Machine Learning and Advanced Analytics for Business Users
LityxIQ 4.0, brings increased functionality and processing speed to a platform already known for its ease of use in data blending, predictive analytics, machine learning, and decision optimization.
LityxIQ 4.0 provides both business users and advanced data scientists the means to leverage machine learning and advanced analytics to increase the ROI for their business.
Contact Us to Learn More About LityxIQ 4.0
“We are extremely pleased with the performance of LityxIQ. We tested and built over a dozen models in a short time and put them into production more quickly than with other available tools.”
– Tony Coretto, Co-CEO, PNT
“The dimensionality of the data drove the success of the predictive modeling, and, ultimately, the programs’ results.”
– Bryan Flynn, DiamondStream
“Moving to Lityx took an expensive, long, outsourced process and brought it in house. It’s cheaper and so far seems a whole lot better. It’s a win for the company and me personally.””
– Alexa Langford, Director of Research & Analytics, Grizzard
Who Uses LityxIQ?
LityxIQ is a powerful analytics platform, used
with many types of business data. The solution enables users to easily apply
advanced analytics approaches to dramatically improve results.
Marketers create marketing dashboards, understand customer value, improve response, or run predictive models, and optimize their budget allocation across products, channels, and customer segments.
Agencies use Lityx to support their clients’ analytic needs by easily providing management dashboards, critical customer insights, or advanced predictive models.
Product Managers & Financial Analysts
Product Managers & Financial Analysts predict future sales or revenue based on historical trends, seasonality, or other predictive factors.
HR managers predict employee satisfaction and determine its most important drivers.
Health Science Users
Health Science Users build predictive models of disease likelihood or geographic prevalence.
Channel Managers determine the optimal budget spending across multiple channels and sub-channels based on prior performance.