How is artificial intelligence useful in configurators?
Digitalization and process automation have been recognized as one of the so-called megatrends - that is, changes of a global and long-term nature. In the IT industry, we feel this trend doubly - we support the digitization of our clients, and at the same time we ourselves are constantly investing in AI solutions to help us in our work.
A good product configurator allows the vendor and customer to save time (often as much as 50%) while working out the solution that best meets their needs. The ideal final product is often the result of hundreds and sometimes thousands of components. At a time when everyone is in a hurry, it is hard to imagine this process without the involvement of artificial intelligence (AI).
What is the role of AI in the preparation of the configurator?
Artificial intelligence can enhance a product configurator in many areas - from its preparation to testing to its operation on a retailer's website. At the preparation stage, it is most useful for relieving the burden on the people who have been responsible in the company so far for the development and elaboration of the product offering. One can imagine what kind of knowledge and time such a specialist needed when putting together, for example, the optimal computer for a customer. The customer indicated the preferred processor, and it was on the specialist's shoulders to select the best motherboard and other components that matched this processor. Artificial intelligence, thanks to the rules of correctness, can do this in a few seconds, and the time freed up by this, can be used to search for customers or build better relations with them.
Howdoes AI know what choice to suggest?
Artificial intelligence greatly assists in recommending products. We often see such systems in online stores. We buy a certain product (e.g. rice for sushi), and the system prompts us to buy soy sauce to complete the set. There is a recommendation rule behind this, which is made possible by teaching artificial intelligence when and what to suggest to the person putting together their purchase. For AI to do this well, it first needs to be taught a historical overview of the relationship between products (in the case of a convenience store) or product elements (in the case of a customized product configuration). Its task will be to understand the rule that the purchase of one item is likely to trigger the need to buy another. But that's not all. In the next step, you need to teach the system to recognize the connections that are currently most likely to be made. This can be achieved by prioritizing those links that occurred most often. Or those that appeared most often not on a scale of the whole history, but only in recent years. Many configurators, however, consider as priority not those connections that occurred most often together at the bidding stage, but those that actually ended up in a purchase. In this way, the customer gets a hint of the item that his predecessors found most apt, because they decided to finalize the purchase.
ShouldAI evolve?
Like any intelligence, including artificial intelligence, it cannot stand still. Every new portion of data, the slightest change in the history of connections, is part of AI's development. If it is to be a true partner for us and do our work for us, it should become more and more proactive over time. At EXSO, we are working to ensure that the AI algorithm not only responds to customers' needs, but also makes its own suggestions on how best to select recommendation rates in the future. We need to ensure that our AI is powered by large portions of data and learns as the market changes. New technologies, fashions, new regulations - all this is what our AI must learn.
WillAI be a permanent fixture in e-commerce?
All indications are that it does. Just look at the leading online sales platforms and how much emphasis is placed there on letting the system help the viewer select the best related products and suggest the purchase of more. Google's search engine works similarly, and it too learns to suggest content based on the most preferred answers of previous users.
"Behind all these solutions is ultimately time," says Michal Klin, an analyst responsible for AI development at EXSO, "and how quickly we all want to access some content or take care of our shopping needs.
And it's the timing that captivates me most about AI-based solutions. Not only does AI do the work for humans, it does it quickly and not stupidly.
And we can use those reclaimed moments to grow the company and ourselves. At the end of 2022, the global AI market was estimated to be worth $450 billion. This is perhaps the best proof that such solutions must be profitable!"
