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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 (David Peck Riikkinen et al., 2018). 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 (Paschen et al., 2020). Among these involves customer communication prospects due to the sheer immensity of engagement requirement and lack of sufficient expertise (Rose et 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 (Singh et al., 2019).
For B2B commerce and B2B communication, AI could help businesses pinpoint wherein the buyer’s journey to deliver solutions (Dubinsky, 1981). 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 (Singh et 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 Riikkinen et 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 (Davenport et 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 many online shopping and clothing businesses are taken into consideration to put specific emphasis into the organisational practical scenario.
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 (Singh et al., 2019). 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 (Singh et al., 2019). 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.
According to Paschen, Kietzmann, and Kietzmann (2019), Customers want firms to be accessible 24 hours a day, to anticipate their needs, and to address issues fast. 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 stud 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”. These two frameworks define the significance of incorporating AI for generating B2B sales.
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 (Paschen et al., 2020). 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 (Paschen et al., 2020).
According to Neeli (2020), 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. 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.
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 Paschen et 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 et al, 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 et al, 2020). 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 (Ferreira et al., 2020). It may assist B2B enterprises in customising their communication and providing a more personalised experience for their customers (Paschen et 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 et al., 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 (Singh et 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 (Saura et al., 2021).
Research Aim: To identify the importance of growing AI for B2B Customer Communications and its impact on sales generation in retail sector of UK.
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.
The literature review is conducted by analysing relevant secondary sources that includes peer-reviewed journals, scholastic articles, PDFs, authentic websites, etc. 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 (Kalu, Unachukwu, and Ibiam, 2019). Customers want businesses to be available around the clock, anticipate their requirements, and resolve difficulties quickly.
As per Chatterjee et al., (2021) for both customers and employees, AI-powered assistants have been used by companies. 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.
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.
As per Paschen, Pitt, and Kietzmann (2020), 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 (Dubinsky, 1981). 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. According to Ferreira et al. (2020), 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 (Syam and Sharma, 2017). Structured and unstructured data are handled differently in this procedure. In each phase, Dubinsky’s 1981 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).
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 (Dubinsky, 1981). 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 (Dubinsky, 1981). 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. According to Felder (2016), 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. 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” (Antonio, 2018). 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” (Antonio, 2018). “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 cress -selling, 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).
(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 (Singh et al. 2019). 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 (Singh et al. 2019). 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.
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 et al., 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 et al, 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 et al, 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 et al., 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 (Kushwaha et al., 2021).
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 (Chen et al., 2021). Robotic process automation (RBA), that effectively automates worker duties, is another AI technology that B2B companies may adopt (Saura et al., 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 (Saura et al., 2021). 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 et al., 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.
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 (Gligor et al., 2021). 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 (Paschen et 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 et al., 2021). To ensure a seamless transition from employing AI in B2B sales, leadership roles should actively engage in this transformation process (Mikalef et al, 2021). In this period of fast change and digitalization, management teams must focus on improving team participation (Crittenden and Crittenden, 2015). As Paschen et 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., 2020). 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 (Kaplan and Haenlein, 2019). Identifying consumers who are reluctant to employ AI and are more used to conventional sales service is a challenge for salespeople (Saura et al, 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 (Kaplan and Haenlein, 2019).
To conclude, 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.
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 (Kaplan and Haenlein, 2019). For both customers and employees, AI-powered assistants have been used by companies (Bag et al., 2021). Since 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 (Saura et al, 2021). 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 (Mikalef et al, 2021). 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 (Pöntinen, 2021).
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 (Paschen et 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 (Pöntinen, 2021).
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 endeavours (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 (Pöntinen, 2021).
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.
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 (Han et al., 2021). There’s a problem, though, with their bot-like conversation. It is 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 (Han et al., 2021). 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) (Han et al., 2021). 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 (Han et al., 2021). 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.
The third chapter of the thesis contains a detailed discussion of the techniques and procedures that were used to gather the data. The study of research technique is both helpful and advantageous in a huge number of situations. As a result, this chapter provides a more comprehensive understanding of the overall research design. One of the key goals of this section is to give a complete framework for doing research (Cypress, 2018). The major goals of the research were established early on, as was the study’s methodological structure. The data analysis provides readers with a detailed way to analysing the feasibility and success of the research, which is beneficial to them. The qualitative analysis was utilised throughout the whole inquiry, it was necessary to examine both primary and secondary sources in order to acquire background knowledge on the issue.
Primary and secondary sources, such as interviews and research papers, were used to obtain information on the difficulties the firm is facing as well as the solutions that may help it prosper in B2B Customer Communications and retail sales in the United Kingdom. This chapter goes into great length on how to conduct research and come up with a legitimate and dependable set of results as a result of this. According to the study’s ethical chapter, the authors outline the processes they used to ensure that their research met all applicable ethical standards and that it was carried out in compliance with all laws and regulations (Mikalef et al, 2021). Additionally, this section has a line stating that it must adhere to all of the research criteria in place.
Research is based on the assumption that data may be understood, analysed, and exploited in the same manner as a phenomenon. Interpretivism and positivism are widely accepted in western philosophy, according to the study (Cypress, 2018). There have been many philosophers, positivists, interpreters, practitioners, and writers who have contributed to the development of the four major research philosophical tendencies. Positivists believe that social reality can be scientifically studied. Philosophies of research include interpretive theory, positivism, and quiet reflection.
