Optimization Use Cases

In this section we will introduce all the use cases we have covered in Pricefx so far in terms of Optimization. Please note that this is merely an introduction. Follow the suggested links to learn more.

Optimization in Pricefx is effectively done through a selection of accelerators that aim to help you quickly implement existing use cases or adjust Pricefx behaviour to your needs. We are constantly developing more accelerators to help customers with common use cases.

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Accelerate Negotiation Guidance

The Negotiation Guidance model aims to assist in making pricing decisions by comparing current pricing situations with similar past scenarios. It leverages segmentation to provide a richer comparison set and align with the pricing strategy, enabling exploration of more possibilities and avoidance of sticking with suboptimal past pricing.

The negotiation guidance, a part of our optimization capability, is specifically designed to assist sellers during customer deal negotiations. It provides recommended pricing for specific customer and product combinations along with a related negotiation band. This guidance is based on the segmentation of products and customers, utilizing historical data to identify key attributes and create segments for pricing differentiation.

The process begins with a basic setup, defining a target outcome for optimization, such as gross margin percentage, and then automatically identifies and prioritizes key attributes to drive prices for customers. The results are visualized in a segmentation tree, providing a summary of key performance indicators for each segment and recommended pricing, including negotiation bands.

Ultimately, the overview of the optimization process allows for an understanding of the predicted overall profit and revenue impact, showcasing the incremental value of using recommended optimized prices during negotiations.

Accelerate Product Recommendation

Pricefx's Product Recommendations, powered by AI, enhances quoting by offering optimized product recommendations that can increase order size and quote conversion rates. This functionality suggests products that better align with customer needs and includes recommendations for related products within the same order.

When creating a new quote, users can select Add recommended products from the items page to view a list of itemized recommendations generated from the customer's historic purchases, product history, and purchases by similar customer segments. Each product recommendation is accompanied by a relevant score for easy evaluation before adding items to the quote.

Additionally, the solution allows for manual recommendations to ensure the presentation of combination products together. This comprehensive approach not only saves time but also facilitates the identification of the most relevant products, ultimately leading to increased order size and quote conversion rates.

Accelerate Price Waterfall Optimization

Price Waterfall Optimization is a powerful accelerator driven by our multi-actor AI engine that allows you to optimize multiple elements of the pricing waterfall simultaneously while remaining aligned to your organizational pricing strategy.

The result is an optimization model that produces multiple optimization results at the same time, such as optimized list prices for products, optimized standard discounts across product and customer groups, and optimized off-invoice discounts for product and customer. Within boundaries, you can set a range of minimum and maximum percentages for list price changes and for each on and off-invoice discount, as well as specify the percentage point movements within the acceptable range.

Business alignment provides you with the ability to configure alignment across products, such as private label or premium products, and ensure that your pricing remains consistent across your portfolio while preventing product cannibalization. Objectives allow you to select whether revenue or margin is a higher priority and set specific revenue targets by the customer group or volume targets against product groups. When you are ready to see the result, you are now provided with an overall comparison between the current and optimized state of the optimization model side by side for each element of the pricing waterfall, as well as a full overview of the financial impact of the optimization. The details of the results are now available for you to review across your product and customer groupings, and our glassbox approach provides your data scientists and pricing analysts insights into how the optimization engine reached its conclusion.

List Price Optimization

List Price Optimization enhances business profitability through strategic pricing by targeting higher margin rates and allowing precise price adjustments across the board or for specific product groups. This approach ensures effective revenue maximization and aligns products based on attributes such as brand or premium status to attract the right customer segments. The accelerator provides data-driven recommendations and insightful dashboards that display key metrics like recommended list prices, expected revenue, and margins, aiding informed decision-making.

Key benefits include the ability to strategically target and achieve a margin rate increase, customize list price adjustments, control price changes by setting limits, and define price boundaries to ensure competitiveness and profitability. The model offers tailored recommendations for list prices by product, with detailed impact assessments at both product and customer levels. These optimized prices can be viewed within the Pricefx solution or accessed through external systems via APIs.

The intuitive dashboards allow users to review and assess optimization results using charts and tables, providing a comprehensive view of the pricing strategy's effectiveness. The optimization process is straightforward, starting with the list price and adjusting for discounts, invoice adjustments, and rebates to calculate net revenue. This approach ensures a realistic view of financial impacts by considering past transactions, discounts, and rebates.

However, there are limitations: the model relies on past transaction data, excluding products with no sales history, and does not currently use price elasticity, which is planned for future updates. Additionally, there are no predefined extension points for custom features, requiring custom code for specific needs, which may complicate updates.