In the face of ongoing industry change, B2B leaders are under increasing pressure to deliver measurable impact faster and more efficiently.

Digital investments must prove value quickly, and every touchpoint—from discovery to reorder—needs to contribute to customer retention and profitable growth.

Personalization offers a high-potential path forward. But B2B personalization is not as simple as “customers who bought this also bought that.” Buyers expect relevant, responsive, human-like experiences across multiple roles, channels, and cycles. Many companies struggle to deliver on this expectation, and even more struggle to connect those efforts to financial outcomes.

For part two of the Profitability Imperative series, How Improved Personalization in B2B Drives Higher Profits, Master B2B conducted a survey of 86 global manufacturing and distribution executives to learn how they're approaching AI and personalization in commerce. The resulting report highlights why B2B e-commerce personalization isn’t just a customer experience initiative—it’s a profitability strategy.

What is AI personalization?

AI personalization uses artificial intelligence to tailor digital experiences in real time. In the B2B context, it means showing the right content, offers, or actions based on a user’s role, behavior, history, and business context. Unlike static rule-based personalization, AI can adapt based on intent and value and optimize for profitability.

Keep reading to explore how leading companies are using AI to turn personalization into performance across three key areas: site search, web experience, and customer service.

Smarter site search fuels conversion—and profits

Search is often your first signal of buyer intent. In B2B, that signal can be especially nuanced: buyers might search by part number, task, product nickname, or even an image. If your site search can’t handle that complexity, you’re not just missing conversions—you’re increasing service costs.

According to the report, only 36% of B2B companies are currently using AI to power site search. Those who are, however, are reaping benefits from a variety of search-related use cases.

The most common use cases among survey respondents who are using AI

• 68% are using it for predictive search suggestions
• 65% are using it for semantic search to understand buyer context
• 52% are using it for tasks like natural language processing, autocorrection, and synonym recognition
• 42% are using it to personalize search results based on user behavior

These capabilities reduce friction, increase relevance, and improve profitability—especially when integrated with product margin data from ERP systems.

Guided selling is on the rise

Some companies are going even further with their search solutions. Guided selling tools use AI to ask customers a series of questions and then recommend the right product based on goals—not just keywords. These tools reduce the burden on sales teams, boost confidence in the purchase, and increase deal size.

For example, an industrial equipment distributor might guide a buyer through specifying environmental conditions, energy efficiency needs, and price range before suggesting the most relevant compressor. That consultative digital experience often converts better than traditional search and adds long-term value.

Website personalization must go beyond clickstream data

Most B2B companies personalize web content using surface-level data—past purchases, page views, or behavior on a single visit. That’s a start, but it’s no longer enough. Modern B2B buyers now expect the same types of tailored experiences in sales and marketing that they receive in B2C.

This means most companies are making decisions about what content, recommendations, or offers to show without any awareness of profitability impact. That’s a missed opportunity—especially in an economy where every percentage point of margin matters.

Connecting every data source matters

B2B companies surveyed currently personalize their websites using a range of available data points. The most common inputs include:

• Past site purchases
• Product view history
• Past website behavior
• Geolocation
• Customer segmentation
• Company the user works for
• User preferences
• The user’s role within their company
• Referral source

AI is the key to taking this wide scope of data and making it usable for a variety of personalization use cases.

With tools that can summarize unstructured content, you can create composite profiles that adapt over time and across roles. In B2B, this is essential—because each customer account includes multiple stakeholders, each with distinct needs. A procurement officer may need to reorder prompts and compliance documentation, while a field technician may look for how-to videos or service information. AI enables companies to deliver the right information at the right time to each individual, ensuring every interaction supports both the buyer and the bottom line.

This type of B2B marketing personalization helps ensure every touchpoint adds value—not just to the buyer, but to your bottom line.

AI chatbots do more than cut service costs—they increase sales

Many B2B companies think of chatbots as a service tool. But when done well, they’re also a revenue accelerator.

That’s why it’s surprising that only 28% of companies surveyed in the report had implemented AI chatbots—especially given Gartner’s projection that these tools will reduce service costs by $80 billion by 2026.¹

But the key isn’t just in having a chatbot—it’s designing one that works effectively. The most effective chatbots go beyond basic automation to simulate human interaction. Bots that use informal, conversational language, respond dynamically, and even mirror a visitor’s tone or name can significantly impact upsell rates. A recent study found that chatbots using personalized, human-style interactions were twice as likely to drive upsells compared to generic bots.²

Chatbots that learn can do more than help—they can inform strategy

The value of chatbots doesn’t end with service. Leading organizations use chatbot data to uncover trends and inform product innovation. For example, Butterball analyzed call center data to develop a new “cook-from-frozen” turkey product based on repeated customer inquiries—an insight made possible by AI summarization and integrated systems powered by SAP.

Bringing it all together: Turning personalization into a profit strategy

Bringing AI e-commerce personalization to life takes more than just implementing the right tools. It requires meaningful access to the right data—shared across systems, teams, and touchpoints. This kind of holistic, integrated foundation enables companies to personalize with precision and drive real business outcomes.

When data from marketing, commerce, sales, service, and finance is connected, every interaction becomes an opportunity. You can turn touchpoints into insights—and insights into actions that matter.

And in today’s climate of economic uncertainty and tighter budgets—where leaders face growing pressure to deliver results with fewer resources—that kind of alignment isn’t just helpful. It’s essential. The most successful B2B personalization strategies aren’t just more dynamic—they’re smarter, more efficient, and more profitable.

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