Impact On Sales Generation In Retail Sector of UK Assignment sample

To evaluate the emergence of AI in B2B Customer Communications and its impact on sales generation in retail sector of UK.

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Chapter 1: Introduction and Rationale

1.1 Research Background

Many people still link artificial intelligence with science fiction dystopias, although that perception is fading as artificial intelligence grows and becomes more ubiquitous in people's everyday lives as technology progresses. Artificial intelligence is already a common term, and although its acceptability in mainstream culture is a recent occurrence, the notion of artificial intelligence is not. However, despite the fact that the contemporary area of artificial intelligence was established in 1956, it required decades of hard labour to make considerable progress toward the development of an artificial intelligence system and the realisation of this technology. Businesses in a variety of industries have incorporated intelligence into their operations in some capacity. For example, artificial intelligence may assist industrial organisations in reducing production mistakes. Using a computer to detect problems in a particular product and remove that item from the production line, a management may save time by not having to make the choice themselves.Now in terms of modern business context, the complexities of operatives have risen extensively. Among these involves customer communication prospects due to the sheer immensity of engagement requirement and lack of sufficient expertise (Roseet al., 2021). To counter against these issues, use of artificial intelligence have attained a substantive recognition in the market. A recent report showed that a large number of B2B customers would look elsewhere if a brand did not accurately anticipate their ‘pain points’. Artificial intelligence will help provide the right source and quantity of data that will aid in crafting better strategies to analyse and anticipate the behaviour of their clients.

For B2B commerce and B2B communication, AI could help businesses pinpoint wherein the buyer’s journey to deliver solutions. With the introduction of machine learning, these results can be achieved in a cost-effective way, allowing businesses to maintain brand loyalty and deliver on their promises to B2B customers. Thus, the implication of improved strategies will also mean that targeted KPIs are hit successfully (Singhet al., 2019).Email and SMS marketing campaigns may take a long time to create in B2B businesses, and this is especially true if they're done well. They need to include some background information about themselves, a grasp of their customers' personal histories, and some facts on how the product or service is the right match for them.

The idea of replacing the 'human touch' in business-to-business communication with artificial intelligence may sound paradoxical, but it will enable the company to customise its communication tenfold. For example, AI will be able to leverage the client's professional background to build highly personalised messages, and with the use of customer segmentation, it may also assist to modify the brand's message to be on point with other B2B enterprises. Clients are also encouraged to build a long-term, mutually beneficial connection using this method (David Peck Riikkinenet al., 2018). Email and SMS marketing campaigns may take a long time to create in B2B businesses, and this is especially true if they're done well. They need to include some background information about themselves, a grasp of their customers' personal histories, and some facts on how the product or service is the right match for them.However, in the application of AI into the B2B communicational framework, there are range of uncertainties regarding the proper implication of AI, key requirements for its integration, such as how it can boost sales or benefit the business and most importantly what challenges the adaptation and development of AI may bring (Davenportet al., 2020). Considering all these facets, the following research takes account of the importance of AI for B2B customer communication and evaluate how it can impact the sales generation of retail sectors in UK. For this specific reason, the organisational context of ASOS is taken into consideration to put specific emphasis into the organisational practical scenario.

1.2 Research Rationale

1.2.1 Theoretical Justification

AI stands for artificial intelligence, which makes use of intellectual processes such as reasoning ability, human qualities, and meaning discovery. Within a company, AI is critical and beneficial since it enables them to grow their consumer base in the shortest amount of time possible. Thus, the literature study focuses on the many and unique views and perspectives expressed by various writers and experts about the development and rising relevance of applying AI in B2B customer communication in the retail sector in the United Kingdom. It is clear that the increasing adoption of AI is significantly contributing to the growth of B2B sales and marketing. Businesses increasingly need to customize and expedite customer support interactions. Customers want firms to be accessible 24 hours a day, to anticipate their needs, and to address issues fast (Paschen, Kietzmann, and Kietzmann, 2019). Multiple eminent scholars have explored the numerous advantages of incorporating AI into B2C firms, as well as the difficulties that leaders and managers have when incorporating AI into B2B sales processes for effective client communication(Paschen, Kietzmann and Kietzmann, 2019). This study aims to clarify the varied perspectives of writers and researchers on the implementation of AI in B2B sectors and how the use of AI in the retail industry contributes to growth and development while also providing a competitive advantage.

