20 Sep

Our latest white paper is now available

Get our latest white paper: How AI and machine learning is improving live chat for customers and businesses.

Find out how the latest in AI/machine learning can help optimise ‘live chat’ channels to improve the speed of resolution, provide more relevant responses and streamline the chat optimisation process.

Many companies are implementing live chat because it offers a better
experience for some queries and some customers as well as cost savings for
companies compared to voice. Indeed the channel has been growing 87% pa
according to CustomerThink.

BoldChat found that top reasons given for why live chat is preferred are
immediacy of responses (71%), ability to multi-task (51%) and customers don’t
like talking on the phone (22%).

However canned responses, complex queries or poor staffing can lead to the
opposite experience, with channel switching, repeat calls and abandonment or
even churn. Misunderstandings can happen more frequently than a telephone
conversation, and with both customers and agents multitasking, there is plenty
of room for error. Offshore chat operations are often compounded with cultural
issues and misunderstandings too.

Many businesses who deploy live chat do so with conflicting reasons i.e. is it to
serve customers’ channel preferences or save money. It is frequently treated as
an ‘alternative’ to voice.

Download the paper for free here.

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01 Aug

Guest Blog on S4RB: TWITTER DATA – SORTING THE PROBLEMS FROM THE POSITIVE FEEDBACK

On our latest guest blog for partner S4RB we take some publicly available Tweets looking specifically at packaging. It’s a timely example to use as it is a growing topic as consumers voice more and more environmental concerns, as well as the usual quality issues relating to packaging.

In a generic text analysis model, you might be lucky to pick up packaging issues at all, or at best to pick them up and assign positive or negative sentiment. However, this doesn’t help to action anything, not without further reading and coding. One must also figure out what the themes are to code in the first place.

With ‘human-in-the-loop’ software like PrediCX and the S4RB model specific to grocery retailer, the new signals are referred to a human as they appear so that nothing is missed, nor does it have to be guessed. The data truly speaks for itself!

Here is just one example from the blog post:

@Tesco check this out! Bacon I can open one handed. Unlike your packaging that I have to attack with a knife because for years the pull tab has not worked once. @AldiUK #voodoomagic #bacon

Beyond sentiment this richness allows brand owners to understand competitive advantage or disadvantages, which can feed into either marketing or product development. The ease of opening on Aldi’s product will be for more than just bacon!

Also,  there’s a hint of long-standing Tesco customer so can add label: “loyal customer”. These tags can both be used to help improve packaging, avoid serious issues, and also improve the brand’s standing to competitors in terms of the features that customers mention. What’s interesting is that a longstanding, loyal Tesco shopper has made an unsolicited comment to Tesco about a competitor. Have they switched? Imploring their favoured brand to improve?

You can see more examples and read the full blog at S4RB

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07 Jun

Featured on S4RB Blog: HOW TO REALLY HEAR WHAT YOUR CUSTOMERS ARE SAYING WITH SOCIAL LISTENING

Warwick Analytics provided a guest blog for our great new partner S4RB:

HOW TO REALLY HEAR WHAT YOUR CUSTOMERS ARE SAYING WITH SOCIAL LISTENING

Social listening has been with us for a while, as has text analytics. These techniques pick out topics and sentiment of issues from social and indeed private data sources to inform brands how their customers are feeling and what they’re talking about.

So, is it possible to get to these ideal, actionable signals, without hand-coding each one? The good news is that it is. AI technology exists which not only generates these granular signals accurately at scale, but also allows them to be tuned and adapted as new signals come in. It flags all potential new signals to a ‘human-in-the-loop’ to validate as they appear so that nothing is missed, nor does it have to be guessed a priori. The data truly speaks for itself.

For example, if new products or services are introduced by your brand or your competitor, if there are new trends in the market e.g. environmental concerns, nutrition trends. By hearing, rather than just listening, you will be able to discern precisely how to delight your customers on a local and brand level. And in the evolutionary arms race, that ability will put you ahead of the competition.

Read the full blog here

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17 May

New partnership with retail S4RB hits US media

Warwick Analytics’ new partnership with grocery retail technology specialist Solutions for Retail Brands (S4RB) has been published in various US trade media.