The primary objective of positivism is to re-establish learning, certainty, and conceptions from resources that impose reasonable frames and trustworthy segregation. With the philosophy of interpretation, sociological criticism is interwoven into science (Ukauskas et al., 2018). According to the interpretive school of thought, only theoretical frameworks like perception, shared ideas, and perceptions may expose one to reality. Thus, the empirical critique of positivism philosophy frequently serves as the basis for this theory’s foundations.” “In this kind of theory, qualitative analysis is typically found to be superior than quantitative analysis (Ukauskas et al., 2018).” “As a result, the metaphysical claim of positivity, which is typically used to aggregate various approaches except for nihilism and functionality, is linked to interpretivism as a philosophical movement (Ukauskas et al., 2018). To interpret important facts and concepts, the researcher used the interpretivism philosophy in the following study, which makes use of both qualitative and quantitative methods of data collection”.
Research can be done in a variety of ways, each with its own advantages and disadvantages. Consequently, it is crucial to take into account the type of the research approach. Next, we looked at how culture spread around the globe using the second technique of inquiry (Van den Berg and Struwig, 2017). Qualitative research is commonly conducted in the manner described below in academic publications. A wide range of documents, including reports and papers, were thoroughly analysed. In spite of the study’s enticing goals and anticipated consequences, the research procedure employs a subjective approach in the manner of industrial notions. Researchers used it to see how different cultures affect people all over the world (Tuffour, 2017).
Based on this appraisal and using both primary and secondary materials relevant to the topic, the study was undertaken. The qualitative research technique is widely used in the following study sample, which includes articles, essays, letters, academic publications, and so on that have been analysed and analysed. This study relies heavily on the second strategy for data processing and interpretation (Woo et al., 2017). It’s a great opportunity for students who are interested in a particular subject to practise their problem-solving skills in the classroom. Research methodology studies give instruction in the selection of techniques, resources, analytical tools, and processes that are appropriate for the subject at hand (Tuffour, 2017).
The researcher picked content analysis as a technique of qualitative research. There are four distinct stages to the research design process, starting with the identification of the information’s source, followed by the collection and assessment of relevant data and, finally, a study focused on the research goal: Rutberg and Bouikidis (2018). The primary goal of mixed methodological analysis is to estimate the total number of people who demonstrate a certain behaviour, belief, opinion, or perspective in society. To ensure that the study’s conclusions are based on correct data, competent data collection procedures, and ethical concerns, the researchers must be held accountable (Cypress, 2018).
Foreseeing future developments, the researchers will conduct qualitative analysis. It is vital that researchers have the opportunity to observe and experiment in order to generate and validate hypotheses and facts, so that academics may continue to examine both the validity of evidence and new information (Rutberg and Bouikidis, 2018). In order to develop a solid evidence base for widely accepted, major concepts, an exploratory research technique driven by evidence collects a wide range of relevant data and then builds on it with both believable and non-sounding findings (Rutberg and Bouikidis, 2018). Qualitative research relies on secondary data to assist researchers get a deeper understanding of the subject.
The methods used to collect and document data are referred to as “Data Collection” in research (Maxwell, 2018). Comprehensive data collection procedures may enhance the collection and testing of hypotheses (Clark and Vealé, 2018). Regardless of the study subject, data collection is the most important and crucial phase. The methodology’s concepts and ideas form the basis of the thesis. By examining all outcomes and issues around them, and the whole report, data is acquired. It’s possible to gather data using secondary sources and subjective information, although subjective information is more often used since it’s difficult to quantify. Other researchers’ results and peer-reviewed articles are the primary sources of this knowledge. Certain data formats, on the other hand, might be very difficult to decompose. Secondary data is utilised by researchers who must do their own analysis of information from other sources (Maxwell, 2018). Certain components of the project need the collecting of data in order to better understand the conclusions of a thesis. The researchers came to the conclusion that using a supplementary group of references that solely supplied fresh data would improve the study’s reliability and accuracy (Clark and Vealé, 2018). However, the report’s meticulous approach to data collection allowed them to totally remove the unethical data. If you’re looking for a reliable source for the information you’ll find in the study’s secondary sources, such academic journals, PDFs, papers, and more.
Data analysis may be used in a variety of ways to get information from a database. Observations and hypotheses generated by the inquiry have resulted in conclusions. During the operation’s Data Analysis phase, the facts and figures were carefully scrutinised (Elliott, 2018). Qualitative approaches were used to examine the data that had been collected. Data from this study was analysed and it was found to be more subdivided than previously assumed into semi-tasks and subgroups (Lester, Cho and Lochmiller, 2020). Validating a study’s hypotheses using data and evidence is a crucial step in the “data and evidence analysis” process. “An advanced data analysis system can handle both types of data, improving the value of data acquired from a range of sources. Data analysis is a tool such as SPSS is also utilised together with the data collection process to achieve their objectives when it comes to collecting and analysing data”. Furthermore, “Narrative analysis is used for quantitative data, whereas content analysis is used for qualitative data. Content analysis is used since the research was conducted in a qualitative fashion”.