 The study uses Dubinsky's (1981) conventional sales model as a description model. Dubinsky (1981) defined the seller's obligations throughout the sales process's seven phases. The sales process is divided into seven separate stages, each with its own set of activities. Prospecting, Preparation, Approach, Presentation, and Dealing with Objections, Close and follow-up. The initial part of the sales process is prospecting, or lead creation. This is the process of acquiring new consumers in the context of marketing segmentation. In the conventional approach, sales managers filter out potential leads, which seem to be prospects raised by sales teams. Singh et al. (2019) investigated the influence of AI on three domains: (a) “the sales profession”, (b) “sales professionals as an organization”, and (c) “sales professionals as individuals”. Through this research, this framework defined the objectives and challenges associated with digital sales and artificial intelligence.Thus, the key theoretical focus of the study is to assess the implementation of AI in B2B retail sales generation as well as the study uses two frameworks namely Dubinsky's (1981) conventional sales model as well as the framework developed by Singh et al. (2019) for investigating the influence of AI on three domains: (a) “the sales profession”, (b) “sales professionals as an organization”, and (c) “sales professionals as individuals”.This two frameworks defines the significance of incorporating AI for generating B2B sales.

1.2.2 Practical Justification

The topic selection is justified as the research seeks to bridge the gap existing in all other researches as well as the research tends to provide authentic and legitimate information concerning the research topic. The research seeks to help retail B2B business like Asos identify the benefit of AI incorporation for sales generation that in turn will help the business gain competitive advantage over other competitors. The research also helps identify the best uses of AI for customer communication as well as recognizes the barriers and limitations of using AI in sales and marketing processes. The various challenges are addressed by recognizing effective ways of implementing AI for B2B businesses. More than 80 percent of enterprises in the retail and consumer goods sectors are expected to adopt AI-driven intelligent automation in the next three years. Among retail and consumer goods firms, supply-chain planning is likely to expand the most, while manufacturing is expected to have the greatest penetration. Thus, the research seems to be important as it also helps B2B businesses recognize the effective ways of AI incorporation in business. Artificial Intelligence (AI) and Machine Learning (ML) are the future of B2B marketing and sales (Neeli, 2020). In order to stay one step ahead of the competition in B2B eCommerce, more businesses are shifting to artificial intelligence (AI). B2B marketing is already impacted by artificial intelligence, and it will continue to do so in the future. AI is changing the way B2B marketing is done. It is possible to use artificial intelligence in the sales and marketing process to analyze social media, data from websites, and contact databases to improve lead generation and quality. Marketing tactics that are hyper-personalized for B2B organizations may result in superior business results as a result of AI and machine learning capabilities. Thus, the research selection is practically justified as the research helps identify the effective methods as well as the significance of AI implementation in B2B retail business.

1.3 Research Contribution

The research will put practical and theoretical emphasis into the role of AI in B2B communication and its key implications within business context. As previously discussed, the following study will place a focus on the close and follow-up on the early portion of the sales process, which is prospecting, or lead generation, in order to maximise profits (Dubinsky, 1981). Obtaining new customers in the context of marketing segmentation is the process of obtaining new customers. As is customary in the industry, sales managers screen out prospective leads that seem to be prospects raised by sales teams. The research will make significant contributions to three domains: the sales profession, sales professionals as an organisation, and sales professionals as persons. The study will conduct a thorough analysis into the effect of artificial intelligence on these three domains (David Peck Paschenet al., 2019). The aims and problems related with digital sales and artificial intelligence were identified via research and the development of this framework. Specifically, the review reveals how artificial intelligence automation could enable firms to employ AI for both customer interactions and back-end operations in the future It is possible for B2B commerce and B2B communication to be complicated, with procurement procedures becoming long and convoluted, necessitating the development of time-consuming marketing techniques (Paschen, Wilson and Ferreira, 2020). These operations may be simplified by using AI, since artificial intelligence (AI) employs automation to find the customer's history and ordering history, making restocking simpler and increasing client loyalty as well as feature chatbots as a mean for communication means. On the back-end of a firm, artificial intelligence may make time-consuming processes such as monitoring inventory and managing supplies more manageable and efficient (Paschen, Kietzmannand Kietzmann, 2019). Automatic stock monitoring frees up time for marketing plans and B2B communication initiatives, which may then be implemented.

