TechSee – Your ultimate source for innovation in Customer Support
The latest technologies, trends & insights about Customer Service & Support of Things, for contact center and field service professionals. TechSee revolutionizes the customer support domain by providing the first cognitive visual support solution powered by augmented reality and artificial intelligence.
According to Gartner, organizations should strive to deliver low-effort customer experiences because simplicity is the most significant driver of loyalty. In fact, 96% of customers who experience a high-effort interaction become more disloyal compared to just 9% who have a low-effort experience. To help companies improve their Customer Effort Score (CES), we’ve compiled a list of 10 call center mistakes that often result in high customer effort. By avoiding these errors, businesses can drive the shift towards frictionless interactions and enhance the overall customer experience.
When customers are waiting for their call to be answered, they’re annoyed about their issue, and their stress level just keeps increasing. Automated callback options and call queuing solutions that keep customers up to date on their queue position have gone a long way toward decreasing customer stress levels while on hold. However, contact centers should continue to finetune how they engage customers immediately, using voice recognition or simple IVR to determine the nature of their issue, for example.
Wasted hold time
If customers must wait on hold, why not make good use of their time? Implementing automated customer identification processes and personalized promotions during wait times can add value to the interaction and reduce customer effort once the agent comes on the line.
Getting off on the wrong foot
Setting clear expectations is an obvious, but often overlooked way to ensure that everyone is on the same page. Customers want to know what’s going to happen, how they’re going to be helped and how long the whole process is likely to take.
Ignoring customers’ feelings
Asking a customer to briefly describe their issue at the start of an interaction can enable companies to use NLP technology to instantly gauge the nature and urgency of the problem – in objective terms and in relation to the customer’s level of frustration. The up-and-coming field of emotion analytics analyzes an individual’s responses in order to understand their mood or attitude, creating valuable opportunities for companies to connect with customers on an emotional level.
Single contact resolutions are the contact center equivalent of one-click ordering. When agents achieve First Contact Resolution (FCR), it means that they properly addressed the customer’s needs the first time they contacted the enterprise. This eliminates the need for them to follow up with a second contact to seek resolution. Having the right desktop tools and resources can turn any agent into a multiskilled product expert, enabling them to resolve the issue at the first time of asking.
Sometimes escalations are inevitable. That’s why it’s vital to make sure Tier 2 agents have access to the same customer histories – ideally visual resources. Customers hate to repeat themselves – it increases customer effort and drives up Average Handling Time (AHT). Intelligent routing is also key, since one of the worst call center mistakes is making a customer wait for a supervisor, only to direct them to the wrong department.
The devil is in the details. One unclear instruction, misleading image, dead link or outdated article can ruin an entire customer episode. Artificial Intelligence holds the key to analyzing huge data sets in order to facilitate QA testing and to identify any high-effort hiccups that might damage the customer experience.
Don’t overburden the customer with too many options. They want to resolve the issue as quickly and painlessly as possible, without exerting unnecessary mental effort on complicated choices. There might be many routes to the same end, but the contact center should be there to guide the customer by recommending the best way forward.
Call deflection to self-service ensures customers receive the answers they are seeking in the most efficient manner. It also reduces the number of inbound calls routed to human agents. Instead, enquiries are rerouted to self-service channels such as FAQs, live chat, community forums, and knowledge center databases. However, these channels must be easily available and flawlessly designed to ensure they actually reduce customer effort.
Being reactive, not proactive
Anticipating a problem before it becomes a crisis is the key to happier customers, reduced customer effort and lower contact center volumes. While the concept of predictive maintenance has been around for decades, it is only recently that advances in AI have enabled organizations like Vodafone to take advantage of the possibilities, by analyzing any potential issues and automatically contacting customers before they need to contact customer services.
When striving to deliver effortless service, avoiding these ten key call center mistakes is critical to creating effortless experience. Paying close attention to negative customer feedback is much more useful than positive “thumbs up” responses as they can identify the exact moments in a customer episode that must be improved.
Customer service calls are usually pretty routine: customers can’t log in, something isn’t working, they need help setting up a device, or they need clarification on a bill. But sometimes, in the midst of the mundane, comes the mind-boggling: crazy, funny or hair-raising B2C customer service success stories that make your day and inspire you to deliver ever-better service.
Every company understands that their customers are their number one priority, and many businesses are ready and willing to go the extra mile to wow their customers… if only they had the right tools. TechSee’s clients use Visual Assistance technology in their contact centers to support customers with a wide range of issues, from product unboxing, setting up and troubleshooting of devices to onboarding and billing issues.
Visual Assistance uses screen-based technology that allows agents to see the customer’s physical environment via their smartphone camera or by sharing their smart phone screen. Using Augmented Reality annotations, agents provide visual guidance, helping customers by showing them the exact steps they need to take.
But sometimes, having the ability to see the customer’s environment results in some weird, wacky or wonderful outcomes. Let’s take a closer look at some memorable customer success stories to highlight those businesses that go above and beyond when it comes to delivering the kind of service that wins over a customer for life.
