Deep learning in e-commerce: AI learns independently

Definition: What is deep learning in e-commerce?

Like the broader topic of machine learning, deep learning deals with the learning processes of artificial intelligence. It is based on so-called neural networks. These are structured similarly to the brain: they consist of different layers, the input, intermediate, and output neurons, which connect the artificial brain. The more extensive this neural network is, the more complex the situations that the deep learning algorithm can process.

To better understand the definition of deep learning, it helps to look at how humans learn. They take in all the details of a situation and weigh them up before making a decision. They then question their decision: if it was incorrect, they correct it and learn from it. If it was correct, this confirms their initial choice.

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Deep learning works according to the same principle: the machine makes decisions independently and questions them. Deep learning is therefore able to develop independently and learn from both mistakes and successes.

Deep learning vs. machine learning: AI beats the brain

Deep learning and machine learning have one thing in common: both make artificial intelligence usable for companies in e-commerce technology. But despite their common basis, there is one key difference:

• In machine learning, humans must intervene in the learning process. They assist with data analysis and set guidelines for the decision-making process.
• In deep learning, humans are only responsible for providing the database. The machine analyzes all the information independently, derives a forecast from it, makes a decision, and learns from the result. Humans do not need to intervene in the learning process of self-learning algorithms. The downside, however, is that deep learning requires an extremely large amount of data.

Example: The model is supposed to learn whether an object is an apple or a banana and is given images of both objects to learn from. In machine learning, a human can specify features such as color and shape to solve the problem quite well. In deep learning, the image, i.e., all pixels, is entered as input. In the output, the network sends a signal indicating whether it believes it is more likely to be an apple or a banana. It then receives feedback on whether the prediction is correct. Based on this, the parameters of the network are adjusted in several stages. This process is repeated until the model can no longer make better predictions or the parameters can no longer be adjusted.

By the way: The machine learns through reinforcement learning based on the principle of trial and error. Initially, it does not have the information necessary to make decisions, but must work this out for itself.

The use of deep learning in e-commerce: practical examples

Artificial intelligence is not a thing of the future —it is already being used successfully in e-commerce data science today. The following examples of deep learning show how you can use autonomous machine learning in online retail.

Potential: What advantages does deep learning offer in e-commerce?

Deep learning offers numerous advantages in e-commerce:

• Higher sales: Personalized offers, optimized search results and prices, and individual customer service—deep learning lays the foundation for greater trust and, as a result, higher sales.
• More relevant marketing campaigns: With the help of deep learning methods, you can tailor marketing campaigns even more personally to each customer. This significantly reduces wastage and enables more effective marketing.
• Internal efficiency: Time-consuming tasks such as customer service or forecasting can be automated and made more efficient thanks to intelligent e-commerce data science. This frees up capacity for strategic tasks.
• Better informed decisions: Deep learning enables well-considered decisions in e-commerce based on facts and experience—even in real time, thanks to real-time analytics. AI can plan for business success instead of just predicting it.

Conclusion: Successfully implementing deep learning in e-commerce

With deep learning, you can improve the customer journey for your customers, create a personalized shopping experience, revolutionize your customer service, and prevent fraud. This allows you to increase your sales, optimize your marketing campaigns, and make your business processes more efficient.

Frequently asked questions about deep learning in e-commerce