Moreover, ever since, artificial intelligence has emerged as a critical component of effective B2B trade and B2B communication. It may assist B2B enterprises in customising their communication and providing a more personalised experience for their customers (Paschenet al., 2020). This may assist to increase brand loyalty while also generating more conversions and leads for the company. With the help of AI, companies may demonstrate to customers that they have been paying attention to their activities and that both parties are taking into consideration the demands of the other party's customers (Saura, Ribeiro-Soriano and Palacios-Marqués, 2021). As a result of using AI-associated data and solutions, companies may reduce burden and devote more resources to effectively promoting their products or services (Singhet al., 2019). Additionally, focusing on data rather than intuition can result in increased sales. If organisations are interested in learning more about how they can utilise artificial intelligence to better their B2B commerce and B2B communication, they should consider how artificial intelligence may assist with marketing efforts, extensive contribution on understanding the AI implication on business context.

1.4 Research aim, objectives and questions

Research Aim: To identify the importance of growing AI for B2B Customer Communications and its impact on sales generation in retail sector of UK.

Research Objectives:

1. To develop basic understanding about the B2B Customer Communications.
2. To identify the role of AI in retail sector of United Kingdom.
3. To discern the requirements of using AI in B2B within ASOS.
4. To examine the importance of AI for communicating with customers and improving their sales growth in ASOS.
5. To evaluate the challenges that would be faced by ASOS while adopting AI in its B2B.
6. To investigate the effective ways for overcoming those challenges associated with AI development within ASOS.

Research Questions:

1. What is the conceptual framework of B2B Customer Communications?
2. What are the roles of AI in retail sector of United Kingdom?
3. What are the requirements of using AI in B2B within ASOS?
4. What is the importance of AI for communicating with customers and improving their sales growth in ASOS?
5. What are the challenges that would be faced by ASOS while adopting AI in its B2B?
6. What are the effective ways for overcoming those challenges associated with AI development within ASOS?

1.5 Conclusion

The research in the following shall be discussing relevant literatures in the next chapter taking in account of AI and its practicalities, usages and key benefits while in use of B2B communication and evaluate its key contribution on sales and performance. The methodology in the chapter three accounts key approaches and methods undertaken, the conceptualises the data analysis, and the discussion summarises the learning to make extensive conclusion on the conclusion chapter in the end.

Chapter 2: Literature Review

2.1 Chapter Overview

The chapter two that includes the literature review is conducted by analyzing relevant secondary sources that includes peer-reviewed journals, scholastic articles, PDFs, authentic websites, etc. (Kalu, Unachukwu, and Ibiam, 2019). Therefore, the literature review mainly brings together the various and distinct arguments and opinions of various authors and scholars on the emergence and growing significance of using AI in B2B customer communication in relation to the retail sectors in UK. It is evident that the rapid implementation of AI is contributing hugely to the increase in B2B sales and marketing. There is a growing need for companies to personalize and speed up customer service encounters. Customers want businesses to be available around the clock, anticipate their requirements, and resolve difficulties quickly.

 For both customers and employees, AI-powered assistants have been used by companies (Chatterjee et al., 2021). Since they are present 24 hours a day, they make an important contribution by engaging with many clients. However, their bot-like dialogue is really not relevant to the users they're interacting with. When the bot uses keyword-based scripts to produce replies, it becomes tedious and uninteresting. The following chapter addresses the evolving trends on how AI is transforming B2B marketing and sales as well as the research also throws light on the possible limitations and barriers of using AI for B2B customer communication. The literature gap discusses the gap in knowledge that this chapter failed to address and the entire content of the chapter is piled up together in the summary section.

2.2 Importance of AI in in B2B Customer Communications

When artificial intelligence (AI) was first studied commercially, it was via the work of McDermott (1982), who created a software to create personalised orders for customers. This application used logical thinking in its research to make a significant impact to sales in the B2B selling process via the use of Artificial Intelligence. Using Dubinsky's (1981) study on the sales process, B2B salespeople's duties might be presented at each level. As an added bonus, Ferreira et al. (2020) as well as Homburg et al. (2011) utilised Dubinsky's (1981) sales process to show how AI plays a role at every step in the B2B sales process, which will be discussed in more depth below. A study by Singh et al. (2019) examined the impact of AI on the sales profession, the firm, and the individual sales professional underlined the five contributions of Artificial Intelligence in sales that have previously been established in prior research.

