How Can Businesses Use AI To Gain Market Share in Their Industry?
How Can Businesses Use AI To Gain Market Share in Their Industry?
Data on the internet today is so rich in valuable information for companies and retail enterprises as it includes aspects such as personal interest, wishes, values to more sensitive data such as financial information, date of birth, individual name, relationships, health information, insurance information among other things central to creating a full individual identity. With data becoming synonymous to understanding and creating an individual’s social identity within the virtual world, and in extension affecting their real world perception, data science and machine learning as constituents of AI have become critical in creating patterns on social trends that can be used to provide quality prediction and estimation for enterprise (automated) decision making. The following report shades light into how business enterprises can utilize AI to gain an greater market share explaining that through access to consumer data, they can build an online portfolio on their customer that constantly tracks, defines and understand their needs, and wants, as such outdoing their competition.
The rise of globalization has manifested an increase in competition for consumer goods and services worldwide. This is a phenomenon described by various economist as the paradox of choice, and it manifests due to increased offerings of the same products, that serve the same purpose to the same line of consumers by different companies, manifesting in a paralysis of choice rather than liberation (Schwartz, 2006). The increase in competition can be related to product and services cost reduction, availability of large and more accessible target markets, quick technological adaptation among companies producing the same products, quick response, quick production and better customer service across the board in many industries (Steger, 2020). For many companies this has been godsend as they now have the ability to increase production, expand, and target a larger market share.
However, in the age of social media, and greater internet access, a stall in one section of the supply chain or production or even a failure in products can greatly work to undermine a company’s market share and effectively its profits since there are numerous companies offering the same product to the same market. As such, companies need to adopt to a variety of solutions to ensure that they secure a larger market share. A larger market share implies greater public visibility of the product, and a greater likelihood of customer retention and even acquisition of more consumers through referrals (Lockett, 2018). It makes the company dominant and their product more trusted by the average consumers.
This essay agrees with this perspective, and advances that AI can be a great tool for general market knowledge. It advances further and states that AI as a marketing strategy tool allows for external market knowledge as it provides analysis of vast amounts of online content on social media platforms, blogs and general websites. It poses a question as to how businesses can use AI to gain superior market share in their industries? The answer that will be explored in short reinstates that marketers with access to AI services can easily create customer personas based on billions of data collected from algorithms and group said personas into various realistic customer models and as such easily persuade them to buy their products.
Artificial intelligence is when a programmer creates algorithms that are able to conduct a set of tasks better than human beings. This is a branch of computer science where programmers deal with the creation of machines that work and think like humans. Machine learning is an application of algorithms to convert raw data into more useful data types (Harrison, 2019). These transformed data can be studied and be used to make better predictions within the real world. Deep learning falls under machine learning.
Data representation is studied instead of task-specific algorithms. An example of AI is seen in Google’s AI-powered predictions, or spam filters that categorically work to limit spamming on emails and numerous websites. additionally, social media companies such as Facebook and Google have come to use this data for purposes of advertising as just a few clicks on their platforms could be enough to identify and group enmasse, for other organizations, their potential buyers, interest, political views, intelligence, customer preference, sexual orientation, gender to exclude others, all of which are important in defining individual company profit (White, 2019). Deep learning can be seen in marketing website when companies apply the use of chatbox to perform tasks such as interaction with computers. Machine learning (ML) a constituent of AI, comes instrumental in refining searches on public websites such as Google (Surti, and Chakraverty, 2021).
Artificial intelligence allows for the analysis of large amounts of data, by capable algorithms to reveal patterns in spending, preferences, likes dislikes, ideologies and well as perception of products (Elaine, 2020). Researchers have as such identified that a critical way that companies can use to increase market share in the modern day and age is by use of artificial intelligence, as there is too much data available from consumers that can be used by the companies to track consumer trends and create an understanding of their demographic for easier promotion, advertising, and sales (Kramer, Brown and Munichiello, 2021). In the sector of business management, for companies to gain a larger market share, there is need for a customer-centric approach in the application of AI.
AI industry experts agree with this stating that customer-centric, personalized and data-driven analysis of their customer online transaction offers more insight to enterprises, allowing them to target specific consumers and offer them what they need when they need it (Scoro, 2020). This is because the technology can allow business to find ideal customer profiles and segments, define scoring leads based on customer’s online behavior and fit into them to prioritize sales efforts, increase automation in order to satisfy customers and quickly expand (Allied Market Research, 2021). Availability of data from a large pool of resources and avenues has worked to increase the adoption of AI.
