Customer relationship management (CRM) encompasses all the processes through which a company collects, structures, and utilizes customer data to enhance every interaction. Far from being limited to after-sales service, this discipline covers marketing, sales, and support, with a common goal: transforming one-off exchanges into lasting relationships.
The solutions that support this management are evolving rapidly. The performance gaps between companies are widening on one specific point: the ability to orchestrate data across channels.
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Orchestration of customer data across channels: the real bottleneck
Most companies already have a CRM, a support tool, and several communication channels. The problem no longer lies in the adoption of these tools, but in their interconnection.
Sinch indicates that a significant portion of companies believes their customer channels remain poorly connected to CRMs, ERPs, or support platforms. Specifically, a customer who sends a message on an online chat and then calls the phone service often forces the agent to manually reconstruct the history. This disconnect degrades satisfaction and lengthens processing times.
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Resolving this issue requires working on three simultaneous levels:
- Real-time synchronization of contact records between the CRM, the ticketing tool, and messaging channels, so that each agent accesses the same consolidated history.
- Standardization of customer identifiers (email, phone number, internal ID) so that the same individual does not generate multiple profiles in different systems.
- Implementation of native connectors or APIs between software components, rather than manual file exports that create data discrepancies.
Specialized platforms in optimizing customer relationships, such as those offered on perceptis.fr, assist companies in this technical integration process. Without data orchestration, personalization remains a theoretical goal.
Agentic AI in customer relations: beyond the classic chatbot
The chatbot has popularized the automation of customer support. Its limitation is well-known: it answers simple questions based on predefined scripts and refers to a human as soon as the request goes beyond the framework. Agentic AI aims to execute end-to-end tasks in the customer journey, not just to answer a question.
Apizee describes use cases where agentic AI handles a complete complaint: customer identification, order verification, solution proposal (refund, exchange, credit), and then execution of the action in the system, without human intervention. The human agent only intervenes in complex or disputed cases.
Concrete limitations to integrate into the strategy
This autonomy poses a documented risk: hallucinations. AI can generate factually incorrect responses, for example, announcing an inaccurate delivery time or a return policy that does not exist. Apizee emphasizes the need to control the quality of automated responses and to avoid false information being transmitted to customers.
Any company deploying agentic AI must plan a verification system for critical responses (amounts, contractual commitments, deadlines). Without this safety net, productivity gains turn into additional complaints.
Transparency on the use of AI: an operational requirement, not an ethical detail
The temptation is strong to deploy AI invisibly to the customer, hoping that the fluidity of the exchange will suffice. This approach backfires on the company as soon as a customer discovers they are interacting with a machine after believing they were speaking to a human.
Clearly explaining how AI is used and offering a human alternative has now become a best practice recommended by players like Apizee. This transparency does not hinder adoption. It consolidates it, as it gives the customer the feeling of retaining control over the interaction.
Three concrete measures make this transparency operational:
- Indicate from the beginning of the conversation whether the customer is interacting with AI or a human agent, without ambiguous wording.
- Offer at any time a transfer to a human interlocutor, accessible with one click or tap.
- Inform the customer about the type of data collected during the automated exchange and its purpose.
This approach reinforces trust, which remains the foundation of any sustainable loyalty strategy.
Personalization of customer relations: moving from segment to individual
Segment marketing (age group, geographic area, category of purchased product) has long been sufficient. Current tools allow for deeper engagement by leveraging individual behavioral data: pages viewed, purchase frequency, preferred channels, usual contact times.
Effective personalization relies on behavioral data, not declarative data. A customer who views the same product page three times without purchasing does not have the same need as a customer who buys at every visit. The former may be waiting for missing information or a trigger (availability, price). The latter deserves a loyalty program tailored to their regularity.
The role of CRM in this granularity
A well-configured CRM aggregates these signals and triggers automated actions: targeted follow-ups, contextual offers, alerts for the sales team. The quality of personalization directly depends on the quality of incoming data. A poorly filled field or a duplicate customer record is enough to skew the entire chain.
Companies that get the most out of their customer relationship management are those that invest as much in maintaining their database as in acquiring new tools. A CRM powered by clean and synchronized data produces better results than a sophisticated tool connected to an inconsistent database.
Optimizing customer relations is no longer about choosing software, but about the rigor with which data flows between systems, the transparency granted to customers regarding automation, and the ability to correct AI responses before they become unfulfilled promises. These three axes today determine the gap between functional customer management and customer management that generates measurable loyalty.