B2B sales managers are incredibly worried about providing excellent customer service at every level of the sales process. Salespeople in the 1940s made B2B sales primarily via the use of manual analogue technology (examples: maps, conventional phones, etc.). It has already been several years since the first mobile phones were introduced, and according to Paschen, Pitt, and Kietzmann (2020), this enhancement in customer-sales interaction has only been amplified by artificial intelligence (AI).

There are a number of different forms of data that may be employed in an AI system, as defined by Narayanan et al., (2012). Both structured and unstructured information may be gathered from the same source. Structured data includes standardised sets of numerical values (e.g., demographics) and unstructured data involves a range of other formats that aren't numerical but nonetheless include a wealth of information (examples: comments, reviews, likes, photos, requests, videos). Rizkallah (2017) estimates that 80 percent of the world's data is unstructured, and they are growing at a rate of 15 seconds faster than structured data. In addition, data are numbers that define an object or person with regard to qualitative or quantitative factors, but only if a data is processed and evaluated can it be utilised for decision-making. Artificial intelligence and business-to-business (B2B) sales rely heavily on the accuracy of data. The decline in interpersonal contact necessitates the involvement of sales teams. Syam and Sharma (2017) underline the greater interaction between people and machines with AI, allowing computers to solve issues with minimum or no human engagement. As a result, salespeople may concentrate their efforts on activities that need them to work closely with customers. In addition, Ferreira et al. (2020) note that new technologies, such as digitization and Artificial Intelligence, have altered the B2B sales model. A variety of previous technologies have had a significant impact on the B2B sales process, whether it was for data collecting, processing, or transmission. Artificial Intelligence, on the other hand, has altered sales decision-making at the very end.

The typical sales process and also how salespeople become involved in each phase of the process must be described in depth in order to get a clear picture of the role artificial intelligence plays in B2B sales. Structured and unstructured data are handled differently in this procedure. In each phase, Dubinsky's traditional sales model is used as a model of description. Dubinsky (1981) outlined the seller's responsibilities in each of the seven stages of the sales process (Figure 1).

classic sales steps summarized

(Source: Dubinsky, 1981)

There are seven sequential phases in the sales process, each with a distinct set of activities, as seen below. “Prospecting, Preparation, Approach, Presentation, and Dealing with Objections, Close and follow-up” are the seven stages of the sales process. Prospecting, or lead generation, is the first phase in the sales process. In the context of marketing segmentation, this is the process of looking for new customers. Potential leads, which are prospects raised by sales teams, are filtered out by sales managers in the traditional form of the process.

Dubinsky's (1981) model is employed as a reference in the study on the effect of Artificial Intelligence on sales by Homburg et al. (2011); “Kock and Rantala” (2017) and “Ferreira et al.” (2020) since the same tools of Artificial Intelligence may be used to acquire the findings. They also note that AI would not replace present suppliers; rather, it will aid in making decisions at every level. Chatbots or bots could be used to improve productivity and change vendors' everyday activities in two additional sales areas: customer retention service and business operations, according to Felder (2016). Step 7 of the conventional sales process includes a follow-up step that includes these two elements. Felder (2016) further stresses the need of cyber security since businesses are dealing with sensitive and secret information. Thus, the IT department will be affected by these changes in the sales process. On the basis of Ferreira et al. (2020), Antonio (2018) identifies five contributions of AI in B2B sales: price optimization in step (5) of the sales process, attempting to sell and crosselling in step (7) follow-up to cover new demands. Other contributions to the sales process include forecasting, based on past sales and current sales performance, and refining overall B2B firm strategy.

The sales process is comprised of seven consecutive segments, each with a distinct job, as outlined below. (1) “Prospecting” (2) “Preparation” (3) “Approach” (4) “Presentation” (5) “Objection Handling” (6) “Closing” (7) “Follow-up”. Prospecting - also known as lead generation - is the initial phase in the sales process. It is the process of identifying prospective customers, which is related to the marketing segmentation job. In the traditional model, sales managers screen prospective leads, which are possibilities identified by sales teams that have the potential to grow into profitable revenue for the organisation.