The internet’s popularity among the modern day populace across the world has been instrumental in providing people access to a variety of information on goods and services, access to brands, ability to monitor the supply chain, among many more things. But its successes have been advanced thanks to the form of reciprocal service that the modern consumer provides when they visit the internet and leave behind their digital footprints (Analytics Insight, 2021). Like the physical foot print, that can be used to trace an individual’s activities, the virtual digital prints can do that and more thanks to the great digital migration experienced within the 21st century that has seen digitization of day-to-day operations to the point where it is becoming incomprehensible to make a difference between online and offline activities.
AI works to provide a better user experience. This can be done by the provision of an avenue for data analysis such as facial recognition. Here companies can produce products that create a more intimate connection with their consumers. Additionally, AI can be used to provide better targeting of consumers by analysis of data on websites they have visited so as to create adverts tailored to things they are, for example, attracted to. It could also be used to provide faster results through metadata analysis to provide finer data (OpenPR, 2021). UX (User experience) defines the process that the marketing design team uses to create a product in the digital world. The process takes into consideration the user’s preferences in order to create a meaningful and relevant design (Grand View Research, Research, 2020). UI (User Interface) is the front end of a website or an application. It is what you see when serving inputs in computer systems. Where the user links to make meaningful interactions. This is where an individual is allowed to make inputs and software.
Use of AI can be applied by businesses to measure potential financial risk, so as to mitigate from losses, or evaluate product offerings and their perception in the market, so as to prevent unwanted products from entering the market and failing or directly tapping into the consumer data points and creating individual social identities and behavior that can be used to understand individual customers who are then grouped into demographics and offered certain products.
First and foremost, big data is used by financial, marketing and retail institutions among others to lessen operational risk and prevent fraudulent behaviors. In addition, it greatly reduces information asymmetry issues and meets all set compliance goals (Grand View Research, 2021). Institutions may have reputable access to real-time data that could aid in the detection of fraudulent actions, as well as gain consumer insights into their product for their own benefit. Digitization in the finance, retail and service sectors has allowed technologies such as artificial intelligence (AI), advanced analytics, big data, and machine learning to permeate and revolutionize how financial institutions contend in the market.
Big corporations are adopting these innovations to carry out digital transformation, fulfill customer demand, and improve their profitability. While most businesses are holding fresh and important data, they aren’t always clear how to leverage its potential since the data is unorganized or hasn’t been recorded inside the organization. Firms need to collect and analyze consumer data as the most sectors today are data-driven like never before. Efficient technology systems that match the superior analytical needs of digitalization will allow institutions to completely harness the potential of big data, detect competitive advantages, and generate market opportunities.
Among other things, AI uses the econometric models to investigate stochastic volatility, optimal portfolio returns, and sequential learning, all key factors in risk management (Johannes et al., 2014). This becomes key in business identifying their potential failures in product and services and through customer data identify ways in which they can improve. Berry, Mohamed and Yap (2019) outline that the demand in advanced data analytic tool is due to a rise in big data trends some beyond human conceptual understanding. It is widely conceptualized that “Data scientists may have the necessary expertise to test systems, but the manual intractability of large-scale data coupled with the complex, sometimes opaque, nature of state-of-the-art models makes it hard even for them to clearly articulate and ascribe trustworthiness to approaches and insights. This is even harder for lay users who lack specialized data science knowledge but are sometimes the users of these systems or those most impacted by them” (Passi and Jackson, 2018). The need for calculated trust to be established and the fact that most of this analysis are complex becomes the basis under which machine learning uptake is seen.
Thirdly, AI allows companies with legitimate internet licenses to do business and provide them with a justifiable platform to use customer in their reciprocal services to offer them information, that is critical in gaining market share. Take the example of the now defunct Cambridge Analytica, a company that used facebook’s data points to gain insight into consumer behavior for billions of people in Africa, USA and Europe and used iot algorithm to create virtual social identities for these people, in turn selling the data to politician campaign platforms as well as big business such as oil, as and cigarette corporation (Goldhill, 2018). While its use of said data was illegal as it had not notified the owners how it sought to use the data, majority of the data was fed into AI, and the results were astounding as it allowed many of the final buyers to predict outcomes to elections, sales and advertising campaigns in a very precise manner and gain the upper hand (Fernandez, 2019). With the adequate compliance to regulation and other legal requirement, access to data and software to manipulate said data, organization can use AI to their own benefit, in expanding market share.
