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White papers

BRINGING MODELS TO LIFE

Whilst there are a lot of fanciful headlines and hyperbole about the latest algorithm, the reality is that to deploy a machine learning model in an operational environment, it needs to be trained well on relevant data, and if the environment changes, to continue to be trained so that it adapts.

This leaves the machine learning experts in a quandary: How can businesses develop machine learning models which automate processes and contact centers not just today, but reliably ongoing? How can they get continually rich insight from models when the data are changing around them. Is the irony that the data scientist cannot bring a model to life like Pygmalion, but actually needs to constantly be the puppet-master: You can't just build a training set of data and then automate, you need to keep feeding training data to keep the models up to date and prevent model degradation.

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AUTOMATED ANALYTICS: Turning Predictive Analytics from a Project into a Product

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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PRACTICAL PREDICTIONS Predictive Analytics for Everyone

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.

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Case studies

How a Contact Center automated their Voice of Customer (VoC) data with PrediCX

A global industrial manufacturing company has one of the largest and most complex contact centers in the world. It receives thousands of interactions every day from engineers using their products in factories. Most of the interactions are via online resources, although thousands still phone in. Time is of the essence, particularly if a manufacturing line is stopped. This, combined with the huge range of complex products means that the company needs a large amount of skilled operators in its contact center, and escalation to specialists for third line support. The company was looking for technology to assist the contact center reduce the time taken for operators to triage the query and retrieve the right information to resolve it. It was also interested in technology which could obviate traffic to the contact center by firstly improving online resources, and secondly looking for insight to enhance the usability and in some cases the reliability of its products.

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PrediCX helps financial services provider improve customer experience and lower costs

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.

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PrediCX helps home appliance manufacturer and retailer to automatically handle customer interactions

A leading manufacturer of home appliances is using PrediCX, the automated predictive analytics platform for customer experience from Warwick Analytics, to automate the analysis of their customer interactions and 'Voice of Customer' (VoC) data. As a result, they are able to dynamically generate key insights which would otherwise remain undetected. The insight generated is being used to support the continuous improvement of their Customer Experience, from design, through to manufacturing quality, user experience and after-sales customer service. The improvements are also driving cost reduction and increases in operational efficiency.

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Leading Airline uses PrediCX as an early warning system for Voice of Customer

The airline industry is ever-evolving with the launch of new services, evolving customer expectations and competitive operational challenges. One specific airline was in the process of launching several new services as well as conducting customer research in order to improve the touchpoints that mattered most to improve customer satisfaction, loyalty and advocacy.

The airline's Voice of Customer (VoC) data were extensive. Tens of thousands of surveys were issued every month and as with any large customer facing organisation, a significant volume of complaints was also received. There were also data from its CRM, transcripted calls,
chat, email enquiries, reviews and other social media. There was valuable Voice of the Staff data also, such as engineering, pilots and crew notes, all of which contained rich operational information. The company had an internal data science and analysis team who was using text analytics to classify some of the VoC data. There were many challenges to this however.

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Global Telco uses PrediCX to Predict Customer Behavior

A global telecoms provider has used PrediCX from Warwick Analytics to automatically understand and predict customer behavior.

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 behavior and situations.

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