Homburg et al. (2011); Kock and Rantala (2017); and Ferreira et al. (2020) used Dubinsky's (1981) model as a regard in their research on the “influence of Artificial Intelligence on sales”, but with only five steps, including (2) Preparation and (3) Approach, (5) Dealing with objections, and (6) Closing, because they can obtain the results using the same tools as Artificial Intelligence. Additionally, the authors emphasise that Artificial Intelligence would not replace present providers; rather, it will aid in decision-making at each stage. Felder (2016) suggested two more sales areas where chatbots or bots might be used to increase productivity and automate vendors' everyday tasks: (1) customer retention support and (2) company operations. These two concepts may be used broadly in the conventional sales process's step (7) follow-up. Felder (2016) also emphasises the need of cyber security, since businesses operate with internal and sensitive data. As a result of these fundamental changes to the sales process, the IT department will be affected. Antonio (2018), in agreement with Ferreira et al. (2020), notes five contributions of AI to B2B sales: price optimization, which can be used in step (5) of the sales process, upselling and crosselling, which can be used in step (7) follow-up to address new demands. Two further contributions are unrelated to the sales process, but to general sales management: forecasting, based on sales history and performance, and optimising a B2B company's overall strategy.

Singh et al. (2019) examined the impact of Artificial Intelligence on three domains: (a) “the sales profession”, (b) “sales professionals as an organisation”, and (c) “sales professionals as an individual”. This framework identified the goals and difficulties generated by digital sales and artificial intelligence via study (Figure 2).

Framework

(Source: Singh et al. 2019)

Businesses are increasing (1) “digitisation of sales channels to simplify buying and selling processes”, (2) “digitalization of the sales hopper through AI-assisted decisions”, and (3) “digitalization of the providing through a digital transformation that allows customers to see the products and services they are purchasing in detail. In the domain” (2) “Sales professionals: organisational difficulties”, the solutions, which are mostly for B2B sales, are more specialised, requiring suppliers to have a broader domain of goods to provide. According to Singh et al. (2019), the majority of suppliers, particularly B2B sellers, anticipate providing solutions tailored to client demands rather than typical goods and services. Thus, digitization combined with Artificial Intelligence might result in co-creation of solutions with customers and the identification of previously unidentified demands.

In domain (3) Sales Professionals: Individual Challenges, Artificial Intelligence may alter salespeople's activities, posing certain issues for individuals in regard to their roles, as well as altering and confronting companies' operations. Additionally, it may help salespeople develop new abilities in the use of these new technologies powered by Artificial Intelligence. Singh et al. (2019)'s paradigm enhances previous research on the contribution of Artificial Intelligence to the sales process by including other dimensions such as the role of the salesperson, the sales professional, and the company. In this way, it demonstrates the impact of AI on all B2B sales operations, not only the sales process and related duties. This framework is critical because B2B organisations' sales operations are not restricted to the sales process; the whole company must be altered in order for Artificial Intelligence to have an impact.

2.3 Evolving Trends: How AI is transforming B2B sales and marketing

Traditional B2B marketing and sales methods are evolving to suit AI and Machine Learning since they are the future. More businesses are turning to artificial intelligence (AI) in B2B eCommerce to remain one step ahead of the pack (Li et al., 2021). Artificial intelligence is already having an impact on B2B marketing and will continue to do so. The future of B2B marketing is being influenced by AI. Marketing efforts that can be fully automated using smart AI technology are decried by some of the largest corporations in the world; however, based on the success of AI-powered robots in customer service industries, it is evident that understanding the customer nuance will not be completely manual or managed by human power (Mikalef, Conboy and Krogstie, 2021).

Artificial intelligence (AI) is not only for B2C enterprises, according to a widespread misconception. As a result of their greater number of customers, B2C companies are thought to have more data in which to work when it comes to AI (Kushwaha, Kumar, and Kar, 2021). The exact opposite is true, since this is completely false. Both a B2B and a B2C company may profit from AI. B2B companies should think about how AI may help them create and deliver better products and services while also improving business processes. Wholesale distributors and other B2B organisations are increasingly relying on intelligent technology to comprehend data at a large scale and create relevant solutions to business difficulties (Gligor, Pillai, and Golgeci, 2021). When used in conjunction with machine learning, AI may assist in manners other than the mechanical one often associated with normal automation.

