Navetti Applies Advanced Machine Learning And Analytics In The Pricing Waterfall

Navetti is Introducing A General Machine Learning And Adaptive Artificial Intelligence Engine In Its State-Of-The-Art Pricing Software Suite Navetti PricePoint

Navetti, a leading European provider of price optimization solutions, is introducing a general machine learning and adaptive artificial intelligence engine in its state-of-the-art pricing software suite Navetti PricePoint.

Advanced machine learning and analytical methods can be applied across all stages in the pricing waterfall, from centrally defined pricing strategies to locally adjusted specific executions for individual customer orders. It means that the system can be customized to individual client needs faster and reduces time to market for new features and developments, and Navetti is working with a range of blue-chip clients to develop customer-specific price optimization solutions. By applying these facilities across the different modules in Navetti PricePoint, Navetti helps its customers understand, optimize and capture additional business opportunities through more precise pricing structures, more efficient internal handling and more predictable business outcomes.

Pricing and price optimization is already a big data business application, taking in and analysing millions of different data points from all aspects of the business environment, such as product specification details, transactional data (who bought what, when, where and how), supply chain situations, competitive environment and channel-specific conditions. Hence, it is no surprise that price optimization systems increasingly are applying advanced algorithms and machine learning capabilities to further enhance system capacity and capabilities.

“Many vendors talk about Artificial Intelligence and Machine Learning (AI & ML) as an add-on to their legacy systems”, says Andreas Westling, CEO of Navetti. “For us at Navetti, this kind of advanced adaptive intelligence has been a key perspective in our development of our solutions for a long time. Our Navetti PricePoint platform and its optimization algorithms have been designed to incorporate applied artificial intelligence and machine learning ever since we started to develop the fourth generation of Navetti PricePoint.

A key difference is that we apply AI & ML across the entire pricing waterfall, which means our customers can use this to improve all stages of the customer journey. Now we are taking the next step by making AI & ML a general engine for all pricing processes and their corresponding modules in Navetti PricePoint, and we have already started to apply this together with several major clients when developing customer-specific solutions for both e-commerce and traditional manufacturing applications. In this way, we are offering our customers the best of two worlds: a fully developed off-the-shelf system that can be configured to our customer’s specifications, fully integrated and implemented in a matter of weeks, and an AI & ML engine for rapid additional developments.

In addition, we expect AI & ML developments to take place across many other aspects of our customers’ businesses, and hence we have made sure that Navetti PricePoint can easily interact and be fully integrated with the AI & ML structures in other parts of our client’s IT solutions.”

“Price optimization is not just about the actual price itself”, Westling continues. “But AI & ML usually starts by methods of recording and learning how different customers respond to different pricing strategies in different situations. By using machine learning techniques like logical inference, neural networks and heuristic search, we can align, adjust and optimize not just the specific price itself but the full customer purchasing experience to suit the transaction process, which has a positive impact on our client’s business objectives. However, most business situations are more complex than that, and hence we are also applying AI & ML in decision processes.”

These include

• Virtual product family creation and market segmentation, i.e. understanding which products and market segments are similar, and how these clusters differ from each other in terms of customer behaviour.
• Customer value attribution, i.e. understanding how different features and product attributes generate value for different types of customers and market situations.
• Deeper business logic understanding, i.e. understanding how complementary attributes such as competitive structures, customer support, supplier t******************* and supply mechanisms influence the pricing mechanics, and how these jointly help define both the optimum price and the total business impact.
• Advanced demand forecasting, i.e. taking the price optimization results and applying them in the supply chain mechanism, allowing suppliers to balance their inventory and production cycles against expected demand levels.

We know that when customers plan and implement price optimization systems, they look at three different benefits:

• Sales and margin growth opportunity, i.e. identifying and turning business opportunities to bottom line results through market-driven and customer value-based price optimization.
• Risk reduction, i.e. companies under pressure from customers and/or competitors with price harmonization needs use professional pricing systems to support them in their pricing logic and operational pricing activities to both reduce risks such as cross-border trading and improve perceived quality.
• Operational efficiency, i.e.  the ability to take pricing decisions faster and more efficiently throughout the organization.

“With artificial intelligence and machine learning across the entire Navetti PricePoint system structure, we are helping customers use price optimization logic across the business operations, with a considerable impact across all three benefit dimensions,” Westling concludes.