Challenge
Classify and analyze parts, then forecast for a pricing policy that generates a better profit margin
Classify and analyze parts, then forecast for a pricing policy that generates a better profit margin
Our client, a global manufacturing giant, has annual revenue of around $13 billion from aftermarket services. The company has an in-house group that sets prices and sells spare parts for the bigger machines 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.
Solution
Nothing short of a paradigm shift
The situation called for the client to move from its legacy. 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.
Take a copy for yourself
Download Pdf opens in a new tabA 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 segments 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 set the stage for using meaningful data for extracting relevant insights with analytics.
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, joint venture and revenue sharing agreement details, and so on. 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. They take into account relationships with sales channel partners and the discounts provided to them as well as the price benefits from the revenue sharing scenario.
Snapshot of the Heuristic Margin Optimizer model
Snapshot of the Value Analyzer model
Impact
A new era in pricing – and customer satisfaction
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 in the first year
- Created a dashboard visualization for the CXO and CMO of the client's business KPIs across different part segments
- The models were on track to generate another $20 million in the last quarter
- Creating a parts' price catalog now takes 70% less time, which allows for far better dynamic pricing