COIT20253 Business Intelligence using Big Data
COIT20253 Business Intelligence using Big Data
Assessment 1
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Executive Summary
This document explores the combination of huge statistics inside the retail enterprise and specializes in how it can lead to cost introduction and commercial enterprise optimization. The film highlights the tremendous possibilities massive data gives to generate new commercial enterprise insights, boost operational performance, and generate new sales. It also discusses how big information is changing the retail enterprise through personalization and innovation. Clutter. The document also showcases relevant open facts from the UCI Machine Learning Repository, indicates its metadata, and explores how it could be used to identify opportunities in the market. How can retailers use huge statistics to benefit aggressive advantage, improve patron revel in, and pressure long-term period growth?
Table of Contents
TOC o "1-3" h z u HYPERLINK l "_Toc175309071" PART A PAGEREF _Toc175309071 h 3
HYPERLINK l "_Toc175309072" 1. Introduction PAGEREF _Toc175309072 h 3
HYPERLINK l "_Toc175309073" 2. New Business Insights and Big Data Opportunities PAGEREF _Toc175309073 h 3
HYPERLINK l "_Toc175309074" 3. Optimization PAGEREF _Toc175309074 h 5
HYPERLINK l "_Toc175309075" 4. Revenue Opportunities PAGEREF _Toc175309075 h 5
HYPERLINK l "_Toc175309076" 5. Industry Transformation PAGEREF _Toc175309076 h 5
HYPERLINK l "_Toc175309077" 6. Value Creation using Big Data PAGEREF _Toc175309077 h 6
HYPERLINK l "_Toc175309078" 7. Porter Value Chain Analysis PAGEREF _Toc175309078 h 7
HYPERLINK l "_Toc175309079" 8. Porter Five Forces Analysis PAGEREF _Toc175309079 h 8
HYPERLINK l "_Toc175309080" PART B PAGEREF _Toc175309080 h 10
HYPERLINK l "_Toc175309081" 1. Dataset Overview PAGEREF _Toc175309081 h 10
HYPERLINK l "_Toc175309082" 2. Justification for Selection Metadata PAGEREF _Toc175309082 h 10
HYPERLINK l "_Toc175309083" 3. Business Opportunities through the Chosen Dataset PAGEREF _Toc175309083 h 11
HYPERLINK l "_Toc175309084" 3.1 Insight Generation PAGEREF _Toc175309084 h 11
HYPERLINK l "_Toc175309085" 3.1.1 Customer Segmentation PAGEREF _Toc175309085 h 11
HYPERLINK l "_Toc175309086" 3.1.2 Product Performance Analysis PAGEREF _Toc175309086 h 11
HYPERLINK l "_Toc175309087" 3.2 Value Creation PAGEREF _Toc175309087 h 11
HYPERLINK l "_Toc175309088" 3.2.1 Operational Efficiency PAGEREF _Toc175309088 h 11
HYPERLINK l "_Toc175309089" 3.2.2 Revenue Growth PAGEREF _Toc175309089 h 11
HYPERLINK l "_Toc175309090" 3.3 Industry Transformation PAGEREF _Toc175309090 h 12
HYPERLINK l "_Toc175309091" 3.3.1 Personalized Marketing PAGEREF _Toc175309091 h 12
HYPERLINK l "_Toc175309092" 3.3.2 New Service Development PAGEREF _Toc175309092 h 12
HYPERLINK l "_Toc175309093" 3.4 Competitive Advantage PAGEREF _Toc175309093 h 12
HYPERLINK l "_Toc175309094" Conclusion PAGEREF _Toc175309094 h 13
HYPERLINK l "_Toc175309095" References PAGEREF _Toc175309095 h 14
PART A1. IntroductionThe current fast globalizing business requires the application of facts via insights to maintain a competitive advantage CITATION Cur16 l 3081 (Curry, 2016). Significant facts, which are distinguished by their scope, rapidity, diversity, and correctness, have developed into a useful tool that organizations may use to improve, beautify, and demand new the structure. This report, which focuses on the retail industry, investigates how big data information from multiple sources, record-keeping, buyer contacts, and online behaviour may be incorporated into business processes to provide new price offers. Through the effective utilization of this data, retailers may enhance their understanding of consumer behaviour, streamline their supply chain, and develop customized marketing campaigns. This report aims to explore the potential applications of Porter's fee chain and five forces characteristics in examining the impact of comprehensive data on retail businesses. Selling goods and talking about the business opportunities it presents. The research aims to offer valuable insights to retail businesses who want to use large data sets to get a competitive edge and significantly increase their growth.