Washing machine woes: one father was desperate to disengage the child lock on his laundry machine, as his daughter was in dire need of a quick wash – they were on their way to a wedding and she’d spilled juice on her outfit. An agent using Visual Assistance came to the rescue, guiding the dad so he could get that dress looking like new.
Diabetic drama: not many service companies can boast that they’ve intervened in a potentially life-threatening situation. But Visual Assistance enabled a help desk agent at a company that supplies glucometers to assist an elderly diabetic with her new device. She just couldn’t manage to draw a drop of blood – until the agent showed her how to set the needle to the correct depth. Crisis averted.
Bill bust: when a telecom customer complained about an unusually large bill, it opened a real Pandora’s Box – after the agent initiated a Visual Assistance session and worked through the invoice line by line with the customer, they soon realized that she’d had been the victim of identity theft! The company got right on the case and with the help of the police, the customer didn’t end up a penny out of pocket.
The rogue router: a customer care agent at a leading telco couldn’t figure out why none of his verbal instructions made sense to a customer whose router wasn’t working. But once they began a Visual Assistance session, it was obvious. The customer was looking at a fire alarm box! After he’d overcome his embarrassment, the issue was resolved in a few minutes.
The jam joke: an agent at a top UK telco was struggling to understand why there was a connectivity issue with a set-top box. With Visual Assistance, the problem became clear – the customer had somehow managed to get a dollop of strawberry jam on the microfilter in the wall socket. Must have been the kids. Needless – or seedless – to say, this bizarre scenario was quickly resolved.
Car calamities: every summer, thousands of motorists forget to top up the coolant in their engines, resulting in blown head gaskets and warped cylinder heads. Using Visual Assistance, roadside assistance providers around the world can now show drivers exactly what they need to do when that warning light comes on – and even when smoke starts billowing out from under the hood.
Whether using Visual Assistance to save the day, spare customers’ embarrassment, defuse critical situations or to help your customers through everyday issues, having TechSee as a tool enables fast, effective problem diagnosis and resolution by agents and customers. To read more of our out-of-the-box customer service success stories, click here.
Artificial intelligence (AI) – the science that deals with the creation of human-like learning and reasoning capabilities – has been catapulted into the spotlight in recent years. It seems like every company in every industry wants to harness the power of AI to enhance operations and positively impact the lives of their customers. Applications based on AI are already visible in healthcare diagnostics, transportation, entertainment and education, to name but a few, and now the customer service industry in particular has recognized AI technologies as having almost unlimited potential to meet consumers’ growing demand for better customer experience (CX), reduce costs and decrease reliance on contact center agents. A Tata Consultancy Services survey found that 31.7% of major companies around the world are currently using AI customer service technologies, the second most common use of AI after IT.
This realization has seen investments in AI rapidly increasing. The two fields attracting the most AI investment last year were automated AI-powered customer service agents, which raised $4.5 billion, and sales process recommendation and automation, which attracted $2.7 billion. According to IDC, “AI is the game changer in a highly competitive environment, especially across customer-facing industries such as retail and finance, where AI has the power to push customer experience to the next level with virtual assistants, product recommendations, or visual searches.”
Forward-thinking companies are increasingly turning to AI-powered customer service solutions to optimize CX and to streamline their back-office operations. In this article, we take a deep dive into the different AI customer service technologies that companies are employing to improve their customer-facing interactions, as well as to enhance their internal processes. As the technology matures, many companies will inevitably look for holistic AI solutions that unify customer and operational data to achieve the most valuable and actionable insights.
Customer-facing AI technologies
AI customer service technologies have given rise to a wide range of customer-facing platforms, all of which help companies provide a level of service beyond human capacity. In fact, Gartner predicts that by 2020, 85% of all customer interactions will no longer be managed by humans. Customer-facing AI technologies are especially relevant to assisting in customer identification, call classification/routing, chatbots and predictive personalization.
Biometrics refers to body measurements and calculations for the purpose of authentication, identification and access control. Physical biometric solutions analyze parts of the human body, such as a person’s face, iris or fingerprints, while behavioral biometric solutions analyze other characteristics, such as gait, voice, or interaction with a device. The field is becoming increasingly mainstream with a 2017 Tractica report predicting that biometric hardware and software revenue will grow to $15.1 billion worldwide by 2025, with a CAGR of 22.9 percent.
Face and voice recognition
Facial recognition identifies and verifies an individual by comparing facial features from a digital image or video to a face database. For example, an AI-based algorithm may analyze the distance between the eyes, the shape of the jaw or the width of the nose, and then use the data to find a match in a database. Voice recognition, meanwhile, digitizes words and encodes them with data such as pitch, cadence and tone, and then forms a unique voiceprint related to an individual. This voiceprint can then be used to identify and authenticate the speaker.
AI continues to make significant improvements to machines’ biometric recognition capabilities, especially when it comes to challenging lighting conditions, angles, and backgrounds. Using biometrics, agents can recognize customers, and greet them in a personal manner. Companies can use biometrics to verify warranties, ensuring that customers receive service for their devices without requiring them to save receipts or warranty documentation. Agents representing financial institutions or insurance companies can use biometrics to quickly authenticate customers while minimizing the risk of fraud. As biometrics become more reliable and cost-effective, more companies can be expected to take advantage of their benefits.
