For years, the purpose of CRM was to manage customer relationships. So for almost two decades, these systems were operating quite efficiently. However, the arrival of new technologies always brings profound changes. Falling behind these technologies often leads to failure.
Since 2023, with the introduction of large language models, the customer experience has undergone enormous changes. Now it is possible to enter information in large volumes into the system; a problem that was not easily solved until recently. Now, with Copilot or any other artificial intelligence tool that is connected to the CRM system, it is possible to make organizational decisions in a stronger and more effective way.

Modern Intelligent CRM Architecture

Following up with customers and interacting with them becomes a complex and difficult process from one point to another. This is because organizations develop and the number of customers increases. To respond to this problem, CRM systems were formed to organize and track relationships with customers and even company personnel.

The difference between traditional CRM and intelligent CRM; has the result changed?

In older models of CRM systems, information is entered into the system on one side and the results are displayed on the other. These results are based on input commands and are processed. Therefore, these systems were called rule-based, but the problem with the traditional method is that as customers and sales channels increase, communication and setting rules become more difficult.

In artificial intelligence-based CRM systems, previous customer data is analyzed and specific patterns are obtained from each.

Getting to know the technical components of CRM
Data Layer: In the data layer, which is considered the CRM infrastructure, basic customer information such as geographical region, age, gender, and similar items are placed. In addition, purchase behavior patterns and any additional information such as email and contact are also included in this section.
AI Layer: In the next layer, NLP, machine learning, and other AI algorithms come into play to predict customer behavior patterns by examining basic information. Now, based on these predictions, future customer behavior, appropriate messages to send, and even the probability of churn will be determined.
What is the difference between Data Interpretation and Data Collection?

Data Collection means collecting raw data. Things like contact numbers, basic customer information, emails, and social networks along with recording customer purchase history, which provides a huge data center for CRM. In these AI-based models, system performance does not decrease with increasing data volume because the collection and analysis is done by artificial intelligence.

Data Interpretation refers to any analysis and analysis of information. For example, at which stage of the sales funnel a customer is currently in. How likely is it that the customer will make another purchase before the end of this month? Performing such analyses without the presence of AI and by human resources is difficult, time-consuming, and error-prone. This is while artificial intelligence performs these processes without errors.

Customer Behavior Analysis Using Machine Learning

You have probably heard a lot about the benefits of machine learning in the last few years. This feature is used in CRM systems to analyze customer behavior and obtain specific patterns.
With machine learning, it is possible to determine how many times each customer enters the site, how many times they make purchases, and how many times they do not make purchases at all. Also, in which time periods of the year each customer makes the most purchases and what products they purchase the most.

A look at some examples of Predictive Models in sales and marketing

As the name suggests, Predictive Models predict a series of behaviors and do this through different models. Here are some examples of predictive models:

Next Best Offer: This model actually introduces a product or service that the customer is most likely to buy. For example, if you have worked with Microsoft Dynamics 365, based on the customer’s purchase history, the system suggests that the customer is very likely to buy product X. So the sales manager decides to increase this possibility by sending incentive messages.

Lead Scoring: Lead scoring is the probability that either a person will convert into a real customer or increase the probability of a customer buying. So the sales expert understands which audiences are worth spending more time on.

Review of the most important indicators of CRM based on artificial intelligence

All analysis carried out in CRM systems based on AI must be based on measurable indicators. Otherwise, the analysis is only emotional and not based on reality. One of these indicators is CLV or customer lifetime value. This means how long each customer is in contact with our business and how much income they generate during this lifetime.
For example, consider a stationery store from which a mother buys. This mother has a 7-year-old child who buys her stationery annually from this site. Assuming that this customer’s shopping cart only includes children’s stationery, this customer’s lifetime is likely to be until the end of her child’s education. However, you should consider that from a certain age onwards, the mother will no longer buy and the child will buy stationery alone. In addition, there are other indicators:

Probability of purchase
Prediction of churn
Customer engagement score
Customer satisfaction
Intention to recommend the brand
Challenges of implementing AI in CRM

So far, we have talked in various ways about the advantages of using AI in CRM systems, but this synchronization also has disadvantages. As we said, for the practical and correct use of AI, expert staff must be trained. Otherwise, the organization’s output will not be satisfactory.

Need for clean and structured data

Artificial intelligence is a professional assistant, but it requires complete information. The more complete the input data to AI, the better the result. So, in the CRM system, you should upload all old forms, customer information, and in general any file that helps AI understand more into the system.

Training staff to interact with Copilot tools

After providing accurate data, your staff should be able to communicate well with Copilot tools. AI systems are of minimal use without proper human interaction. Sales and support teams should be fully familiar with Microsoft tools and be able to use AI as an intelligent assistant.

Data Security and Privacy Considerations

These days, more than ever, data security in AI is being talked about because without a specific framework, data given to the system can be processed at a deeper level. This means using customers’ private information at a large scale. So, controlling access levels and encrypting data should not be underestimated.

Interpretation Error of Language Models in Persian Environment

Another challenge of integrating AI with CRM system is interpreting language models. Most language models are developed based on English. Therefore, in Persian language, they may not be properly informed about the meaning and significance of each word, which may lead to errors.

Moving from Predictive to Prescriptive CRM

We said that CRM synchronization with AI will make systems move from Rule-based to Predictive; that is, predict customer behavior. However, the future of CRM with the presence of AI is going to go even further and become Prescriptive CRM. Prescriptive or prescribing means that these systems not only predict customer behavior but also provide the necessary actions.

The role of AI in decision-making automation and Hyper-Personalization

It is predicted that in the future, AI will have models with the ability to “think”. This may be a far-fetched expectation from AI at the moment, but with the advancement of Deep Learning, we can expect this from AI in combination with CRM.
With Hyper-Personalization, unique offers are designed and implemented for each customer. In this case, customers will no longer be grouped together, but each will be a separate unit.