It’s 2020 and anyone in the contact center space can clearly see CX and customer journeys have become the most pivotal points when it comes to successful operations and business growth. The transformation has been on-going, but with rapidly changing technological advancements and thereby varying touchpoints, CX has come into sharp focus. So where is customer experience heading this year? What are the expectations? And more importantly, how will you meet them? Customer convenience remains key Customers are becoming more demanding, more discerning and definitely more reactive if expectations aren’t met. Aren’t we all! According to studies by Walker, it is expected that by the end of 2020, customer experience will overtake price and product as the key brand differentiator.* There are four ways you need to ensure this customer-centric experience: Enable Omnichannel support. Your customer expects omnichannel connections with companies. They want to connect with companies on the go, in different contexts and at different times and will use whichever device and channel that is most convenient at that point. You need to make it easy for them to do this. Go Regional. Customers in 2020, and therefore contact centers, are relying more on regional language support. Whether you are a small business or a large international enterprise, localized, authentic communication is key to good CX. Do this by localizing your call center operations, and enabling vernacular voice or chat bots. Guarantee Privacy. Customers are now concerned with privacy regarding their data and transparency from companies. maintain checks and measures to guarantee this kind of data privacy. Enable personalization. In spite of concerns on data privacy, customers are ok with companies storing their data and using it for improving their experience, as long as they keep it safe. Contact centers balance humans and bots With the advent of chat and voice bots, AI usage will continue to flourish. Customers will appreciate this when they want quick resolutions. But if you don't provide an easy way to connect with a live agent when they want a human interaction, they will get frustrated. Businesses will need to understand how to balance the experience for customers. they will need to carefully decide which interactions to automate, and which to hand over to trained, capable agents. Agent engagement gets its due credit A disinterested or uninvolved agent will naturally lead to a subpar interaction. For customer service to thrive, you need engaged, enthusiastic employees. Businesses are only recently coming to realize the correlation between agent performance and customer experience. Gamified feedback, intuitive dashboards, easy access to data and live stats are just some features that we are constantly enhancing. Agent User Experience has come to the forefront. Data improves decisions. Know thy customer. AI-powered speech analytics will make it possible to know your customers better. Information gathered from call recordings are sieved through and analyzed faster at the back end, without the need for human intervention. Sentiment or intent analysis and advanced features, like tracking the customer sentiment throughout a call, can be a goldmine [...]
Everyone says 2020 is going to be a year of upgrades. And we don’t deny that. Call Center Trends for 2020 look at upgrading call center efficiency, speed of response and productivity. It looks at improving and playing an important role in shaping customer experience. Here are the top 5 trends we predict: Call Centers use self-service to scale up. Scaling up call center operations has never been easier. On one hand voice bots, conversational IVR, and chatbots have matured tremendously over the past year. On the other hand, customers are more willing than ever before to engage with these technologies for faster resolutions. According to CCW, more than 70% of customers are open to using bots. 1 The results are already beginning to show. For instance, last year call centers who implemented self-service IVR reported five times higher productivity. In other words, they were able to handle five times greater call volumes with the same number of agents. These interactions included checking their ticket status, bookings, cancellations, FAQs and other L1 queries. The Omnichannel journey matures. Enabling a 360-degree view of the customers is a strong priority in 2020. Customer Success leaders will shift focus from call centers to omnichannel contact centers. They will achieve this by integrating digital channels and enabling tight CRM integrations. As a result, agents will be able to access customer information with ease, leading to faster resolutions and personalized interactions across channels. New digital channels emerge. Phone calls continue as the most popular customer support channel. This is followed by live Chat. But new channels constantly emerge. This year, WhatsApp will be one such channel. This is because last year the backend technology for enabling large scale customer support via WhatsApp matured considerably. Procedures for getting a WhatsApp Business API number also became far easier and more streamlined. Smart speakers such as Alexa and Google Home may also be poised to become new customer support channels. In the US, nearly 60 million people use smart speakers. In India, these speakers have penetrated 20.9% Internet Households.3 Functionality and options keep improving on this popular channel. It is highly likely that by year-end, call centers could integrate their voice bots with these smart speakers so that customers can ask Alexa (or Siri) to update them on their delivery status or other such details. AI gives Call Center Data its due. Call Centers collect a massive amount of data. This includes customer complaints, pain points, desires, and suggestions. This kind of data is invaluable for improving customer experience, creating user personas, developing marketing programs, measuring product success or developing new features, products, and services. With new AI-based tools, it is now possible to structure and glean these insights from the call recordings where they reside. Speech Analytics and Sentiment Analysis tools will massively improve how call centers manage their operations. But more importantly, it will transform how they contribute to product development, marketing, and overall business growth. Customers willingly talk to your bots. Voice bot technology [...]