Call Classification / routingIntent prediction
Intent prediction refers to the science behind figuring out the customer’s next-step requirements. Customers signals – such as clicks, views and purchases – are translated into predictions that deliver value-added personalization before customers even request it. Predictive solutions combine customer data with AI to determine intent and select the right next step to deliver the relevant customer support. For example, the technology can identify patterns that indicate a customer’s intent based on web activity or text and route the call or chat to the appropriate agent. Intent prediction enables call centers to up their game by giving customers the assistance they need in the way they want it.
Emotion analytics analyzes an individual’s verbal and non-verbal communication in order to understand their mood or attitude. For example, if someone is smiling and nodding their head, they are probably happy, whereas if someone’s eyes are wide and mouth is hanging open, they are probably shocked. Emotion analytics can be used to classify a customer’s mood with the right priority and route it to the right agent. For example, an angry customer might be routed to the customer retention team, and a happy, satisfied customer might be routed to the sales team to be pitched a new product or service. Emotion analytics produces data that can then be used to understand a customer’s experience with a product, new packaging or interaction with a representative of the company, as well as to uncover any weak links that cause negative customer reactions.
Conversational AI customer service platforms – known as virtual assistants or chatbots – represent a promising technology that is already projected to cut business expenses by as much as $8 billion in the less than five years. This is likely one reason why Oracle found that 80% of sales and marketing leaders say they currently use or plan to deploy chatbots in the near future. Major enterprises such as Apple, Microsoft, Facebook, Disney and Google are all actively engaged in the race to build virtual assistants and chatbots that can respond to customer queries and scale the delivery of quality AI-powered customer service. Customer service has clearly benefited from bots as these virtual assistants can store endless amounts of data, predict customer behavior and access relevant information in real time. Today, humans and AI-based bots can collaborate to maximize interactions with customers. Collaboration can be applied in two primary ways: for the augmentation of human intelligence and the enhancement of human capacity.
Natural Language Processing (NLP) refers to the application of computation techniques to language used in the natural form – written text or speech – to derive analytical insights. For example, a company can employ NLP to determine whether the writer’s perception of a specific topic is positive, negative or neutral. This type of sentiment analysis has become a key tool for making sense of the multitudes of opinions expressed every day in texts on review sites, forums, blogs, and social media. This type of analysis also allows companies to extract product suggestions and complaints from online product reviews in order to proactively address any issues. These technologies enable companies to gain insights on a micro level — by understanding the emotions of each customer – as well as on a macro level, by keeping their finger on the pulse of their customer base’s opinions.
Organizations now have access to huge amounts of data about their customers that can be used to provide personalized service and recommendations to targeted consumers. Sprint uses an AI-powered customer service algorithm to identify customers at risk of churn and proactively provide personalized retention offers, a practice that has dramatically improved its retention rate. Netflix’s AI-powered customer service algorithm uses data such as demographics, viewing history, and personal preferences to predict what the user would like to watch next, with a level of accuracy that saves the company $1 billion a year in terms of customer retention.
The technology can also be used to predict technical and maintenance issues before they develop. UPS reports that it has already saved millions of dollars by implementing an AI predictive maintenance solution that reduces delivery truck breakdowns. ThyssenKrupp claims that its predictive maintenance solution has dramatically increased elevator availability by employing real-time diagnostics that reduce out-of-service time. Meanwhile, Cisco uses predictive maintenance to optimize network performance and troubleshoot issues faster.
AI technologies have come a long way toward optimizing back-end customer service processes, ensuring companies are as efficient and cost-effective as possible. Utilizing robotic process automation (RPA) in contact centers has been proven to reduce costs and increase operational efficiencies. Back office AI customer service technologies are especially relevant to assisting in workload management, agent productivity and high-level data analysis of contact center performance.
Contact Center workload managementComputer vision AIfor object/issue recognition
Computer vision AI technologies involve the processing and analysis of digital images and videos to automatically understand their meaning and context. Their accuracy for object recognition enables the machine to identify an object within an image, classify and distinguish it from other objects, and identify parts within the object. Computer vision AI customer service technologies can reduce the workload of contact center agents either through assisted service mode – to route visual customer enquiries, interpret them and assist the agent with visual decision support tools – or in self-service mode, where customers visually interact with visual assistants that visually guide them to self-resolution, thereby alleviating pressure on human agents.
Agent decision support
The same computer vision AI technology that interconnects humans with technology to provide superior CX can also be utilized to reduce contact center agent workload through better agent decision-making and company-wide knowledge sharing through the creation of dynamic visual knowledge bases. The agent and machine collaborate during the agent-customer interaction, with the agent’s performance enhanced by the computer’s ability to provide real-time resolution suggestions. This model is especially effective when the contact center is required to handle large call volumes or highly complex episodes.