It is impossible to emphasise the significance of research sampling in acquiring useful data for a study. Systemic assessment answers may be found by advancing or analysing outcomes and issues (Sharma, 2017). In order to get to clear conclusions and identify answers to difficulties, the thesis includes a comprehensive examination. As a consequence, academic papers and research journals are used to gather evidence and data. A mix methodological evaluation is therefore a primary purpose of this study, which aims to analyse the results and conceptions associated with the research (Lester, Cho and Lochmiller, 2020). An analysis is a rigorous examination necessary to get an accurate conclusion and identify answers to problems. The investigation focused on data analysis and sample collection in order to present a comprehensive picture of the investigation. Therefore, secondary sources were actively used in this research, and the data were mostly taken from these sources.
The achievement of research objectives is impossible if the highest ethical standards for research are not followed. “As a researcher, it is essential to adhere to a set of ethical norms in order to guarantee that the scientific objectives of your study are satisfied. For the sake of achieving a specific research aim, research ethics refers to a set of norms and standards that assure the protection and safety of a wide range of research procedures. In the majority of cases, a study review should be carried out in accordance with research ethics guidelines” (Roth and von Unger, 2018). This does not rule out the possibility of a two-way conversation on research ethics. When it comes to determining the impact of culture on international business, ethics are the most crucial issue to consider. As a result, data collection and study for the purpose of developing research ethics standards are the most important areas of ethical concern. Assistance, consent, and protection are all examples of formal ethical meanings or standards that can be found in a study report. It is important to be mindful that validated study findings may be used in a report to misrepresent key data problems. In order to achieve the specific study purpose, a variety of concepts and standards were employed in order to improve the numerous processes in research ethics. The extraction of information from a broad variety of websites, including copyright clearances, and the retrieval of this information have received more consideration. When researchers adhere to the basic principles and standards of scientific investigation, they may be certain that the outcomes of their experiments will be correct (Roth and von Unger, 2018).
For conceptualising practical and actual insights regarding the concerned subject, a comprehensive survey involving 15 participants is formalised within the following context. Therefore, the key questionnaires and their respective responses and evaluations are being illustrated in the following.
Q1. What is your Age?
Among the 15 respondents approached, the survey analytic conceptualised three different age variations on the selected populace. As per the responses gained utmost of 5 individuals are categorised into the age group of 18-24. There are 8 personnel ranging between the age of 25-34. The variations of 35-44 age groups ranges across 2 individuals. There is no personnel identified with age range over 44 years.
Q2. What is your Gender?
While evaluating the genders of the associated respondent group, for key options for responses are made available. Among 9 responses are made as Man while 6 responses are made into Woman. No responses are made into the options of “Non-binary” or “Prefer not to Say”. This implies that majority of the respondents were males with subtraction of females as well.
Q3. What is your job role in the organization?
While asked about the specific job roles of individual respondents, 15 different responses are recorded. As per which it is perceived that individuals with different stakeholder interests are approached by the research to attain diverse responses. Nevertheless, among the 15 responses latest responses are recorded to be the Marketing Intern, Sales Operations and Brand Marketing Executives. Apart from them there were also e-commerce and web design specialists, Marketing Executives, Strategy Managers, CRM Marketing Managers, Senior Customer and Media Mangers as well as Students, Technical Marketing Executives, Social Media Marketing Experts etc.
Q4. Do you consider the importance of AI in modern business function?
While asked about the importance of AI in modern business functions, respondents provided comprehensive responses. Among these responses 7 implied with promotive response on the question whereas other 7 remained passive. There was also one detractor. Nevertheless, with the value of 40 NPS the overall responses are conceptualised towards positive response.
Q5. Does your organisation implement AI for driving sales in its business operations?
Regarding the question of whether to agree that implication AI make drive sakes and business operations. 10 respondents among all 15 replied with a “Yes”. Apart from this there were 2 responses who optioned with “No”. The rest 3 individual respondents implied with “Maybe”. Considering these responses in concern, it can be implied that majority of respondents with their diverse background, job roles and experiences considers AI implication to be advantageous for business operations and sales boosting as they have agreed with the statement that AI can drive sales and business operations.
Q6. Do you think AI implementation in your organisation drives sales and boosts customer communications?
Regarding the question of whether AI implications drives the sales and boosts the customer communications, majority of respondents like the previous respondent with an “Yes” to the statement. This like the previous question implies a much positive emphasis on the statement with so much stress on the positive effects of AI on sales and customer communications. However, there is still 1 response “No” and 4 other responses on “Maybe”. Yet, the majority of the responses implies a positive inclination.
Q7. Do you consider the significance of AI implementation for driving B2B customer?
In terms of business-to-business communications, AI implications also have significant role to play. Nevertheless, as per the recorded responses almost 14 responses are laid on “Yes” while only 1 response is made on “No”. Based on this metric it can be thoroughly implied that AI implementation may have potency for driving B2B customer communication in efficacious manner.
Q8. Do you agree that AI implementation can optimize work pressure and improve B2b communication effectively?
On the question associating the role of AI on work optimisation, an extensive emphasis on responses is recorded. According to that context, majority of respondents with their response promoting the statement implied positive response by 7 promotive responses. There are 6 other responses on being passive while lastly there have been 2 detractors. Overall, there is positive inclination on the question with NPS of about 34 which implies that AI implementation can optimise the work pressure and improve B2B communication effectively.
This study has been undertaken using the SPSS software to analyse data and meet research aims and objectives.
