Today's B2B customers prefer to do their own research before calling sales. According to a study by Gartner, 67% of B2B buyers prefer a representative-free buying experience, and 70% want a fully digital self-service. For simple products, this works well. For a portfolio with hundreds of variants, self-research reaches a limit: the customer finds too many similar items and does not know which one fits their system.

This is where the difference between a public chatbot and an assistant in the login area becomes clear. One answers general questions. The other knows the logged-in customer and advises them on their specific situation.

Why generic answers are not enough in B2B

Complex products often differ by a single attribute: voltage, connection, diameter, software version. To the customer, the series looks identical; for the selection, exactly that one deviation matters. An assistant that only knows the public catalog cannot resolve this question and answers in general terms. If the customer then selects the wrong variant, a return or downtime follows.

In addition, there is the so-called translation gap: the customer describes their request in everyday language, while the knowledge is stored in technical terms. "The small valve on the 200 series" must be translated into the correct part number. This mapping is only successful with the right context, which is provided by the login area. How this translation works in detail is shown in the article on the digital product advisor.

The login area makes consultation personal

As soon as the customer is logged in, the assistant knows who they are talking to. It knows the installed products, their configuration, the stored conditions, and the order history. A general piece of information thus becomes a concrete one: the compatible spare part for exactly this system, the valid price for this customer, the realistic delivery time.

This personalization changes the consultation. The assistant guides the customer to the appropriate item with targeted questions, along the attributes that actually narrow down the selection, and hides what is not eligible for their system. Guided Selling in the login area combines the dialogue capability of the language model with what the company already knows about the customer.

Consistency instead of contradiction

A second advantage carries significant weight in B2B. The same Gartner study shows that 69% of buyers experience contradictions between the information on the website and the details provided by sales. Such contradictions erode trust.

An assistant whose answers are tied to verified sources and real customer data provides the exact same, correct information everywhere. The answer in the portal matches the data sheet and what sales says, because everyone draws from the same source. If the assistant does not find a reliable data point, it hands over to a human instead of guessing. A huge loss of trust would occur if a bot recommended a spare part that is not even available for purchase in the B2B shop.

Roles and data separation in the portal

A login area comes with responsibility, as the assistant works with sensitive data. Upon login, the portal transfers the user's role to the assistant so that each user group only receives the content authorized for them. A purchasing agent sees different conditions than a service technician, and the data of different customers remains strictly separated.

For data privacy, where processing takes place also matters. Processing in Germany, backed by a data processing agreement, is the reliable path in B2B. How role-based knowledge provisioning looks in practice is described in the article From Documents to AI.

What consultation in a service portal requires

For an assistant to provide reliable advice in the login area, three things must come together.

The product data must be structured, with attributes, variants, and compatibility rules as data relations. The connection to the legacy systems must be established so that the assistant can access customer-specific data from ERP and CRM, such as availability, price, and installed base. How this connection is technically achieved is shown in the article on CRM and ERP integration. And the handoff to a human must be defined, because for final complex decisions, according to Gartner, many buyers still seek confirmation from a human expert.

How Mercury.ai implements this

Mercury.ai brings Guided Selling to the login area by combining these three building blocks. The Knowledge Hub holds the structured product knowledge and links every answer to a verifiable source. Via integration, the assistant accesses transactional data from ERP and CRM, making the information customer-specific. The portal transfers the user's role, and the assistant respects data separation. An orchestra of specialized models checks the source before formulating, significantly reducing the risk of invented answers.

How viable this is in industrial B2B is demonstrated by the case of Böllhoff. The specialist for fastening technology has resolved a complex portfolio and the usual media breaks in service with Mercury.ai, generating qualified leads from dialogue on a daily basis. An entry point is offered by the solution for product search and consultation.

Frequently Asked Questions

What is Guided Selling in the customer portal?
Guided Selling in the customer portal is guided product advice that knows the logged-in customer. The assistant uses identity, installed products, and conditions to guide them to the appropriate item in a customer-specific way.

Why is a chatbot in the login area better for complex products?
Because it knows the customer's context. It resolves the confusion of similar variants by accessing the specific system, configuration, and compatibility of the logged-in customer, instead of answering in general terms.

How is customer data protected in the portal?
The portal transfers the user's role, each group only receives authorized content, and the data of different customers remains separated. With Mercury.ai, data is processed in Germany.

Does the assistant replace sales?
No. The assistant handles recurring consultations and pre-qualification. For final complex decisions, it hands over to a human, whom many B2B buyers still seek for confirmation.

What data does the assistant need for consultation?
Structured product data with variants and compatibility rules, as well as access to customer-specific data from ERP and CRM such as availability, price, and installed base.

Consultation begins with context

For simple products, a public chatbot is sufficient. For a broad portfolio requiring explanation, the context determines the value. The login area provides this context, and a general information provider becomes an advisor who knows the customer and their system.

Those who structure product data, connect systems, and strictly separate roles give their B2B customers the digital self-service they expect while maintaining the reliability of their own catalog.

Would you like to bring product consultation to your customer portal? Talk to us or view the solution for product search and consultation.

About the author: Dr. Hendrik ter Horst is Chief Product Officer at Mercury.ai. He holds a PhD in computer science from the CITEC Institute at Bielefeld University, specializing in machine information extraction and data processing. For ten years, he has been working on dialogue-based AI systems and is responsible for the conception, implementation, and validation of AI-supported applications from the initial idea to productive use.

Sources

• Gartner (2025/2026): B2B Buyer Survey. 67% of B2B buyers prefer a representative-free buying experience, 70% a fully digital self-service.

• Gartner (2025): B2B Buyer Survey. 69% of buyers report contradictions between information on the website and details provided by sales.