2. New Business Insights and Big Data OpportunitiesWith the help of deeper insights, optimization, and the opening of new income streams, plenty of data presents an excellent opportunity to transform businesses. Massive record analytics gives retailers the ability to capitalize on how customers are seen to behave, make choices, and exhibit trends. Retailers may construct precise customer profiles that go beyond the public by obtaining and analysing data from a variety of sources, including social media, online video games, and transaction history. This data enables companies to anticipate consumer purchasing behaviour, customize advertising and marketing campaigns, and anticipate future requirements. For example, data pertaining to consumer loyalty or decision-making would enable retailers to enhance their tactics, so boosting customer satisfaction and ultimately driving revenue and consideration. Play a significant role in operations and cost minimization. Retailers can predict demand, manage logistics more effectively, and keep an eye on stock levels by utilizing real-time data to improve their supply chains. An information-driven strategy may assist merchants in avoiding the risk of out-of-stock or out-of-inventory goods, guaranteeing that products are available where and when customers need them, and reducing the cost of products.
Additionally, by looking through export data, companies may identify inefficiencies like delays or discrepancies and take prompt action to address them. This optimization guarantees on-time delivery and delivery, which lowers operating costs while also increasing customer satisfaction. New income streams are being established by retailers. Stores may modify prices depending on customer behaviour, price competitiveness, and conversion rates when they have access to real-time market data and consumer insights. As a result, they can maximize their income and lead competitive lives. Important details also allow retailers to read client purchasing trends and recommend more items to them when they make a purchase, which helps them spot sales possibilities. These strategies increase the average possession value and lead to more transactions. Through the examination of customer feedback, product reviews, and social media exchanges, retailers may identify market gaps and develop new products that cater to customer needs. The delightful process of producing this product serves to both enhance consumer satisfaction and generate a competitive advantage by setting the store apart from its competitors. Additionally, to satisfy client enchantment and sales technology demands, insights from huge data analytics might guide the search for new company models, including as subscription solutions or targeted advertising CITATION Che15 l 3081 (Chen, 2015).
Figure SEQ Figure * ARABIC 1: Big Data in Retailing
Source: CITATION Bra17 l 3081 (Bradlow, 2017)
3. OptimizationBig data has the power to enhance retail in every way, increasing its effectiveness and efficiency. Inventory management is among optimization's vital components. Retailers may estimate demand and modify inventory based on that prediction. This lowers stock levels and lowers the possibility of overstocking or out-of-stock items by guaranteeing that popular products are available when needed. Retailers may benefit from real-time data analytics by having simpler items, better logistics, and quicker delivery times. This all-inclusive approach to supply chain and inventory management lowers costs, boosts operational effectiveness, and enhances service delivery all of which eventually result in corporate success.
4. Revenue OpportunitiesThe income of the retail industry has expanded significantly because of the smart use of big data. Retailers who possess comprehensive data on customer behaviour, purchasing habits, and company models may utilize this data to craft marketing strategies and customized promotions. Retailers may increase conversions and sales by developing attractive and engaging offers by studying data on client preferences and shopping patterns. Dynamic pricing techniques, which may modify prices in real time according to competition, demand, and other factors, are also supported by big data. This modification enables shops to secure more sales at peak or off-peak times while also optimizing their pricing methods to maximize profits and revenues CITATION Abd23 l 3081 (Abdollahi, 2023).
5. Industry TransformationThe retail industry is currently experiencing a change in established business processes and the creation of new opportunities due to the transformative potential of big data. Retailers may use the information to create new workspaces that suit to client preferences by including online marketing and advertising or subscription services. The development of new products and services that suit to the wants and needs of customers may be aided by data-driven insights. Retail businesses may take the lead in business integration processes and adjust their strategies to get a competitive advantage through constant action and statistical analysis. This effort will increase operational efficiency and enable the shop to better meet the demands of its customers and identify new development opportunities. Leadership in the sector and long-term performance are both influenced by these elements.
6. Value Creation using Big DataRetail price discovery improves the fields of factory operations and consumer relations with statistics-driven insights using big data. A crucial domain is client segmentation, enabling retailers to accurately divide their consumer base into groups. Retail outlets may improve their advertising and marketing campaigns with targeted, rhythmic segments that achieve exactly that by researching consumer behaviour, demographics, and preferences. This low-tech method guarantees that marketing initiatives are not only more fitting but also more appropriate, resulting in and requiring significant intervention and modification expenses.