Agent motivation and productivity
Research shows that disengaged employees cost U.S. companies up to $550 billion a year. With advanced AI technologies, such as computer vision and augmented reality, agents can work faster and more efficiently. These tools include call prioritization, customer identification, recommendation engines and smart agent monitoring and training. Providing agents with AI-powered customer service tools and solutions to extend their abilities, enabling them to gain mastery and provide better service, is an effective way to improve job satisfaction and reduce attrition. Empowering agents with top-notch solutions and encouraging them to perform better using these tools raises their sense of self-worth and increases the pride they feel in their work. When agents are empowered, they become invested in every customer interaction. The results are reflected positively in the agent’s KPIs, further motivating them to use these innovative tools to succeed.
Contact center decision makers understand that better tools are the key to reducing contact center training times. Contact Center Pipeline reports that increasing the focus on coaching and development for agents is a top priority for contact centers worldwide. AI-based training tools such as gamification, visual assistance and self-monitoring, cut down on agent onboarding time and ensure agents are fully engaged from day one. Another solution, Virtual Employee Assistants (VEAs) – or digital buddies – have been tapped as an effective solution to help contact centers support their agents with on-demand learning, foster intra-company communication and assist with other administrative tasks. Gartner predicts that by 2021, 25% of digital workers will use a VEA on a daily basis, up from less than 2% in 2019.
When gamification is introduced into a call center environment, agents compete to complete objectives and outpace other agents in specific KPIs such as hours worked, lessons learned or average speed to answer. Gamification can be an immersive, exciting experience that engages and motivates agents. Rewards may include recognition on leaderboards, physical prizes or alternative rewards like preferred shifts or free parking. Success lies in full transparency and comprehensive reporting that ensures a fair competition, which can be based on any activity tracked by the platform, such as resolved cases, average handle times, or timesheet submissions.
High-level data analysisProcess improvements
Inefficient processes cost organizations as much as 20 to 30 percent of their revenue each year. As companies scale their customer care operations or respond to new marketplace realities, changes to their processes are inevitable and necessary. Rather than relying on instinct or team decisions, process improvements should be factually substantiated based on data analytics. AI helps companies harness their data to make useful decisions about process changes that will drive the organization forward.
Customer Lifetime Value (CLV) is a measurement that tracks how valuable a customer is to a company over an unlimited time span. CLV is based on the premise that retaining existing customers delivers a higher return on investment than acquiring new ones. Studies have found that the likelihood of selling to a first-time customer is 5-20%, whereas for an existing customer the probability is 60-70%. Using high- level AI-driven data analysis to pinpoint where in their lifecycle customers are churning or to identify target customers for loyalty promotions helps to optimize CLV. Understanding CLV gives companies the data they need to continuously improve or to pinpoint areas of excellence; it is a number that should be top of mind for every contact center agent fielding calls from customers.
Companies are increasingly adopting AI to identify trends and gain insights into the huge volumes of data they hold in order to aid decision-making. AI-driven holistic solutions are being utilized to automate business intelligence and analytics processes based on transactional data found in their databases. By detecting changes and patterns, companies can use the resulting insights for a wide range of business applications, such as new service requirements, location-based trends or new product development.
Where it’s all heading
The tremendous impact technologies have had on AI customer service – both for customer-facing and back office applications – has already been felt by companies across multiple industries. It is a space where new and improved AI applications are being deployed at a rapid rate to provide omni-channel experiences for both customers and agents.
In the insurance industry, for example, leading companies are now using AI to power every aspect of the policyholder experience and the claims process. Companies use facial recognition to identify customers and computer vision AI to compare images of damage with photos of objects already in their systems to calculate the cost, while gauging the urgency of the scenario in order to process the claim as quickly as possible.
Today, AI is at the epicenter of technological convergence across multiple sectors brining together disparate technologies to create a seamless union of customer-facing and behind-the-scenes AI-driven systems.
The insurance industry is at the forefront of the digital revolution. Mounting competition from non-traditional players cannibalizing market share with innovative business models has forced P&C insurers to integrate digital technologies into their operations to keep pace.
In addition, insurers recognize that customer satisfaction stretches far beyond the purchase of a policy. Customers demand peace of mind throughout the contract term, with a short and efficient claim life cycle and quick payment when a claim is filed.
Digital processes have enabled not only an elevated claims experience for customers, but a reduction in average cost per claim, and improved loss prevention and overall profitability. According to McKinsey, digital claims processes can drive a 20% increase in customer satisfaction score and 25-30% reduction in claim expenses.
In the fiercely competitive insurance sector, implementing visual claims has emerged as a solution that can improve entire claim life cycle. A remote visual communication platform enables customers to transmit images and videos of damage to contact center agents for immediate assessment.
Let’s see how this plays out in the latest issue of Superagent:
A new digital player in the insurance sector has been taking away market share from Experico, a traditional P&C company. But the real problem lies closer to home. The evil Byoorekrat has the agents under his control, tying their hands when it comes to assisting customers and slowing down the entire claim life cycle. What on Earth can they do?
It’s Superagent to the rescue!