In the correlation analysis, this study has considered two variables such as “AI is important in modern business and AI reduces work pressure and B2B communication”. The above correlation analysis indicates that the value of the Pearson correlation is 0.410. It has to be mentioned here that in correlation when the Pearson-correlation value is not far from +1, it indicates a positive association. In the context of the above correlation, positive bonds are identified among variables. Here, it can be stated that AI played a significant role in improving B2B communication and reducing work pressure.
This study has undertaken another correlation analysis among multiple variables. In the above correlation analysis, the Pearson-correlation value is 0.254. In the context of correlation test in SPSS, when the Pearson-correlation value is close to +1, it considers positive relationships among variables (Sujarweni and Utami, 2019). The above correlation table indicates that AI played a significant role in an organization to boost sales and communication.
In the next stage, the Pearson-correlation value is 0.102. In this test, the above Pearson correlation value is not far from the +1. It has been identified that when the value of Pearson correlation is far from +1, it chooses a negative association. Therefore, the above value indicates the value of Pearson-correlation indicates the positive association among the variables. Here, it can be stated that AI creates a significant impact on business by influencing the communication flow. Therefore, by focusing on the above discussion, it can be stated that AI creates a significant effect on the business drive and communication flow of an organization. However, by focusing on the above correlation analysis, it has been identified that the Pearson-correlation values are not reliable to indicate a significant positive association among variables.
Null Hypothesis (H0): Implementation of AI does not reduce work pressure in B2B business (p>0.05)
Alternative Hypothesis (H1): Implementation of AI reduces work pressure in B2B business (p<0.05)
This report has undertaken a regression analysis to meet research objectives. In this regression model, the dependent variable is “The importance of AI in modern business function and independent variables are AI implementation to reduce work pressure”. In this regression model, R square value is 0.182. The value of R square indicates the predictor factor may fit a 1% chance of independent variable in this regression model. P-value is 0.300. In the context of regression analysis, if the p-value is less than 0.05, it chooses an alternative statement (Pardoe, 2020). However, in this regression model, the p-value crossed the 0.05 level. Therefore, the above regression test selects the null hypothesis. Therefore, it can be stated that the implementation of AI does not reduce work pressure in B2B business.
Null Hypothesis (H0): Impact of AI does not create a significant effect on sales and communication in B2B business (p>0.05)
Alternative Hypothesis (H1): Impact of AI create a significant effect on sales and communication in B2B business (p<0.05)
This study has undertaken a regression analysis in SPSS to meet research aims and objectives. In this regression model, the dependent variable is the “importance of AI in modern business function and independent variables are implementation of AI to drive sales and customer communication, the significance of AI in driving B2B customer communication”. Implementation of AI to reduce work pressure and improve B2B communication is another independent variable. In this regression model, R square value is 0.184. The value of R square indicates that independent variables may predict 1% of variances among independent variables. Further, the p-value is 0.507. It has to be mentioned here that when regression analysis, a p-value is achieved at the level of significance (0.05). It chooses a null hypothesis (Ciaburro, 2018). Therefore, by focusing on the above discussion, it has been identified that the regression model chooses null statements over alternative hypotheses. Therefore, it can be stated that the Impact of AI does not create a significant effect on sales and communication in B2B business. Here, it can be concluded that the implementation of AI does not create a significant effect on the growth and sales drive of B2B business.
In the fashion and luxury industry, an increasing number of companies are using smart technology and consumer data to make better economic choices. With the ever-increasing granularity and size of customization in the world of online fashion shopping, it is difficult to handle without the help of AI and automated procedures (Guha et al., 2021). As of 2021, Gartner expects that 85 percent of client interactions with an organisation will take place without any human intervention (Lomas et al., 2021). During July and September of 2021, next.co.uk had the biggest share of voice among the top fashion websites in the United Kingdom, at nearly 12 percent (Tran, Pallant and Johnson, 2021). Following ASOS in the rankings was ASOS, which had lesser exposure in Google UK’s search results. It had an 8.16 percent voice share (Guha et al., 2021).
Customers’ expectations of service and customisation are being raised by an increasing number of businesses that have embraced these new technologies. Nearly half of all fashion stores in the United Kingdom are at risk of insolvency. Many organisations are now finding it difficult to compete because they could not discover a method to effectively incorporate AI, and this might be one of the reasons behind this (Pillarisetty and Mishra, 2022). As a result, it is critical that AI be implemented as soon as possible. Using AI effectively allows smaller businesses to compete with the larger players by reducing costs and offering the type of customer service that consumers have come to demand (Esch, Cui and Jain, 2021).
Visual recognition is a popular use of artificial intelligence in the online fashion industry. Customers may use this technology to find clothing that looks similar to their own. Customers can always find what they’re looking for thanks to visual recognition on online shop detail pages (Sivaram et al., 2021). Online fashion shops may save time by using visual recognition to propose acceptable product tags when adding new items to the store (Jain and Gandhi, 2021). As a buying department, you may leverage visual resemblance with previous items to better estimate the quantities required, reducing overstocking.
Fashion trends may be predicted by analysing social media and other data sources, as well as by looking at what has worked in the past (Dwivedi et al., 2021). Marketing departments and product development teams may utilise this information when making product selections and planning marketing efforts.
