01 Mar

In the Press: Finance Digest

Warwick Analytics has been featured on Finance Digest, the publication providing in-depth coverage and analysis of the global financial community.

CEO of Warwick Analytics discusses ‘How Banks Can Use The Latest AI Text Analytics To Improve Customer Loyalty’. The article features a case study of a major UK high street that bank was looking to improve its customer loyalty. It was already using the latest analytical tools including social listening, sentiment analysis and a large data science team but they were experiencing limitations and not making enough progress.

The bank was keen to find more opportunities to improve customer loyalty and reduce their operational costs by gaining more customer insight. They were also interested to see what online feedback their main competitors were receiving.

You can read the full article here.

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28 Feb

In the Press: Connections Magazine

Warwick Analytics has been featured on Connections Magazine, the magazine for the call center industry . The article THREE PIECES OF KEY INFORMATION YOUR CALL CENTER IS MISSING WITHOUT MACHINE LEARNING will also appear in the March/April issue.

With the amplification of social media, as well as the ease and increase in the ability for customers to complain, issues can quickly turn into operational and PR crises. Yet this is just the beginning; issues happen every day that cause customers to interact with contact centers. The intended customer experience can be impacted by taking up unallocated resources to deal with day-to-day issues.

The article looks at three ways machine learning for text analytics can be applied within a contact center to unlock key data that can ensure the intended customer experience is achieved.

You can read the full article here.

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25 Feb

In the Press: Applied Marketing Analytics

Warwick Analytics has been published in the latest volume of the peer reviewed journal Applied Marketing Analytics.

The paper ‘The Coming Democratisation of Emotions Analytics’ looks at the growing volume, diversity and complexity of customer feedback data and the increasing limitations with sentiment analysis. As a result, analysts are looking for the next steps in the capabilities of text analysis. We discuss these advancements later in this paper but first we look at the current limitations and opportunities that have acted as the drivers for a more emotional approach to AI text analytics.

Whilst sentiment analysis has served text analytics well for a long time, the challenges and opportunities being presented are becoming more significant. Machine learning has promised much in the way of assisting text analytics to uncover more hidden customer data but the skill required and complexity has thus far proved a barrier to the models which can unlock true sentiment.

Now there are techniques and methodologies appearing which can democratise data science, more specifically voice of customer data, to enable business analysts rather than data scientists to turn customer sentiment not just into charts but into actions, satisfaction and profit. $3.1 trillion is IBM’s estimate4 of the yearly cost of poor quality data, in the US alone, in 2016. The Harvard Business Review5 also highlights the following statistics:

  • 50% — the amount of time that knowledge workers waste6 in hidden data factories, hunting for data, finding and correcting errors, and searching for confirmatory sources for data they don’t trust.
  • 60% — the estimated fraction of time that data scientists spend cleaning and organizing data, according to CrowdFlower7.

Reducing these costs requires a new way of thinking. The latest in AI Emotions Analytics looks much deeper at the origins and overall content of the data and solves many more root causes of issues. The benefits of improving data quality with this latest technology go far beyond reduced costs though. Improving data quality is a gift that keeps giving — employing the right analytics and the right level of automation vs human in the loop will become a self-fulfilling cycle producing more efficiencies every day, enabling firms to more easily pursue other data strategies.

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14 Feb

In the Press: Compare the Cloud

CEO of Warwick Analytics Dan Somers is featured on Compare the Cloud talking about how using concepts instead of keywords with the latest in machine learning and AI can transform your text analytics.

When different people are voicing different issues, they will use different words and sentiments. Current analytics typically identifies just the keywords used, but this runs the danger of failing to miss the entire context behind the communication. Often a customer will merely imply a sentiment or intention instead of explicitly expressing it with specific keywords e.g. a customer in a restaurant might say ‘by the time my meal arrived, the food was cold’, the keyword would be flagged as ‘cold food’, when in fact the main issue was the slow service. There are also other limitations with using just keywords such as sarcasm, context, comparatives and local dialect/slang. The overarching message can often be missed and so the alternative is to analyse text data using ‘concepts’ instead of ‘keywords’.

You can read the full article here.

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24 Jan

Should AI Text Analytics be artificial or augmented

Most experts define artificial intelligence as technology with the capability of thinking of itself and making decisions based on its own ideas. It’s going to be years before this becomes a reality, if it actually does…as some experts argue. More philosophically, why would we want computers not to require guidance and input from time to time where the situation is new or uncertain? Particularly when dealing directly with customers.

What we should be using and aiming towards, instead of Artificial Intelligence then, is Augmented Intelligence. That is, man plus machine rather than man versus machine. The definition of this is inherently vague but essentially it is where software supports human decision-making and actions, and when it carries out repetitive or known tasks but defers to a human for more complex or unique ones. Unless one is familiar with the state-of-the-art of the technology, it is easy to believe the hype. The reality though is that even the most sophisticated AI applications ironically require armies of data scientists to develop and maintain them. For many, the Holy Grail in Augmented Intelligence is an application that is trained and guided by a non-data scientist, in particular so that the front-line personnel are not directly doing all the tasks, but they are guiding the bots which help them.

