The Daisy Intelligence Process

 
 

 We use artificial intelligence and our proprietary Theory of Retail™ and Theory of Risk™

to uncover smarter operational decisions in your operational data.

INCREASE REVENUE FROM YOUR POINT OF SALE DATA

Daisy’s Theory of Retail™

Daisy utilizes a patent-pending approach to machine and reinforcement learning that analyzes large databases to uncover insightful answers to complex optimization questions.

 Yes, we’re very much into math.

Our algorithms mimic biological processes and continuously improves itself to provide more accurate and effective results. It’s also predictive: its outcomes lead to specific actionable recommendations. And our algorithms can be customized to specific parameters or constraints to shape specific outcomes.

We execute our processes on Daisy’s massively parallel in-memory computational platform using MPI. Our proprietary master-slave architecture parallelize computations and accelerate performance to deliver near real-time results.


UNLOCK THE PROMISE OF BIG DATA.

 

Artificial Intelligence

Artificial Intelligence is an approach to solving very complex problems using computer programs that can learn new actions and iteratively adapt when exposed to new data. It continuously improves defined outcomes without human intervention and has the ability to learn without being explicitly programmed.

Daisy uses the most advanced approaches in machine learning to analyze vast quantities of our client’s data to find intricate relationships between variables and find solutions to complex problems.


OUR THEORY OF RETAIL™ ANSWERS CORE MERCHANDISING QUESTIONS.

 

Daisy’s Theory of Retail™

The theory of evolution. The theory of general relativity. Newton’s laws of motion. There are many theories that work to help us connect the relationships between variables to better understand our world.

Daisy has developed a mathematical Theory of Retail™ to answer core merchandising questions.

This model is designed to maximize total revenue by finding optimal solutions to highly complex trade-off situations. This model analyzes extremely large data sets and finds the relationships between variables like cross-category cannibalization, promotional cadence, associated product affinities, price sensitivity and seasonality. It’s complicated, but our approach makes this possible.