Customer Churn Debacle

To address customer retention challenges, a large BPO vendor built a dashboard to get a 360 degree customer view for all their 40 clients.

Every one on the client team was pleased to see all the information in one place. When the model predicted an 80% chance of a customer churn, the account manager was unimpressed as he knew this 6 months earlier and the dashboard did not solve his fundamental problem.

When Axiomatic stepped in, we analyzed the clients ticket data to identify three things the vendor should do to prevent churn for a third of what they already spent.

Our model predicted a 90% chance of retention if three specific things were completed in 3 months. That resulted in the customer renewing the contract.

Intelligent Data Pipelines

Naval artillery on battleships generates a lot of data that is analyzed by engineers on a base.

The data doesn't reach the base until the ship reaches a port due to limited bandwidth of satellite networks. This causes huge delays in the Navy's ability to deploy new guns.

We used machine learning to identify test data to download and optimize the bandwidth, when engineers request what they need based on smart summaries generated by the models.

Engineers use a dashboard to manage their data requests withing the confines of bandwidth limitations.

This reduced the total testing time on gunships from many months to a few weeks.

Smarter Negotiations

When purchasing $2B worth of hospital supplies every year for 17 hospitals across 3 regions from hundreds of vendors, it is very challenging to negotiate the right prices for thousands of specialized hospital supplies.

The company wanted to see how much savings can be had by negotiating with vendors.

Combining the purchasing data with ECRI benchmark data using Alteryx, Tableau and machine learning models written in R and Python, the team built an intelligent smart negotiation dashboards to help negotiators. The system ensured that the hospital system pays close to the median price for most of its supplies in the region.

This resulted in $40M savings as predicted by the machine learning models.

Marketing a New Drug

A bio-pharmaceutical company needed to segment 10,000 doctors from a pool of a million doctors for a year long sales outreach program.

CMS data on providers has 381M visits, 91 Specialites, 22 Cities, 27M prescriptions, 1.6B medical services and 1B drug services.

The model needed to learn and adapt based on weekly sales.

A series of deep learning models and using patent demographics, physician performance, sales efficiency and location statistics we were able to help the company beat their sales targets.

The sales dashboard gave the executives a weekly view of how their new drug is performing.