Businesses that provide goods or services for profit have a lot of information about their products, consumers, and relationships with each other. In a way that no human being can, machine learning systems that tap into these sources can adapt to new circumstances as they emerge, learning from data in the real (Kushwaha, Kumar, and Kar, 2021). AI in e-commerce may be able to assist firms in improving customer experiences, making better business choices, reducing costs, increasing productivity, and accelerating time to value.

It is imperative that B2B marketers put in the time and effort to get to know their consumers in order to meet their clients' ever-increasing demands while also maximising the number of new customers they can acquire. Online clicks and searches, chat and email interactions, live marketing, website visits and purchase choices produce a digital trail for both end-users and corporate customers (Gligor, Pillai, and Golgeci, 2021). With so much data to manage, analyse, and collect, it's imperative that high-quality automated technologies like AI be used to learn about customer mindsets, demographics, and behaviour.

All current business procedures will be improved by AI's ability to replicate and surpass human intelligence. Deep machine learning approaches enable AI-powered computer systems, which are very clever, to function without any need for programming codes to solve problems. Robotic process automation (RBA), that effectively automates worker duties, is another AI technology that B2B companies may adopt (Saura, Ribeiro-Soriano and Palacios-Marqués, 2021). Using Automation Anywhere's RBA solutions is easy for any firm, not only those in the IT sector or those with IT capabilities on staff.

If artificial intelligence is utilized in the sales and marketing processes, social media accounts, data from websites, and contact databases may be evaluated for insights that help enhance the quantity of leads created and the quality of those leads. AI and machine learning allow for hyper-personalized marketing strategies for B2B businesses, which may lead to better business outcomes (Chen et al., 2021). Chatbots as well as other AI-powered communication technologies allow the human workforce to concentrate on more vital activities now that they can offer customer service around the clock. Predicting customer demand based on trends and purchase habits may be done with the help of artificial intelligence. This might be a significant benefit to brand marketing experts who can use AI's results to learn more of what customers are likely to want in the pre-sale phase.

In B2B sales and marketing, artificial intelligence has become a reality. Organizations may utilise AI and machine learning to manage the avalanche of data in order to construct real-time prediction models and engage with customers while remaining competitive advantage (Gligor, Pillai, and Golgeci, 2021). Marketers may unlock new revenue streams in B2B sales and marketing by honing their storytelling skills. Through pushing the boundaries of innovation, AI will help companies boost their bottom lines in the long term by enhancing user experiences.

2.4 Possible barriers and limitations to the use of Artificial Intelligence for B2B sales

Artificial Intelligence in B2B sales might be problematic because of the enormous quantity of data accessible and the quick change in client preferences, a lengthy sales process with so many influencers making purchase choices, as well as market shifts. AI systems also require signals from the environment and processing of this data in order to create information (output) that may be sent directly into the environment (Paschenet al., 2019). Individualization of information is a present habit that must be changed if the sales team is to create the environment data. For this, it may be difficult to get the desired outcomes. Employees that have been involved in the sales process or concerned about their existing roles may create these hurdles or resistance, according to Gligor, Pillai, and Golgeci (2021). To ensure a seamless transition from employing AI in B2B sales, leadership roles should actively engage in this transformation process (Mikalef, Conboy, and Krogstie, 2021). In this period of fast change and digitalization, management teams must focus on improving team participation (Crittenden and Crittenden, 2015). As Paschenet al. (2019) argues, leadership must make it apparent to the team that human touch remains a crucial part in the sales process. When more data is gathered and kept, security technologies become more critical. Internal rules and procedures must be reviewed by the firm's leadership to ensure the security and privacy of consumer and corporate data. This phase of adaption requires extensive training. Workers must learn new skills to reap the benefits of AI systems (Kaplan and Haenlein, 2019), and training is crucial to assist workers adapt (Pachen et al. The sales crew must be well-versed about AI's strengths and drawbacks in order to effectively sell it.

Sales processes may benefit from the use of AI, but there must be a time of transition and client support. An AI-powered customer experience is possible, but each organisation is at a different stage of implementation. Identifying consumers who are reluctant to employ AI and are more used to conventional sales service is a challenge for salespeople (Saura, Ribeiro-Soriano and Palacios-Marqués, 2021). Controlling the customer experience is seen as a higher order construct that covers specific cultural attitudes, strategic orientations, and firm skills that are focused on controlling each point of contact during the customer's journey.

