The Retail Industry and Big Data Analytics
In this era of information and digitalisation, big data analytics have become an essential part of businesses, particularly in the retail industry. The retail industry uses machine learning that allows computers to access big data, which is then analysed and makes decisions for the business. The same is true for the car industry, which has recently seen a boom in new business models. The car industry uses AI technology for its innovations. Consequently, AI uses big data from the car to make predictions and decisions.
The AI has been successful in using descriptive analytics, which is the analysis of past performance data to understand what happened; predictive analytics, which uses data to predict future events; prescriptive analytics, which uses data to recommend options that will lead to desired outcomes; and automation analytics, which use automation solutions.
The automotive industry has taken advantage of big data analytics to improve its products, services, and business operations. As a result, they can continuously innovate and meet their customers’ needs. For instance, the emergence of self-driving cars has made it possible for vehicles to use big data from sensors to decide how best to navigate traffic or avoid accidents—companies like Tesla Motors Inc (Kumari & Bhat, 2021). The company recently rolled out its Model S electric vehicle with Autopilot capabilities that allow drivers to take hands-free control of their cars.
In Additional, artificial intelligence (AI) is helping to propel the industry into its next era. AI has become essential in several areas, from streamlining production to improving maintenance operations. By leveraging data from background sensors that monitor everything from vibrations and noise to tire pressure, AI can help predict when maintenance will be needed and drastically reduce maintenance costs by facilitating proactive repairs.
The retail industry is entirely dependent on the future. Predicting what customers will want tomorrow and next year and what they will not buy is the key to success. However, that is not enough to know; the business has to keep the proper inventory and have a good way of managing its shipping and receiving process and sales channels. Consequently, predictive analytics is so crucial in the retail industry. With the help of data analytics and algorithms, retail companies can analyse their data, come up with predictions and make plans to satisfy customers while making profits at the same time.
In today’s marketplace, data analytics is a necessity to stay competitive. With the advent of artificial intelligence and machine learning, companies can track customer sentiments, suggestions, and ideas about the company’s brands in real-time. It is important to note that data analytics is not only for big businesses. Even smaller companies can leverage their customer data to gain insights that help them compete more effectively in the marketplace. For example, by analysing customer sentiment over time, the business can determine how the customers feel about a brand—and whether those feelings are changing. With that information in hand, a company can adjust to keep customers satisfied with its product or service and ensure they remain loyal.
The Automotive Industry and Artificial Intelligence
Retailers have been on the lookout for a way of eliminating the guesswork associated with product pricing (Fulgoni, 2018). The retail industry has been around for centuries, and it is only recently that retailers started using AI to set their prices. Previously, retailers used to rely on their intuition when setting prices. AI has made it easy to select the best pricing strategies for products. It can identify different factors affecting a product’s pricing using its machine learning capabilities and develop the right price for the product. It’s not just about finding the right price but also determining whether or not the pricing strategy will be effective in attracting customers. Retailers want to ensure they are making a profit while selling their products at an affordable price that won’t scare off potential customers.
One of the disadvantages of artificial intelligence and machine learning is data security. For instance, data in retail shops can be accessed by hackers, which could be a catastrophe for the business. Hackers can quickly access the data stored on the devices where the system is housed (Mahmoud et al., 2020). The systems can also be infected with malware, viruses, or spyware. Consequently, there will be wrong predictions on pricing and inventory keeping.
Part B: Solving business problems using analytics
Descriptive analytics analyses past data to understand what happened in the past. The purpose of descriptive research is to learn from historical data so that a business can make better decisions in the future. Consequently, the type of analytics aims to summarise and visualize the past data to prepare for the future. Further, the summaries include the mean, median, standard deviation, and frequency counts.