He introduces visual claims to the contact center, taking away Byoorekrat’s power and putting it back in the hands of the agents. Remote visual claims enable agents to see the full visual picture in real time, without leaving their desks. They can assess the damage, determine the level of urgency, authenticate the necessary documents, and resolve the claim on the first notice of loss, sometimes in only a few minutes!
But that’s not all: Superagent introduces an AI computer vision engine that helps the adjuster provide an accurate estimate and recommend an approved vendor in the vicinity of a customer’s home or workplace.
By powering the claim life cycle with computer vision, Superagent liberates the entire insurance claims process.
Claim life cycle time is down! No more loss of market share! Customers are satisfied again! The insurance company is saved!
Can Superagent help improve YOUR customers’ claims experience?
For your reading pleasure, we proudly present “SUPERAGENT – The Agent of Innovation Liberates the Insurance Claim Process.”
Click here to download the next exciting installment of our Superagent series!
For years, companies have battled to strike the right balance between customer service KPIs. Focusing on one metric would often harm others. For example, heavy emphasis on low AHT would often negatively impact customer satisfaction. Conversely, when trying to improve NPS, agents would often spend additional time building relationships with their customers, driving up call durations.
Recently, an innovative technology has emerged, transforming contact center operations and customer experience across a wide range of industries: Visual Assistance. It enhances all customer service KPIs, including those measuring contact center productivity and CX quality. The technology is unique because it delivers KPI improvements without cannibalizing others. All metrics progress simultaneously, reinventing contact center culture and improving the bottom line, thanks to significant cost savings and higher customer retention.
TechSee has analyzed data from our clients, comparing it with data collected from control groups. The results provide eye-opening insights into how Visual Assistance enhances customer service KPIs across numerous industries.
Covering 70 clients, 220 contact centers and help desks and 30,000 agents, the report highlights the impact of Visual Assistance on customer service KPIs over time. Complete confidentiality has been maintained with regard to sensitive or customer-specific data.
Data was collated from clients in sectors including telecom, consumer electronics, utilities, insurance, and medical technology. The results focus on five core KPIs: truck rolls, FCR, NPS, AHT and product returns.
Customer Service KPIs – The Results
Truck rolls reduced by 19%
FCR improved by 22%
NPS enhanced by 45%
AHT lowered by 12%
Product returns reduced by 17%
What is Visual Assistance?
Visual Assistance uses screen-based technology that allows agents to see the customers’ physical environment via their smart device, and visually guide them using Augmented Reality. It enables fast, effective problem diagnosis and resolution between tech support agents and customers.
The technology is currently evolving, enabling virtual assistants to offer customers the option of full self-service. Powered by Computer Vision AI, these systems can now recognize devices, identify issues, suggest resolutions and provide step-by-step visual guidance to the customer, resulting in even faster and more efficient call resolutions and a more satisfying customer experience.
The technology works across a wide range of use cases, from the unboxing, setting up and troubleshooting of devices to onboarding and billing issues.
KPI #1: Reduce Truck Rolls/Tech Dispatch
Truck rolls – or the need to dispatch a technician to a customer’s location – represent one of the largest costs in customer service operations, across numerous industries.
Impact of Visual Assistance on Truck Rolls
Better remote resolution: a more efficient customer support process eliminates 90% of NFF dispatches.
Due diligence prior to dispatch: agents can determine the exact nature of the issue and dispatch the right technician with the correct information and parts.
Remote consultation: technicians requiring further support in the field can consult with a remotely located expert.
Visual Assistance decreases tech dispatch rates across industries by 19% after just eighteen months. Click here for the full report.
KPI #2: First Contact Resolution (FCR)
FCR is the customer service KPI used to gauge a contact center’s ability to resolve the issue the first time the customer reaches out to the company, eliminating the need for them to follow up with a second contact to seek resolution.
Impact of Visual Assistance on FCR
Faster issue identification: agents can see the problem with their own eyes, eliminating the need for them to rely on the customer’s description of the issue.
Elimination of confusion: customers receive precise Augmented Reality guidance on their screen, so the point of reference and required course of action are crystal-clear.
Easier data verification: when visual proof is required, such as for billing disputes, promotional eligibility or warranty authentication, customers can simply show the agent their documents via their smartphone.
Verification of resolution: visually confirming that the issue is indeed resolved goes a long way toward avoiding repeat calls about the same issue.
Visual Assistance increases FCR across industries by 22% after eighteen months. Click here for the full report.
KPI #3: Net Promoter Score (NPS)
NPS is the customer service KPI that measures customers’ overall perception of a brand and the likelihood of them recommending the product or service to a friend.
Impact of Visual Assistance on NPS
Faster service: customers appreciate resolving their issue as quickly as possible, at the first time of asking.
Reduced escalations: frontline agents become multiskilled product experts, capable of resolving all but the most complex cases.
Elimination of unnecessary tech dispatches: by empowering customers to self-resolve issues, the frustration of waiting for a technician visit is often avoided.
Increased call deflection to self-service: self-service options reduce waiting, saving valuable time for customers.
Greater personalization: providing support via the customer’s preferred communication channels has been proven to drive brand loyalty.
