You will find below a selection of videos, white papers and case studies – all designed to showcase our specific products and use cases.
The field of ‘Predictive Analytics’ is receiving significant attention lately due in a large part to the rise of ‘Big Data’.
However this presents significant practical challenges in terms of extracting timely and valuable insight quickly from disparate, dirty and unstructured datasets, without the need for an army of data scientists.
This paper aims to give a broad overview of the current state of Predictive Analytics, the various common techniques and their applications and limitations. It will also attempt to challenge a few myths along the way.
We will then show some of the latest emerging techniques and how these are able to drive reliable insight and prediction in a timely manner for business users without the need for data scientists, even with disparate, ‘dirty’ and indeed unstructured datasets.
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Manufacturing firms globally are investing heavily in the adoption of Big Data techniques and systems that will drive them ahead of their competitors. Plant managers and engineers need no longer rely on experience and existing analytics to locate root causes or accept No Fault Found issues. New technologies, developed specifically for the manufacturing industry, are harnessing Big Data to deliver significant savings in COPQ, warranty resolution, life cycle costs, time taken to market and environmental impacts.
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This paper aims to introduce a new approach and a new type of technology, as exemplified by “SigmaGuardian” enterprise software from Warwick Analytics that stems from over a decade of academic research in the US and UK and originating from six-sigma failures in complex manufacturing.
The field of ‘Predictive Analytics’ is receiving significant attention lately, due in a large to the rise of ‘Big Data’. However this presents significant practical challenges in terms of extracting timely and valuable insight quickly from disparate, dirty and unstructured datasets, without the need for an army of data scientists.
Healthcare organisations are under increasing pressure to increase quality targets and service delivery whilst at the same time saving costs. This is combined with the growing challenges of an ageing patient population which puts increasing constraints on these organisations to achieve these aims.
High profile problems are in A&E, stroke and other acute pathways, but due to co-morbidities and interdependencies, it is not straightforward to fix one pathway without adversely affecting others. This can lead to variations in care, unplanned attendances, hospitalisation and longer lengths of stay.
A global telecoms provider has used Y-Analytics – the automated predictive marketing suite – from Warwick Analytics to automatically understand and predict customer behaviour.
From the analyses, the telco was able to provide to the retailers the dynamic segmentation of customers and how it changed over time, as well as the specific factors which explained or predicted key decisions such as those listed above. These factors could be used for a variety of purposes, for example determining which segments to target and which locations, media and channels to use to communicate with them. Further, there was inbuilt flexibility for the retailer to generate its own questions, for example to understand the factors behind different customer behaviour and situations.
Motorola is not only one of the first and largest mobile telecoms manufacturers, it is also the originator of Six Sigma – the quality standard strived for by the majority of engineers worldwide.
Whilst they still widely implement Six Sigma techniques across their factories and processes, Motorola recently went one step further and utilised new technology developed by the academic co-founder of Warwick Analytics to solve a No Fault Found (NFF) issue and significantly reduce their Cost of Poor Quality (COPQ).
In the production of a major pharmaceutical companies product, used in the treatment of haemorrhoids, a higher-than-expected impurity formation required a solution.
The Site Operational Excellence Lead at the plant where the product is made says: “The product is formed from a complex series of reactions from intermediate chemicals. It is very sensitive to many factors and has been a challenge to control the impurities for a long time.
“Clearly it is imperative to better understand the factors which drive impurity formation so that we can maximise the quality and avoid rejections. Not only does this save on the expensive raw materials and process, but it is also better for the environment reducing waste product and energy.”
Copernicus Technology provides test equipment & data solutions to maintenance organisations in aircraft, transport & technology. Many of their aerospace customers are consistently dealing with complex quality issues including No Fault Found – transient problems which are extremely hard to detect.
One of the key challenges is the effective classification of maintenance logs. Maintenance, Repair and Overhaul (MRO) is one of the largest operating costs for both civil and military air transportation, and relies upon good information. Analysis of maintenance & repair ‘free text’ data currently needs considerable human input by experienced personnel throughout the analysis cycle to ensure that the information is consistent and accurate.
Copernicus Technology is overcoming the issue for their customers and aims to improve it further with the introduction of new software from industry analytics specialists Warwick Analytics.
A global automotive brand has used WAC (Warranty Automated Classifier) from Warwick Analytics to automatically classify warranty claims to reduce costs and improve quality.
A major gas and electricity provider recently used big data analytics from Warwick Analytics to increase their predictive maintenance capabilities and insight.
A retail financial services company was struggling with unpredictable call volumes to its call centres. They wanted to be able to predict when customers would call and the likely issues they were calling about. This would enable them to recommend ‘next best action’ on the operators’ screens, update their website accordingly and send pre-emptive outbound text messages. This would ultimately improve the customer experience and reduce costs. They also wanted to understand the aggregated issues that mattered, so that they could prioritise the issues that most affected customer satisfaction.
They turned to PrediCX from Warwick Analytics. PrediCX is a predictive analytics engine which takes all heterogeneous data and processes it dynamically to make recommendations to operators in terms of ‘next best action’ based ultimately on optimising the customer experience (CX).
Customers can be segmented and scored in terms of the company input versus their potential lifetime value to enable the appropriate levels of service delivered to the right customers. This can be shown to drive both customer satisfaction scores (NPS and CES) as well as reducing cost and potentially increasing a customer’s lifetime value.
The core technology is based on over a decade of academic research in automatic predictive analytics from heterogeneous data. Proprietary algorithms mine and validate predictive signals from CRM notes, transactions and records, and even social media data.
For the customer services and customer experience executives, there were many tactical and strategic pre-emptive outputs available: knowing when best to send outbound texts and emails; improvements to be made to website interactions; making chat options available proactively; prioritising actions for the operations teams. These all helped to improve customer experience and save operational costs.
Furthermore, by segmenting customers and identifying the factors which predict churn and cross-sell/up-sell, the sales team was able to identify opportunities to secure leads with more relevant offers and outbound conversations.
The Head of Customer Services said: “If someone had said to me we can improve customer experience, increase the potential lifetime value of our customers and save money at the same time, I would not have believed them. However that’s exactly what PrediCX does. It enables us to know what we didn’t know before. We can operate smarter and improve the metrics that our customers and our organisation cares about. The entire operating expense is reduced as we are focused purely on the actions and SLAs which are relevant to driving customer satisfaction.”
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