Almost all companies with a large number of customers have different CRM solutions. We do not want to talk about what CRM solutions are, because our goal in this blog is to show you the difference made by CRM solutions based on artificial intelligence and machine learning models. How many times have you, as a user, received an offer from a company even though you have already completed the purchase or cancelled it, more precisely, you did not receive it at the time when you were interested in a service or product? Have you had offers that do not fit your profile at all, e.g. an offer from a bank for a cash loan, and you already have enough money in your account, so a savings offer would be a much better offer for you? Precisely because of these situations, every company that introduces CRM or switches to another solution, encounters challenges such as:
How AI based CRM can change your business?
Lack of real-time decisions
Integration with other company systems
Data collection from all available sources
It is not enough for CRM to monitor only historical data, but it is necessary to make decisions in real-time, based on events. If the client appears at the place of interest (logging in to the application, opening the portal, physical appearance, etc.), it is necessary to react and offer the client a product/service with the highest probability of a positive outcome. To make this possible, CRM must be integrated with all the company’s systems and must successfully collect and process data from all available channels on which the client is located.
It used to be enough for CRM to aggregate customer data and help a company automate processes. Today, CRM is far more than that. Thanks to artificial intelligence and the application of advanced machine learning models, CRM is becoming a powerful predictive and analytical tool that takes business and customer relations to a new level.
The amount of data that companies collect nowadays is incalculable. However, companies often do not use the full potential of the data they have. The application of different analytical models enables companies to use the full potential of the resources they have. With machine learning models, customers can be segmented according to the behavior they exhibit towards the company when using different channels and products of the company. By knowing the segments in which customers are located, companies can improve their approach to the customer by personalizing the offers and campaigns they send.
Furthermore, different predictive models learn based on previous client behavior and then predict numerous parameters of interest. For example, what is the next best offer for the client (Next Best Offer), what value will the client bring in the future (Customer Lifetime Value), whether will he become a premium client or stop using the company’s product or service (Churn prediction)?
In this way, companies not only integrate data from all available sources but also obtain valuable conclusions, thanks to the application of artificial intelligence, which indicates correlations in the data. Thanks to these conclusions, companies nurture the relationship with existing customers and increase the base of loyal customers by sending content that is relevant to each of them.
Advanced models of machine learning are, in addition to clients who are individuals, also clients who are business entities – corporations, small and medium enterprises. Predictive models provide the ability to calculate the risk of this type of client for the future. When a company knows which customers will become risky, it can react promptly, take preventive measures, and reduce the impact of an existing or potential problem on its business.
By using CRM solutions based on artificial intelligence, companies influence the improvement of both internal processes and customer relationships. These solutions enable the company to gain an advantage in the domestic and global market, thanks to the full use of the potential of its resources, process automation, and real-time response, using the latest technologies.
