How we improved sales cycle efficiency for a machine learning startup

How we improved sales cycle efficiency for a machine learning startup
December 20, 2017 Sameer Karmarkar

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

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