The customer was sitting on a mountain of B2B sales data but was solely dependent on the expertise of veteran salespeople, which wasn’t scalable in real-time and didn’t optimize sales margins.
An agribusiness firm specializing in soybean exports and other commodities-related activities, the customer had more than a century of distinguished experience in the commodities industry. However, it needed to become more efficient and agile through technology to stay ahead of competitors and keep pace with today’s market realities.
That’s why the agribusiness firm’s leadership team approached Pythian for machine learning (ML) advice to help improve margins and sales team efficiencies. But they weren’t sure which use cases to pursue – while the company’s IT team boasted several very talented data scientists, Pythian was able to lend support by bringing our SMEs’ vast knowledge of ML solutions into the fold. It was also highly dependent on the corporate knowledge of a group of veteran sales people.
After attending a machine learning workshop organized by Pythian and conducting a risk/reward assessment of each idea, Pythian and the customer soon identified bid-acceptance prediction as the highest value use case.
Pythian helped consolidate the company’s notable expertise into a tool that allows for lightning-fast decision-making, decision verification around price, and risk assessment, helping them scale their sales team and optimize workloads.
- The customer can now optimize their pricing on high-volume transactions based on data-driven probabilities while better recognizing the risk of not dropping the price in certain situations.
- The sales team can more easily scale by optimizing their workload and making more sales, while conducting transactions more confidently.
Pythian’s ML model architecture and design expertise, history of successfully tackling complex ML problems, and long partnership with Google ensured a successful project handoff. The ability to instantly see the last time a specific customer bought a product, at what price, and during which market conditions have already helped the customer improve their margins—and that’s just through a relatively limited PoC.
In the future, the agribusiness firm could enjoy similar cost optimizations across a broader range of products with further implementation of the PoC while relying on Pythian’s always available support services for AI and ML.
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