Overstocking is a major concern for fashion shops. Predicting which items will be in demand in the future and at what volume can be done with the help of artificial intelligence (AI). In order to reduce overstock, this choice is based on the buying power of the consumers and the present supply. Predicting supplier price fluctuations and recommending appropriate purchase dates may also be done using certain algorithms (Zou and Wong, 2021). On-site, Agent Provocateur uses just a little amount of AI. They haven’t given much thought to expanding their on-site usage yet, preferring to concentrate on other possibilities (Esch, Cui and Jain, 2021).
Inventory management is such use of artificial intelligence that fashion merchants believe has a lot of potential. Artificial intelligence has been used to assist fashion businesses enhance stock turnover by taking into account the “desire” to sell older products as quickly as feasible (Purc?rea et al., 2021). When it comes to fashion stores, this is a significant AI use case since the longer you have your goods in stock, the less likely it is to be sold off (Esch, Cui and Jain, 2021).
Another method AI is being utilised in fashion e-commerce is via AI chatbots, often known as smart assistants. Online businesses might deploy these AI chatbots to impersonate customer care professionals and assist their consumers in finding what they’re searching for on the website (Luce, 2018). A retailer’s worldwide conversion rate may be increased by using this highly scalable technique of customer support. Customers may utilise Levi’s chatbot to help them discover the right pair of jeans via the retailer’s online store (Luce, 2018).
Topline development may be influenced greatly by pricing. If one gets it properly, one will be successful (Luce, 2018). If you do it wrong, you run the danger of irreparably harming your company. In order to do it right, it’s vital to take advantage of new possibilities brought about by digitization and leverage psychological elements of pricing; and most crucially, match price with the desire to pay (Luce, 2018). In particular, artificial intelligence and machine learning may be used to dynamically and continually alter price to fit the desire to pay and assist control sales volumes (Shi and Lewis, 2020). With prices changing every 10 minutes, Amazon is a perfect illustration of how this works. Specialists in revenue management might use pre-packaged or custom solutions to keep track of market demand and willingness to pay shifts and adjust price accordingly. In addition to product price, AI may be used to improve discounting and promotional activities (Shi et al., 2021). Customers’ preferences and willingness to pay may be taken into account by analysing data on buying habits and past promotions. Optimizing promotion mechanics, frequency and depth; increasing conversion rates; minimising stock excess at the conclusion of a season are all benefits of AI (Luce, 2018).
Sales should take precedence over all other aspects of a company’s operations after a sound strategy, marketing plans, and pricing are in place. Strategic advantages are gained in the struggle for revenue development when fashion and luxury firms use systematic channel optimization and sales force management (Banerjee et al., 2021). Today’s CEOs may utilise AI as a foundation for evaluating their sales groups critically. You must use a cross-channel strategy that incorporates both online and off-line transactions (Moncrief, 2017). Many high-end fashion and luxury firms need clients to download an app in order to have access to extra services and perks at the shop, as an example. Apps and online businesses may be utilised to gather data that can be used to increase offline sales. However, this data may be used for purposes other than merely boosting sales (How and Luce, n.d). To make the most of physical store inventory, data from online sales may be pushed back into the production system to affect future designs and volumes. As a result of the Covid-19 dilemma, internet sales have grown increasingly more crucial (Marr, 2019). Until today, only brick-and-mortar retailers employed customer activation methods to drive sales. In the beauty and fashion industries, for example, applications and tools are being developed so that clients may virtually try on clothes and cosmetics. Customer involvement may be boosted with the aid of cutting-edge internet technologies, which are improving all the time (Banerjee et al., 2021).
As customer preferences shift, new fashion and luxury firms have an opportunity to acquire a leg up on the competition. Having the correct assortment plan in place is critical if one want to stay on top of things (Banerjee et al., 2021). AI can help brands keep an eye on their assortment plans and make adjustments as needed. Future market trends may be predicted using AI (Dwivedi et al., 2021). In this way, designers and marketing managers may adopt a more fact-based approach and lessen their reliance on intuition. The search and filtering habits of online shoppers may be used to tailor the purchasing experience for each individual (Vla?i? et al., 2021). The abundance of internet data is nothing new, but it might be used by other teams to make future product design decisions and assortment plans more informed (Rangarajan et al, 2020). Furthermore, AI is gaining traction in physical shops as well as the online economy. Retailers are utilising artificial intelligence (AI) to better understand their consumers’ buying habits and how they respond to their in-store offering (Neeli, 2020). Companies may, for example, use sales and return data to improve their in-store product presentation and choose what products to sell and/or promote in certain locations. A well-executed inventory management strategy may reduce the need for discounting and write-offs of unsold goods by bringing supply and demand into balance (Banerjee et al., 2021).
Customer loyalty programmes and targeted marketing initiatives are two examples of this. The marketing function must progress in lockstep with changing customer demands. Increasingly, leading fashion and luxury firms are using customer-centric AI to deepen and customise their consumer interactions and drive broader top-line development objectives (Murgai, 2018). AI can help one understand business clients and build stronger interactions with them if one has access to relevant data. Leading fashion and luxury firms capture customer information at every point of connection, but they also provide loyalty and rewards programmes that reveal a wealth of consumer preferences (Singh et al., 2019). In order to adapt online recommendations and send tailored ideas to shop personnel on their mobile devices, several fashion businesses leverage data supplied willingly via loyalty programmes. Increasing customer engagement is the key to improving customer lifetime value in the marketing context of AI, which may be used in a variety of ways (Sahai and Goel, 2021). AI can assist raise revenue and profitability by increasing conversion, lowering acquisition costs, increasing retention, and increasing the number of customers who return for more transactions (Singh et al., 2019).