SOLUTION AND CONCLUSIONS
Notwithstanding the potential ethical concerns that comes with collection and use of consumer data and data points, the best and most imperative use of AI can be gained from the evaluation of individual social identities online, curating their behavior and personalizing their online feed (social media, website visits, etc) into what they want. These data points define consumer profiles by evaluating their moves, what they are interested in, why they like it, how they use the product, their general preference as well as their overall perception of other brands. Organization with access to such data can change accordingly and target individual buyers or a group of buyers of their product and services with accuracy knowing fully well what they need, and why they need it and even in some cases when, to increase their sales, brand perception and overall quality of service as such gain good market share.
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Include the details of data, quantitative analysis and other necessary information to back your problem, goals, and solutions.
At least you MUST include a table that summarizes the CRAAP test result of the at least 10 references you used in your report. You can find a sample in the assignment instructions.
Note: This is NOT part of the body. 5-10 pages PLUS Cover page, REFERENCES, and APPENDIX.
Table 1: Summary of the CRAAP Test Results
Article 1 Article 2 Article 3 Article 4
C-1 No date 6-3, 2001
C-2 No date No date
C-3 Yes Yes
C-4 n/a n/a
R-1 Very much Very much
R-2 College students Professionals, Researchers
R-3 Yes Yes
R-4 No Yes
R-5 No Yes
AU-1 No author John Jones
AU-2 Unknown Professor of mgmt at Famous U.
AU-3 Unknown Yes
AU-4 Unknown Yes
AU-5 Canvas .edu
AC-1 Canvas Journal of FGCU Management
AC-2 No Yes
AC-3 No Yes
AC-4 No Yes
AC-5 Biased No bias
AC-6 Yes No
P-1 Persuade Inform
P-2 No Yes
P-3 Opinion Probably fact
P-4 Subjective Objective & impartial
P-5 Political bias Ideological bias
Your Overall Assessment Not credible Credible
Table 1: Continued
Article 5 Article 6 Article 7 Article 8
Table 1: Continued
Article 9 Article 10 Article11 (if necessary) Article 12 (if necessary)
How Can Businesses Use AI To Gain Market Share in Their Industry?
Artificial intelligence (AI) has been transforming industries and businesses in various ways. AI involves the use of computer algorithms and programs to perform tasks that would typically require human intelligence. AI has become a vital tool in business, enabling companies to gain a competitive edge in their industries. In this essay, we will explore how businesses can use AI to gain market share in their industry.
II. AI in Market Share Analysis
Market share analysis is a critical aspect of any business strategy. It involves analyzing the sales of a company in relation to its industry’s total sales. With AI, market share analysis has become more efficient and accurate. AI can analyze large amounts of data in real-time, which enables companies to make informed decisions on pricing, marketing, and customer segmentation.
AI also allows businesses to gain insights into their competitors’ strategies and market trends. For example, companies can use AI-powered tools to analyze their competitors’ social media presence, customer feedback, and online reviews. These insights can help businesses make strategic decisions on product development, marketing, and pricing.
Real-world examples of AI in market share analysis include Amazon and Alibaba. These companies use AI to analyze customer behavior and preferences, enabling them to personalize their offerings and target specific customer segments effectively. They also use AI to optimize their pricing strategies and improve their supply chain operations.
The future of AI in market share analysis is promising. As AI continues to evolve, businesses will be able to analyze customer data from various sources, including social media, online reviews, and search engines. This will enable companies to gain more comprehensive insights into their customers’ preferences, behaviors, and trends, which will help them make informed decisions on product development and marketing.
III. AI in Marketing
Marketing is another area where AI can help businesses gain market share. AI-powered tools can analyze customer data, enabling companies to create personalized marketing campaigns that target specific customer segments. This can improve customer engagement and increase customer loyalty.
AI can also help businesses optimize their advertising spending by analyzing customer behavior and preferences. For example, AI can analyze customers’ browsing history, search queries, and online purchases to determine the best advertising channels and messages.
