AI-Powered Pricing Simulator Helps Industrial Manufacturer Uncover Optimal Costs


A B2B industrial manufacturing company wanted to strengthen its product pricing strategy. They hoped to identify market changes and channel dynamics and discover the impact of fluctuations in their input costs on their overall profit and loss (P&L). The manufacturer wanted to receive information on optimal pricing for specific products.

Evalueserve created a cloud-based pricing simulator to help each business unit optimize prices. The simulator served as a central location for various teams to access pricing-related data, which our experts maintained and enriched to improve model accuracy.

The Challenge

The industrial manufacturer wanted to optimize its prices and improve its margins. The market shifts brought on by COVID-19 made the pricing process more challenging and complex. Decision-makers could no longer rely on historic data to anticipate future conditions. They needed to factor real-time cost fluctuations into their pricing models.

Most of the client’s data sources for pricing information were stale and disjointed. Its models and model outputs were all designed for on-premise use. Even in cases where the client actively gathered current data, such as scraping competitor pricing from the web, its teams had no centralized platform for storing and using data in real-time. Instead, data was sent to the business units in Excel sheets, and analysis was performed on an ad-hoc basis.

The manufacturer wanted to analyze instances where clients refused to pay the list price, hoping to expose any systematic root causes and adjust prices accordingly.

Pricing Simulator Uncovers Optimal Pricing

Our Solution

Evalueserve created a cloud-based pricing simulator for the B2B industrial manufacturing company.

The AI-Powered pricing simulator:

  • Modeled price elasticity based on the most current sales data and competitor pricing
  • Allowed users to play with various factors to understand the impact of potential scenarios on profitability
  • Showed what the ideal pricing would look like in each simulation
  • Forecasted changes in component prices.
  • Compared list price to the average price at which products sold.
  • Offered an interactive dashboard and customizable visualizations.

Users could easily sort by various dimensions in the simulator. For example, if a user was only interested in the price of a particular product in North America, they could sort the data that way with just a few clicks.

The tool analyzed whether potential clients still made deals when their requests for the price to be lowered from the list price were denied. For example, if a user were interested in whether repeat clients are less price-sensitive (i.e., more likely to pay the list price), they could investigate that using the pricing simulator.

On the back end, our data engineers, data scientists, and developers improved data quality, governance, and workflows to improve model accuracy and maintain platform usability. We built APIs, pulled in disparate Excel sheets, and processed unstructured data so that various sources of sales data and competitor pricing insights could be fed into the platform on an ongoing basis.

Our experts enriched the sales data, making it more granular by mapping it to BOM data. Previously, this data was grouped into higher-level product types, and business units could not access the historic pricing of individual materials. Evalueserve also indexed material prices to macroeconomic factors to manage risks.

Business Impact

By receiving detailed analytics through the pricing simulator, the industrial manufacturer understood how to adjust their prices for market conditions while keeping them at a level that customers would accept. They received analytics providing insight into whether a potential customer would still buy from them if they refused to lower their price. The pricing simulator provided insights into their competitor’s pricing, helping the client gauge normal price ranges in the current market.

One of the client’s business units estimated a $1.25 million margin increase for the July 2021 pricing cycle from the tool.


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