How can businesses develop machine learning models which automate processes 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, 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. Now the business problem is starting to emerge: You wanted to use AI to automate a process based on historic data so that you could free up the human resource that is currently being deployed in the process. But if all those humans disappear and you let the model loose and operationalise it, then ironically the source of fresh training data also disappears. The computer is left to mark its own homework. If the model is to be kept up to date, then you need to keep some or all of those humans around, and it isn’t at all calculable how many you need. This is the bane of a data science team, i.e. having to curate models and constantly be involved in refreshing and validating, when there is plenty of new data science opportunities to focus on to generate value. Also the curation process itself is almost always sub-optimal as there is no way the data science team would be able to second-guess if new signals have appeared without validation and involvement from users. Further, if the business process itself needs to change due to demands from the business, then the whole model could be left redundant with no relevant training data for the new model.

However, the latest in technology being used to analyse customer data uses machine learning to be able to classify interactions accurately to guide processes and generate early warnings of issues and trends. A perfect example of Augmented Intelligence. The first trick is that it ‘understands’ when it is uncertain about something, and it invites a human for assistance (sometimes this is referred to as ‘human-in-the-loop’). The human doesn’t need to be a data scientist, they only need to understand the domain to impart their judgement and knowledge to the machine and once it’s done, it’s done forever. The second trick is that it does this in an optimal way, asking for the minimal amount of input from a human to maximise the performance, and therefore the accuracy, of the machine learning. In this way, it can process more and more tasks to an acceptable threshold accuracy, and hand off to a human seamlessly when it is below this.

Warwick Analytics is one such company which has cracked this. It is a spin-out from The University of Warwick and it has developed software called PrediCX which uses machine learning to learn from various customer interactions to be able to classify them accurately to guide processes automatically.

Let’s take chatbots for example, they need to be curated to constantly improve and learn to new and changing signals from customer intents. There is no reliable feedback loop, even if the customer ticks “helpful” or “please can I speak to a human”, there is a lot of things that could go badly wrong to use this for training and maintaining. Further, it is critical to understand and classify the topics being talked about across all channels, to encourage and facilitate the right channels for the right topics, i.e. self-service/chatbots for FAQs and for complex queries to be quickly routed to a human on the ‘phone, with chat and other semi-synchronous channels perhaps somewhere in the middle. The latest Augmented Intelligence facilitates an ongoing virtuous circle of harmonised classification across all and any channel, to break down silos, improve internal processes, save costs and most importantly optimise customer satisfaction.

In conclusion, AI is here and very much here to stay. However, AI is Augmented, not Artificial Intelligence and for the foreseeable future, if not forever, blends the best of machines with the best of humans to make the perfect customer experience.

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07 Dec

In the Press: IBS Intelligence

Warwick Analytics has been featured in IBS Intelligence, the
independent news, analysis & research source relating to global financial technology markets.

In the article Dan Somers, CEO of Warwick Analytics comments on how sentiment analysis may not be giving banks useful results and that AI text analytics can now uncover valuable hidden customer feedback and intent to turn customer words not just into charts but into actions, increased customer satisfaction and more profit.

You can read the full article here.

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31 Aug

Warwick Analytics in the Press: Customer Service Manager

Check out the latest issue of Customer Service Manager in which Warwick Analytics talks about ‘The Missing Sentiment Analysis from Contact Centre Data’.

Dan Somers of Warwick Analytics explains how machine learning is improving the lack of accuracy and relevance associated with sentiment analysis

Sentiment Analysis is widely used to supplement the analysis of text data in surveys, complaints, reviews and other contact centre data. In theory, Sentiment Analysis categorises opinions expressed in a piece of text just as a human might.

However, as the volume and complexity of customer data grows, growing issues with Sentiment Analysis are providing flawed information and preventing companies from getting the full picture from their data. Key customer feedback that could drive positive business change is being missed.

Read the full article here.

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22 Aug

Warwick Analytics in the Press: MarketingTech

PrediCX technology from Warwick Analytics has been featured in the latest issue of MarketingTech.

Sentiment analysis is widely used to supplement the analysis of text data in surveys, complaints, reviews and other customer feedback. In theory, sentiment analysis categorises opinions expressed in a piece of text just as a human might.

However, as the volume and complexity of customer data grows, growing issues with sentiment analysis are providing flawed information and stopping companies from getting the full picture from their data; key customer feedback that could drive positive business change is being missed.

Read the full article here.

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01 Aug

Warwick Analytics in the Press: Elite Business Magazine

CEO of Warwick Analytics Dan Somers has been interviewed by Eric Johansson as part of a feature on predictive technology in the latest Elite Business Magazine.

The article ‘Predictive technology leads to crossroads for risk or reward’ looks at how startups have developed tech that can accurately predict the future. While it’s easy to see the benefits, innovators still have to face a slew of both technical and ethical challenges. Dan talks about how AI and machine learning technology can have huge benefits for firms of all sizes.

You can read the full article here.

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30 Jul

Warwick Analytics joins EIT Digital Accelerator

Warwick Analytics has joined EIT Digital’s Accelerator.

Dan Somers, CEO of Warwick Analytics explains: “By joining the accelerator of EIT Digital we are going to benefit great support to help us reach new markets in Europe and also access a network of potential partners and support of growth outside our frontiers”

Data Scientists spend over 80% of their time preparing data for analysis. This increases costs of analysis and makes the results being out-of-date quickly.

Founded in London in 2011, Warwick Analytics has developed PrediCX, a text classification tool using proprietary machine learning algorithms which asks for minimal human input to maximise the performance of the machine learning models. The tool can be deployed by business users as well as data scientists and handles unstructured data including text.

The use cases are early warning and root cause of issues, as well as automation and harmonisation across different data silos. The benefits are significant increases in loyalty and satisfaction as well as reducing the costs and speed of service

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