Apps that combine virtual and actual worlds are becoming more popular with consumers shopping for clothes online. Gucci’s sample version of a premium clothing store developed a platform to illustrate the value of experience-based e-commerce. Zara has also jumped on the AR bandwagon, creating AR platforms in several of its locations. Because of the pandemic, the advent of such platforms has arrived at an ideal moment (Vladimirovich, 2020). The creation of an elusive world in contrast to its own via one-of-a-kind clothing, dignified runway presentations, and digitally augmented models in fashion magazines. Virtual and Augmented Reality Fashion and the Fashion business is the orange of the future. Businesses may claim that it is easier than it sounds to do research and innovate (Paschen, Wilson and Ferreira, 2020). There are tools and software that help companies do research on their behalf; all they need to do is input the specifications, since they have established how technology has permeated their life. For those in the business world, technology may be used to keep up to date and make well-informed business decisions (Zaki, 2019). In contrast to those who wait until the textile-apparel and fashion industry has already evolved before moving forward with new ventures or diversifying into new areas, companies that use marketing research tools like TexPro, which originates rich and up-to-date data daily, have the upper hand, could indeed make faster and detailed decisions, and are always up to date on new technologies, market prices (Pandey, Nayal and Rathore, 2020). In contrast, smaller retail technology start-ups are filling in this need for larger corporations. Edited, a London-based start-up, offers real-time market data to its store clients using live data analytics software. Brands like Marni, Boohoo, Tommy Hilfiger have fallen in love with it, and it can analyse the worldwide market in seconds (Zaki, 2019).
It is now possible to employ artificial intelligence (AI) technology to help salespeople identify items that fit the body type, physical traits, and particular preferences of their customers. It is possible to narrow down the results to provide the most relevant suggestions for the various scenarios in which consumers could be making their purchases (Vieira et al., 2019). With the use of an AI clienteling tool, sales associates now have access to professional outfit styling for any item in store. Customers may benefit from increased personal style capacity without the effort and expense of training a sales associate to become a personal stylist (Bag et al., 2021). An AI Remote Selling solution may link their in-store sales staff to clients anywhere in the globe to expand your current sales capacity and improve their customer service quality at no extra expense. Shoppers are more likely to spend more money if they have a personal stylist working with them. By using AI, a retailer can recognise returning consumers at every point of contact, no matter which channels they’ve used in the past; this is a significant step forward for conventional retail (Campbell et al., 2020). A devoted consumer who visits a retailer’s physical shop for the first time or at a new location may expect the same personalised attention they’ve come to expect from the retailer’s internet presence (Bag et al., 2021). It is now possible for sales representatives to quickly and easily access a client’s profile, which allows them to instantly learn about the style, preferences, and purchasing history of that consumer.
Artificial Intelligence (AI) is a critical component of contemporary business, and this research looked at the association between these two factors. According to the aforementioned correlation study, the Pearson correlation is at a value of 0.410. In correlation, Pearson-correlation values that are close to +1 suggest a positive relationship (Paschen et al., 2020). A positive correlation may be found between variables in the context of the aforementioned correlation. Artificial intelligence (AI) has played a crucial role in enhancing B2B communication and lowering workload.
Furthermore, AI is thought to have a big influence on business via its ability to alter the flow of communication. As a result of the above debate, it can be concluded that AI has a substantial impact on an organization’s drive for business and communication (Moncrief, 2017). As a result, Pearson-correlation values are not trustworthy to identify a substantial positive link between variables, as shown in the preceding correlation analysis formalised in this study.
Thus, as a significant correlation analysis has been performed in this research, including a number of factors. Pearson’s correlation coefficient is 0.254 in the example above. Correlation tests in SPSS evaluate positive correlations between variables when the Pearson-correlation value is near to +1. (Sujarweni and Utami, 2019). This connection table shows that AI has a substantial impact on an organization’s ability to increase sales and communicate effectively.
The Pearson-correlation value for the following step is 0.102. The aforesaid Pearson correlation score is quite close to a +1 in this test. It has been discovered that when the Pearson correlation coefficient falls much below a +1, a negative link is likely to result. As a result, the aforementioned correlation coefficient value suggests a positive connection between the variables. Here, it can be said that AI has a huge effect on business since it influences the flow of communication (Paschen et al., 2020). As a result of the above debate, it can be concluded that AI has a substantial impact on an organization’s drive for business and communication. As a result, Pearson-correlation values are not trustworthy to identify a substantial positive link between variables, as shown in the preceding correlation analysis.
Nevertheless, from this comprehensive analysis it can be conceptualised that customer service encounters are becoming more personalised and time-sensitive. It’s important for firms to be available around the clock, seven days a week, to anticipate their customers’ demands, and react quickly to issues. Corporations have used AI-powered assistants for both customers and employees (Chatterjee et al., 2021). They must constantly engage with a big number of clients in order to have a major influence. However, there’s an issue with their discussion that seems like it’s being conducted by a bot. Customers don’t care about that. When the bot relies on keyword-based programmes to generate responses, it quickly becomes tedious. There are several benefits and cons of employing artificial intelligence (AI) in business-to-business communications (Herhausen et al., 2020). B2B marketers must invest time and effort to get to know their customers in order to meet the ever-growing demands of their clients while also boosting the number of new customers they can acquire. For both consumers and end-users, internet activity leaves a trail of digital traces (Kumar et al., 2019). It is impossible to learn about customer sentiments, demographics and behaviour from all of the data without the use of artificial intelligence (AI) and other high-quality automated technologies. All current business operations will benefit from AI’s ability to replicate and exceed human intelligence.
