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Machine Learning in GrandNode, cross-platform e-commerce

Created on:  15.05.2018

Nowadays, each company throws buzzwords like "Machine Learning!", "Big Data!", "Artificial Intelligence!", "Marketing Automation!". Is it new for you? Probably not, wherever you look, you will see this words. But you have to ask yourself, is this definitely properly used? 

Companies are using new technologies, they implement new algorithms to provide better services. You will see many customized and personalized offers. The digital world is changing and the companies which want to count in this race must adapt their services to the newest trends.

As you know, everybody talk about Machine Learning, but a few companies or softwares really use machine learning. Let's look how we covered it in GrandNode.

Segmentation, targeting and personalization - be precise

Targeted ads are nothing new in e-commerce. Starting from Facebook Ads, ending on personalized pop-ups and banners on your stores. Targeting is closely related with e-commerce. 

If you compare real stores and online stores, you will see many similarities. Good seller after a short conversation has a practically finished image of the buyer. He knows your preferences, he knows your needs, he knows your demands. With this knowledge, it's easy to find something useful for you. In the online store it's harder to achieve. 

In the offline, real store the seller immediately after the entry will suprise you, ask you many questions. It's easy to catch a contact with buyer. In online stores, it's also possibile, you can integrate your store with live chat, messenger. But is it the same? 

Unfortunately not. The whole process requires complex knowledge. Without proper tools, knowledge of programming, statistics and analysis is required.  You need to know to how to write a program that will invite users to shopping, ask questions, answer wisely. You need to have a complicated algorithms which will offer the right products to your customers. 

I'm pretty sure, that store owners don't have to know that. They have more important things to do than extra customer service. It should be automatical. Machine learning is the key in this case. Your customers should teach your store as many important things as possible. 

First step is to do the customer segmentation. Next step is to target your ads effectively. You can probably guess that if your customers like Japanese moto brands, there is a big chance that when introducing German cars into the offer and ads, the response will be lower than expected. But, if you make a promo on Japanese cars, you can probably break the bank. 

As I mentioned before, machine learning is responsible for that. It's not possible to know all customers. There is no man, who would be able to do it. We need to trust machines, machine learning, artificial intelligence and it's the future of e-commerce.

Product Recommendations

When we talk about AI and Machine Learning, probably it's the first think which comes to your mind. It's something normal, don't worry. 

The most popular examples of perfectly implemented recommendations engine are Amazon and Netflix. As Amazon said, their product recommendations engine is responsible for almost 35% of their sales. Next great example is Netflix, for sure you know their complicated algorithms responsible for recommending you next films or serials to watch. 

It's also one of the most powerful tool in GrandNode. Since you are able to automatically assign roles or tags you can create a powerful recommendations system. It's highly connected with previous chapter, recommendations must be based on earlier segmentation, targeting then personalization. Only well-choosen recommendation has a chance to attract your customers. 

Fixed and manually set recommendations or related products are no longer valid. Taking into consideration the number of stores and ads, you must be predictable.

For your information, Microsoft released a great library to ASP.NET Core - It's cross-platform, open source, machine learning library, which will successfuly improve the work of the online store. More details can be found on the official Microsoft blog here.


As Wikipedia says, Machine Learning is a field of computer science, that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. 

Predictability in e-commerce doesn't have to be related with recommendation. You can predict prices, interests, traffic and mentioned recommendations. It's a powerful weapon in the machine learning hands. 

Pricing Optimization

Pricing is important. In my opinion, pricing is one of the most important thing in the store. If you set too low price, customer will become suspicious, why it's so cheap? Probably it's broken, without guarantee or something similar, but unfortunately the same is happening at the other end. Why it's so expensive? This store is crazy!

But let's be honest, you never suggested a price? Of course yes. I did it many times. Prices in the online store are the most critical issue. There is no place for calculations, "Hm.. in another store it is cheaper, but I have 20km to it, I will lose on fuel, so I will take more expensive one.". It's impossible. If your price won't be properly calculated, you will lose customer. 

The customer completely  won't lose anything, if they abandon shopping from you. It's next field where machine learning makes a difference. You can create a dynamic pricing, which will be flexible and will adapt to your customers needs.

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