Scientists organize plants, animals, bacteria, and other life forms into biological taxonomies known as classes, phyla, orders, families, and the like. In the same way, businesses are beginning to see the advantages of grouping products, consumer profiles, and other market data into categories. Using specialized algorithms, these companies combine similar attributes for data points and generate suitable groupings. This artificial intelligence (AI), alongside its ability to improve itself through machine learning, estimates how likely two products belong to the same class. The practice of classification with AI is taking on an increasingly substantial role in modern business.
How AI Classification WorksAI classifications works when the business feeds the AI data points, such as product stock, along with their predetermined categories. The algorithm studies the information in this database. For each category, it creates a model based on what it learned that likely represents the type of product in that category. It then applies this model to new products to decide which category they belong to.
Types of Classification AlgorithmsWhile you don’t have to understand the details of your AI classifier, here is a basic overview of some strategies computers use to categorize data points in business.
- Using Bayes’ Theorem, which predicts the probability that a product belongs to a class based on its features. If many parts of the product seem to share characteristics with the category, then it’s likely part of it.
- Decision Trees. You’ve likely heard of this type if you’ve played the “20 questions” game. The algorithm slowly deduces the attributes of the product by asking questions to narrow down where the data point belongs.
- K-nearest Neighbors. This algorithm compares a new piece of data with other similar data points already in the database. It’s has a population application predicting the prices of goods in the marketplace and providing product recommendations.
- Neural Networks. This buzzword is popular in the machine learning field since it simulates how the human brain itself picks up new information. Arguably the most challenging type of AI to develop, neural networks have caught the interest of large enterprises like Facebook, Google, and Amazon for their versatility and virtually endless applications.
Practical Business Applications for AI-Driven ClassificationAI, in general, has found a multitude of practical use cases for modern businesses. While classification has always been a handy tool, its increased efficiency due to automation has made it invaluable for companies to take full advantage of their data. For example:
- You can categorize marketing channels to find the ones with the highest chance of returning a high profit margin and focus your funding there.
- You can also use it to organize products in your store to make it easier for customers to find what they’re looking for.
- AI classification is also useful for recommending new products based on a customer’s past browsing and purchase history. Delivering personalized content with AI categorization is a widespread practice for large businesses like Netflix, Spotify, and Pandora.
- Businesses cluster consumer data to generate marketing predictions and adapt to changes in user preferences.
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