand; and as importantly
- Cannot understand the concept of, for example, customer loyalty outside of quantitatively ‘crunching’ numbers.
So, from the above points, AI must not be seen as a cure-all for an organisation’s marketing woes but rather a tool to assist the firm in achieving better results in the marketplace.
APPLICATION OF AI IN MARKETING
AI, and systems like Lucy (there are numerous others), will undoubtedly have a huge impact on content marketing as they become more affordable and more popular. They will help companies better understand their audience and the data that are garnered by means of AI will allow marketers to position brands more effectively in the minds of current and future customers and put together more effective strategies so that organisational objectives may be attained. AI will also help them understand what outcomes they can expect by pinpointing accurate customer expectation so that customer-specific targeting can be better planned based upon more reliable forecasting and market intelligence. According to the publication Smart Insights: The Financial Brand (March, 2018), the applications of AI in marketing can be found in Figure 1 below:
Figure 1: Application of AI in marketing
At present Cookies and other engagement tools follow customers as they interact with websites, products, and applications by providing various data sets that will form a personal ‘ecosystem’ that is programmatically targeted by tools and systems. Here relevance is the key to successful engagement by the consumer with variable pricing bases upon the propensity of interest and purchase.
As can be observed in Figure 1 above, AI can have an explosive impact on marketing throughout the organisation’s relationship with its customers… from demand generation through to the instilling of customer loyalty. It can therefore be used to cement strong and mutually rewarding relationships with customers and help to maximise the lifetime value of the customer. It can have a profound influence on the marketing mix, the consumer adoption model and as importantly Customer Relationship Management (CRM). In essence it can generate awareness, instil interest, create desire and likewise important, facilitate action (AIDA). To further explore the above figure and its content, let us examine the four stages of the application:
- REACH: Reach is the initial stage of the buyer’s relationship with the marketer. The idea is to attract potential customers and provide them with an appealing experience that will lead to product trial. Reach commences with smart content curation (selection), which is the stage showing potential customers content relevant to what customers with similar perceived needs are interested in. The second phase is concerned ad targeting, with using programmatic media buying. In other words, by using propensity (tendency) models to effectively target advertisements at the most relevant customers. AI can be used to identify the best media and sites (web pages, areas etc.) to place advertisements. Thirdly, AI generated content writing programmes can select the right customer appeals and then personalised content for targeted prospects. Lastly, AI can be employed for voice search (made use of by Google, Amazon and Apple) to improve structured search traffic by applying digital assistants like Lucy as discussed above.
- ACT: The second stage of the customer journey (Act) is intended to grab the customer’s attention and make them aware of a firm’s products and services. It consists of four elements, namely propensity modelling, which uses copious amounts of historical data to make predictions. AI at this juncture helps the marketer to direct customers to the correct messages and locations on websites and to generate outgoing personalised content. The second element is predictive analytics which employs propensity models to process large amounts of data that perform best on selected people at specific stages in the customer buying process, which permits more effective advertisement placements and message content than traditional methods. The third element is predictive analysis. This is implemented to determine the likelihood of attracting customers, predicting what price they are prepared to pay for the offering and equally important to establish what customers are most likely to make repeat purchases. The last element under ‘act’ is lead scoring, which is the process of using predictive analytics to determine how interested the potential customer is and likewise if the lead (potential customer) is worthwhile pursuing in order to covert him or her to a supporting customer.
- CONVERT: This is the stage of converting a prospect into a customer. Here the first element is dynamic pricing, which uses AI (machine learning) to develop special offers for potential customers that are most likely to purchase the product or service. By doing this, one can increase sales and maximise profits. The next element is re-targeting, where once again, propensity models are used to determine what content is likely to bring customers back for more. This facilitates the re-targeting of advertisements to make them more effective and customer-centric. Re-targeting is often based on the past customers engagement levels with the initial product offering and interest at the onset. This is frequently based on a series of the same or similar advert / content being sent to the customer and the interaction multiple times and during various traffic and time zones dependent on the brand and category. The third element is web and application personalisation, which once again employs propensity models to personalise a web page or application in the position where the customer is in the purchasing decision making process. Lastly, chatbots use AI to mimic human intelligence in order to interpret customer enquiries and to complete orders. Facebook has created instructions on how to build Chatbots.