Hence, Artificial intelligence and machine learning may be used by businesses to develop hyper-targeted marketing tactics that improve the success of the organisation (Chen et al., 2021). Artificial intelligence-powered communication tools like chatbots and other means of conversation may provide 24/7 customer assistance, freeing up human employees to focus on other critical tasks. It is possible to utilise artificial intelligence to predict client demand, for example, based on patterns and previous purchases (Sestino and De Mauro, 2021). In the pre-sales stage, marketing experts may benefit greatly from using AI’s insights to better grasp what clients want. Researchers used a mixed method, which involved the use of secondary and primary data.
There has been an investigation on the use of artificial intelligence (AI) for client communication in the B2B clothes online market. Secondary results included an investigation on the use of artificial intelligence in customer communication between businesses (Han et al., 2021). Artificial intelligence has the potential to boost sales and improve customer service, according to the overwhelming majority of survey respondents. Thus, instigate solutions on the key issues ascertained use of IT innovative technologies can become the key solution. For instance, as implied in the findings, use of AI clientelling technology may allow sales associates to access expert outfit styling for every item in the shop. Customers may have access to a personal stylist’s expertise without having to invest the time and money in training a sales representative (Banerjee et al., 2021). Customers across the world will be able to access your in-store sales personnel through an AI Remote Selling solution at no additional cost. Having a professional stylist working with you increases the likelihood that you’ll spend more money when shopping (How and Luce, n.d). An AI-powered store is able to recognise recurring customers no matter which channels they’ve previously used; this is a big advancement for traditional retail. It’s not uncommon for customers to anticipate personalised service when they visit a retailer’s physical store for the first time or in a new location. Because of the ease with which customers’ profiles are now accessible, salespeople are better able to have a better understanding of their buying habits.
In order to fulfil their clients’ ever-increasing needs while also maximising the number of new customers they can attract, B2B marketers must put in the time and effort to get to know their consumers. Both end-users and corporate clients leave a digital trail when they engage with online services (Marr, 2019). This includes clicks and searches, chats and emails, live marketing, website visits, and purchase decisions (Gligor et al., 2021). Automated technologies like artificial intelligence (AI) are essential in order to learn about client attitudes, demographics, and behaviour in the face of so much data.
AI’s potential to mimic and outperform human intellect will revolutionise all present business practises. Computer systems driven by artificial intelligence (AI) can handle issues without the need for writing codes thanks to deep machine learning techniques. Another AI tool that B2B enterprises may use is RBA, which efficiently automates worker activities (Saura et al., 2021). RBA solutions from Automation Anywhere are accessible to every company, not only those in the IT industry or those with IT professionals.
Using artificial intelligence in sales and marketing may assist increase the amount and quality of leads generated by analysing social media accounts, website data, and contact databases. To improve business results, firms may use AI and machine learning to create hyper-targeted marketing campaigns (Chen et al., 2021). Using AI-powered communication technology like chatbots and other AI-powered communication tools, the human workforce can now focus on more critical tasks, including providing 24/7 customer care (Chen et al., 2021). Artificial intelligence can assist predict client demand based on trends and purchasing patterns. Using AI’s findings to learn more about what consumers are likely to desire in the pre-sale phase might be a huge advantage to brand marketing specialists.
To sum up, it can be stated that using AI in B2B commerce and B2B communication may help firms identify the best places to provide solutions to their customers. Using machine learning, these benefits may be delivered at a minimal cost, enabling firms to sustain brand loyalty as well as deliver on their commitments to B2B consumers. As a result, better strategies will lead to better KPIs being achieved (Singh et al., 2019). Marketing initiatives for B2B companies may take a long time to put together, particularly if they’re done well. Some background information about themselves, a knowledge of their consumers’ personal history, and some facts about how the product or service is a good fit for them. The study’s goal is to demonstrate to retail B2B companies the advantages of using AI for sales generation, which will allow them to obtain an edge over their competition. Using AI in marketing and sales has its advantages and disadvantages, and this study sheds light on the pros and cons of each. With the help of AI, B2B companies may overcome a wide range of obstacles. As a result, the evaluation of the literature focuses on bringing together the numerous and unique views and viewpoints of many writers and researchers on the development and rising importance of employing AI in B2B customer communication in connection to the retail sectors in the UK. Increasing B2B sales and marketing are clearly being aided by the fast use of AI.
Companies should customise and expedite customer service interactions. Customers want companies to be accessible 24 hours a day, seven days a week, to anticipate their needs, and to respond rapidly to problems. AI-powered assistants have indeed been utilised by corporations for both consumers and staff (Chatterjee et al., 2021). In order to make a significant impact, they interact with a large number of customers at all times. However, the problem with bot-like conversations is that it’s irrelevant to their customers. It gets boring and dull when the bot employs keyword-based programmes to generate answers. Here we look at how AI is changing business-to-business communications, as well as the potential pitfalls and drawbacks of using it in these types of interactions. In order to fulfil their clients’ ever-increasing needs while also maximising the number of new customers they can attract, B2B marketers must put in the time and effort to get to know their consumers. It is possible for both end-users and business customers to leave a trail of digital footprints via their online activity. AI and other high-quality automated tools are essential for learning about client attitudes, demographics, and behaviour in the face of such a mountain of data. AI’s capacity to mimic and outperform human intellect will enhance all present business processes. In order for AI-powered computers, which are very intelligent, to work properly, deep machine learning methodologies must be used.
