Commercial Intelligence
Commercial Intelligence turns your data into actionable recommendations, from identifying the next high-potential customer and optimizing pricing decisions to predicting at-risk contract renewals.
AI-powered Commercial Intelligence uses artificial intelligence to transform data from CRM, ERP, and service systems into concrete recommendations for sales, pricing, marketing, and service teams. Rather than spending time analyzing additional reports, teams receive direct, actionable insights that support their next business decisions.
Industrial companies possess vast amounts of sales, pricing, service, and customer data, yet much of its potential remains untapped. This includes ERP and CRM records, service tickets, and installed base histories. Data that already exists across multiple systems but is often insufficiently connected and underutilized for decision-making.
A perfect data foundation is not a prerequisite. In many projects, data quality and integration improve step by step alongside the implementation of prioritized use cases.
AI-powered scoring and recommendation models have become highly effective, fast to implement, and economically viable.
The required data is usually already available. The greatest opportunity lies in making existing information usable across systems and business functions.
Purchasing decisions are increasingly influenced by AI-driven systems. Buyers research, compare, and evaluate suppliers based on value and relevance long before their first interaction with sales.
Commercial Intelligence brings together four interconnected disciplines: Sales, Pricing, Marketing, and Service. Each derives recommendations from the same underlying data ecosystem.
This approach ensures that customer-facing functions work in concert. Marketing intent signals feed lead-scoring models, sales and service data strengthen account prioritization, and pricing intelligence supports more effective quoting and deal execution.
Prioritize customers and opportunities more effectively, identify revenue potential earlier, and align sales activities with the opportunities that create the highest business value.
Next Best Action analyzes CRM, ERP, service, and customer-interaction data to recommend the most valuable next step for each account. This enables sales teams to identify cross-selling and upselling opportunities earlier, allocate resources more effectively, and develop customer relationships more systematically.
Modern Sales Intelligence solutions uncover buying signals, continuously prioritize accounts, and help sales professionals prepare for customer interactions. The result is a more focused sales organization with higher conversion potential.
Revenue growth has stalled despite a customer base large enough to generate significantly more business.
Sales priorities are largely driven by intuition rather than measurable opportunity potential.
Cross-selling, service, and renewal opportunities are identified only after they have already been lost.
Make better pricing decisions and sustainably increase profitability.
Pricing Intelligence combines transaction history, willingness-to-pay signals, market dynamics, and customer characteristics to recommend optimal prices, discount levels, and bundle configurations.
Rather than relying on static price lists or individual negotiation styles, commercial teams receive contextual pricing guidance precisely when decisions are made.
Modern pricing solutions reveal pricing inconsistencies, identify margin leakage early, and help teams make more consistent pricing and discount decisions.
Discount levels vary significantly across comparable deals.
Pricing decisions are primarily based on experience rather than data.
Margin erosion occurs without a clear understanding of the underlying causes.
Identify high-potential target accounts, detect market shifts earlier, and become visible where purchasing decisions begin.
Marketing Intelligence combines market and competitive insights with digital engagement data to identify and address target audiences more effectively. This helps organizations recognize changing market conditions earlier and optimize marketing investments.
Through the continuous analysis of market trends, competitive activity, customer needs, and digital interactions, companies gain the ability to identify emerging opportunities and engage buyers more effectively.
Modern marketing solutions help organizations identify new target accounts through approaches such as lookalike modelling, better understand digital buying journeys, and strengthen visibility against competitors. At the same time, content can be optimized for search engines, AI assistants, and other digital discovery channels.
Prospective customers discover competitors before they discover your company.
Marketing generates traffic and leads but only limited qualified pipeline.
Market changes and customer needs are identified too late.
Transform installed base, service, and asset data into proactive revenue opportunities and outstanding service execution.
Service Intelligence combines service history, asset utilization, IoT data, and lifecycle information to identify replacement opportunities, retrofit potential, contract renewals, and spare-parts recommendations before customers experience operational disruption.
