Digital pricing: The perfect solution for a major aircraft manufacturer

Our client, a global manufacturing giant with annual revenue of around $13 billion from aftermarket services, used an in-house group to set prices and sell spare parts for the bigger machines that it manufactures. One of the client’s key objectives this year was to upgrade its pricing strategy to achieve an overall impact of $100 million. But it faced real challenges:

• Pricing pressures from small players, who were building sections of spare parts with reverse engineering and selling them at lower price
• Multiple contracts with end customers that had price escalation caps, preventing the client from benefiting from price increases. What’s more, the firm had different revenue sharing agreements with various partners
• These conditions made a simple price increase impossible, so the client wanted to optimize pricing to incrementally improve net margin

The situation called for the client to move away from legacy systems and processes. Under this new approach, the value proposition to the end customer plays a key role in determining prices. With these insights, Genpact helped the client re-engineer its pricing strategy along with developing a continuous feedback loop for monitoring the impact of these pricing actions on key process metrics.

A deep dive into analytics

Our first task: Get the lay of the land. To do that:

• We performed a rigorous analysis of granular data on part attributes and on transactions with end customers
• We identified target outcomes by assessing historical sales trends, the impact of new technologies, and shifts in customer preference and buying patterns
• We identified key levers for setting the best prices to boost net cash flow for the customer

• We devised a logical system for grouping more than 130,000 individual parts and segment them according to their value and use, based on engineering insights. We considered the technological complexities of each part, the ease and cost of repairing each unit, and part segments which had limited lives. That enabled us to use analytics to extract relevant insights from meaningful data

Establishing the right pricing models

Our next step was to digitally calculate optimal prices.

• We created a repository for hundreds of customer-level contracts on price caps, based on economic parameters including consumer price index (CPI), labor index, industrial material indices, inflation, and so on. This helped us set up a price escalation scenario model, so the client could visualize the market bearing price acceptance point
• We created a margin optimizer model that projects the risk score of a price increase at the piece part level. It considered the following levers: technology, repair availability, internal versus external consumption, used sales penetration, any alternate material threat from the outside, and joint venture and revenue sharing agreement details, among others. The model projects the proposed price and forecasts the revenue impact of the price change
• Based on the composite risk score, the model proposed an optimum price and forecast the revenue impact at the new price. Cross-product benchmarking of the same group of parts, ranked according to their technology and value parameters, clearly showed where to go for higher price premiums
• We used these models to determine the best-price tipping point for units sold, taking into account relationships with sales channel partners and the discounts provided to them as well as the price benefits from the revenue sharing scenario

A structural change to contracts with customers, partners, and distributors heralded the arrival of a new era – one that prices our client’s products optimally. Here’s the proof:

• The price optimization models generated $99 million revenue impact – and the models were on track to generate another $20 million in the last quarter
• The dashboard we created provides CXOs with a comprehensive visualization of the client’s KPIs across different part segments
• The firm is now able to create a price catalog for parts in 70% less time, allowing for better dynamic pricing