How to boost your website conversion rate with a predictive UX based on explainable AI?



Tous les articles


As many web-based marketplaces, banks rely on their website and mobile app to propose products like loans and insurances to the users. The conversion rate, that is to say the percentage of visitors that purchase a product, is often pretty low. The reasons are multiples: a great diversity of offers, non-targeted propositions, doubtful users… This article presents how Craft AI boost a website conversion rate by proposing adapted products to the users using predictive UX and hyper-personalized content.

Understanding user behavior...

Before proposing hyper-personalized content, the system needs to understand the user behavior on the platform. If a new user lands on the website, what path will they take? What element will they interact with? Obviously, this is not a simple task, there are hundreds of different pages on the website and a user can take thousands of different paths. As a human operator, creating an adaptive UX using rules that take into account all of the possible actions the user can take would require a tremendous amount of work. Moreover, adding new products and new pages on the website means that the operator needs to regularly update those rules.

Feeding analytics data to an AI enables the understanding on how a user can evolve in this environment. Craft AI is able to predict the most probable paths that any visitor will take. It is able to find the product which is the most likely to trigger the conversion and which path leads to it.

… To create a goal oriented AI…

In order to meet the company’s objectives, the AI needs to have a goal related to them. It is possible to fix the goal to be all pages related to a specific subject (e.g. real estate) on a specific period (e.g. September), a specific product type (e.g. insurances) or simply the user's most adapted product. With this information given to the AI, it can hyper-personalized the website's UX.

… That will hyper-personalized the UX

An user arrives on the website! The AI predicts possible paths that they can take based on their profile. By using a tree search algorithm, similar to what is used in Game AI, and in particular AlphaGo, it finds the best path to one of the goal pages. This personalized path will lead the user to their conversion.

We can suggest the user to take this path in multiple ways depending on the platform they are on: we can show a banner to the next page, push a notification, display information related to the topic…

The UX adapts itself to the user and evolves with their exploration of the website to show hyper-personalized content.

Results: increasing the user engagement

Choosing Craft AI to realize a predictive UX will make you able to push hyper-personalized content to user related to your goal. It will increase the user engagement as the proposed product is chosen according to their profile. Moreover the AI will evolve automatically with the website and with the new proposed products and new pages that would be added. This AI is directly integrated in your existing website with the help of an expert data scientist from craft ai who drives you from the expression of your need to the production launch of the solution.

This AI can be adapted for any e-commerce website to find in which products the user can be interested in. In an e-commerce website for example, it would display a link to the next products based on the already seen ones.

Of course, predictive UX can be used in a lot of sectors and on every platform that use UX. Craft AI is notably applied on the pilot interface for a project with Dassault Aviation to minimize the informations displayed to the pilot depending on the current situation.

À télécharger

Vous pourriez également apprécier

IA de confiance

Garder l'humain dans la boucle avec l'XAI

Quelle place l’explicabilité (XAI) occupe-t-elle aujourd’hui en Machine Learning et en Data Science ? Le challenge de ces dix dernières années en data science, a plutôt été de trouver la bonne “recette algorithmique” pour créer des modèles de ML toujours plus puissants, plus complexes et donc de moins en moins compréhensibles.

Lire l'article


L'industrialisation de l'IA & le concept de MLOps

Le MLOps apparaît comme une nécessité pour pallier les difficultés lors du passage à l’échelle de l’IA au sein des entreprises : la reproductibilité, le versionning, l'intégration continue... C’était l’objet de l’une des conférence sur l'industrialisation de l'intelligence artificielle dans le cadre de l'Enjeu Day Industrie & Services 2022. Vous n’aviez pas pu y assister ? Retrouvez le replay.

Lire l'article

IA de confiance

A guide of the most promising XAI libraries

Using Machine Learning to solve a problem is good, understanding how it does is better. Indeed, many AI-based systems are characterized by their obscure nature. When seeking an explanation, looking to understand how a given result was produced, exposing the system’s architecture or its parameters alone is rarely enough. Explaining that a CNN recognized a dog by detailing the Neural Network weights is, to say the least, obscure. Even for models deemed as glass-box such as decision trees, a proper explanation is never obvious.

Lire l'article