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For a direct-sales business, pricing can either accelerate growth or quietly undermine it. When every deal depends on thousands of frontline account managers making pricing calls, inconsistency and ad‑hoc discounts quickly erode margins, trust, and the ability to scale.

Hilti, the family-owned construction solutions provider known for its red tools and direct sales model, faced exactly this challenge. Pricing had become transactional, complex, and difficult to explain to customers and new sales hires alike. For a company adding hundreds of account managers each year and expanding from hardware into software and services, that was not sustainable.

At Manufacturing Pricing Excellence 2026 – Power of 50, Hilti’s Head of Fair Pricing Competence Center, Nazar Lukasevych, delivered a keynote exploring how the company rebuilt its pricing model based on data, segmentation, and analytics. More than half of global revenue now runs through an automated price engine, supported by predictive models and structured price-setting governance.

From Deal-Making to a Defined Pricing Philosophy

A recurring issue in B2B is that pricing is treated as a negotiation tool rather than a strategic system. At Hilti, this was visible in how new account managers struggled: every sales call began with questions such as “What price should I give?” and “Is this price fair compared to similar customers?” Those uncertainties slowed ramp-up and made growth heavily dependent on individual pricing judgement.

The company deliberately chose to replace this transactional mindset with four principles underpinning its fair pricing model:

• Fairness: Comparable customers should be treated consistently on price.  
• Consistency: Prices should be stable and reliable over time, not dependent on timing or promotions.  
• Simplicity: The model must be easy to explain to a new sales hire and easy for them to explain to a customer.  
• Transparency: Customers should see the same price logic across channels, including e-commerce, instead of calling their account manager to circumvent the website.

Fair pricing became the name of the global pricing team, the design brief for the model, and a guiding principle for cross-channel pricing. It also forced clear decisions, preventing deals on the fly and pricing that only worked if customers knew when to ask for promotions.

For senior leaders, the key insight is that a pricing transformation gains traction when it is anchored in a small number of non-negotiable principles that directly address frontline pain points and customer experience, not just margin targets.

Building a Price Engine Around Customer Potential

Turning those principles into a working model required a shift from individual discount decisions to an automated price engine as the core pricing mechanism.

Hilti’s price engine is built around two segmentation criteria: customer trade and customer potential. Potential is defined in a pragmatic way, with company size as the primary driver. For example, the number of employees in a contractor’s business is linked to an expected basket of tools, inserts, and other products per employee in that specific trade.

Customers are then grouped into five potential classes, from large accounts to small “father-and-son” operations. Prices in the engine are calibrated to each potential class so that customers receive pricing that already reflects their likely long-term business volume and profile, without having to negotiate volume discounts order by order.

For customers, this has several implications:

• They no longer need to wait for occasional promotions to secure acceptable pricing.  
• They are not forced to consolidate large orders to trigger volume discounts.  
• They see a coherent logic for their pricing level, which supports trust and long-term engagement.

For Hilti, this architecture has created a clear hierarchy of pricing tools:

• Around 50%+ of the business runs through the price engine.  
• About 40% runs through individual price agreements, mainly for large key accounts and specialised or co-developed products.  
• Roughly 10% is managed via one-off overrides, treated as true exceptions rather than a default behaviour.

An engine-driven model limits uncontrolled discounting but demands much higher discipline and capability in initial price setting. Once the engine is in place, a poor pricing decision can propagate across markets and customer segments at scale. That shifts the strategic bottleneck from negotiation to price setting.

Elevating Price Setting: Governance, Value, and Tools

An automated price engine only works if the organisation can set and maintain robust, market-aligned prices. In many B2B companies, that capability is underdeveloped; Hilti was no exception. The company responded by structuring price setting across three dimensions: organisation, methodology, and analytics.

1. Clear roles and responsibilities

Hilti placed primary responsibility for price setting with product managers across three organisational levels and found the sweet spot at the regional product management level. These managers:

• Own the product’s value proposition.  
• Are close enough to local markets to understand competitive dynamics.  
• Are still connected to global strategy and portfolio considerations.

