"deepVoxel's prediction models for customer behavior had a stunning degree of accuracy. The success of our customer retention program was directly due to the diligence and technical capabilities delivered by deepVoxel"

-J.S., Customer Information Manager, B2B


Business Solution Examples

The following case examples illustrate how deepVoxel Analytics has helped commercial clients leverage the power of event analytics:

  • Customer retention
  • Behavioural response modeling
  • Multi-channel analytics

Customer Retention

Problem: Find customers 'at-risk' for churn.

A multi-billion dollar wholesaler aims to reduce the number of clients that leave, taking their business to competitors. They need a system that will detect client defection as early as possible, enabling them to intervene and recover potentially lost revenue.

Solution

Using advanced data-mining methods, deepVoxel develops a highly accurate system to detect customer churn. Customers who are at risk for defection are identified for outreach to win back both their loyalty and future revenue.


Customer Response Modeling

Problem: Find the most 'responsive' customers

Profitability increases dramatically when customer responsiveness can be predicted. The ability to distinguish 'persuadable' from 'dead' customers means that companies can focus their efforts on the customers that are most likely to respond.

Solution

deepVoxel develops algorithms that mine complex, multi-channel transaction data to estimate customer responsiveness. The high accuracy of these statistical models enable the client to contact only the most responsive clients.


Multi-Channel Analytics

Problem: Use multi-channel data to it's fullest potential

Customer behavior is recorded in a vast number of ways, ranging from point-of-sale transaction histories to clickstream activity on the internet. Analysts need to formulate models that leverage all forms of data to create powerful predictors of customer behavior.

Solution

deepVoxel develops novel visualization technologies to interactively explore high-dimensional data. These technologies enable analysts to formulate models that are robust and explainable.


Market Basket Analysis

Problem: Find Targets for Upselling

Which customers are loyal and which ones are 'cherry-picking' your product lines for the best deals? Are you getting their full share-of-wallet, or are they going elsewhere for their largest purchases?

Solution

The content of a customer's basket of purchases reveals how they are using you as a supplier. Statistical analysis of their product basket uncovers opportunites for growing your share of their wallet.