How are you gauging the quality of your customer experience? How are you deciding whether your customer is really satisfied or not at the end of each call? Is it part of your agents’ ACW? In that case, how objective is your agent in marking your customer’s mood? Or are you are using a survey system? Sadly, we know that surveys are not always answered. Besides, every customer judges on a personal scale. One customer’s 4-star rating can mean they’re not completely satisfied, whereas that may be the highest rating another customer gives. The scale just isn’t standardized. Or are you still using metrics such as first call resolution to judge satisfaction quality? Do these metrics give you a complete picture? For example, let’s consider a customer who calls, speaks to your agent for 5 minutes, hangs up and doesn’t call again. Was that really a call resolution or have you just lost a customer? And, we all understand that even the best QA team can only go through a small sample set of call recordings. We don’t know how many unhappy (or happy) customers hide within the call recordings we missed. What you really need is an objective and intelligent analysis of customer sentiment at the end of every single call. That’s where Sentiment Analysis comes in. Sentiment Analysis is a function of your Speech Analytics system. A Speech Analytics system first transcribes every word your customer speaks to text. Then it analyzes the text using Natural Language Processing to understand what they meant when they said what they did. Based on the words used, and the context, Sentiment Analysis is able to gauge whether the customer’s mood was: a. Positive b. Neutral or c. Negative. And here is why it helps: Fastest Feedback Loop. A great plus point is that the results are instant. Your agent gets immediate feedback. Studies show that immediate feedback is the most powerful way of improving performance. 100% analysis. 100% of your calls are analyzed. You don’t miss out a single negative (or positive) remark. This makes a judgment of agent performance 95% more accurate than before.* Deep Dive. The results can be further scanned with various keywords to find common causes and trends. For example, are the negative results all pointing to a product/service flaw? Are there common complaints that can be used to improve your product offering? Are there some triggers in your script that cause a change in customer sentiment? Find your star players. Sentiment can also be scanned at the beginning and end of the call. Are there some star agents that can turnaround customer sentiment from negative to positive? You can automate call routing of certain customers to your star agents. Take immediate action. You can develop APIs that allow barge-ins or call redirects whenever sentiment drops to negative. Go beyond first call resolutions. Let’s go back to the customer who calls, speaks to your agent for 5 minutes, hangs up and doesn’t call again. Was this a [...]
At the beginning and end of every call, your Speech Analytics system displays customer mood on your Agents dashboard. How does a machine understand whether your customer is happy or not? It uses two things: Natural Language Processing Sentiment Analysis Natural Language Processing. Conventionally, people used programming languages to “speak” to computers. But now, we see Alexa, Siri and Cortana, following instructions we give in our “natural“ language. This is thanks to NLP. NLP or Natural Language Programming is the ability of a computer program to understand natural human language. It is an aspect of Artificial Intelligence. Sentiment Analysis. People don’t just communicate information using language. We communicate emotions too. Sentiment Analysis is a layer placed over Natural Language Programming, where a program is “trained” to understand sentiment in a text passage. How does this work? The short explanation Very simply speaking, your SA first translates your call recordings to text. It then scans for positive and negative words. It relates each word to the surrounding 5 words. Then it uses these words as context to understand word meanings. Like humans, machines to get better and better at understanding language through exposure and experience. So the very short explanation would be: Your Speech Analytics system can tell if your customer is happy or not, because of experience. The slightly longer explanation What we mean by experience is that your Speech Analytics System has been exposed to billions and billions of words—until it’s learnt to understand meanings. This is similar to how a well trained, educated—and possibly middle aged— person gets good at understanding what others mean to say. It learns to understand slang, humor and sarcasm. For example, It learns to differentiate this sentence: That is so sick! From this sentence: I’m sick of you. It also assigns intensities. For example, in this sentence: I am very unhappy with your service. SA will consider this sentence negative because of the presence of unhappy. And then assign it an intensity of +2 due to the presence of very. And then gives you an aggregate. For example, in this sentence: I am happy with your service but your product is too costly. SA will be trained to assign costly as negative. It will add ++ for intensity. But then it will calculate for the positive emotions. Then, it will use appropriate aggregators— like sums or averages— to assign a final value to your sentiment. How accurate is SA at analyzing sentiment? SA is more accurate than humans. BUT, this statement comes with two disclaimers. Disclaimer 1: SA is only more accurate than humans over large quantities of data that have to be analyzed within tight deadlines. This is because humans get bored with repetitive tasks. Pressure to meet deadline, tiredness and boredom can affect human accuracy. Disclaimer 2: SA is not 100% accurate. On an average, SA can be said to be is 60-85% accurate. But, know this—within the professional setup of gauging sentiment from text, humans are not 100% accurate either. [...]
Speech Analytics is 95% more accurate and 15 times faster than a QA team. That's why it's taking over Quality assessment in the call center. Contact Centers have thousands of valuable data bytes stashed away as call recordings. Processing this data manually is time-consuming. And this main reason this data went unused. A trained personnel takes 3.5 minutes to listen to a 2-minute call for quality and training purposes. This means that listening to every single call is an impossibly long process. This is why even dedicated QA teams within call centers cant listen to more than 5%-10% of their call recordings. That means 90%-95% of their data is left unused. Not anymore. Speech Analytics is a combination of Natural Language Processing (NLP) with Machine learning. It can analyze the complete set of your call recordings in minutes. This means that with Speech Analytics, the quality monitoring within your call center is far more accurate than ever before. Moreover, the monitoring happens really fast: 15X faster than before. So fast, in fact, that you get the results in real-time. This means that agents don't have to wait to judge agents' performance at the end of the month or even the end of the week. They can barge-in, or whisper to agents to help them in real-time. Dashboards can be set up for agents to see their performance in real-time as well. For example, a Speech Analytics enabled dashboard can show an agent when their speech volume is too high, or too low. Also Read: How Call Center managers use Speech Analytics. Why you need real-time Speech Analytics in the Call Center?