Firms may employ artificial intelligence and machine learning to construct hyper-targeted marketing strategies in order to enhance company performance (Chen et al., 2021). Chatbots and other artificial intelligence-powered communication technologies can offer round-the-clock customer service, the human staff can devote their time and energy to other vital duties. For example, Artificial intelligence may be used to forecast customer demand based on trends and past purchasing histories, for example. Marketing gurus may find it quite beneficial to use AI’s results in order to understand more about what customers are likely to demand throughout the pre-sales period. The research was carried out utilising a mixed approach, which included the utilisation of both secondary and primary data.
Research was conducted in the B2B online clothing industry to assess the usage of artificial intelligence (AI) for customer communication. The application of artificial intelligence in B2B customer communication was also investigated via secondary outcomes. The vast majority of respondents agree with the above response, which states that artificial intelligence has the ability to increase sales and enhance customer service. Both issues suggest that the assumption is being overstated in terms of the positive influence that artificial intelligence (AI) may have on sales and consumer relations. Despite this, only one individual responded with a “No,” while four others responded with a “Maybe.” The vast majority of responses, on the other hand, reflect an inclination towards good. Artificial intelligence (AI) technology is now accessible to aid salespeople in selecting items that are most suited for their customers’ body types, physical characteristics, and special tastes, among other things. It is possible that the findings will be paired down in order to give the most suited suggestions for the many different situations in which consumers may make their purchases. With the use of artificial intelligence (AI) clientelling technology, sales associates may obtain professional outfit styling for any item in the store, regardless of its price range.
It is possible to teach sales employees to be personal stylists for their clients, enabling them to profit from an enhanced personal style capacity without having to invest the time and money in formal training. At no extra expense, an artificial intelligence-based remote selling system may link your in-store sales workers with consumers anywhere in the globe, allowing you to expand your current sales capacity and increase customer service quality without incurring new costs. It is more likely that you will spend more money when you shop if you have a professional stylist working with you. Artificial intelligence can now be used by merchants to recognise repeat clients at every point of contact, which represents a significant advancement for conventional retail. When devoted consumers visit a retailer’s physical shop for the first time or in a new location, they may expect the same degree of personal attention that they have come to expect from the retailer via their online presence to be extended to them in person. Customer’s interactions with B2B retail firms as a consequence of AI deployment are significantly improved.
B2B marketing success is largely determined by their ability to manage and keep an established client base. B2B lead generation is the process of finding and engaging with potential consumers who will be interested in your product or service and will purchase it (Chen et al., 2021). Sales and marketing teams in B2B companies must pay attention to this. It was difficult to generate leads prior to the advent of artificial intelligence (AI).
Modern AI helps to focus on what and who is most essential to the business, allowing one to concentrate on what and who is most vital to the company (Paschen et al, 2020). AI may save the marketing and sales staff numerous hours of work, as well as precious corporate resources.
Every company aims to transform potential consumers into customers. Creating leads is one thing; converting those leads into paying clients is another, and much more challenging. No matter how many prospective consumers that marketing effort generates each month, nobody of them will buy the goods (Singh et al., 2019).
Increasingly popular and used by many businesses, artificial intelligence marketing is the foundation of 21st-century business. Chatbots and other AI-powered communication tools can help businesses maintain the lines of communication around the clock.
AI can handle anything from answering queries to distributing pre-planned messages. Research shows that it may now be utilised in a conversational manner to keep an audience as interested as if they were chatting to an actual person (Saura et al, 2021).
One of the most effective marketing strategies is sending consumers messages that are specifically tailored to their needs and wants. In order to construct effective tailored communications and advertising campaigns, AI will aid businesses in sourcing relevant data and information (Paschen et al, 2020). These granular targeting capabilities would result in more contextually relevant marketing communications and a better overall consumer experience.
There are a growing number of tools and technologies that allow AI to act as a blogger for any company. While a robot with artificial intelligence (AI) may not be able to write convincingly just yet, the distinction between human and machine-generated material is becoming increasingly blurred owing to AI-powered content tools (Singh et al., 2019). The Word Point, for example, still provides more acceptable commercial alternatives to article writing services.
The key limitations of this research is the lack of empirical data evaluations, although the study conceptualised a comprehensive statistical analysis on the survey conducted but evaluations of market trends, analysis of key metrics and statistical evaluation of key information can be taken into consideration which are primarily not stressed in this research.
Though the research fulfils all the criteria relating to the research topic, there still tend to pertain some gaps that in turn provide scope for conducting future research. This is because the study employed only mixed techniques, which means that the field of future research utilising various ways has been expanded. Furthermore, due to the limited scope of the research, it was not possible to cover all aspects of the issue in this study, leaving opportunity for future research. As a result, future research might examine AI application in a variety of different industries, and researchers could choose other nations to perform the research in order to get more knowledge. Interviews with management experts might be conducted in order to get further real and accurate information.
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