Machine Learning in E-Commerce

A type of machine learning where the model is trained on labelled data to learn the relationship between input and output.

The process of categorizing data into predefined classes, such as spam detection.

The process of predicting a continuous value from a set of input data, such as predicting house prices.

A type of machine learning where the model is trained on unlabelled data to discover patterns and relationships.

The process of grouping similar data into clusters, such as customer segmentation for marketing.

A type of machine learning where an agent learns to make decisions through trial and error by interacting with an environment and receiving feedback in the form of rewards or penalties.

A set of data with labels or predefined outputs associated with each data point, used to train supervised learning models.

The input features used by a machine learning model to make predictions, often represented as numerical values or categories.

Machine learning (ML) is a type of artificial intelligence that involves training computers to learn from data and make decisions without explicit programming. Instead of being told what to do, these systems are fed vast amounts of information and use algorithms to figure out how to perform a task.

ML algorithms can analyze a customer's past purchases, browsing history, and interactions to suggest products they might like. This makes shopping more personalized and efficient.

By analyzing data from sensors and past maintenance records, ML models can anticipate potential equipment failures before they even occur. This helps prevent downtime and reduces repair costs.

ML models can examine satellite images or drone footage of crops to assess their health, identify diseases, and optimize fertilizer and irrigation use. This leads to better yields and more efficient farming practices.

Machine learning algorithms can analyze financial transactions, identify unusual patterns, and flag potentially fraudulent activities. This helps banks and financial institutions mitigate risks and protect customer funds.

With ML, algorithms can assess market data, news, and economic indicators to make faster and more informed trading decisions than traditional methods.

ML algorithms enable self-driving cars to perceive their surroundings, make decisions, and navigate safely. This involves analyzing data from cameras, sensors, and maps in real time.

Machine learning models can optimize delivery routes by analyzing factors like traffic conditions, distance, and time constraints. This makes deliveries more efficient and cost-effective.

The desired output or target variable that a model is trying to predict. In supervised learning, each data point is labelled with its corresponding outcome.

A mathematical representation of a real-world process. This representation is created during training and learns the relationship between features and labels, allowing it to make predictions on unseen data.

The output of a machine learning model when provided with new data. It is a prediction of the label for the new data based on the relationship between features and labels learned during training.

Using historical data and external factors to predict future demand. It helps optimize inventory levels and reduce stockouts.

Predicting the total revenue a customer will generate throughout their lifecycle. It helps identify valuable customers for retention strategies.

The process of gathering labeled data (where each piece of data has an assigned label). This data is used to train the model.

Identifying and transforming features from raw data into a format suitable for machine learning models. It involves creating new features or modifying existing ones.

Groups customers based on labeled data like demographics, purchase history, and browsing behavior. This enables targeted marketing and improved ad campaign return on investment (ROI).

Predicts which products a user is likely to buy based on past purchases and behavior, using algorithms like logistic regression, decision trees, or deep learning models. This increases sales through cross-selling and upselling, enhancing the user experience.

Supervised models trained to recognize patterns in fraudulent transactions, labeled as either "fraudulent" or "non-fraudulent." This helps prevent payment fraud in real-time.

Uses historical pricing, demand, and competitor data to predict optimal prices for products, aim to maximize revenue. This is seen in dynamic pricing strategies like airline ticket pricing or flash sales.

Models customer behavior to predict the likelihood of churn (e.g., inactivity, canceled subscriptions), using algorithms like decision trees, neural networks, or logistic regression. This enables proactive retention strategies and increases customer loyalty.

Analyzes customer reviews to classify sentiment (positive, negative, or neutral) using algorithms like Naive Bayes, SVM, or deep learning with NLP. This helps identify areas for product or service improvement and enhances brand perception.

Discovers hidden customer segments, optimizes product organization, and identifies trends in unlabeled data. This contrasts with supervised learning, which relies on labeled data.

A technique in unsupervised learning that groups similar customers based on their actions (browsing history, spending habits) or characteristics (demographics). Helps identify customer segments for targeted marketing.

An unsupervised learning method used to organize products into groups based on their attributes (product descriptions, price, images), making product search more intuitive and reducing manual tagging efforts.

A method in unsupervised learning where algorithms like DBSCAN or autoencoders identify outliers or unusual patterns in data. Useful for fraud detection, system monitoring, and identifying unusual customer behaviors.

Analyzing customer actions on a website to identify common paths or patterns (products viewed together, cart abandonment tendencies) and optimize user journeys, reducing friction and improving conversions.

The process of collecting unlabeled data from sources like user interactions, sales records, and product attributes. It's the first step in applying unsupervised learning.

Identifying the most relevant features or characteristics of data for unsupervised learning. Examples include click rates, product descriptions, and purchase times.

Choosing the appropriate algorithm (k-means clustering, hierarchical clustering, PCA) for your unsupervised learning task based on the data and desired outcome.

Evaluating the results of unsupervised learning using metrics like the silhouette score to assess the quality of clusters or groups and gain actionable insights.

AI tools that can design visually appealing ad creatives, like banners, infographics, and product images, using machine learning to suggest elements that resonate with target audiences.

AI tools that create short, engaging video ads by combining pre-existing footage, templates, and automated voiceovers.

Videos featuring AI-generated or virtual human-like avatars that can speak, demonstrate products, or deliver information.

AI continuously tests and refines ad elements, like headlines, images, and calls to action (CTAs), to maximize performance using A/B testing and multivariate analysis to find the best combinations.

A tool that offers AI-powered templates for creating visually appealing ad creatives.

A tool that uses AI for logo creation and ad designs.

A tool that converts scripts or blog posts into video ads.

A tool that transforms text into videos using AI.