While descriptive analytics has formed the backbone of business intelligence for years, artificial intelligence and machine learning have made it possible to use descriptive analytics as part of a predictive solution (Mandal, 2018). For instance, a company can use past trends and shopping patterns to calculate the likelihood that the customer will buy a product in the future. Companies use descriptive analytics to understand what happened in a given period. They can evaluate how well they met their financial goals, how many customers bought their products, or how many widgets they sold.
With descriptive analytics, companies can also use descriptive data to predict internal processes and supply chains disruptions. They can identify weak spots in their production or sales processes and take steps to improve those operations. Descriptive analytics helps companies understand where they are current position and plan for the future. Consequently, the company will minimise losses and make a lot of progress in the coming days.
Predictive analytics is a term that describes how businesses use data to predict the future. It uses historical and current data, statistics, and machine learning algorithms to predict consumer behaviour, weather patterns, and even trends in stock prices.
Businesses use predictive analytics in a variety of ways. For example, a retail chain might use it to predict which products will sell best at various stores throughout their chain. This can help them with inventory management and merchandising. Another example is how some companies use it to predict customer retention or whether or not a new customer will make future purchases, so they can focus on retaining customers who are likely to leave or winning back ones who’ve already left. Predictive analytics can also predict when equipment might fail so that maintenance can be scheduled accordingly.
The Success of AI with Descriptive Analytics, Predictive Analytics, Prescriptive Analytics, and Automation Analytics
In conclusion, descriptive and predictive analytics are complementary since they work together to provide the most valuable insights possible (Ahmed et al., 2021). For example, a business might use descriptive analytics to understand how sales were affected by a recent price change and then uses predictive analytics to project future sales with different prices.
Amazon Business uses data analytics to improve its operations and make the shopping experience for its customers faster and more efficient. Amazon Business combines predictive analytics, which is used to predict what customers will buy or do, with descriptive analytics, used to determine what customers have already done (Bouakel & Zerbout, 2021). With these two types of analytics working together, Amazon Business can provide a better customer experience than its competitors.
One example is when a customer orders an item from Amazon that they know is in stock and later finds out it isn’t available anymore. This could be because the item has been sold out or returned by another customer who ordered it first but didn’t want it anymore.
In both cases, Amazon would use descriptive analytics to determine how many people had ordered the same item to determine how likely it was that they’d be able to fulfil their future orders without running into any problems with supply shortages or returns due to incorrect information being provided at checkout time (such as when someone else might have ordered first).
Every business should impress with the use of modern technology. For instance, the 5 G and cloud computing development are among the technologies every company should implement. However, the essential part of modern technology is business analytics. It should become a crucial part of any organisation. Businesses are increasingly investing in data-driven decision making and business analytics to improve their performance and gain a competitive advantage over their competitors (Puscar, 2020). Business Analytics is a broad field that includes Data Science, Artificial Intelligence, Machine Learning and Predictive Analytics.
Analytics is helpful when making decisions on keeping an inventory, finding new customers, and even knowing the best routes for supply (Akter, 2019). For example, if a company has too much of a particular item in stock and it is not selling well. The company can then use data to decide whether to keep the product or sell it at a discount price. In marketing, data can show how customers react to your marketing strategies. The data would also allow the company to see what marketing campaign works well with your customers. Further, businesses need to understand their supply chain to know where their products are coming from and what steps are involved in getting them from point A to point B. consequently, they can make sure that their products are not sitting on shelves for too long before being sold or shipped off again for distribution elsewhere so as not to waste any space unnecessarily. Finally, business analytics helps companies predict how much demand for their products.
In conclusion, adopting analytics into business processes creates a deeper understanding of the business. This understanding is critical for making decisions that improve business performance. Indeed, the adoption of analytics into business processes can be beneficial if it is done with the correct mindset and applied to the right problems. Before acting with prescriptive analytics, businesses need to use descriptive and predictive analytics to understand the business problem. They need first to understand what has happened in the past and why it happened and then make predictions about future trends and scenarios before making precise decisions about how to respond to those scenarios.
