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Home / Corporate Blog / Perspectives of Artificial Intelligence and Machine Learning Usage in CRMs

Perspectives of Artificial Intelligence and Machine Learning Usage in CRMs

Published: May 3, 2017
Updated: March 23, 2023
Categories: Expertise

With the recent GPT chat getting all over the news, the subject of utilizing Artificial Intelligence in digital solutions has risen in popularity with renewed force among businesses. Meanwhile, Artificial Intelligence has long been present in various applications and many companies as well as individuals have been using this technology for quite some time.

Working on the background of digital ecosystems and software, Artificial Intelligence helps companies significantly improve their customer services as well as streamline their inner workflows. That’s why businesses, especially in the highly-competitive fields such as sales, retail and e-commerce, actively upgrade their customer management software with Artificial Intelligence technology.

One of the main systems that greatly facilitates customer relationship management is CRM. In this article, we’ll have a look at how companies can benefit from the CRMs upgraded with Artificial Intelligence and AI-related technologies such as Machine Learning.

Benefits of AI and ML for CRM

The implementation of Artificial Intelligence (AI) and Machine Learning (ML) technologies in CRM systems allows for bringing customer experience to a new level. The technologies can help make each customer service more personalized, increase and speed up conversions, and significantly improve customers’ journeys to the final goal – product purchasing. Apart from that, with AI-upgraded CRMs, companies can more effectively organize post-purchase experiences, turning their customers into loyal followers and making them return for more purchases. Let’s have a closer look at the advantages AI and ML can offer to businesses once implemented in CRM systems.

Effective Data Processing

With time, CRM systems started gathering more customer data from various sources. This has become possible due to the development of customer omnichannel service strategies implemented by many marketers today. With AI and ML implementation, companies can more effectively process large amounts of obtained data and get deeper insights into their customer wants, needs, and preferences.

Apart from data collecting, AI-enhanced solutions make it much simpler to enter data related to customer service in the system. This way, the technology can gather all the customer data at a centralized database and provide a smoother customer experience over several communication channels.

Enhanced Customer Satisfaction

AI and ML technologies in CRM systems can provide companies with advanced marketing tools with elements of social listening and deep analytics instruments. For example, they can include sentiment analysis tools for monitoring customer and follower attitudes to a company’s brand or products in social media posts and over the internet. Or include predictive lead scoring analytics that will provide the data on converting leads into customers, and much more.

Improved customer and follower monitoring and analytics allow businesses to get a complete picture of who their customers are and what pain points they have. This helps businesses better address customer needs, create relevant to their target audience content, and contribute to greater customer satisfaction.

Automation

Automation is one of the main reasons why many businesses empower their digital solutions with AI technologies. Artificial Intelligence helps marketers and sales representatives automate much of manual data entry tasks, resulting in better productivity.

Automation in CRMs can include automated data entry, automatic lead engagement through chatbot interactions, phone calls, or emails, customized responses to current and potential customers via various communication channels, and much more.

Apart from automated data input and response to customers, AI-powered CRMs can create daily/weekly/monthly reports, collocate documents, create call lists, share data with other systems, or perform pre-set actions such as automated invoicing, payments, and much more.

Use Cases

Many companies already successfully implement AI and ML technologies in practice. There are a number of cases of how AI and ML utilized in CRMs can help businesses improve their workflows and customer services.

AI Technology in CRMs

Artificial intelligence acts as an advisor in the decision-making process along with data mining and machine learning. A system analyzes data to provide managers with predictions on sales, marketing, or customer service. It operates on the basis of pre-designed algorithms that include important data for analysis. The system itself can’t make decisions, only suggest the most suitable ones, e.g., it analyzes customers’ behavior, purchase history, and interests in social media to give sales specialists a clear view of the product that might be interesting.

AI can become an autonomous unit and be responsible for a certain part of decisions. It can give recommendations on top of the results of machine learning. Usually, this use case is applicable for some routine actions that don’t require final approval. For example, a chatbot can automate customer service.

Machine Learning and CRMs

Among a number of different AI-related technologies, machine learning is one of the most powerful additions to existing CRM systems. It gives businesses an opportunity to change themselves from reactive to predictive customer services.

Initially, the system collects information about a wide range of things. Then, this data is analyzed and processed with the help of machine learning to create patterns and identify trends. For example, Microsoft Dynamics CRM machine learning is using the Azure cloud platform to build the model for automatic cross-sell product recommendations.

On top of that, the more information such machine learning CRM processes, the more precise and intelligent it becomes in its predictions. Based on SCAND’s experience, the results of machine learning assist in foreseeing:

  • Consumer behavior that is hinged on the data about tastes and choices collected from many users (collaborative filtering).
  • Better target audience segmentation and, therefore, services or product promotion based on customer preferences.
  • Demand for a certain product or service. That is about content-based filtering which is used to recommend the items a user might like according to his/her preferences.