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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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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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. 

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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
market.

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.

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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.

 

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