Big Data not only improves segmentation but also significantly raises visitor interest. Retailers gain important information regarding consumer happiness and quality suppliers by analysing comments, critiques, and social media interactions. This feature makes it possible for businesses to pinpoint and resolve difficult issues, fix supplier services, consider gender preferences and choices, and fulfill customer interactions. It is also intended to give customers more positive and individualized information, which may increase their loyalty and encourage them to re-engage.
Another significant area where big data adds value is business efficiency. Practical strategies for promoting variety in standard retail establishments can be found in real-time statistics analysis. Record-pushing information, for instance, might be used to enhance supply chain management by predicting demand trends and modifying inventory levels accordingly. This promotes uniform stock management and lowers the possibility of excess stock or stockouts. Big Data may also be utilized to create product placements and store layouts that are solely dependent on the probabilities and patterns of client buying. This boosts sales prospects and operational efficiency but no longer enhances the shopping experience. In general, vendors may improve consumer happiness, streamline corporate procedures, and achieve remarkable profitability through the smart utilization of big data.
7. Porter Value Chain Analysis
Figure SEQ Figure * ARABIC 2: Porters Value Chain Model
Source: CITATION Cuo20 l 3081 (Cuofano, 2020)
Porter's value chain analysis offers a framework for understanding how big data may improve each connection in the supply chain. Predictive analytics, for instance, may be used in inbound logistics to improve stock control and the supply chain. Using data for more accurate projections, retailers may use records to enhance their purchasing processes, lower stock levels, and ensure that a product's availability meets sales criteria. Retailers may identify inefficiencies, optimize processes, and reorganize operations for better, more remarkable, more efficient operations by using statistics driven insights. Enhancing operational effectiveness raises average production in addition to lowering running costs. Enhancing the precision of predicting customer requirements may lead to more effective routes and planning for transportation, resulting in faster and more precise transportation. This innovation makes it easier to minimize delays by guaranteeing punctual delivery, cutting down on transportation costs, and improving the general clientexperience. Evangelism plays a significant part. By examining consumer behaviour, opportunities, and purchase history, retailers may develop advertising strategies tailored to individual customers. This personalized approach boosts engagement and advertising efficacy, improving conversion rates and customer loyaltyCITATION Zie20 l 3081 (Ziegler, 2020).
Lastly, big data will support customer analytics and improve after-income providers in offers. Retailers may identify emerging issues, evaluate the quality of their carriers, and implement solutions with the help of input from reviews, discussions, and provider interactions. This modern approach to provider control makes customers feel valued and promptly attends to their problems, which boosts customer satisfaction and fosters long-term loyalty. In general, comprehensive data enhances every aspect of Porter's value chain, resulting in improved customer satisfaction, increased operations, and increased profitability CITATION Jul19 l 3081 (Juliana, 2019).
8. Porter Five Forces Analysis
Figure SEQ Figure * ARABIC 3: Porters Five Forces Model
Source: CITATION Adl23 l 3081 (Adly, 2023)
Porter's Five Forces learning provides illumination on the important variables influencing retail business competition. Retailers may reduce the threat created by new competitors because to their greater capacity to gather enough information. These stores may impose cost-effective pricing policies that provide significant barriers to new competitors, enhance their product offerings, and modify their consumer studies with the use of records-pushed data. New competitors find it challenging to match designers in terms of output and customer involvement due to this detailed utilization of information, which lowers their risks. Managing large amounts of data effectively Boost the visibility of suppliers into the overall operation of the organization and its suppliers. Retailers can negotiate better terms and find opportunities by examining data on supplier dependability, price, and shipping. The data-driven strategy tips the scales in favour of retailers, enabling them to obtain better contracts and reduce their dependence on individual sales representatives. The huge data sets show the consequences of gender CITATION Ger19 l 3081 (Gerard, 2019).
Retailers may utilize data to develop marketing strategies, provide effective advertising, and provide consumers with personalized information. This kind of customization can increase customer pride and loyalty while reducing the possibility that other businesses will entice customers. Shops profit when customers become less likely to switch to other brands and more aware, as this lowers their negotiating power and encourages long-term loyalty. Retailers can adapt to the risks associated with their business to remain ahead of changing consumer preferences and shifting marketplaces. Shops can adjust their methods and find components that improve the identification of various items through analysing data CITATION HuY16 l 3081 (Hu, 2016).
