ElizaSixtyFour

Recognise

Uncover valuable data-driven insights from your images with our innovative image recognition software.

Our planogram image recognition tool, Recognise, allows automated compliance checking of retail fixtures from a photo.

Recognise compares a photograph to the planogram assigned to a fixture, then analyses the photo to identify which products are on shelf. By comparing what is identified to what should be on the planogram, our tool instantly identifies out of stock products and compliance discrepancies, which enables merchandising and replenishment of stock to be efficient and accurate.

We have worked with our partners, Dee Set, to integrate our technology into their merchandising systems, helping them to boost merchandising compliance, increase product availability and increase efficiency.

By using our API it is possible to integrate the power of planogram recognition technology into your own business so your internal teams can unlock the data you require.

Consultancy Services

We provide Data Science Consultancy Services, covering knowledge transfer, ad-hoc pieces of work and bespoke solutions.

Our areas of experties include:

Machine Learning

We design and train machine learning models for classification and predictive analytics, including deep convolution neural networks for image analysis

Dataset Modelling

We can model large datasets to gain insight and make data driven business reccomendations

Data Engineering

We can help you build data pipelines and manage the data that you already have. At ElizaSixtyFour we have the expertise to unlock the full potential of your data.

Case Studies

Fixture Compliance Product Recommendation Sales Forecasting Price Elasticity Modelling

Fixture Compliance

Our client is a large merchandising agency, who merchandised greetings cards for a well known supermarket chain across 47 stores.

By using Recognise (our automated planogram checking software) to ensure planogram compliance, they were able to drive an increase in on-shelf availability of 1% over 20 weeks, and effect an increase in sales of 1.8% (compared to stores not merchandised by our client).

Product Recommendation

Our client is one of the UK's largest eBay retailers, and as part of their growth plan for 2020, they were keen to grow their existing product range and expand into new categories.

We were able to source online retail market data, and used machine learning to identify product categories which matched their product profile and were performing well in the market.

They anticipate that our recommendations will lead to hundreds of new listings across new and existing product categories.

Sales Forecasting

Our client, a leading male grooming brand, sells products through multiple supermarkets, each with a unique customer profile and complex promotional schedules.

We built a bespoke forecasting model which is able to accurately predict sales (per SKU per retailer) 3 months into the future, while taking into account seasonal trends and planned promotional periods. Our model was able to predict future sales 15% more accurately than our client's previous method.

With a long lead time for their products, our forecasts enable them to be more efficient in their ordering and reduce their monthly stock hold.

Price Elasticity Modelling

We worked with an online retailer on a project to investigate price elasticity - a measure of how demand for a product fluctuates in response to a change in its price.

Focusing on one of their key ranges, we modelled the price elasticity of every product based on historical sales data. Using the models we created, we were able to recommend specific price movements which positively influenced demand whilst maximising profitability.

The Team

We are a team of Data Scientists and DevOps Engineers who specialise in helping companies make the most out of their data.

We work mainly in Python to build and train machine learning models, carry out dataset modelling, and deploy our solutions.

Greg has a background in retail, having previously worked as a Sales Director and led a multi-site food retail business.
As a long suffering Newcastle United fan, Greg enjoys football and boxing though has recently hung up his own boots and gloves for the more civilised pursuits of cycling and golf.

Greg Philips

CEO

David previously worked for Stagecoach as their Chief Technical Officer for the UK and North American divisions, responsible for their global technology strategy focusing heavily on business technology and modernisation.
In his spare time David is an avid golfer, however if you can’t find him on the Green you will find him fueling his passion for travel and exploring a new cities.

David Pugh

CIO

Having previously worked as a research ethologist, Josiah has been able to apply his experties in data science to the understanding of consumer behaviour. He specialises in using machine learning to extract insight and meaning from unstructured image data.
Outside of work he enjoys climbing hills.

Josiah Halstead

Chief Data Scientist

Jon has worked in IT since 1989, specialising in Cyber Security Professional and Managed Services for the last 10 years. He's focussed on solving business problems using innovative technology and services.

Jon Fisher

Senior Software Sales Manager

Phil Barton is a Linux Systems expert and senior Developer specialising in APIs, Machine Learning and IOT.
You'll find him building and developing with Raspberry Pi or Arduino in his spare time, when he's not cycling down mountains or kayaking in the local lakes.

Phil Barton

Senior DevOps

With a decade of experience in retail analytics, Chris brings a deep understanding of every step of the retail supply chain. He enjoys learning about new methods in Data Science, and applying using them to find solutions to problems.
He can be found relaxing in Llandudno most weekends throughout the year.

Chris Taaffe

Data Scientist

Having completed a Major in Business Analytics with focus on Machine Learning, Teja is enthusiastic about solving problems using Data Science. He loves to apply his data capabilities on the most pressing business issues to help improve efficiency.
He can be found playing tennis on most weekends, so let him know if you want to play a game if you are in or around Stockport.

Teja Gali

Junior Data Scientist

Denis is a recent graduate of The University of Manchester, obtaining Master’s degree in Physics with Astrophysics. Prior to joining the ElizaSixtyFour team, he worked on a project which used neural networks to automate the targeting of radiotherapy treatments in hospitals, which was his introduction to the world of data science and machine learning.
Outside of work he have a keen interest in astronomy, music and his beloved Northampton Town FC, who he watches every Saturday.

Denis Page