There are a number of warning signs that an advisor can pick up on to identify if the caller on the other end of the line is vulnerable but without extensive and proper training these are difficult to spot and the volume of communications is just too much to carry this our manually.
However, the latest in speech transcription and text analytics, supported by AI technology, is now able to automatically detect vulnerability and hints at vulnerability from conversations with customers.
Many companies are implementing live chat because it offers a better experience for some queries and with some customers. It also offers cost savings for companies compared to voice. Indeed, the channel has been growing 87 percent per year, according to CustomerThink.
However, canned responses, complex queries, or poor staffing can lead to the opposite experience. This results in channel switching, repeat calls, abandonment, or even churn.
However, new techniques in AI and machine learning make the analysis of live chat both easy and immediately actionable. Dan gives three ways these tools can transform chat optimization.
In this webinar Lee Mostari of Ember and Dan Somers of Warwick Analytics explained how you can analyse your customer interactions across all customer touch-points, and use this insight to define and optimise your channel strategy. We also discussed the benefits of undertaking this piece of work with operational efficiencies and customer experience as two of the key drivers for building an omni-channel operating model.
Recent years have seen a proliferation of channels for customer interactions – and most organisations have focused simply on keeping up with the latest addition. But the more channels you have, the more challenging it is to maintain a coherent customer experience across all of them and to understand how each can best be applied to enrich that experience.
This webinar can help bridge that gap. It focuses on how you can define, and refine, your channel strategy.
Identify which channels your customers are best suited to – and which work best for specific types of interaction.
Understand the causes of channel failure and what drives customers to switch.
Reduce customer effort by delivering service in the customer’s preferred channel first-time.
Fill in the form to watch our webinar recording now.
Contact-centres.com has published ‘How to get from Aberdeen to Wells avoiding ‘Other‘, an article by Dan Somers of Warwick Analytics that looks at the issue of incorrect wrap up codes/dropdowns being selected by agents and also how to avoid ‘other’ being a catch all category.
Inputting wrap-up codes is a standard piece of ACW work a contact centre agents typically completes. It allows a call to be classified so insight can be extracted on popular topics, locations, type of caller and so on. Some contact centres also use the accuracy of codes as a KPI for individual agents.
However, too many lengthy dropdown lists are proving too laborious for many contact centre operatives – we call this ‘Aberdeen Syndrome’.
So how can you get from Aberdeen to Wells avoiding ‘other’?
The answer is in auto-tagging or auto-classification, made possible by the latest in AI and machine learning. Specific information can be requested and automatically identified from a call, email or other piece of customer feedback such as online chat. The wrap up codes are populated automatically, significantly reducing call wrap up time.
Understanding the mix of topics, emotions and outcomes for different days of the week and time of the day can make a huge difference.
For example, do people use internet banking on a weekend? Are customers (and agents) happier at the beginning of the day/week?
By analysing incoming queries for topics, sentiment analysis and identifying emotional intents, a bank was able to understand when customers were more likely to tolerate delays and issues. This allowed them to forecast and staff more strategically, while flexing their skills and resources accordingly.
And in the feature: Why Is It Getting Harder to Recruit Good Contact Centre People? Dan talks about how there are many ways that better technology and analytics can support better processes that make ‘being great’ easier and more standardised as well as routing digital queries to the best people. This makes it easier to find and retain the best people and take on more inexperienced staff.
Wooden figures of people. The red man comes out with a team of workers. The concept of choosing a new leader. Choice of person. Hiring and recruiting. Human resource management. Selective focus
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.
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!
@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?
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.
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.
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?