Nowadays, providing unique personalized online experiences to customers is a must. Companies continuously want to match the content of their websites and applications with each customer’s unique profile and expectations to increase the generation of additional revenues.
In short, companies need solutions that aggregate many sources of on- and off-line information, extract data sets quickly, and ease the deployment of models to compute real-time compelling recommendations.
However, this is easier said than done. Firstly, a tailored online experience requires acquiring and then linking together a massive amount of data related to the customers. Every transaction made, every page viewed, every search, as well as time spent on each page is recorded and integrated with the customers’ and the companies’ data. Secondly, it requires building and using machine learning algorithms for demand forecasting, product search ranking, product recommendations, deals propositions, translations, etc.
The main challenge though is to manage sub-second processing time to generate online recommendations. The algorithms must be able to react immediately to changes in a user’s offline and online data. Moreover, if the products catalogue or the inventories change, the recommendation needs to change as well. The live evolving content needs to be tailored in real time.
The Digazu platforms (end-to-end data engineering platform, MLOps data platform and end-to-end data science platform) are batch and real-time big data platforms, designed for real-time data processing and the industrialisation of data science models to help companies tailor their customer recommendations. The objective is to link customer online data in real time with the client profile data and historical data. When you combine those data, you can create and feed machine learning models that are able to tailor the online experiences of customers to their needs and preferences, hence increasing their satisfaction.
Digazu’s platforms allow companies to aggregate online and offline information about customers, to analyse the behaviour of each customer in real time, and to compute and personalise offers and recommendations. For example, the collected data shows that Tina is 3O, she has just changed her address, and she is looking for a credit provider to buy a new car. Seconds after visiting the website, she receives a tailored credit offer… coupled with a special offer for the opening of a saving account for children.The Digazu platforms enable companies to make compelling recommendations for all users in real time.
A perfectly timed, relevant and personalised discount offer or product suggestion can do wonder to convince customers to engage in business with you. With Digazu, it is possible to automatically aggregate data, train and deploy data science models, and change and display content in real time based on users online behaviour.