It is possible to examine huge amounts of data, especially unstructured data, in real time using an artificial intelligence system. However, decision-making relies heavily on human intellect. Emotional and social abilities, which are vital in B2B sales and will remain to be critical in human duties inside the Artificial Intelligence sales process, are restricted by artificial intelligence.

2.5 Effective Ways of using AI for generating B2B sales

There is a growing need for companies to personalize and speed up customer service encounters. Customers expect firms to be available around the clock, anticipate their requirements, and resolve difficulties quickly. For both customers and employees, AI-powered assistants have been used by companies (Bag et al., 2021). Because they are available 24 hours a day, they make an important contribution by engaging with many clients. However, their bot-like communication isn't always relevant to consumers. When the bot uses keyword-based scripts to produce replies, this becomes tedious and uninteresting. Every year, new technological developments help companies by opening up new channels for reaching out to customers. According to Kovanen (2021) Artificial Intelligence (AI) is a game-changer in the business-to-business world, and it's causing quite a stir. Even while some marketers have dabbled with machine learning algorithms, there is still a lot of territory to cover in terms of predictive analytics, statistical analysis, personalization, and the development of new leads. B2B marketing success is largely dependent on having a well-defined target audience (Pöntinen, 2021). Artificial intelligence (AI) analyses all of the data and creates categories for consumers depending on the parameters that are entered into the system. AI may be used to evaluate and prioritize leads for account-based marketing focused on the demographic data, in order to segmenting the current audiences. Businesses may utilize this data to create realistic consumer profiles for whom they can create unique marketing materials.AI has made it simpler than ever for B2B marketers to rate prospects based on their potential of becoming customers (Agnihotri, 2020). In order to better target their marketing efforts, firms may use predictive lead scoring technology to automatically group leads into personas.

Using this predictive technology, customers have complete control and insight over which fields have the most impact on lead scoring, which results in a faster and more accurate score. The same AI machine learning that enables lead scoring also powers behavior scoring, which notifies B2B marketers when their leads are ready to purchase (Han et al., 2021). Leads may be mapped to specific accounts using predictive behavior scoring, and new prospects within those accounts can be identified. Leads and accounts are automatically linked using deduplication criteria. To reach a larger audience, AI might create lookalike accounts based on existing consumers. B2B sales rely heavily on next-best offerings like upsells, subscription renewals, and cross-sells. However, it might be difficult to determine which offer is best for a particular client at any given moment (Agnihotri, 2020). This mystery can be eliminated and the next-best offer process made substantially simpler with the help of AI.

Artificial Intelligence (AI) can immediately begin calculating ways to give customized content and offers that will entice customers to engage with the company again and again. Discounted pricing and promotional discounts for value-conscious purchasers, or free trials and sample products for usability-focused clients, are examples of this.

Nurture campaigns powered by AI may be used to collect online data by sending personalized emails depending on what a user has viewed or downloaded from your website. For offline data, AI may be used to measure intent data, allowing marketers to understand the implicit meaning of customers' activities (Bag et al., 2021). A customer's purpose is more important than the number of clicks or downloads they make. When consumers buy in person or online, the information they provide about their purchases might be useful for marketing purposes. Artificial intelligence can help businesses better understand their clients by combining online and offline data. In B2B marketing, there really are numerous chances to use AI to utilize customer data to create tailored experiences across all channels (Agnihotri, 2021). Using AI, it is possible to merge the online and offline experiences of the customers, segment the target market, and create a unique experience for each one. Conversational AI is a real-time technology that understands and responds to human discussions. With intelligent virtual assistants, chatbots, and speech bots, it makes it possible for machines to communicate with one other in a natural way (Paschenet al., 2019). The use of digital technologies by organizations, particularly retail enterprises, may help automate customer contact operations.

For B2B marketing to be successful, marketers must focus on and satisfy the demands of their target audience. Consequently, businesses are always on the lookout for extensive information on their customers. Artificial Intelligence (AI) has a wide range of platforms from which it may collect a great amount of information (Han et al., 2021). AI can help bridge the gap between both the sales force and the prospective customers by selecting the ideal B2B targets for inside and outward marketing campaigns. An effective use of artificial intelligence (AI) may boost both the number and quality of leads produced, as well as limit the time that is spent on tedious and repetitive operations.