- ENGAGE: Here we find the stage after a purchase has been made. Where traditionally once a sale was concluded by a salesperson it was customary to make a quick exit before the customer changed his mind. In a modern context however, it is important for a firm to continuously engage with customers in order to build mutually beneficial relationships and to facilitate recurring business and referrals. The first element here is customer service, where AI, though predictive analytics, can be used to determine which customers are most likely to become dormant (stop purchasing) or stop supporting the marketer altogether. With this insight, the firm can reach out to these customers with offers, prompts or assistance to prevent them from churning. The second element is marketing automation. This is when AI is availed to determine when (the best time) to contact customers and what message to use when such contact is made. This facilitates insight into where the firm can improve the effectiveness of its automated marketing. The last element is dynamic emails where predictive analytics using propensity models can use previous custom behaviour to market better targeted offerings via automated email as part of the customer acquisition and retention strategy. The results emanating therefrom can be employed to improve future results by uploading them into the models.
As can be seen from the above, the greatest advantage of AI in marketing is its ability to deliver personalisation in a customer-centric manner and in a large scale. In today’s rather complex world, with numerous channels of distribution, complex supply chains, many customer touchpoints and retail options, customers are being overwhelmed every day with messages on traditional media and on digital/social platforms in novel and unique ways. This random bombardment of marketing messages has already fallen on deaf ears and blind eyes as people want to be treated individually and no as a number. The beauty about AI is that it can help organisations to create consistency and personalised experiences across channels for their customers over the long term.
AI AND ITS IMPACT ON CRM PROCESS
Customer relationship management (CRM) is an approach to managing a company’s interaction with current and potential customers. It uses data analysis about customers’ history with the company to improve business relationships, specifically focusing on customer retention and ultimately driving up sales growth. CRM is also known as a strategy that companies use to manage interactions with customers and potential customers and helps organisations streamline processes, build customer relationships, increase sales, improve customer service, and increase profitability.
The relationship usually starts with the customer becoming aware of the organisation (marketer) via the marketer’s promotions activity or by means of word-and-mouth. When commercialisation of an offering begins, marketers use various aspects of the promotion mix to create product and brand awareness, and thereafter attempt to facilitate product trial and then retrial (repurchase of the offering). By astute and customer-driven marketing, the next step for a marketer is to attempt to generate customer loyalty, then insistency and finally advocacy. By performing the latter, loyal customers become the marketer’s unpaid salespeople in the marketplace. Furthermore, the cost of promoting goods and services to these loyalists and ambassadors reduces as they have already built a strong relationship with both the marketer and its offerings. Finally, being risk adverse, loyalists and advocates, they are nor very price sensitive, which makes them very profitable.
When one examines Figure 1 above, one can see that AI can be used as a strategic tool to acquire new customers, motivate them to try its offerings and then through the use of technology and marketing savvy, retain them by creating long-term relationship based upon mutual trust, understanding and co-dependence. This path to purchase ultimately results in mutual need satisfaction for both the marketer and its customer. So, with a closer understanding of what customers want and need by means of the effective and efficient employment of AI, closer relationships can be forged thereby making it easier for the marketer to manage the mutually binding relationship.
AI AND ITS IMPACT ON THE CUSTOMER ADOPTION PROCESS
The Customer Adoption Process is a 6-step mental process which all customers experience while adopting a product; from learning about a new product to becoming a contented and loyal user of that product. During the process the customer may choose to either decline to buy the product or defer the purchasing thereof. The process of a customer moving from a cognitive state toward the emotional state and finally reaching the behavioural or conative state is another way to explain the Customer Adoption Process. The three stages are as follows:
- Cognitive State, which can be defined as being informed and aware of the product and marketer’s existence;
- Emotional State, which can be defined as the preferences of the customer; and
- Behavioural or conative state, which can be explained as taking the decision to purchase, decline to purchase or defer the purchase.
By examining the three above-mentioned points and Figure 1 above, it can be noted that AI can be used to create awareness of the product and the marketer, influence the decision-making process, reinforce preferences and finally assist in motivating the potential customer to buy. According to Cunningham (2018:178), the customer adoption process has six steps. In Table 1 below, one can observe these steps/stages as well as how AI can influence the process:
Table 1: The customer adoption process
Level of adoption
Influence of AI in relation to the various AI stages
|To be created by the marketer in order to inform the customer of the existence of the offering||
Reach stage: Reach is the initial stage of the buyer’s relationship with the marketer. The idea is to attract potential customers and provide them with an appealing experience that will lead to product trial. AI uses technology not only to make potential customers aware of an offering and organisation but to use information that has been garnered to ensure that the right message is communicated to the right audience. The strategy at this stage is to alert the potential customer by means of employing the right promotions mix. The idea even at this early stage is to lay the foundation on which future relationships will eventually be built.
