AI and Machine Learning for business. Artificial Intelligence is the discipline of computer science dedicated to the study and development of systems capable of autonomously pursuing specific activities with capabilities similar to those of human beings such as reasoning, learning, planning and problem-solving.A fundamental characteristic of these systems is therefore the ability to operate autonomously, adapting and improving their behaviour based on the effects of the choices made, thanks to processes of automatic learning, the so-called Machine Learning.
If the concept of Artificial Intelligence includes all the technologies that reproduce the functioning of human intelligence, Machine Learning represents a subset. Through machine learning the output of a process based on artificial intelligence continues to optimize, reducing the margin of error as the amount of new data analyzed increases.Once the necessary data has been collected and processed, AI systems are capable of operating autonomously and can support the decision-making processes given.
AI and Machine Learning: examples and applications
Artificial Intelligence systems allow to profitably manage and analyze a huge amount of data, data that are fundamental for companies. Applying AI to the analysis of Big Data allows the rapid processing of large amounts of data, developing models to identify trends, perform forecasting analysis and support decision-making processes, always starting from the objectives and business constraints of a company. A competitive advantage that allows making data-driven choices in the markets, mainly in marketing & sales. For example, AI can support companies in the dynamic determination of prices or suggest discounts for certain customer clusters. Going into more detail about the use cases of AI and Machine Learning to support the optimization of a company’s commercial activities, here are some examples. Here, then, are some examples and applications of AI and Machine Learning.
AI and Machine Learning: predicting the risk of customer churn
One of the most strategically relevant activities for companies is understanding the dynamics of customer churn, especially in the context of service delivery. Understanding the drivers of abandonment and the characteristics shared by customers who have abandoned a brand in the past helps companies predict future churn rates and identify the best preventive strategies. Creating a model for calculating customer churn rates (Churn Analysis) allows companies to identify the customers most likely to stop interacting with the company and the reasons for this choice. Thanks to the use of Machine Learning algorithms, it is possible to analyze a large number of variables that have an impact on the choices of a customer and to determine an articulated scenario of the dynamics of abandonment, obtaining indications such as the percentage of risk of abandonment of individual customers, the main causes that will lead to this choice and suggestions of commercial actions to be conducted with a view to retention.
AI and Machine Learning: dynamic pricing models
Dynamic pricing allows companies to stay up-to-date and respond to the dynamics of a constantly evolving market, characterized by more knowledgeable customers and the use of resources and channels for price comparison. Through Machine Learning technologies and the use of a large amount of data, it is now possible to vary the price of products and services in real-time basedon changes in a series of variables that impact demand, thus intercepting demand at the optimal point at every stage of the sale and for every customer.
AI and Machine Learning: customer segmentation
For those involved in marketing, the main objective is to offer the right product or service to the right person at the right time. Machine Learning, through clustering algorithms, makes it possible to segment customers into groups based on specific characteristics, such as demographic data, browsing behaviour and affinity, and associate these characteristics with specific buying and consumption patterns. A huge competitive advantage that allows you to maximize the effectiveness of your marketing investments, generating personalized offers that are truly in line with customer needs.
AI and Machine Learning: pre and post-sales services
Thanks to AI systems, it is possible to optimize services in the pre and post-sales phases, generating added value in terms of effectiveness, savings on the resources used, timeliness and personalization of the service.
The applications for businesses are many: an example can be that of chatbots within e-commerce sites or service portals. In these cases, AI can engage at different points of the customer journey, facilitating the user in product search, price comparison and product or service-related features. Similarly, these digital assistants can help customers in solving post-sales issues, replacing customer service in most cases, in less complex cases.
Another significant application of AI relates to After Sales services: Predictive Maintenance. In predictive maintenance, data are collected to monitor the state of tools and machinery, to identify models that can support the company in predicting and preventing faults.
AI and Machine Learning: the characteristics of the right partner in a digital transformation project.
Today, the digital transformation of companies passes through the integration of Artificial Intelligence and Machine Learning systems to support business processes, from Sales to Marketing and Customer Service.
The introduction of these technologies allows to increase the competitiveness of the company on the market, to reduce the time of analysis, to increase the marginality and allows the team to save time in activities with high automation and to devote themselves to activities with high added value, difficult to perform by a machine.
At Premoneo we provide our clients with solid know-how derived from years of data analysis and pricing optimization, with a highly qualified and specialized team. We support the company throughout the digitalization process, leveraging also on a network of authoritative partner companies. As we often say to our customers, our strength lies in the combination of artificial intelligence and the human factor, characteristics that allow us to build with them, 4 hands, the best solutions based on their needs.