Real-world examples of AI in marketing include Coca-Cola and Sephora. Coca-Cola uses AI to analyze customer data and create personalized marketing campaigns that target specific customer segments. Sephora uses AI to analyze customers’ skin tones and preferences to recommend personalized makeup products.
The future of AI in marketing is exciting. As AI continues to evolve, businesses will be able to create more personalized marketing campaigns that resonate with customers. AI-powered chatbots will also become more sophisticated, enabling businesses to provide personalized customer service and support.
IV. AI in Sales
Sales is another area where AI can help businesses gain market share. AI-powered tools can analyze customer data to predict which products or services a customer is most likely to purchase. This enables businesses to tailor their sales strategies to specific customer needs, increasing the likelihood of closing a sale.
AI can also help businesses identify new sales opportunities by analyzing customer data and identifying patterns and trends. For example, AI can analyze customer purchase history and identify products that customers are likely to purchase together. This enables businesses to create bundled offers that increase the average order value.
Real-world examples of AI in sales include Salesforce and Shopify. Salesforce uses AI to analyze customer data and provide sales representatives with personalized recommendations on how to close a sale. Shopify uses AI to provide businesses with personalized insights on their sales performance and customer behavior.
The future of AI in sales is promising. As AI continues to evolve, businesses will be able to use it to automate sales processes, enabling sales representatives to focus on more complex tasks. AI-powered tools will also become more sophisticated, enabling businesses to identify new sales opportunities and improve their sales performance.
V. AI in Customer Service
Customer service is another area where AI can help businesses gain market share. AI-powered chatbots can provide customers with personalized support and assistance, reducing wait times and improving customer satisfaction. AI-powered tools can also analyze customer feedback and identify areas where businesses can improve their customer service.
Real-world examples of AI in customer service include Bank of America and H&M. Bank of America uses AI-powered chatbots to provide customers with personalized support and assistance. H&M uses AI to analyze customer feedback and identify areas where they can improve their customer service.
The future of AI in customer service is exciting. As AI continues to evolve, businesses will be able to provide more personalized customer service and support. AI-powered chatbots will become more sophisticated, enabling businesses to provide more complex support and assistance.
VI. AI in Operations
AI can also help businesses gain market share by improving their operations. AI-powered tools can analyze data and identify areas where businesses can improve their efficiency and reduce their costs. For example, AI can analyze supply chain data and identify areas where businesses can optimize their inventory management or reduce their transportation costs.
AI can also help businesses improve their product development processes by analyzing customer feedback and identifying areas where they can improve their product offerings.
Real-world examples of AI in operations include UPS and Ford. UPS uses AI to optimize their delivery routes, reducing their transportation costs and improving their efficiency. Ford uses AI to improve their product development processes, enabling them to create more innovative and customer-focused products.
The future of AI in operations is promising. As AI continues to evolve, businesses will be able to optimize their operations and reduce their costs more effectively. AI-powered tools will become more sophisticated, enabling businesses to identify new opportunities for improvement and innovation.
In conclusion, AI can help businesses gain market share in various ways. AI can be used to analyze customer data, create personalized marketing campaigns, improve sales performance, provide personalized customer service, and optimize operations. As AI continues to evolve, businesses will be able to leverage it to gain a competitive edge in their industries. The future of AI in business is promising, and businesses that embrace AI will be well-positioned for success.
The Business Impact of AI” by McKinsey & Company. This report explores the ways in which businesses can use AI to improve their operations, drive growth, and gain market share.
“AI in Marketing 2021: The Next Frontier in Customer Experience” by Salesforce. This report examines the use of AI in marketing and how businesses can use it to personalize customer experiences and drive revenue growth.
“How AI Can Help You Boost Sales” by Harvard Business Review. This article discusses how businesses can use AI to improve their sales performance, identify new sales opportunities, and increase revenue.
“AI in Customer Service: Using Bots to Enhance Customer Experience” by Gartner. This report explores the use of AI-powered chatbots in customer service and how businesses can use them to improve customer satisfaction and loyalty.
“The Future of Work: How AI Will Transform the Employee Experience” by Deloitte. This report discusses how AI can be used to improve employee productivity, engagement, and satisfaction, ultimately helping businesses gain a competitive edge.