By connecting service, asset, and usage data, companies gain a comprehensive view of their installed base. This enables earlier identification of maintenance requirements, renewal opportunities, and additional revenue potential.
Your installed base is managed operationally rather than commercially.
Service, sales, and aftermarket teams work with inconsistent data and limited transparency.
Growth depends primarily on new equipment sales rather than customer lifetime value.
potential reduction in unnecessary discounting through AI-powered Deal Guidance
higher cross-selling conversion rates through targeted Next Best Action recommendations
of installed-base service revenue potential often remains untapped without proactive customer engagement
Projects typically begin with an initial prototype that makes a specific use case tangible. This is followed by a proof of concept using real company data to validate business value, feasibility, and scalability. In a third step, the solution can be deployed and scaled across the organization.
Early Stage: Illustrative Prototype
An interactive mock-up built with synthetic or sample data makes the selected use case visible at an early stage. This allows stakeholders to evaluate business value, user experience, and functional requirements before investing in data integration and implementation.
Intermediate Stage: Functional Proof of Concept
The next step brings the use case to life using real company data and connects it to existing systems such as CRM, ERP, or service platforms. The objective is to validate business impact, data readiness, and expected value under real-world conditions.
AI-powered Commercial Intelligence uses artificial intelligence to transform data from CRM, ERP, pricing, and service systems into actionable recommendations for sales, pricing, marketing, and service teams.
Rather than providing static dashboards, it delivers clear recommendations on which account to contact, what price to offer, or which service contract is at risk and should be renewed.
Next Best Action (NBA) is an AI-generated recommendation that identifies the most valuable next step for a specific account.
Examples include a cross-sell opportunity, a contract-renewal conversation, or a customer-retention activity. Recommendations are based on patterns identified across CRM, transaction, and service-history data.
Deal Guidance is an AI-powered pricing and discount recommendation presented to sales representatives during quote creation.
Using historical win-loss outcomes, margins, and transaction data from comparable deals, it replaces intuition-based discounting with a data-driven pricing corridor. This helps reduce unnecessary discount leakage while maintaining win rates.
Willingness to pay is determined by analyzing historical transaction data, including won and lost opportunities, discount levels, product configurations, and customer segments.
Rather than relying on generic benchmark prices, AI models use these patterns to estimate an appropriate price range for each new product configuration and customer context.
AI-powered Marketing Intelligence helps structure content and positioning to improve discoverability across both traditional search engines and AI-driven answer systems through Generative Engine Optimization (GEO).
It also transforms digital engagement signals such as content consumption, configurator usage, and technical-document downloads into intent data that can feed sales-prioritization models such as Next Best Action.
Installed-base monetization refers to the practice of turning data from already deployed equipment into recurring commercial opportunities.
Examples include contract renewals, spare-parts cross-selling, retrofit programs, and upgrade offers. Usage data, service history, and support interactions are leveraged to identify opportunities before customers actively request them.
The installed base is therefore treated as a commercial asset, not just an operational one.
Manufacturers with long sales cycles, engineered-to-order products, and distributed dealer or service networks generate large volumes of valuable but fragmented data across CRM, quoting, and service systems.
AI-powered Commercial Intelligence converts this data into consistent, actionable recommendations, solving a pattern-recognition challenge that would be too complex and resource-intensive to address manually at scale.
An initial prototype built with illustrative data and designed to demonstrate a specific use case, such as Deal Guidance or Next Best Action scoring, can typically be developed within a matter of weeks.
A fully functional proof of concept connected to real company data represents a medium-term initiative whose timeline depends on the complexity of the use case and the company's data landscape.
A strong starting point typically includes CRM data such as accounts, opportunities, and win-loss history, transaction and quotation data including prices, discounts, and configurations, and, where relevant, service or installed-base data.
The data does not need to be perfectly structured or fully cleansed. An initial assessment usually reveals which existing sources can already support the selected use case and where the most important data gaps exist.
