How we improved sales cycle efficiency for a machine learning startup
Problem
- Our client is a leading machine learning and analytics startup that provides machine learning based software for quantifying risk by analyzing e-commerce transactions.
- In order to enable evaluation of their software, their customers needed to use their data. The software had to be installed at their customer data centers (public/private cloud) & that required some dedicated time from prospect/customers.
- This meant customers couldn’t see proof of concept analysis on their data.
- This was one of the main reasons their PoCs were extending the sales cycles.
Solution
- CloudHedge team analyzed the product, identified components & converted them into Docker container based products.
- Once the docker containers were created, we could use CloudHedge Cruize service to deploy those containers within minutes directly into customer’s public cloud (Azure, GCP or AWS).
Benefits
- Client’s development team did not have to handhold customers/prospects for setting up PoC.
- The docker based product setups enabled more PoCs that the previous method, that was dependent on availability of key personnel.
- The sales cycle bottleneck was reduced & it was possible for quickly demonstrating the value of the product without getting bogged down by setup steps or logistical roadblocks.
Tools Used
- Kubernetes, Docker
- CloudHedge Cruize
- CloudHedge Transform
- Python
- Kafka
- React
Platforms
- AWS, Google Cloud, Azure Cloud
“WhiteHedge offered solution was a dream come true for the customer by offering innovative customized solutions for better user experience and a low cost advantage. WhiteHedge enabled the customers to assess feasibility before both parties committed to a long term engagement.”
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