Visual Assistance boosts NPS across industries by 45% after eighteen months. Click here for the full report.
KPI #4: Average Handling Time (AHT)
AHT measures the duration of each customer episode with the goal of ensuring contact center efficiency, planning headcount and reducing operational costs.
Impact of Visual Assistance on AHT
Quick grasp of the issue: agents instantly see the nature of the problem, enabling them to understand and resolve it faster.
Clear visual guidance: using Augmented Reality, the customer is shown precisely what actions are required, allowing them to complete the process in a fraction of the time.
Verification of resolution: agent can visually confirm resolution much more quickly than by asking the customer for verbal verification.
Elimination of irrelevant cases: rapid identification of issues that are outside of scope, for example by seeing that a customer’s warranty has expired.
Reduction of post-call work: capturing images of defective devices is much faster than writing a lengthy report.
Visual Assistance shortens AHT across industries by 12% after eighteen months. Click here for the full report.
The prevalence of consumer returns of non-defective electronic devices represents a massive pain point for electronic brands and retailers as this 2019 TechSee survey reveals.
Impact of Visual Assistance on Product Returns
Prevention of returns: a more engaging and interactive experience – during initial setup, configuration, troubleshooting or for regular maintenance – reduces the likelihood of returns.
Virtual “try before they buy”: when interacting with a product live is not possible or convenient, interactive video has emerged as an effective alternative.
Promotion of self-service: more personalized and effective than product videos, Visual Assistance with Augmented Reality capabilities incorporates interactive feedback and the ability to correct the customer when needed.
Visual Assistance reduces product returns across industries by 17% after eighteen months. Click here for the full report.
For more details about these findings, including charts tracking KPI improvements over time, as well as case studies highlighting how specific clients have improved their customer service KPIs with visual assistance, click here to read the full report.
Everyone knows that time equals money, right? Nowhere is that truer than in call center training – bringing new agents up to speed on products, processes and the productivity expected of them. Traditionally, contact centers have trained new agents using one-on-one coaching or classroom-style role-playing, where the focus is on imparting knowledge and customer service etiquette. While these non-technological training styles center offer a number of benefits, they can consume a considerable portion of a call center’s resources in terms of time and cost. These old-school methods are also inconsistent, as there are so many human factors involved, and the lack of measurability further complicates the call center training process.
Call center training priorities
Contact center decision-makers understand that better tools are the key to improving agent performance and reducing call center training times. Contact Center Pipeline, the magazine for contact center professionals, reports that the number one challenge for contact centers in 2019 is the lack of suitable desktop tools, especially for CRM and collaboration.
The survey further highlights that improving employee engagement and empowerment is the number one priority for contacts centers in 2019, while increasing the focus on coaching and development emerged as another top priority in the Workload/Performance category. To help contact centers meet their goals of both engaging agents and improving training methods, we’ve compiled a list of 7 market-leading technologies that are redefining call center training and development.
Virtual private tutors
One size doesn’t fit all. Agents have different strengths and weaknesses and training modules must be responsive to their requirements. Lessonly for Chrome is an engaging onboarding platform that provides agents with individualized learning, practice opportunities in real-life customer service scenarios, and clear feedback and insights that help pinpoint areas for further improvement.
Socially acceptable collaboration
It’s the virtual water cooler. In-house social networks and forums promote the sharing of best practices and knowledge across customer service teams. When learning becomes a shared activity, agents engage more and are more successful. One innovator in this area is Slack, an online communication platform that sees users logging 100 million collective hours online per month. Its flexible public channels, along with small private groups, can facilitate group learning as well as smaller workshops where ideas can be shared in real time.
Get in the game
Make the workplace fun again. When gamification is introduced into a call center environment, agents compete to complete objectives and outpace other agents in specific KPIs such as hours worked, lessons learned or average speed to answer. Gamification is an immersive, exciting experience that engages and motivates agents. Rewards may include recognition on leaderboards, physical prizes or alternative rewards like preferred shifts or free parking. Success lies in full transparency and comprehensive reporting that ensures a fair competition, such as that provided by Microsoft Dynamics 365 – Gamification, a solution that drives collaboration and competition to increase agent performance. Competition can be based on any activity tracked by the platform, such as resolved cases, average handle times, or timesheet submissions.
We all learn best through practice – not theory. The new breed of AI-based platforms reduces call center training times by delivering timely next best action advice during each customer episode. Jacada’s autonomous CX platform provides interactive and intuitive guidance to help agents respond to customer enquiries in real time. Help your agents learn exactly what to do, when to do it, and how to do it, by offering instant, contextual guidance during each interaction.
See the bigger picture
TechSee has taken the concept of AI-based decision support and added the crucial visual element. Building on its core contact center product – Intelligent Visual Assistance – which enables agents to visually connect with customers and guide them using Augmented Reality annotations, it also provides AI technology that helps a company to rapidly expand its own visual knowledge base. It’s what they call the “crowdsourcing of expertise.” Every customer interaction is tagged by the agent, enabling both employees and the system itself to grow smarter and more resourceful, right from day one.