Salesforce automation software, CRM databases, and other B2B applications may all benefit from the use of artificial intelligence (AI), which frees up salespeople to focus on more creative endeavors (Pöntinen, 2021). Artificial Intelligence will also assist identify accounts of current and new consumers based on predetermined parameters, allowing for access to many more suitable clients. Salespeople will indeed be able to make more accurate decisions while prospecting since AI is more focused on data-driven facts and less about intuition.

2.6 Literature Gap

The literature review covers almost all aspects related to the topic, however, there still persist gap in knowledge that the literature review failed to acknowledge. The significance of AI in boosting sales in B2B businesses is focused however, every detail regarding the effective use of AI in B2B business could not be covered in this chapter. Numerous notable researchers have discussed the numerous benefits of implementing AI into B2C organizations, as well as the challenges encountered by leaders and managers when integrating AI into B2B sales processes for efficient customer communication. However, the researcher came across no considerable in-depth examination of these topics in the context of particular industries. As a result, the researcher was unable to include these points into this evaluation. These are the areas in which future scholars should concentrate their efforts. As a result, these are the elements in this study that might be termed "gaps." Also, due to the enormous amount of information present in the topic every detail concerning the topic could not be brought together that in turn creates a void in knowledge.

2.7 Summary

To put it all together, it can be said that for the most part, the literature review is a compilation of many writers and researchers' views on the importance of employing AI in B2B customer communication for the UK retail industry. Artificial Intelligence (AI) has had a significant impact on the growth of B2B sales and marketing. Personalization and speeding up customer service encounters are becoming increasingly important. Customers want firms to be accessible 24 hours a day, seven days a week, and respond immediately to issues. AI-powered assistants are used by businesses for the benefit of both consumers and staff. They have a significant impact on the business since they are available all the time and interact with a large number of customers. There's a problem, though, with their bot-like conversation. It's irrelevant to their customers. It gets boring and dull when the bot employs keyword-based programs to make answers. There are several ways in which AI is reshaping the B2B market, and this chapter focuses on how AI is influencing B2B customer communication. The literature gap elaborates the gap in information that this chapter failed to acknowledge and the complete material of the chapter is heaped up together in the summary section.

Many different types of data may be used in an artificial intelligence system. The same source might provide both structured and unstructured data. An example of unstructured data would be a collection of comments, reviews, likes, images, requests, and videos that are not numerical but nonetheless contain a richness of information (e.g., demographics). The veracity of data is critical to both artificial intelligence and business-to-business (B2B) sales. Sales teams are needed because of a decrease in face-to-face communication. New technologies like digitalization and artificial intelligence have also had an impact on the B2B sales model, according to Ferreira et al. (2020). The B2B sales process has been impacted by a number of past technologies, whether it was for data collection, processing, or transmission. When it comes to sales decision-making, though, artificial intelligence has had a profound impact.

It is based on Dubinsky's (1981) notion that artificial intelligence may affect sales. These two more sales sectors might benefit from the usage of chatbots or bots: client retention service and company operations. Digitalization of sales channels, AI-assisted decision-making, and a digital transformation of the business are all becoming in importance for companies. Artificial Intelligence (AI) and Machine Learning (ML) are the future of B2B marketing and sales. In order to remain one step ahead of the competition in B2B eCommerce, more companies are turning to artificial intelligence (AI). B2B marketing is already impacted by artificial intelligence, and it will be continuing to do so in the future. AI is changing the way B2B marketing is done. Some of the world's greatest firms decry marketing efforts which can be totally automated utilizing smart AI technology; nevertheless, considering the success of AI-powered robots in customer service sectors, it is clear that comprehending the consumer subtlety will not be wholly manual or handled by human power. In order to fulfil their clients' ever-increasing needs while also maximizing the number of new customers they can attract, B2B marketers must spend the time and effort to really get to understand their consumers. End-users and business customers alike leave a digital trail of their clicks, searches, chats, emails, live marketing, website visits, and purchases made online. As more and more data are managed, analyzed, and collected, high-quality automated solutions like artificial intelligence (AI) are essential for learning about consumer attitudes, demographics, and behavior.

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