RETAIL GLOSSARY

Machine Learning (ML)

A subset of artificial intelligence that enables computers to learn and improve from experience, used to develop more accurate demand forecasting models.

What is Machine Learning (ML)?

Machine Learning (ML) is a subset of AI that enables computers to learn from data and make predictions without explicit programming. It identifies patterns, makes decisions, and improves over time, leading to more efficient tasks and data-driven decision-making in various applications and industries.

How ML works

Data Collection: First, relevant data is collected and prepared for training. This data consists of input features (attributes) and corresponding output labels (target variables).

Training: The ML model is trained using the prepared data. The algorithm uses this data to learn the underlying patterns and relationships.

Testing: After training, the model is tested on a separate set of data (test set) to evaluate its performance and accuracy in making predictions.

Evaluation and Optimisation: The model's performance is evaluated, and adjustments are made to improve its accuracy and generalisation to new, unseen data.

Deployment: Once the model achieves satisfactory performance, it can be deployed to make predictions on new, real-world data.

Continuous Learning: Some ML models support continuous learning, allowing them to adapt and improve over time as new data becomes available.

Overall, ML enables computers to learn from data, recognise patterns, and make informed decisions, revolutionising industries like healthcare, finance, marketing, and more.

Pros of ML

Accurate Predictions: Machine Learning algorithms can analyse vast amounts of data and identify complex patterns that may not be apparent to humans. This ability allows ML models to make accurate predictions and decisions based on data, leading to improved outcomes and performance in various applications.
Automated Data Analysis: ML automates the process of data analysis, saving time and effort for businesses and researchers. Once trained, ML models can continuously analyse data, identify trends, and make real-time decisions, enabling businesses to respond quickly to changing conditions.
Personalisation and Recommendation: ML is widely used for personalised recommendations, such as product recommendations on e-commerce platforms or content recommendations on streaming services. By understanding user preferences from data, ML can deliver tailored experiences to individual users, enhancing customer satisfaction and engagement.

Cons of ML

Data Dependency and Quality: Machine Learning models heavily rely on high-quality and relevant data for training. If the data used to train the model is biased, incomplete, or of poor quality, it can lead to inaccurate and unreliable predictions. Additionally, acquiring large and diverse datasets can be challenging and expensive.
Model Interpretability: Some complex Machine Learning models, like deep neural networks, can be difficult to interpret. The lack of transparency in certain models can make it challenging to understand how they arrive at specific predictions or decisions. This lack of interpretability raises concerns, especially in critical applications like healthcare or finance.
Overfitting and Generalisation: Overfitting occurs when a model performs well on the training data but poorly on unseen data (testing data). It can happen when the model becomes too specific to the training data and fails to generalise to new data. Striking the right balance between model complexity and generalisation is crucial to avoid overfitting and ensure robust performance.

FAQ

Below you will find answers to common questions

How can Machine Learning improve demand forecasting for our retail business?

Machine Learning can significantly enhance demand forecasting by analysing historical sales data, customer behaviour, and external factors like seasonality or promotions. By using advanced algorithms, the system can identify patterns and trends, leading to more accurate predictions. This enables retailers to optimise inventory levels, reduce stockouts, and improve overall supply chain efficiency.

How does Machine Learning personalise product recommendations for customers on our e-commerce platform?

Machine Learning utilises customer data, such as past purchases, browsing behaviour, and preferences, to build personalised recommendation models. These models leverage collaborative filtering, content-based filtering, or hybrid approaches to suggest products that align with each customer's unique interests. By offering tailored recommendations, retailers can enhance customer satisfaction, increase engagement, and boost sales conversion rates.