Retail outlets may protect itself against exchange risks and continue to operate as a marketplace by modifying their inventory or investigating other marketplaces based on data. Level enhances common experience and competitive mind. Retailers may utilize records to learn about the pricing, sales, and promotions used by their competitors. This enables them to identify opportunities for differentiation and growth performance and implement new approaches. Retailers may continue to maintain aggressive pressure while maintaining a competitive advantage by using data to continuously outperform competitors and adapt to the dynamics of the marketplace. In general, large data sets are essential for addressing a variety of issues facing the retail industry. They help merchants find solutions, take expansion possibilities, and receive positive results CITATION Wan17 l 3081 (Wang, 2017).
PART B1. Dataset OverviewThe "Online Retail Dataset" from the Kaggle was selected as the dataset. It holds the record for being the primary online gift hold of all time. All the changes made to this document between 01/12/2009 and 09/12/2011 are included here CITATION Lak19 l 3081 (N, 2019).
2. Justification for Selection MetadataThis database was selected because it offers a wide range of crucial data, such as details on customer transactions, goods, and behaviours. Because it can analyse sales, client segmentation, and purchase trends, it is pertinent to the retail sector. Due to the nature of this data, which covers every year of the product, there are several opportunities to provide insightful data and, as a result, achieve good results in the retail sector.
Dataset Structure:
Data Type: The dataset is structured, stored in a CSV format, making it easy to analyse using common data analysis tools like Excel.
Datasets size: 45 MB
Author: Lakshmipathi N
Key Attributes:
InvoiceNo: Individual invoice numbering for every transaction input
StockCode: An individual number assigned to every product.
Description: Text description of each product.
Quantity: The quantity of items purchased in every transaction.
InvoiceDate: Transaction's date and time.
UnitPrice: The cost of the product per unit.
CustomerID: Unique number assigned for identifier for each customer.
Country: The country where the buyer executed the transaction.
Data Volume and Quality:
Size: The dataset contains about 500,000 rows, representing transactions made over 12 months.
Quality: The dataset is notably clean, however there are a few lacking values, especially within the CustomerID field, which might need to be addressed earlier than evaluation. The Description area additionally consists of text facts which could require preprocessing (e.G., disposing of duplicates, coping with typos) to make sure correct evaluation.
Source and Accessibility
Data Source: The dataset is publicly to be had at the UCI Machine Learning Repository, a well-known platform for sharing educational datasets.
Accessibility: The dataset may be freely downloaded in CSV layout from the UCI internet site. There aren't any restrictions on its use for educational and research purposes.
Link for access datasets: HYPERLINK "https://www.kaggle.com/datasets/lakshmi25npathi/online-retail-dataset" https://www.kaggle.com/datasets/lakshmi25npathi/online-retail-dataset
3. Business Opportunities through the Chosen Dataset
3.1 Insight Generation3.1.1 Customer SegmentationThrough purchase style analysis, categorize clients entirely according to their purchasing behaviour. For example, common consumers, high-value customers, and seasonal shoppers can all be distinguished. By using this segmentation, marketing and advertising strategies may be more customized, and customer retention rates may improve.
3.1.2 Product Performance AnalysisThe dataset makes it possible to assess a product's performance and reputation over time. Stock control, price strategies, and advertising campaigns may all benefit from knowing which goods sell the most, for how long, and in which locations.
3.2 Value Creation3.2.1 Operational EfficiencyBy reducing stockouts, reducing extra stock, and forecasting product demand, insights from the dataset can improve inventory management. For example, by knowing how long a certain product sells, a shop may adjust their restocking plan, accordingly, saving money.
3.2.2 Revenue GrowthGo-selling and upselling possibilities may be found using the dataset. To increase average transaction values, the store should bundle goods that are typically sold together or run targeted promotions based on its assessment of customer purchase patterns CITATION Sey23 l 3081 (Seyedan, 2023).
3.3 Industry Transformation3.3.1 Personalized MarketingThe shop may create personalized marketing campaigns that target certain customer categories with tailored offers by utilizing client data. For example, during the holiday season, incentives may be targeted towards consumers who frequently purchase gifts.
3.3.2 New Service DevelopmentTo produce new goods or services, the dataset should assist in identifying gaps in the product range or unfulfilled customer requests. For instance, the retailer may launch a brand-new bundled product if a significant number of customers are purchasing similar goods one at a time.
3.4 Competitive AdvantageBy the incorporation of these insights into the business enterprise plan, the store may set itself apart from competitors by giving more personalized customer evaluations, improving procedures, and introducing new items.
ConclusionThere are many chances to create value when integrating big data into the retail sector. Retailers can streamline operations, find new income sources, and improve consumer experiences by utilizing both organized and unstructured data. Utilizing Porter's Value Chain and Five Forces analysis highlights the strategic benefits of Big Data in sustaining a competitive advantage and promoting industry transformation.
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