CRM and AI as the key to customer centricity

Inspire customers and thereby achieve all corporate goals

What is the company's greatest asset? Sales, growth, brand awareness? The philosophy of customer centricity puts the customer at the center and makes them one of the highest assets. However, the practical implementation of the customer-centric mindset is a complex task. A powerful CRM system acts as a tool for implementation because data-based decision-making processes are among the core components of the approach.

What is the difference between customer orientation and customer centricity?

At this point, some people will say: We've always been customer-centric. That is probably true. However, there are crucial differences between "customer-oriented" and "customer-centric".

Customer orientation describes a strategy in which companies aim to be as customer-friendly as possible. In practice, this means, for example, offering a good service and improving product features, for example in response to customer feedback. However, customer orientation is usually reactive. Only when there is criticism do adjustments follow. Moreover, customer orientation is usually only one aspect of decision-making in business matters.

Customer centricity, on the other hand, places the customer uncompromisingly at the center. This means that the entire corporate culture is geared towards this. Accordingly, customer centricity must be anchored in all departments, not just in sales and marketing. It is crucial that customer centricity takes place proactively. Your company does not wait for feedback, but puts itself in the customer's shoes, determines their wishes and optimizes its offering with foresight.

What exactly does the implementation of customer centricity look like?

Various measures are required to ensure that a company's behavior and actions are no longer just product-centric, but above all customer-centric. In order to take the best possible care of customers, you need to know them as well as possible.

Challenges in the integration of CRM, AI and customer centricity

On paper, it reads simple: introduce CRM, implement AI tools, evaluate data, think customer-centrically and inspire customers. However, the individual steps require good preparation, suitable managers, a team of motivated employees, a concrete timetable, training and sufficient financial resources so that the concept can be put into practice step by step. Everyone must pursue the same goal and remove obstacles on the way to becoming a customer-centric company.

The following best practices highlight three common problems and their solutions.

1. improving data quality through a company-wide policy

As already mentioned, understanding customer needs is crucial for a customer-centric approach - especially in the B2B environment. However, companies often make the mistake of making gut decisions about what might be important to buyers, even though analytical, data-based strategies are much more reliable.

A necessary prerequisite for the data-driven approach is standardized data maintenance in CRM. Only then can a high-quality treasure trove of data be created that is available to all departments in the company. A company-wide guideline should therefore be drawn up for all employees, specifying the requirements to be observed for data maintenance.

2 Customer centricity as a living corporate culture

A customer-oriented corporate culture is crucial for the long-term success of your company. In fact, all departments must think from a customer perspective, not only sales and marketing, but also development, production and support. In order to create and maintain a customer-centric culture, it is crucial that management levels exemplify customer centricity and support it with suitable measures such as training and workshops.

This is why customer centricity is so important:

1. Customer satisfaction and loyalty: When all employees, regardless of department, put the customer at the center of everything they do, it leads to a better customer experience and increases customer satisfaction and loyalty.
2. Efficiency and productivity: As all employees focus their efforts on meeting customer requirements, this leads to greater efficiency and productivity throughout the company (e.g. through improved communication, faster problem solving, more targeted use of resources, etc.).
3. Employee motivation: Employees who work in a customer-oriented environment and whose contributions to customer satisfaction are valued are generally more motivated and committed.
4. Innovative strength: A customer-centric culture with motivated employees promotes a company's ability to innovate because employees are committed to looking for ways to improve products and services and adapt them to customer requirements.
   
   
   

3. provide infrastructure for customer centricity

Providing a suitable infrastructure for customer centricity is crucial to effectively meeting customers' needs and building long-term relationships. In addition to the CRM system, the following technologies are useful:

• Business intelligence technologies for data analysis:
  BI helps to analyze data, especially unstructured data from social media. These analyses provide a deeper insight into customer behaviour and promote a personalized approach, which increases customer satisfaction.
• Marketing automation tools to support personalization:
  They help deliver personalized content and offers in real time, resulting in higher customer loyalty and satisfaction as customers feel understood and valued.
• Omnichannel platforms for seamless communication:
  Utilization ensures that customers have a consistent and seamless experience across different channels. This is important to ensure a consistently positive customer experience.

What role does artificial intelligence play in customer centricity?

Artificial intelligence (AI) enables companies to analyze large amounts of customer data to identify patterns and trends in customer behavior. By using AI-powered tools, specific customer profiles can be created based on first-party data. As soon as you merge demographic information, transactions and interactions from your CRM and other sources via interfaces, AI can help you structure your data. This makes it possible to identify which products or services are of interest to certain customers so that personalized offers can be made to them.

How AI can "predict" customer behavior

1. data analysis and pattern recognition
AI-supported systems analyze historical data on customer interactions, purchases and preferences. Through machine learning, these systems can identify patterns that indicate future behavior.

2. rfm analyses
(Recency, Frequency, Monetary (RFM for short) analyses use AI to determine the value of a customer and predict how likely they are to buy in the future. These analyses focus on factors such as the frequency and value of previous purchases to calculate purchase probabilities.

3. prediction of customer churn
An important use case for AI is the prediction of customer churn. AI models analyze past customer behavior to calculate churn probabilities. Customers with a high risk of churn can then be targeted with special offers or incentives to retain them.

4. sentiment analysis
AI can also analyze unstructured data such as customer reviews and feedback to assess customer engagement and satisfaction. These sentiment analyses help companies to identify problems at an early stage and adapt their strategies accordingly in order to improve customer loyalty.

Conclusion: Customer centricity - don't just catch the eye, stay in the memory of your customers!

Customer centricity therefore means putting the customer at the center of your business activities and aligning your activities accordingly. Don't just create the product your customers want, create the feeling that they are making the best choice with you.