FIND OUT HOW PREDICX IS TRANSFORMING PREDICTIVE ANALYTICS

A selection of videos, news, case studies and white papers
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White papers

CONCEPTS VS KEYWORDS

Machine Learning can be used in text analytics to classify text based on
'concepts' rather than using keywords and rules. This paper shows the
differences between these two techniques with a real case study and dataset.

Everyone who uses text analytics directly or indirectly intellectually understands the theoretical differences between
machine learning concepts versus keywords yet there is still somewhat of a disconnect in the market place between what
each technique is good for and the capabilities and limitations. Which one should I use? What difference will it make? There
are also some fanciful claims on proponents of both sides which only add to the confusion.

The objectives of this research were to qualify and quantify the differences between ML concepts versus keywords based around exemplary datasets. Whilst this paper was sponsored by Warwick Analytics, a machine learning company, the research was conducted at arm's length using two distinct datasets and blind controls. The other text analytics companies are market leaders and not named herein. For the purposes of clarification, we will be using the word 'label' to describe each count of the issue for both techniques. This is synonymous with 'tags' in other literature.


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.


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.


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

Uncovering Hidden Profit for Global Telco from Social Media Data

The UK telco market is mature and highly competitive with four mobile networks and more virtual (or 'MVNO') players. The expectation of consumers is high. Brand loyalty, churn and differentiation are the main KPIs for operators.

WA applied its PrediCX software to publicly available customer data, primarily from Twitter. It did this across all the UK telcos with a social care feed, namely O2, EE, Vodafone, Three, Carphone Warehouse, GiffGaff, ID Mobile and Tesco Mobile. It focused on O2.

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How an AI-driven recommendation engine can increase Expedia profits by $5m per annum

In the travel and hospitality markets customers have ever-increasing expectations of a memorable and trouble-free experience. And whilst there is an increasing disillusionment with the reliability of reviews, consumers and providers still want to gain the insight they need to make decisions and take actions.

Warwick Analytics applied its PrediCX software to publicly available reviews for the travel booking company, Expedia. The aims of the analysis were as follows:
• To improve the profitability of Expedia
• To improve the profitability of Expedia's partners
• To improve the customer experience
• To present better information to customers to improve their decision-making

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Using PrediCX to reduce Helpdesk Costs while Improving Service Levels

In a large company an internal helpdesk can be as large and complex as any external CRM and keeping down costs whilst keeping up service levels are high priorities.

The customer was an enterprise providing software and consultancy with around 5,000 employees and around 30,000 tickets per year. Warwick Analytics applied its PrediCX software to the helpdesk tickets from ServiceNow (it can also work with BMC Remedy, Zendesk, Salesforce and others). After a short training period of a couple of days, PrediCX was already classifying unstructured data i.e. the text notes within the tickets as well as the notes of the solutions and corrective actions. There were several use cases:
• Early warning of issues and hints for root causes to minimise risk and lost productivity
• Root causes of common issues to obviate tickets and cost, and improve service levels
• Identify opportunities for automation and self-serve both direct and to support agents
• Automated, accurate classification

The view was both retrospective and forward-looking, i.e. to identify the opportunities it could have saved in the past had it been implemented at the time, as well as identifying opportunities going forward.

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Using AI text analytics to uncover drivers of loyalty and churn in restaurants

When it comes to the restaurant market, along with the rest of the hospitality market, customer tastes can change and their expectations only grow. Every brand needs to stand for a memorable experience.

Warwick Analytics applied its PrediCX software to publicly available reviews, in particular TripAdvisor reviews for London restaurants. The analysis was centred around use cases that would improve the profitability of restaurants including:
• Understanding the issues which drive churn, loyalty, yield and advocacy
• Operational early warning with granular analysis of issues
• Marketing effectiveness in terms of looking at voucher and campaign feedback
• Compare against the competition, by chain and by location

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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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