Using Big Data Analytics to Improve the Automotive Industry
The company’s leadership needs to understand the importance of analytics in the organisation and its role in helping them meet their strategic objectives. They should collaborate with all departments to provide continuous support for analytics initiatives. Strong leadership ensures that all employees view data-driven decision-making as a critical part of their role within the organisation and not just a task assigned by senior managers (Sivaram, 2021). It creates a sense of ownership among employees who are motivated by their desire to achieve success using data-driven insights rather than external pressures or rewards; hence, they are more likely to stick with these initiatives over time instead of dropping them after the initial enthusiasm wears off when things get difficult or challenging along the way.
Funding is one of the most critical aspects of business analytics. Generally, an organisation needs a lot of funding to incorporate new technologies like 5G, cloud computing, and software (SaaS). This funding can be sourced from sponsors or the company’s profits. Further, an organization can partner with other companies and exchange services, saving the organization from spending money. In the first place, an organisation can source funding from sponsors. Sponsors may be individuals or other companies interested in supporting an organisation’s activities; this could be done through donations or collaborations.
The distribution of analysts across the organisation is crucial, considering that analysts are rare and expensive to hire. The problem is that the analysts tend to be concentrated in the same departments, where they only add value to a small number of projects. They leave the rest of the projects to run on their own.
Data analysis is crucial for a business to succeed, as it provides the foundation on which effective strategies can be built. An organisation needs to have competent analysts who can extract insights and generate predictions from structured and unstructured data. In addition, an organisation should also hire analysts who have both descriptive and predictive analytical capabilities to ensure that the company has experts in all areas of analytics (Weber & Schütte, 2019). Finally, the criteria for distributing analysts across different departments should be clear to ensure that each department receives adequate support and that the company gets maximum value from its analytics team.
Further, a highly centralised data analytics model offers a high level of standardisation for the production of business intelligence solutions, ensuring that all follow company processes and best practices. However, in this case, different units might complain about not being able to customise their solutions to their needs, which can cause a decrease in motivation and a reduction in trust between the business side and the analytics team.
References List
Ahmed, M., Zheng, Y., Amine, A., Fathiannasab, H. and Chen, Z., 2021. The role of artificial intelligence in the mass adoption of electric vehicles. Joule, 5(9), pp.2296-2322.
Akter, S., Bandara, R., Hani, U., Wamba, S.F., Foropon, C. and Papadopoulos, T., 2019. Analytics-based decision-making for service systems: A qualitative study and agenda for future research. International Journal of Information Management, 48, pp.85-95.
Bouakel, M. and Zerbout, A., 2021. Perspectives of Big Data Analytics’ Integration in the Business Strategy of Amazon, Inc. In Big Data Analytics (pp. 201-220). Apple Academic Press.
Ireland, R. and Liu, A., 2018. Application of data analytics for product design: Sentiment analysis of online product reviews. CIRP Journal of Manufacturing Science and Technology, 23, pp.128-144.
Kumari, D., & Bhat, S.,2021. Application of artificial intelligence in tesla- A case study. International Journal of Applied Engineering and Management Letters,20, pp.205-218.
Mahmoud, A.B., Tehseen, S. and Fuxman, L., 2020. The dark side of artificial intelligence in retail innovation. In Retail Futures. Emerald Publishing Limited.
Mandal, S., 2018. An examination of the importance of big data analytics in supply chain agility development: A dynamic capability perspective. Management Research Review.
Prasad, G.N., 2021. Use of Artificial Intelligence in Motor Claims The Future is Now. Bimaquest, 21(2).
Sivaram, M., Porkodi, V., Kandasamy, M. and Sasikala, A., 2021. AI transformation in retail sectors. International Journal of Public Sector Performance Management, 8(3), pp.230-235.
Weber, F. and Schütte, R., 2019. A domain-oriented analysis of the impact of machine learning—the case of retailing. Big Data and Cognitive Computing, 3(1), p.11.
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