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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09 Feb

Digital Transformation or just better FAQs?

A lot has been made of digital transformation, and how many businesses are using self-serve web-based applications to engage with their customers, employees and other stakeholders to be able to enhance and in some cases reinvent the customer experience often with both a stickier customer journey and lower service costs. Uber and AirBnB are often held up as the poster-boys but there are many businesses who are not ‘digital native’ companies emulating in their own way.

As with so many buzzphrases, there is usually a less-sexy way of saying the same thing which has been around for a long-time. In the field of customer interaction, most people will think of digital transformation as the growth in chatbots and social media-enabled communication. However I would argue that the main bastion of change has to be directed at FAQs.

Sexy or not, FAQs used to be the only way to read about self-help and avoid calling a contact center. They are frequently cited as inherently flawed as these blogs from the UK Government and eloquently in this technical writers blog. Yet if you stop and think, a well-structured FAQ if it is searchable with natural language is a critical asset as it is really the same thing as a chatbot, but perhaps without the charm or manners.

In a more measured manner, really FAQs are part of a spectrum of communication channels (one-way and two) where customers can solve problems. They sit alongside forums, social media, chatbots, chat, phone and email (see diagram below).

Also surveys and reviews can trigger interactions depending on the content. The current state of most organisations is that all these elements are separate silos, and whilst the customer experience team are trying hard to break these silos down, there are few who would see FAQs as on the same spectrum as chatbots and forums. Also people’s expectations of FAQs are to see a laundry list of requests which is not how they desire to interact. Imagine though if you could write your query in any way you wanted into a search bar and it would retrieve the correct response. Imagine also that the search was entirely consistent across all channels. Is this just a fantasy?

Machine learning for text is capable to classify interactions to be able to automate responses to natural language. However as we see with chatbot fails, it is hard to get this right due to the complexity and variability of human dialogue and chatbot containment rates are still below where their proprietors would want them to be. Further complexity is that human dialogue varies immensely across the channels: People don’t write in emails how they use chat which is different again to forums, nor how they speak or write a complaint, or even fill in a survey. By way of an example, a study at an airline found that the average topic in a chat was just over one whereas in a call it was nearer to two and in a complaint it was two and a half. People use different channels for different things, and also use different channels for the same thing in different ways. If the company is trying to classify aka tag or ‘label’ each interaction, then it will very easily fall into the trap of having different categories or tags for different channels, not by design but because it is hard to normalise them whatever technology you’re using. This phenomenon doesn’t really have a formal name but it is rife and disruptive. The ideal is some kind of ‘homogenization’ of the tags i.e. so that “late shipping” can be the same concept whatever channel. This then allows the guardians of the customer journey to understand what’s going wrong (and right), get a global view, and also understand if customers are calling back about the same thing on a different channel because they didn’t get it resolved. This also means that the customer journey and knowledge base can be fixed once for each breach, in the knowledge that it is fixing things across the board.

Machine learning can help this harmonization process although it is fraught with challenges, not least because the models for each tag need to be built especially for each channel, for example the “late shipping” tag for chat will need to be a different model to the “late shipping” tag for email or complaint. What data scientists know is that the process of building the machine learning models is intense: New York Times estimated that up to 80% of a data scientist’s time is spent “data wrangling”. CrowdFlower estimates “data preparation” at 80%. Further, assumptions and errors are an inevitable part of the process where human judgement and skill is required. More than this, 76% of data scientists view data preparation as the least enjoyable part of their work. Furthermore someone needs to build a training set for the models and that typically involves a human somewhere labelling the various interactions into topics and a topology that can drive the correct response. This is laborious in a linear fashion.

There are a number of different approaches to this problem. One company addressing this problem in a novel way is Warwick Analytics which is a spin-out from The University of Warwick. It has developed a proprietary technology called ‘Optimized Learning’ which puts a ‘human-in-the-loop’ in a very effective way: What this means is that the technology classifies the customer interactions in a meaningful way but when its certainty is low, it asks for assistance for a human to classify or ‘label’ the interactions which provide the most information back to training the models. Therefore it is theoretically and practically guaranteed to involve the minimum human interaction to maximise the performance of the models and hence the accuracy. The human trainer can be offline, as well as involving the customer in certain circumstances. The company has worked with many enterprises to improve chatbots, automate contact centers, complaints handling and improve the quality of self-service and FAQs.

So in conclusion, FAQs are an old-fashioned and much discredited digital experience, yet in the new world of digital transformation and harmonization, they can come back to center stage thanks to some clever technology and the human-in-the-loop.

 

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27 Sep

Warwick Analytics selected by BMW Financial Services to join Innovation Lab

Warwick Analytics is one of the five start-ups selected to join the BMW Group Financial Services Innovation Lab, the first financial technology business ‘incubator’ in the automotive sector.

The five finalists present a range of innovations that could revolutionise how consumers own and insure cars in the future, from opening up entirely new types of leases to consumers, through to tackling the barriers young drivers face.

bmwfsinnovationlab2016_w268Mike Dennett, chief executive BMW Group Financial Services, said: “Despite being vital for many drivers to access vehicles, the auto-finance sector has failed to innovate in the same way that most other areas of the automotive industry have in recent years.

“Our industry is ripe for disruption and each start-up we’ve selected to enter the Innovation Lab offers the potential to change how thousands of consumers gain access to and use vehicles, and I’m confident that our support will help us both work towards this goal.”

Warwick Analytics was selected by a panel of BMW Group Financial Services representatives following an intense pitch day this month.

Warwick Analytics will receive access to BMW Group Financial Services, in order to support innovation and fast track the growth of their business.

This includes:

·      Dedicated office space at the BMW Group Financial Services head office in Farnborough

·      Mentorship from members of the business’s senior leadership

·      Product validation and testing opportunities

·      Access to investors at corporate innovation specialist L Marks

·      First class education programme based on the ‘disciplined entrepreneurship’ method from MIT.

Stuart Marks, technology entrepreneur and partner of the Innovation Lab, said: “Every start-up that attended the pitch day presented a new idea for changing automotive finance, which meant that selecting only five to participate was a difficult challenge.

“However, we’re certain we’ve selected five strong companies who will benefit tremendously from being members of the Innovation Lab – it’ll be thrilling to watch them develop over the next ten weeks.”

The Innovation Lab will officially launch October 3, run for 10 weeks, and close with a demonstration day on December 8.

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