Psst. When a contact center manager can not only listen in on a call but speak to the agent without the customer knowing, it adds a whole new dimension to call center training. Whisper coaching, provided by companies such as Voicent, enables managers to listen in on agents’ calls, and when it’s appropriate, they can share advice with the agents, coaching them in real time. This is especially helpful when bringing new agents up to speed or helping more experienced agents through a difficult call. Think of it as the equivalent of a TV producer talking to a presenter through his earpiece.
Empowering agents by letting them analyze their own performance engages them more effectively than a top-down approach. Beyond Verbal uses patented Voice and AI technology to detect changes in vocal range that indicate emotions like anger, anxiety, happiness or satisfaction, and picks up on nuances in mood, attitude and decision-making characteristics. Call centers have applied this technology to help customer service representatives monitor their own performance, alerting them if they start to get annoyed with the customer and enabling them to correct their tone before the interaction turns sour.
Training contact center agents is an ongoing process and there’s no single training method that works for everyone. Contact center managers must be able to identify the right technologies that address individual agents’ skill gaps and implement practical coaching and development practices that are both time-efficient and results-driven. Whether using virtual private tutors, social collaboration, gamification, on-the-job training, visual assistance, whisper coaching or self-monitoring, innovative call center training technologies will help you cut down onboarding time and ensure your agents are fully engaged from day one.
Call deflection is the process of routing a customer enquiry to an alternative service channel. The goal is both to ensure customers receive the answers they are seeking in the most efficient manner and to reduce the number of inbound calls routed to human agents. For that reason, successful call deflection strategies allow enquiries to be deflected to self-service channels such as FAQs, live chat, community forums, knowledge center databases and virtual agents.
Consider this scenario:
James is having trouble programming his smart sprinkler system. He navigates to the Contact Us page of the supplier’s website and easily finds the Customer Support phone number. He calls, waits on hold for a few minutes and then a Customer Service agent walks him through the steps until he has the system programmed exactly as he wants it. Success! Or is it?
Now consider this alternate scenario:
James is struggling to set up his smart sprinkler system. He visits the Contact Us page of the manufacturer’s website and is prompted to click on the issue he is experiencing: identifying parts, installing the system or programming it. He clicks on programming and is met with a series of videos explaining exactly what he needs to do. He follows the video instructions until he has the system programmed exactly as he wants it. Now that’s true success!
As self-service becomes more widely adopted across customer care, contact center innovation leaders are seeking better call deflection strategies to help route costly customer enquiries to those alternate channels. Let’s explore four proven methods of boosting call deflection:
User-friendly self-service channels
With over 50% of customers preferring to solve issues themselves rather than relying on customer service agents, companies must ensure that their self-service channels are intuitive and efficient as part of their call deflection strategies. Make sure each of your self-service channels is fully functional. Remove any dead links, awkward or confusing language or irrelevant information. Make sure the entire help funnel is seamless and crystal clear. If necessary, use conversion-funnel analytics to determine exactly where customers are abandoning the self-service channel, and implement a solution to overcome that barrier.
Proactive customer communications
There’s no better way to deflect calls than heading them off altogether. Taking the initiative in your support strategy by identifying and resolving customer issues before they become problems can go a long way toward reducing incoming calls to your customer care center.
Utility companies can proactively notify customers about outages in their area, software platforms can flag technical issues, and consumer electronics companies can check in with customers to see if they need help with new features. These proactive communications eliminate huge volumes of incoming calls to report outages, open support tickets or enquire about product functionalities and are essential elements of the most successful call deflection strategies.
It seems like almost all websites these days have turned toward conversational AI platforms – known as chatbots – to automate and scale one-on-one interactions. The business case is clear: studies show that it’s realistic for companies to aim to deflect between 40% and 80% of common customer service enquiries to chatbots. As the technology becomes more sophisticated, and as chatbots become smarter, more and more enquiries will be successfully deflected, freeing up agents to deal with more complex cases.
Computer Vision AI
Computer Vision AI refers to the processing and analysis of digital images and videos to automatically understand their meaning and context. The technology allows a virtual assistant to quickly identify a customer’s issue via their smart phone camera, enabling it to easily diagnose the problem and visually interact with the customer in self-service mode.
The virtual assistant can also use Augmented Reality to automatically guide the customer toward a resolution via a clear, visual step-by-step process. Computer Vision AI can also detect motion, enabling the virtual assistant to correct the customer in case of errors, ensuring that the resolution is successful.
As a highly effective method of deflecting unnecessary calls, Computer Vision AI has been proven to have a positive impact on contact center volumes through faster issue identification, smarter routing and enhanced self-service capabilities and has emerged as a core component of the world’s best call deflection strategies. Click here to learn more.
It’s no secret that emotions drive behavior. Happy people whistle. Angry drivers crash cars. And now, with the help of emotion analytics, more companies are tuning into their customers’ feelings in an attempt to learn what makes them tick.
Take, for example, a new telco customer and the wide range of emotions they could experience along their personal journey with the company: excitement when unboxing, frustration during installation, annoyance at being transferred between departments, relief at troubleshooting an issue, and a sense of satisfaction when everything finally works as expected.
