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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08 Jan

Warwick Analytics become an AWS Partner

Warwick Analytics has successfully completed the AWS Partner Network (APN) upgrade process.

The AWS (Amazon Web Services) Partner Network (APN) is the global partner program for AWS. It is focused on helping APN Partners build successful AWS-based businesses or solutions by providing business, technical, marketing, and go-to-market support.

Why Work with an APN Partner?

APN Partners are focused on your success, helping customers take full advantage of all the business benefits that AWS has to offer. With their deep expertise on AWS, APN Partners are uniquely positioned to help your company at any stage of your Cloud Adoption Journey and to help you achieve your business objectives.

Dan Somers, CEO of Warwick Analytics says: “We are delighted to become an official AWS Partner, providing software solutions that are either hosted on, or integrated with, the AWS platform.”

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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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09 Oct

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.

 

How PrediCX was applied to ServiceNow data

 

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.

What PrediCX found

 

  • The support teams spend much of their time dealing with these situations rather than enhancing the service. Whilst there will always be emergency situations, the opportunities are to spot the common issues as quickly as possible with alerts, as well as identifying the root causes from the notes of investigated tickets. This can isolate the relevant failure mode quickly and hint at the corrective action required, as well as identifying whether the issue is new or a repeat of something in the past. This is compared to the alternative of manually classification which does not pick out the rich detail of the ticket symptoms (or solutions) and is often inconsistent and inaccurate.
  • Early warning of payroll issues – The insight allows managers to quickly see when certain failure modes are reappearing e.g. the Expense error FM2 which reoccurred from the first incident on 16 March 2018 and reappeared on 4 May 2018. If PrediCX had been used at the time, it would have helped to implement a permanent fix during the first incident. It also would have shrunk the time of impact of issues by providing the earliest warning of an issue and hints at root causes e.g. the Submission error FM2 from 4 March 2018 to 25 March 2018. PrediCX can help obviate future failure modes and facilitate projects to implement preventative and corrective actions that can be executed ‘offline’ without disrupting service levels.
  • Hidden laptop issues – the analysis revealed a driver incompatibility issue that took 9 months to resolve. With PrediCX, it’s easy to see that it correlates with a particular Windows error and memory error too. Alert triggers within PrediCX would have picked this up.
  • Opportunities for automation and deflection – PrediCX looked at tickets over a period of 14.5 months and provided an analysis of whether issues are to do with staff growth and activity, wear & tear (for hardware), a repeating issue which can be deflected (i.e. estimates based on common root causes), a repeated issue which can’t be deflected (i.e. where no common root causes) and issues which appear to be non-repeating. These hint at the potential opportunities for deflection and automation. It shows that 25% of the total tickets analysed
    could have been deflected and 29% could be automated to some degree. Given there are about 40,000 tickets per annum and based on a typical cost of solving a ticket, it is estimated that PrediCX identified savings of around a third of the cost of the helpdesk. There may be further opportunities to save on wear and tear too, e.g. by further insight into the supply chain and whether alternative suppliers or processes can prolong the life of assets. There are also opportunities to classify the rest of the tickets automatically and more consistently which leads to more accurate triage and resolution.

Conclusion

 

Warwick Analytics is able to generate actionable insight and automation at both a strategic and tactical level for helpdesks. It enables helpdesks to optimise their costs whilst maintaining service levels to meet the expectations of their internal customers.

 

Download the full case study here

Using PrediCX to reduce Helpdesk Costs while Improving Service Levels

 

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02 Oct

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

PrediCX is an automated machine learning platform that quickly and accurately generates models for text, using ‘human-in-the-loop’ technology i.e. it only needs minimum input from a non-data scientist. It took only a few hours to generate meaningful output, no matter how large the dataset, based on concepts instead of keywords and sentiment scoring.

What PrediCX found

  • By looking at all of the second level concepts being talked about by diners in London, aggregated for all reviewed restaurants and normalised as a proportion of total reviews, we can see the concepts that diners talk about most frequently. The two most common issues are both negative – small portions and bland food – followed by a positive one – good drinks selection etc. This view can be aggregated in any way required: By geography, by branch, over time, segment, sector etc.
  • Overall, bad service was the main driver of churn at Level1 and at Level2 – small portions, bland food, poor cooking and rudeness were the main causes. This could be used at the brand or branch level to set KPIs and ensure that levels are maintained appropriately.
  • At Level 1, excellent food and ambience were cited although excellent service was less essential. At Level 2, the view, drinks selection and entertainment were drivers.
  • Loyalty and churn indicators were also analysed for one specific London restaurant, TGI Friday’s in Covent Garden.

