Why use Xineoh Automated Predictive Analytics?
Most businesses cannot compete with the tech giants when it comes to recruiting top data scientists
Building custom solutions are expensive and success is not guaranteed
Huge unexploited incremental value
Netflix Algo $1 Billion in Annual Revenue
The power of an elite data science team unleashed in your company.
How it works:
We take your transaction data…
…run it through our platform…
…and return the mathematical optimal encoding of user, item, date, time etc…
…which can be used to answer use cases like…
Optimal Market Segmentation
Traditionally businesses created customer segments from experience and intuition and then placed each customer into thins pre-defined mold.
Now businesses have the power to use data to let customers themselves tell the business which segments they should be divided into. Thereby, each segment can be much better served.
Audience Affinity Discovery
Flowing from optimal market segmentation audience affinity can be gauged much faster and more accurately and the business can be rapidly positioned to service their previously undiscovered needs and wants.
Results can be used on a Macro level for…
Improved Marketing Targeting
Improved Inventory Management
Optimized Pricing Strategy
Improved Logistics Management
…or on a consumer level for…
Product or Service Recommendations
Most Similar Users
If you use our platform as a recommendation engine, then is will have these advantages:
The Xineoh Predictive Analytics Algorithm
We have created a cutting edge generalizable automated analytics and prediction algorithm that uses artificial intelligence to figure out the relationships between items and the people who use these items.
- Instead of using the standard encoding methods of item-based or user-based predictions, the Xineoh Predictive Analytics Algorithm converts items and users into abstractions.
- Some of the abstractions are carried to the next problem, which makes the prediction of behaviour more accurate than would be possible by applying any other method.
- By using this method, Xineoh has been able to make the algorithm behave more like a human.
These numbers are based on forward-looking accuracy (recall)
The same speed as item-based but with massively improved results. No ratings needed
40 million entries train in 30 minutes vs. DeepNets that can take weeks
Improved cold start
The way the problem is encoded allows reduced cold start penalty. 6-8% precision
Proof of Concept
VideoLlama is an implementation of the Xineoh Algorithm.
It uses the algo to recommend movies and TV shows to users and also serves as a case study for the potential value of the algo in the SaaS business model.
Go to www.videollama.com and try it for yourself.
What others are saying
“I have never seen data extracted using the language and systems Vian Chinner has developed. This is going to be huge. Fortune size companies receive up to 80% of their sales through machine learning recommendation engines.”Dr. Maciej KrausPhD, Director: Revenue Optimization Advisory at PwC.
“The recommendations look awesome!”Debbie StackisCOO/CTO at Pureflix, formerly VP Strategy at Cisco.
“I’ve seen many data warehouses and data science teams unable to provide basic insights. Xineoh quickly goes beyond the current state of the art.”John RobisonFormer CTO at Move, Inc, VP IT Operations at Netflix & VP Engineering at Yahoo. Xineoh board member.