TUBR Predictive Analytics for EPOS Platforms

How does TUBR add value to your EPOS Platform?

Integrate Highly Accurate Predictive Analytics into your platform - quickly, simply, seamlessly

TUBR’s machine learning analytics platform enables your customers to empower their restaurant managers and directors to make better decisions based on highly accurate predictions.

TUBR’s unique and proprietary technology, our physics-based methodology, significantly improves the accuracy of predictions of key metrics such as:

  • Product Sales
  • Bookings and Utilisation
  • Stock Control
  • Staffing/Resourcing and Rostering

This enables management to consistently anticipate changing trends in demand and adjust key levers such as bookings, stock, staffing and promotions to drive revenue and prevent wastage.

Our machine learning as a service can make predictions from one minute intervals through hours, days and weeks into the future allowing your customers to effectively manage your assets while reducing costs and improving customer experience.

What does this mean to you? Well your customers gain added functionality whilst offering you opportunities for incremental product revenue, differentiation from competitors and ultimately a more attractive proposition to enable you to engage, recruit and retain your customers.

What do people say?

TUBR Accuracy

“We’ve achieved accuracy in predicting product sales per hour in quick service restaurants in excess of 83% accuracy”

MD of a leading EPOS Platform

 

Quick and Easy Integration

“We were able to be up and running within days through TUBR’s integrations and toolkit – significantly speeding up realising the day-to-day benefits of the platform”

Owner of a group of Hair Salons

How does TUBR Work and Compare?

Traditional Machine Learning

Statistical Methodologies

Statistical Machine Learning typically requires large amounts of data to identify patterns that have occurred in the past in order to understand when the same trends are going to occur again, this doesn’t take into account ‘real-world’ events or behaviours which could affect the reoccurrence of the trend.

Model depends on data

Assumes available data to be the only variables to represent what is happening in an area.  Data quantity and quality will have a disproportional impact of the quality of predictions.

Can't handle chaos

Does not account for chaos in an area, this effectively prevents accuracy being achieved.

Requires lots of data

Requires the full and complete data to make accurate predictions, this is often unavailable or not timely enough to provide the granularity customers demand.

TUBR Physics-Engine Based ML Methodology

Physics Methodologies

Physics-based methodologies uses information about the dynamics of the area in which it’s predicting to inform the model as to what’s happening enabling the machine learning to identify a changing trend even before it’s been seen, enabling timely and informed operational decisions to be made.

Model built dynamically

TUBR understands all the elements that occur in an area and brings understanding from the available data, enabling correlation/causation patterns to be identified and tie those to impact factors.

Accepts chaos

Accounts for chaos in an area by understanding what the chaos means on the remaining datasets, an example could be a missing days data due to say a bank holiday closure.

Requires enough data

Accepts sparse or partial inputs so the data you have is enough to still create prediction feeds that are meaningful and useful.

How it works

Provide your data

Our advanced algorithm is accurate with complete or incomplete data

We analyse data

No “in-house” manual data crunching or data science capabilities is required

Get your predictions

In less than 21 days you can get your highly accurate predictions and insights

Book a Call

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    How does TUBR add value to your EPOS Platform?