Businesses based on the platform model are gaining in popularity. Their potential is hidden not only in the territorially unlimited, multi-level network of value exchange. The platforms also provide enormous amounts of data on customer purchasing preferences, vendors’ sales opportunities and new market trends. In-depth analysis of this data allows for the proper implementation of a development strategy and facilitates scaling.
The key to fully exploiting the potential of platform businesses is to build a unique customer experience that translates into a growing base of loyal users.
Advanced analytics based on artificial intelligence and machine learning enables this unique customer experience through hyper-personalization in the most crucial areas including individualized offers and content, dynamic recommendations and service automation.
Hyper-personalization of sales and dynamic recommendations
A key consideration in building a competitive advantage in the platform model is sales personalization. However, customers expect more than traditional personalization based on historical data analysis.
To maintain customers’ interest, they must be provided with the highest quality interactions with the platform. This can be done by adjusting the offer to their individual purchasing preferences “here and now”. The availability of tools based on cognitive technologies and artificial intelligence provides a transition from basic personalization to hyper-personalization, enabling the real-time analysis of customer needs. An example of an innovative approach to personalized recommendations is the intelligent, virtual fitting room deepsense.ai developed. Customers can upload their photo to the application, which simulates their appearance in the garments of their choice. The system can also propose a specific size and adjust complementary styling elements. Multi-stage data analysis based on generative adversarial neural networks takes the customer to a new level of interaction with the offer.
Advanced machine learning models also make it possible to optimize recommendation engines and transform recommendations into a personalized offer tailored to the needs of a specific customer.
Data analysis facilitates not only tracking customers during their journey to the next stages of the sales funnel, but also predicting a specific moment of conversion. This gives a business the opportunity to immediately respond to the client’s needs and propose a highly personalized offer. An example of this approach is present in an AI solution developed by deepsense.ai for one of the leading european banks, where, a machine learning model creates personalized offers for customers with an efficiency exceeding forty times the baseline approach. The algorithm automatically searches the database in order to identify customers matching a specific profile, individually selects the elements of the offer and analyzes customers’ readiness to buy.
Content hyper-personalization and intelligent search
Highly personalized content makes it possible to build a customer relationship with the platform and maintain customer loyalty, providing each user with a unique experience.
Leading brands like Facebook, Amazon, Spotify and Starbucks have long been taking content personalization to a new, higher level using predictive personalization. AI has made it possible to extract additional information about customer preferences in real time. Taking into account the relevant data, the models are able to predict what type of content a given client will most likely engage with.
Intelligent search also supports content hyper-personalization. Deep learning elevates search from a simple understanding of keywords to an understanding of intent and context.
Understanding the semantics and real meaning of user queries is essential to intelligent search. Neural networks learn to correctly interpret queries and adjust the information users expect. Such solutions facilitate the effective search for content, navigate information in platforms with complex structures and ensure a quick transition of customers to the issues they are interested in. An example application of the above technology is a system created by deepsense.ai for a platform offering access to scientific literature. To equip users with efficient content search, natural language processing models that can recommend items with similar topics are applied. Thanks to this, the platform’s clients quickly find the materials they are interested in.
Intelligent tools that automate the sales process keep the user engaged and support conversion. But it is not only about simple solutions for improving customer service.
Algorithms make it possible to efficiently guide the customer through the purchase process by providing dedicated information obtained in the learning process, based on the service of previous users. Additionally, by eliminating manual operations, they enable real-time customer service. An instance of such automation can be found in the deep neural networks deepsense.ai created to power an intelligent system for reporting and assessing motor insurance claims. Thanks to advanced image recognition algorithms, the system identifies car parts and classifies those that have been damaged. It then automatically assesses the damage and estimates the cost of repair. This solution helps estimate the value of the claim within a few seconds based on photo documentation sent by the customer.
Platform businesses are a response to the pandemic reality that has forced customers to push deeper into the world of online shopping. The platforms have developed explosively. Going forward, the dynamics of this development will be dictated by the speed with which platforms respond to individual customer needs and advances in data analysis.
The article was published in the Polish edition of the MIT Sloan Management Review, in June 2021.