Analytics has become one of the most powerful tools available to retailers. Retail data analytics involves using data and analytics to understand and improve retail business operations using data and analysis to inform business decisions in the retail industry. It consists of collecting and analysing data from various sources, such as point-of-sale systems, customer relationship management systems, and market research, to understand customer behaviour and identify trends and opportunities. Retail analytics can be used for customer segmentation, optimise pricing, inventory management, marketing campaigns, churn prediction, fraud detection and other aspects of retail operations. Some standard techniques in retail analytics include descriptive, predictive, and prescriptive analytics. Descriptive analytics involves summarising and reporting past performance, while predictive analytics uses statistical modelling to forecast future outcomes. Prescriptive analytics involves using advanced algorithms and machine learning to recommend actions and optimise decision-making. By using analytics, retailers can make more informed, data-driven decisions that can help improve their operations and increase profits.
The types of data required for retail analytics depend on the specific goals and objectives of the analysis. Some common types of data that may be used in retail analytics include:
- Sales data: This includes data on the products or services being sold, such as the product name, price, and quantity sold.
- Customer data: This includes data on the customers purchasing products or services, such as their demographic characteristics (e.g., age, gender, income), contact information, and purchase history.
- Market data: This includes data on the market in which the retailer operates, such as data on competitors, market trends, and economic conditions.
- Supply chain data: This includes data on the flow of goods from suppliers to customers, such as data on inventory levels, order lead times, and delivery times.
- Financial data: This includes data on the financial performance of the retailer, such as data on revenue, expenses, and profitability.
- Web analytics data: This includes data on customer behaviour on the retailer's website, such as data on page views, time on site, and conversion rates.
- Social media data: This includes data on the retailer's presence on social media platforms, such as likes, comments, and shares.
Continuing from the business analytics module in year 3, the module is designed to deepen the student's knowledge of data analytics and further develop analytical skills. Retail analytics often involves solving complex business problems and requires strong problem-solving skills. To analyse data successfully and make data-driven decisions, students will need to be able to identify relevant issues, gather and analyse data, develop hypotheses, and test and validate their ideas. This process can involve working with large and complex datasets, using various software tools and techniques, and thinking creatively and critically.
In addition, retail analytics often involves working in a team setting. Students must collaborate with others, share ideas and resources, and communicate effectively to achieve shared goals. This includes working with team members with different backgrounds, skills, and perspectives and effectively contributing to the team's efforts.
Students will be better prepared to tackle complex business challenges and succeed in various professional settings by developing their problem-solving and teamwork skills through studying retail analytics.
- Introduction to Data Analytics/Big Data: This includes an introduction to data analytics; customer-centric data mining; a business case for data mining; an understanding of how data mining can solve business problems; pitfalls to avoid.
- Data Analytics Methodology: Understanding the data analytics project life cycle.
- Data collection and preparation: This may include data mining, data cleaning, and data transformation.
- Data visualisation: This may include topics such as creating charts, graphs, and dashboards using tools such as Tableau, Power BI, and Excel.
- Machine learning/ Predictive analytics: This may include supervised learning (Regression/classification tasks) and unsupervised learning (clustering).
- Industry-specific applications: This may include pricing optimisation, inventory management, marketing, customer churn and customer segmentation as they relate to the retail industry.
- Applications of retail analytics for sustainability
- Supply chain sustainability
- Sustainable product design
- Energy and resource efficiency
- Customer behaviour analysis
- Challenges and best practices for implementing sustainable retail analytics
- Data bias, inclusiveness and ethical consideration
- Ethics and privacy in retail analytics
- Ethical considerations in data collection and analysis
- Privacy laws and regulations
- Best practices for ethical data use
- Capstone Project: Student projects applying retail analytics for sustainability
-
This module mainly utilises project-based learning that emphasises hands-on, active learning and applying knowledge and skills in real-world situations and authentic projects. The module teaching method is an instructional learner-centred approach that allows students to take an active role in their learning and to develop essential skills such as problem-solving, analytical skills, team working, collaboration, and critical thinking.
- Lectures: Traditional lectures can be an effective way to introduce students to new concepts and theories. They can be delivered in-person or online and supplemented with visual aids and interactive activities to make them more engaging.
- Hands-on learning: Labs and workshops can provide students with hands-on experience using data analysis software and tools, such as Excel, Tableau, and Rapidminer.
- Online courses: There are many online/web-based courses (e.g. MOOC) and resources available on data analytics tools, which can be a convenient way for students to learn at their own pace how to use the analytics software Tableau and Rapidminer. These courses often include videos, readings, and quizzes to help students understand the material.
- Group discussions and debates: Encouraging students to engage in discussions and debates with their peers to foster critical thinking and problem-solving skills.
- Assignments and quizzes: Assignments and quizzes can reinforce learning and assess student understanding.
- Project-based learning: Students learn by actively participating in real-world, authentic projects. Students will work on a real-world business problem or challenge in a team setting. This can allow students to apply their knowledge and skills to a meaningful problem and develop practical skills such as problem-solving and teamwork.
- Group Presentation: Students with the opportunity to practice their presentation skills, as well as to work on their ability to communicate technical concepts to a non-technical audience.
- The learning environment will be practical, integrative and hands-on.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Other Assessment(s) | 100 |