They are empowered to define the prices that feed into the engine, with central pricing providing governance, tools, and methodology. This division of labour recognises that pricing decisions are ultimately product and market decisions, not purely financial ones.

2. Value-based pricing as a discipline

Given Hilti’s differentiated product portfolio and innovation focus, a value-based approach to pricing is a natural fit. In practice, this means moving beyond cost-plus and discount structures to reflect the specific productivity gains, performance, and risk reduction delivered to customers.

The company treats this as an ongoing discipline. As new products are launched—more than 150 per year across markets—value understanding and monetisation must be refreshed. The implication for leaders is that value-based pricing needs sustained investment in training and governance, not just a methodology deck.

3. Analytics as daily support, not a side exercise

To prevent price setting from becoming an overwhelmed, spreadsheet-heavy activity, Hilti invested in a structured analytics toolkit for product managers.

• Descriptive analytics: Foundational reporting and queries to understand sales, pricing levels, and performance across markets and products.  
• Diagnostic analytics: Aggregated indicators such as a price acceptance score that combines common pricing KPIs into a traffic light at product-market level, guiding where attention is needed.  
• Predictive analytics: Models that estimate how changes in price will affect customer purchase decisions and overall business.

Jumping directly to advanced analytics would have overwhelmed users and undermined adoption. Instead, each stage solved a tangible problem and prepared the organisation for the next level of sophistication.

Crossing the Chasm to Predictive Pricing

Moving into predictive analytics was not a minor extension of the existing toolkit. It required new skills, new partners, and rigorous validation.

Hilti’s advantage as a direct-sales company is that it owns the full transaction history, including quotes and orders. This allowed the team to treat quotes and subsequent orders as observable yes/no decisions by customers at specific price points.

Working with an internal data science team and external partners, Hilti built a machine learning model that:

• Ingests product attributes, customer characteristics, and deal features.  
• Estimates the probability of purchase for each deal at different price points.  
• Aggregates these probabilities to simulate volume and revenue outcomes for product portfolios or business units across a range of potential price levels.

The model is now used in two primary scenarios:

• Repricing specific product categories in a given market, particularly in the context of frequent list price changes in recent years.  
• Simulating expected volume developments when planning annual price adjustments.

Several elements are notable for leaders considering a similar move:

• Cross-functional cooperation is essential. Predictive pricing sits beyond typical pricing team capabilities and demands close collaboration with data science functions.  
• External validation builds credibility. Hilti systematically checks model outputs against real transaction data and uses pilot projects to test reliability before broader rollout.  
• Adoption depends on trust and timing. The predictive model was deployed only after simpler analytics had been embedded and accepted, reducing scepticism and resistance.

Predictive pricing is not primarily a technology challenge. It is a sequencing, capability, and mindset challenge.

Conclusion

Hilti’s fair pricing model illustrates how pricing can evolve from ad‑hoc negotiation support to a structured commercial system that scales across products, markets, and channels.

Several themes stand out for industrial and B2B leaders:

• A clear pricing philosophy, expressed in simple principles, can align sales, pricing, and customers around a common understanding of fairness and reliability.  
• An automated price engine anchored in customer potential can reduce negotiation friction and support direct-sales and e-commerce channels—but it raises the bar for price setting quality.  
• Building price setting as a capability requires explicit roles, value-based methods, and an analytics roadmap that moves from descriptive to predictive in manageable steps.  
• Advanced analytics in pricing only deliver impact when embedded in real decisions, validated against real data, and supported by organisational readiness.

Pricing excellence in B2B is less about finding the perfect price and more about creating a coherent system where decisions are transparent, explainable, and consistent over time. Hilti’s experience suggests that when pricing is treated as a core commercial infrastructure, not just a margin lever, it can become a durable source of customer trust and scalable growth.