Solutions for Retail Brands (S4RB) has partnered with Warwick Analytics to integrate PrediCX natural language processing into its Affinitysolution, offering a higher level of insight than existing sentiment analysis solutions. The partnership will enable grocery retailers to utilise machine learning and AI to generate detailed analysis about the root causes of customer sentiment, emotion and intent as well as the reasons for customer churn.

The partnership has been publicised by popular US trade press The Shelby Report and My Private Brand.

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13 May

Warwick Analytics speaking at NEW CX Emotion conference

Dan Somers, CEO of Warwick Analytics, will be speaking at the up-coming CX Emotion conference, 20 June in London at Trafalgar Square (http://cx-emotion.com). If you can make it, use the SPEAKER registration code for 20% off.

CX Emotion is a new conference. We will explore sentiment, opinion, and behavioral analysis tech, solutions, and data. The focus is the intersection of emotion and AI — for market research, customer experience, and consumer insights — and brand and market strategy.

Dan will be speaking on:

EMOTION ENGINEERING: USING ROOT CAUSES ANALYSIS FOR CUSTOMER SATISFACTION

The proliferation of brands connecting with customers through social media has put customers closer to brands than ever before. The real-time element of the interaction ensures it is a prime opportunity to understand what emotionally drives customers, their loyalty, or forces them to churn. Current social listening tools do a fantastic job of capturing and presenting that information to enterprises, but are not often equipped to capture granular concepts, actionable insight, or emotional intent. Dan Somers of Warwick Analytics will present how the next generation of AI utilising a ‘Human-in-the-Loop’ can supercharge enterprises insights from social media, provide a better customer experience, and uncover hidden factors leading to a reduction in customer advocacy, and an increase in churn. Warwick Analytics have used this approach in their wide-spanning market research to identify how one UK bank could prevent £240m of churn simply by applying complex concept, sentiment, and intent analysis.

I hope you can join us. It should be informative and enjoyable. See you there?

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16 Apr

Warwick Analytics partner with S4RB to enable grocery retailers to utilise machine learning and AI

A new partnership will enable grocery retailers to utilise machine learning and AI to generate detailed analysis about the root causes of customer sentiment, emotion and intent as well as the reasons for customer churn.

Grocery retail technology specialist, Solutions for Retail Brands (S4RB) has partnered with Warwick Analytics to integrate PrediCX natural language processing into its Affinitysolution, offering a higher level of insight than existing sentiment analysis solutions.

Warwick Analytics’ technology is based on highly accurate machine learning models that can be applied to textual data such as customer feedback, complaints, calls, CRM notes and chat, helping to optimise the customer experience, identify root causes of churn and loyalty, as well as emotional causes of purchasing decisions.

Sentiment analysis is used widely in the grocery sector, but studies have shown that its accuracy can be as low as 58 per cent. Furthermore it typically misses detailed signals such as specific nuances within customer concerns, plus emotional intents such as signifying churn, loyalty, repeated issues and customer effort – which become ‘unknown unknowns’, or unidentified risks for retailers.

By contrast, PrediCX can accurately and automatically pick up these subtle, actionable signals. The partnership allows S4RB to combine the unique PrediCX technology (patent pending) with S4RB domain expertise to create a machine learning model specific to grocery retail. The model is continuously optimised by bringing in a ‘human in the loop’, which can be a non-data scientist, to validate anything it might not be sure about and then trains and updates itself with the input.

In creating an industry specific mode for the grocery retail market, it has been able to accurately determine the root causes for dropped baskets; churn from particular stores, products or grocery brands; recommendations for future products, packaging, recipes and store layout; and early warning of previously unforeseen quality issues.

This technology will be available for retailers on S4RB’s Affinity platform, which enables retailers to access a complete view of product performance alongside other product and supplier KPI, also sharing this information with own brand suppliers to enable retail teams and suppliers to collaborate more effectively on winning products and brands.

James Butcher, CEO of S4RB, said:

“We see so much data coming into retailers from customers and other sources. Without proper categorisation we are only able to realise a fraction of the value that it brings for our retail customers. PrediCX enables us to unlock that value with consistent and specific labeling and brings exponentially more value to clients and their customers.”