Reducing churn

PrediCX can be used to pick up the reviews which contain concepts for churn, negative advocacy or the root causes of churn. They can be quickly intercepted by the restaurant to try to recover the customers with an appropriate message or offer, as well as decreasing the negative advocacy on the web. It can also be used for marketing effectiveness, e.g. picking up concepts of where people have used vouchers and the associated experience and loyalty.

Identify fake reviews

One of the banes of social media is the growing issue of fake, solicited and gamified reviews, the latter being where review sites work with companies to encourage or invite positive reviews and discourage negative reviews in a non-transparent way. There is no way to stop this entirely, but PrediCX can help to train on known fake reviews, remove suspicious or simply glib reviews such as: “everything” [5 stars], or “excellent” [5 stars]. Clearly more reliable data would come from a properly weighted survey, or from the CRM system.

Conclusion

Warwick Analytics is able to generate actionable insight at both a strategic and tactical level for of opportunity for any chain of restaurants, bars or other hospitality. It enables chains to maintain their brand promise whilst at the same time having the ability to react quickly to issues at an aggregate and even a specific customer level to optimise customer experience and maintain loyalty.

Download the full case study here

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

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25 Sep

Warwick Analytics launch disruptive new AI Text Analytics

Warwick Analytics has launched a disruptive new approach to text analytics. ‘MaaS’ (Models-as-a-Service) is a quick, easy and low-cost way to apply AI to text to get powerful automated insight.

MaaS provides a shortcut to getting started with text analytics AI. Existing models are provided for specific industries and use cases which can be fine-tuned or used standalone.

Until now ‘AI-based Text Analytics’, the use of machine learning to classify text, has been cited as an expensive solution requiring data scientists to craft bespoke models for datasets.

Warwick Analytics is best known for its AI text platform PrediCX that can generate accurate machine learning models without the need for a data scientist. It is this ‘human-in-the-loop’ technology that has enabled them to create MaaS, a disruptively low-cost AI solution. For as little as a few hundred dollars per month, a model can generate predictive insight that other analytics costing ten times the price can’t deliver.

Models are available across multiple industries such as restaurants, hotels, leisure, banking, insurance, retail, ecommerce, CPG, transportation, manufacturing, utilities and healthcare. Use cases for each industry range from identifying root causes of churn and loyalty, predicting sales of new products, predicting marketing effectiveness, finding root causes of brand equity, as well as automation use cases for CRM and helpdesks. The datasets can vary too from social media posts and reviews to surveys and CRM notes and contacts.

Warwick Analytics will continue to expand the range of models and is happy to ‘build for free’ for new customers who have new challenges or datasets.

To demonstrate the effectiveness of MaaS, Warwick applied it to publicly available data in different industries to identify key insight and savings. In one example MaaS found one leading UK telco (O2) could automate 45% of the Tweets, chats and direct messages into its contact center, as well as identifying easily-addressable root causes of churn and customer purchase difficulties worth millions of pounds per year. In another example MaaS looked at addressable root causes of churn for Expedia and the savings were estimated at over $5m per year. When looking at TripAdvisor reviews for TGI Fridays churn and loyalty root causes, not previously found by one of the other leading text analytics provider, were identified. The number of churners identified was 10 times higher with a much higher precision rate of 88% versus 54%.

Dan Somers, the CEO of Warwick Analytics comments: “We are delighted to launch MaaS. Not only is it exciting to bring tangible financial benefits to customers with actionable analytics and automation, but it is a privilege to help disrupt the market in a positive way, removing some of the ivory towers of data science and smashing some of the myths and claims. Democratizing data science means that data scientists still do the ultra-cool stuff and get the credit they deserve, whilst for everyone else in both large enterprise and smaller companies, they get transparent, powerful, cost-effective tools to make their businesses customers happier and their businesses more profitable.”

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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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