How much data is needed to build an AI solution
We explain different AI use cases and the amount of data each of these AI algorithms needs in order to be trained and work properly
If you are considering implementing AI in your organization, you may ask yourself this question: "How much data do I need to start an AI project?".
There is no one universal answer to this question, as the amount of data depends on the use case, as well on the complexity of the problem that is being solved with the help of Artificial Intelligence. Still, we can estimate the amount of data necessary to start having acceptable results.
In this article, we explain different AI use cases and the amount of data each of these AI algorithms needs in order to be trained and work properly.
Customer Clustering consists in the use of a mathematical model to discover groups of similar customers based on finding the smallest variation among customers in each group. Customer clustering is often used in marketing. The goal is to accurately segment different customers in order to understand the profile of customers in a more granular way than customer personas, or socio-demographic profiles. It can also be used to achieve hyperpersonalized marketing.
Data needed for conducting customer clustering is >1.000 data examples.
Sentiment Analysis is the interpretation and classification of emotions (positive, negative, and neutral) within text data, by using text analytics techniques. Sentiment analysis allows businesses to identify customer sentiment towards products, brands, or services.
Data needed to construct sentiment analysis is >1.000 data examples.
Image classification is a computer vision technique that allows machines to interpret and categorize what they "see" in images or videos. Often referred to as "image classification" or "image labeling", this task is a component in solving many computer vision-based Machine Learning problems. Anomaly detection with computer vision is often used in many manufacturing industries.
Data needed for image classification is >1.000 data examples.
Personalized recommendations has several denominations: predictive marketing, hyperpersonalized marketing, individualized marketing, etc. It consists of improving the customer journey and increasing conversions by showing customers the product recommendations that are relevant to them. In Machine Learning terms, this is called a recommender engine.
Data needed: approximately around 10.000 data examples are needed to train a personalized recommendation algorithm.
Anomaly detection refers to identification of items or events that do not conform to an expected pattern or to other items in a dataset. These abnormal events are usually undetectable at large scale by a human expert. For that reason, AI is an excellent tool for fraud detection.
Data needed: approximately 10.000 data examples are needed to train an AI algorithm to detect anomalies.
Customer Churn prevention
Customer churn, also known as customer attrition, occurs when customers stop doing business with your company. Acquiring a new customer can be as 25 times more costly than retaining an existing customer, making it critical for any business to minimize customer churn. Churn prevention consists in using machine learning techniques and predictive modeling to estimate the likelihood that a customer or customer group will churn. This concept is often used to power marketing campaigns. Detecting and preventing customer churn requires approximately 10.000 data examples, both internal client data and external data.
Lifetime Value Prediction
Customer Lifetime Value (CLV) measures all the potential profits a customer can bring to the organization. Measuring and predicting this value can be one of the most important factors when it comes to maximizing the company's profitability.
Data needed: predicting customer lifetime value demands approximately 10.000 data examples.
Dealing with more complex Machine Learning Models
More than 100.000 data examples are needed for dealing with more complex Machine Learning models, such as: Predicting market trends, Predicting Complex Customer Behaviour, Extracting data from unstructured sources and more. Do you have a project in mind? Our team's focus is to help our customers identify what is the best AI-based solution for their needs, and choose the best technologies for their business.
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