Interest and information
|The marketer needs to spark interest so that the potential customer is motivated to look for more information||
Act stage:The second stage of the customer journey is intended to grab the customer’s attention and make them familiar of a firm’s products and services. The focus here is on stimulating interest so that the potential customer may want to obtain additional information about the offering and organisation. AI at this juncture helps the marketer to direct customers to the correct messages and locations on websites and to generate outgoing personalised content.
|Here the customer evaluates the offering against competitor products or product substitutes||
Act stage: At this important phase the potential customer seeks as much information as possible so that he or she can make a constructive and well-balanced decision about the offering compared to that which is offered by alternative marketers. During this phase AI employs predictive analytics to determine the likelihood of attracting customers, predicting what price they are prepared to pay for the offering and equally important to establish what customers are most likely to make repeat purchases.
|Here the marketer desires the customer to try the product, its features, advantages and benefits. The idea/strategy is that hopefully this will lead to retrial and permanent adoption as a product or brand||
Convert stage:This is the stage of converting a prospect into a customer. AI provides dynamic pricing to ensure that the targeted customer can afford the offering and to also re-target where once again, propensity models are used to determine what content is likely to bring customers back for more. This facilitates the re-targeting of advertisements to make them more effective and customer-centric.
|Here the customer has adopted the product with the marketer’s intent to retrial, loyalty and insistency||
Engage stage: Here we find the stage after a purchase has been made. Unlike in the sales orientation stage where sales were transactional in nature, here the focus is on continuously engaging with customers in order to build mutually beneficial relationships and to facilitate recurring business and referrals
|Should the offering fully appease the needs of the customer then he or she will move from insistency to advocacy where he or she will be willing to recommend the product||
Engage stage: The first activity here is customer service, where AI, though predictive analytics, can be used to determine which customers are most likely to become dormant (stop purchasing) or stop supporting the marketer altogether. A customer recovery strategy should be put into place to establish why the customer is not purchasing or why he or she has migrated to competitors. With this insight, the firm can reach out to these customers with offers, prompts or assistance to prevent them from churning. AI also facilitates marketing automation to contact customers at a convenient time and what message to use when such contact is made. This facilitates insight into where the firm can improve the effectiveness of its automated marketing. AI also uses predictive analytics and propensity models to investigate previous customer behaviour to market better targeted offerings via automated emails as part of the customer acquisition and retention strategy. The results emanating therefrom can be employed to improve future results by uploading them into marketing and business models.
Source: Table developed by Wakeham, M., Wakeham. C.N. & Hamman, M.
CONCLUSIONS AND RECOMMENDATIONS: It may be noted in Table 1, that AI can have a profound impact on the way a customer adopts a product, service or retailer. Organisations should therefore use AI as a strategic tool to enhance customer satisfaction, appease the needs of all the stakeholders in the equation and finally enjoy the benefits of a co-dependent relationship. An organisation that does not pursue this strategy will be myopic and will do so at its peril. What an organisation therefore needs to accomplish is aptly depicted in Figure 2 below:
Figure 2: Migration from descriptive analysis to prescriptive analytics
Looking at all the analytic options above can be a daunting task. However, luckily these analytic options can be categorised at a high level into four distinct types. No one type of analytic is better than another, and in fact they co-exist with, and complement, each other. In order for a business to have a holistic view of the market and how a company competes efficiently within that market requires a robust analytic environment which includes:
- Descriptive analytics, which use data aggregation and data mining to provide insight into the past and answer: “What has happened?”
- Diagnostic analytics, which uses data to provide insight into: “Why did it happen?”
- Predictive analytics, which use statistical models and forecasting techniques to understand the future and answer: “What could happen?”
- Prescriptive analytics, which use optimisation and simulation algorithms to advise on possible outcomes and answer: “What should we do?”
AI has a profound impact all of the above types of analytics and should be used in a marketing context for the benefit of all the stakeholders who are involved with the firm.