AI-Powered Pricing Strategies for a Leading Manufacturing Company

Problem

A global materials science company renowned for its specialization in crafting a diverse array of labelling and functional had long relied on a 'cost-plus' pricing strategy. However, following comprehensive market research, they uncovered a significant disparity between the perceived value of their products and their current pricing structure.

In establishing the ideal selling price, the company encountered the challenge of precisely evaluating potential costs and numerous variables at play, rendering the process multifaceted. Moreover, the company grappled with the challenge of efficiently updating quotes in response to customer-requested feature changes. This process consumed time and risked losing potential deals, necessitating significant manual effort.

Solution

Transforming pricing mechanism

Our solution tackles the challenges of modernizing an outdated pricing system form a cost-based pricing to value-based pricing. It aims to achieve the following objectives:

Price prediction

• We used the power of machine learning models to predict the base or initial price of products.
• Users benefit from real-time pricing estimates based on a concise set of features, streamlining the Configure-Price-Quote (CPQ) process.
• Our model identifies the critical features that significantly impact pricing decisions.

Price elasticity

• It measures how responsive consumer demand is to price changes.
• It calculates the ratio of the percentage change in quantity demanded to the percentage change in price.
• If the price elasticity value exceeds one, demand is elastic, and consumers react strongly to price fluctuations.
• Conversely, if the value is less than one, demand is inelastic, and consumers are less sensitive to price.

Business benefits

• Revenue

  Improved revenue and sales with value-based pricing optimization using AI/ML.

• Efforts

  50% reduction in manual resources needed to price products according to customer requirements.

• Time

   75% reduction in the time it takes to quote prices.