Dan Somers CEO of Warwick Analytics adds:

“The success of machine learning models is enhanced by making it industry, and indeed customer and situation specific. This is why we’re so excited to be working with S4RB who bring deep retail domain expertise.

“S4RB can use our PrediCX technology integrated with Affinity to identify customer intent and insight to both the brand experience and also for the first time to product insight and the root causes for dropped baskets. No other analytical product was able to do this in a blind test.”

For more information about Solution for Retail Brands’ Affinity platform, please visit: https://info.s4rb.com/affinity

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03 Apr

Major high street bank uses PrediCX to improve customer loyalty

A major UK 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.

The bank invited Warwick Analytics to carry out analysis using the automated text analytics platform PrediCX.

Some key outcomes from the analysis included:
• £200m per annum of churn mitigation was identified through operational opportunities
• £240m per annum of churn mitigation was identified through CX opportunities
• Customer Service savings opportunities of £20m per annum through automation
• Ongoing tactical opportunities identified for marketing effectiveness and propensity

PrediCX analysed Tweets associated with the bank, and 23 of their competitors, over a 3 month period. Sentiment Analysis can present issues with accuracy, context and multiple sentiments. PrediCX overcomes these by using the latest in machine learning and AI to classify by ‘concepts’ instead of ‘key words’. The result is a highly tuned model, built in a single day, with thousands of classes that pick up signals of issues, customer sentiment and more importantly customer intents.

A number of key recommendations for the bank were identified just by this analysis alone:

  • Estimated 20% of churn is caused by incidents and this represents maximum opportunity.
  • 10% improvement is c. £200m pa revenue compounded yearly
  • Comparable best-in-class churn e.g. Nationwide is 25% lower.
  • Potentially a further 10% i.e. c. £200m opportunity with improvements
  • Online and mobile banking is a key issue, and is causing direct churn
  • Sentiment is middling. It does not appear to correlate to churn for the market
  • Drivers of churn are mostly customer service, branch closures, marketing offers, interest rates and vulnerability issues
  • Early warning can help predict churn tactically and intercept likely churners
  • 28% of Tweets and potentially all non-voice queries can be automated. This could be £20m pa saving
  • Business banking, current accounts and ancillary services have the highest churn, and insurance the highest negative advocacy
  • Mortgages, current accounts, savings and overdrafts cause the most attritional set-up.
  • Opportunity to improve the journey
  • There are distinct patterns and opportunities to change customer services planning over the week and day to reduce churn and costs

It’s clear to see how the adoption of the latest machine learning for text analytics can present far more insight, a much deeper level, than traditional sentiment analysis and text analytics. More significantly it is able to correlate these signals to operational issues to reduce costs and improve customer loyalty.

With PrediCX, this level of insight can be set up in a matter of days, delivered in near real time and without the need for a data scientist to maintain the model.

You can download the full case study here.

Major high street bank uses the latest AI text analytics to improve customer loyalty
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12 Mar

In the Press: Data Driven Investor

Augmented Intelligence is more intelligent than Artificial Intelligence.

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 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 AI text analytics, 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.

The latest technology being used to analyse customer data uses machine learning to be able to classify interactions accurately to guide processes and generate actionable early warning insight. 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 judgment 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 maximize 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. PrediCX from Warwick Analytics is a perfect example of this.

Read the full article here

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12 Mar

In the Press: CRMXchange

An article titled ‘How Contact Centers can use the latest AI to improve customer outcomes and reduce call volume’ by Warwick Analytics has been featured on the publication for customer experience CRMXchange.

Understanding why outcomes vary between agents can prove difficult, requiring proper categorisation and real-time tracking of outcomes. A contact center provider, handling thousands of customer contacts on a daily basis, recently used the latest in AI text analytics to improve customer outcomes, set intelligent KPIs for their agents, and simultaneously reduce overall contact volume.

The contact center used a Machine Learning platform called PrediCX from Warwick Analytics that automatically classified their interactions for early warning signs, insight and the next-best-action to be derived.

Sentiment and sales intents models were also built to allow the reassessment of existing agent KPIs, maximising their performance without the need for CSat surveys.

Read the full article here

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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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