Discount based order prediction for OEM

Problem & Context

Within a national automotive sales organization, the client faced a critical financial imbalance: their promotional discount spend was increasing year-over-year, yet vehicle orders failed to keep pace with that investment growth. The business urgently needed to reverse this trend because the compounding inefficiencies were aggressively eroding their gross margins. Solving this challenge mattered immensely to protect overall baseline profitability. However, optimizing this spend was highly complex due to severely fragmented, poor-quality historical data. This was further complicated by frequently changing discount strategies and incentive types over the last years, making it incredibly difficult to isolate what actually drove sales.

Approach & Solution

To tackle this, we ran cross-functional workshops with sales and finance stakeholders to map existing workflows and align objectives. We developed a predictive order forecasting model and an interactive simulation tool designed to let teams test how varying discount values would impact vehicle demand. However, embedding this into workflows faced severe friction. A key department blocked all validation attempts, choosing to keep discount structures almost entirely static. While full operational integration wasn’t achieved, the project successfully delivered a proven simulation framework, exposing the cultural and data silos the organization must dismantle to achieve true margin optimization.

Results & Impact

The initiative successfully audited the organization’s incentive history, proving that promotional spend grew significantly faster than order volume over a multi-year period. It definitively uncovered that a large share of the discount structures had remained completely static for consecutive years, failing to react to market shifts while new discount strategies were tested without thorough evaluation. By quantifying this disproportionate growth, the project exposed severe, systemic budget inefficiencies. Additionally, it unified multiple years of fragmented sales data into a cleaned data asset for future optimization.

+15–25%

improvement in order forecast accuracy versus baseline planning

3

core commercial drivers linked to order development

8 years

historical discount and order data consolidated for simulation

Your Contact

Dr. Steffen Illig

Partner and Expert for Data Analytics

+49 176 579 82284

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