Artificial intelligence applied to e-commerce

Artificial intelligence applied to e-commerce

Imagine a world in which we all used autonomous but autonomous cars without having the perception of the situation or the context, that is to say, “blind”. Cars would know what their passengers want and they would have rules to follow for operate in a robotic manner according to the wishes of each individual. However, not having an external view of the context, they would have no idea what other cars are doing.

The result would be clear: chaos. Even if each car was technically driven, it would be very easy for them to fall. The problem, obviously, is that these cars have no way of knowing what other cars are doing around them, what they can probably do next or where the other cars are on the map.

In fact, the most likely thing is that you never thought about using a car of this type, let alone applying it to a company. This is exactly what many e-commerce sites in the world do.

“The vast majority of so-called e-commerce automation technologies work in the same way as stand-alone cars without context, they look at individual intent and use rules to try to provide relevant but superficial experience. They set rules for automatically generated results, in the hope that human intelligence will outpace everything else, including computers. “ Says Alberto de Torres CEO of Nektiu and director of the ICEMD’s top artificial intelligence program.

Many e-commerce sales automation tools simply do not work.

As with the example of cars, it’s not really that they work badly, but that they do not have a vision of behavior of the multitude of people and the context.

The reality is that these types of e-commerce sales automation tools just do not work. This automation, instead of getting real relevance, you get a conjecture in the individual purchase intent based on a simplification of human behavior, which is generated totally outside the context of the broader tendencies and the wisdom of the crowd.

See a simple example of e-commerce automation based essentially on the “IF / THEN” rules: IF “visitor” = “woman” AND “month” = “June THEN recommends fashion to the range.

Is this simple logic the one that really gives us the truth of each visitor? Of course not. So, how can the proposed recommendations really be relevant to each visitor? Even this superficial relevance is an illusion, which inevitably hinders the customer’s experience and therefore sales.

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Real-time intelligence

But what is the solution to this theoretical question of the car? More rules? If we were still in the 1930s and the roads were largely deserted, maybe. But with the excellent development of real-time data processing, means that cars controlled by the rules simply would not work in a car, on the contrary, they should be smart.

To function properly, they must be adaptable, intelligent and critical, able to learn continuously in real time.

The same goes for automating e-commerce. Any solution that is not smart is not a real automation and will not offer a real relevance that – by its nature – must adapt in real time and reflect the complexity of human behavior.

“Simple Automation”, the five key issues

Rule-based approaches, in essence, suffer from a few important flaws:

  • They only simplify – data such as gender and season, as in the example, are clearly insufficient to predict and react to the likely intention of the buyer.
  • They personalize too much of a particular “behavior” – the true relevance comes from understanding the behavior of a group, not that of an individual.
  • Those that are based on rules depend on human intervention (for example, to define the length of the summer season).
  • Rules-based rules change quickly and become uncontrollable, as there are too many rules to handle effectively: changing a rule and consequences for other rules (good or bad) are unknown effects because is too late.
  • Rules and the people who use them simply can not handle the complexity of data fast enough to react in real time to changing context and behavior.

Features of a smart automation tool

The tools of The artificial intelligence completely eliminates the dependence on the rules, replace them with advanced automation that teaches, responds and adapts in real time to provide real relevance.

It has been proven that it is transforming business performance and reducing costs, while alleviating the thankless task of managing hundreds or even thousands of rules in automation tools.

The application of artificial intelligence seeks to achieve relevance in digital marketingboth in the products presented to the buyers, and when and what they must show. The whole process is automated and requires only a high level human intervention. Digital marketing decisions must be made if you want a strategy to optimize conversion, revenue or profits.

This is possible because the product exposure is calculated not according to the rules defined manually, but according to a set of integrated artificial intelligence algorithms that represent the individual behavior of the visitor on the whole site and for all the visitors, in real time.

How does the artificial intelligence tool work?

The artificial intelligence tool aims to show the most relevant products to a visitor at a given time and in all possible contexts. Each product and variant is assigned a relevance score for each query.. The relevance score is used to compare the products in the query.

Relevance is context-based and the context is based on four different components; the type of sign used, the e-commerce business strategy (including adjustments to the exhibition strategy, promotions and margin), the historical behavior of the visitor and the historical behavior of other visitors with the same profile as the visitor. current visitor.

Panels that accept the “Search by” argument can classify products based on their relevance using Top Sales based classification algorithms.

The ranking order of relevance is obtained with an algorithmic control methodand the order of sort recommended in most cases of use. Select a ranking algorithm based on the search panel. This is the sort order that will be used to take full advantage of the configuration of the exhibition strategy, promotions and margin sales.

Optimization and profitability

With an artificial intelligence-based ecommerce optimization system, it is possible to increase key key performance indicators of e-commerce. For this we can establish a strategy based on artificial intelligence algorithms this will allow us to develop in several ways, either by conversion (gain of market share), by income (increase of turnover) or by profit (increase of profit).

Being the main benefit of artificial intelligence-based optimization, the first would be to achieve business objectives in terms of margin, profitability and ability to react to real-time shopping trends. The second provides operational efficiency because it automates repetitive and sensitive aspects in real time. And especially an optimized customer experience, because machine learning and big data analytics make it possible to understand each customer’s interactions in real time and adjust product exposure to deliver individually relevant shopping experiences.

Do you want to learn how to use artificial intelligence tools and algorithms without having previous programming knowledge? Train with the ICEMD’s Advanced Artificial Intelligence Program.

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