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.

Share this
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.

Share this
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.

Share this
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.

Share this
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.

Share this
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.

Share this
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

Share this
09 Jul

Warwick Analytics in the Press: Connections Magazine

Warwick Analytics has been featured as the cover story in the July/August issue of Connections Magazine – the premier call center magazine for the teleservices call center industry.

The article: AUTOMATION SUCCESS REQUIRES HUMAN INVOLVEMENT looks at how human in the loop machine learning technology is best introduced and how to get the right level of human intervention and at which point.

Automation of contact centers yields promise, although not without humans-in-the-loop to maintain its performance. There are many different flavors for human-in-the-loop AI automation. With new technology appearing, an optimized system is possible with a minimum number of humans who don’t need any data science skills. There is now no reason why the contact center of the future needs to look like those of the present. The same applies for the customer experience too.

You can read the full article here. 

Share this
06 Jul

Warwick Analytics in Global App Analytics Market Report

Warwick Analytics has been included in the latest MarketsandMarkets App Analytics Market – Global Forecast to 2023.

CEO at Warwick Analytics commented in the report: “The app analytics market is growing strongly but faces inhibiting factors that could slow it down. The main one is information overload, i.e., humans not knowing how to use the analytics to take decisions. The way to resolve this is to use AI on the unstructured data alongside the structured data to contextualize analytics to recommend actions.”

Executive Summary
The app analytics market is at its nascent stage and is gaining traction owing to deeper smartphone
penetration and growing number of mobile app users. The market is primarily categorized into 2 types,
namely mobile analytics and web analytics. The mobile analytics software segment dominates the global
app analytics market. Organizations integrate app analytics software into their applications to monitor the
users, revenue, app performance, and ad monitoring and marketing. With these applications, organizations
are focusing on increasing revenues from their business.

A user analytics solution includes user behavior analytics (app buttons clicked, app ads clicked, articles
read, or screens viewed), visitor analytics (location, gender, age, and language of the user, whether the
user is new or old, type of device, operating system, and manufacturer of device), user experience analytics
(to identify popular business flows in the app, usage, and user journey across web, mobile, and wearable
devices), use heatmaps to view performance, problems, popular app screens, and usage data.
The app performance analytics includes cross platform comparison analytics (to analyze how the app is
functioning on different platforms), carrier latency, Application Programming Interface (API) latency, and
uptime, crashes, exceptions, errors, and data transactions of app. The advertising and marketing analytics
includes tracking the viewership of app content, tracking the latest app installs, registrations, shares,
invites, and in-app ads. The revenue analytics includes in-app payments and in-app purchases.
The global app analytics market is divided into 5 main regions: North America, Europe, APAC, MEA, and
Latin America. North America has witnessed the significant adoption and is expected to grow at the CAGR
of XX% during the forecast period, mainly due to technological advancements and recent developments
pertaining to the market. Companies in the region have been involved in partnerships, acquisitions, and
new product developments which are mentioned in the company profiles section in detail. APAC, on the
other hand, is expected to grow at the highest CAGR of XX%, owing to the rapidly growing number of
smartphone users and mobile app downloads in major APAC countries, such as China, India, Japan, and
Australia, and the rapid development of IT infrastructure and the adoption of new technologies in 2016
and 2017.

The app analytics market is divided into various verticals, such as BFSI; retail; media and entertainment;
logistics, travel, transportation, and hospitality; telecom and IT; and others (education, energy and utilities,
and manufacturing). The BFSI vertical is expected to hold the largest market size; however, the telecom
and IT vertical is projected to grow at the highest CAGR during the forecast period.
Increasing use of apps for mobile advertising, implementation of digital strategies, deeper smartphone
penetration, and growing number of mobile and web apps, and smartphone and internet users are the
major factors driving the growth of the market. However, privacy concerns may restrain the growth of the

Major market players, such as Google Inc. (Google), Yahoo Inc. (Yahoo), Amazon.com, Inc. (Amazon), Adobe
Systems Incorporated (Adobe), International Business Machines Corporation (IBM), Segment, appScatter,
TUNE, Inc. (TUNE), AppDynamics, Appsee, ContentSquare, Countly, Swrve Inc.(Swrve), Amplitude,
Localytics, AppsFlyer, Heap Inc., adjust Inc. (adjust), MOENGAGE, App Annie, Apptentive, Taplytics, Inc.
(Taplytics), CleverTap have been leading in offering app analytics software and services to their commercial
clients across regions.

Share this
05 Jul

Warwick Analytics in the Press: Elite Business Magazine

The latest chatbots research from Warwick Analytics has been featured in the latest issue of Elite Business Magazine.

The research shows that most chatbots are disappointing their business owners in terms of functionality and output.

Elite Business is a really cool publication that provides fresh perspectives and representing disruptive solutions. They focus on the startups and SMEs that are spearheading Britain forward. From tech unicorns to entrepreneurs transforming healthcare with AI, we cover the movers and shakers making enterprising exciting.

You can register for free to read the online issues here.


Share this

© 2019 Warwick Analytics. All rights reserved. Registered in England & Wales. Number 07724630. Registered address 35 Kingsland Road, London, E2 8AA. VAT 120435168.