This customer’s emotions will eventually determine their brand loyalty and likelihood of churning. That’s why monitoring customer emotions is becoming an increasingly important way to improve customer experience. As you’d expect, it’s all about the data.
What is emotion analytics?
Emotion analytics measures an individual’s verbal and non-verbal communication in order to understand their mood or attitude. The idea is to evaluate a customer’s experience with a product or their interaction with a representative of the company and to uncover any weak links that cause negative reactions.
Many popular KPIs – such as NPS and CES – are single questions, with or without a free text option. At best, they provide a narrow snapshot of likelihood to recommend or level of effort. Such feedback is collected at the end of a customer interaction and is biased by the outcome – it doesn’t tell the story of the ‘ups and downs’ of the episode.
Emotional language is limited – is there a material difference between being happy and elated, frustrated and dissatisfied, or surprised and confused? Then there’s the uncertainty principle – the idea that the evaluation itself will impede upon the system being evaluated in unpredictable ways. In other words, it’s essentially unscientific to ask a customer to evaluate their own emotional reactions.
That’s why emotion analytics represents a ‘secret weapon’ for any business looking to get ahead by getting inside the heads of their customers. Research indicates that over the next five years, the emotion analytics market will register a 60.8% CAGR, and the size of the global market will reach $2,420 million by 2024, up from just $140 million in 2019.
A smile is worth a thousand words…
Emotion analytics is an effective and objective measure of feedback, relying on artificial intelligence and technology to detect and analyze data, without requiring the customers to take any additional action. In other words, you can’t fake your feelings. Examples of technological methods for analyzing emotional data include:
Text (Sentiment) analysis uses algorithms to analyze text and determine whether the writer’s perception of a specific topic is positive, negative or neutral. Sentiment analysis has become a key tool for making sense of the multitudes of opinions expressed every day on review sites, forums, blogs, and social media.
Speech analysis refers to the process of analyzing voice recordings or live customer calls using speech recognition software to find useful data, such as stress in a customer’s voice. For example, smart speakers can measure your mood and select music to match it. The technology can also be used in fraud prevention, analyzing the unique vocal characteristics that may indicate dishonesty or concealment of information.
Facial Analysis uses facial recognition technology to analyze a person’s expressions within a photo or video, such as raised eyebrows, smirks or wide smiles. By setting specific parameters around different facial reactions, educators can spot struggling students in a classroom environment, while security forces can detect individuals with malicious intent at public events.
While these are exciting uses of algorithm-based technology, the goal for enterprises is to apply the lessons learned from analyzing emotions to improve their relationship with customers.
The devotion to emotion
Leading B2C providers are now taking these lessons “to heart,” holistically combining the various technologies to optimize customer assistance at every stage of the journey.
Better call routing
Emotion analytics can be used to pick up on a customer’s tone and mood, and to classify it with the right priority to the right agent. For example, an angry customer might be routed to the customer retention team, and a happy, satisfied customer might be routed to the sales team to be pitched a new product or service.
When an agent is in tune with a customer’s feelings, the conversation can be tailored to ensure empathy, thereby enhancing CX. For example, a frustrated customer might be greeted differently than a happy customer, and a sad customer might appreciate a few warm words at the start of the conversation.
Tracking reactions over time
Data provided by emotion analytics is multifaceted and can provide information on every aspect of the interaction at each moment of the episode. For example, contact centers might tweak their processes when emotion analytics indicates that while a friendly introduction is effective, the follow-up identification process is seen as intrusive and annoying.
Delivering corporate-level analytics
Decision-makers benefit from a goldmine of data that helps them understand at the macro level which of their products or services elicit specific emotions. For example, a perfume manufacturer might rely heavily on emotion analytics to finetune its formulas based on customer reactions to specific notes of fragrances, or an ad campaign may be pulled when analytics detect that a specific percentage of people grimace when they see a particular image.
The ability to read a customer’s emotions is clearly a game-changer when it comes to improving CX. And the introduction of computer vision has upped the ante, as new advanced technologies enable computers to both see and interpret the customer’s emotions simultaneously, creating unprecedented possibilities for intuitive service.
Visual Assistance: the key to holistic emotion analytics
Visual Assistance is an emerging technology that enables agents and product experts to visually guide customers using augmented reality during live video sessions. With the introduction of dual camera recording, companies can leverage split-screen snapshots taken simultaneously with both front and rear smartphone cameras, providing a glimpse of both a customer’s facial expressions and their environment.
Real-time insights into customers’ emotions can help agents engage with them in a highly personalized manner and deliver empathetic service, a vital quality in today’s customer-centric business environment. For example, agents issuing instructions for setting up a smart TV can see confusion registering on a customer’s face, enabling them to repeat or simplify the steps.
Speech analytics may help an agent detect high levels of frustration and provide personalized service that addresses the customer’s specific issue. When there’s a language barrier or a noisy environment, a voice-to-text app will enable agents to benefit from sentiment analysis, providing insights into a customer’s mood when speech or facial analysis is not possible.
Of course, there are mixed feelings
While these are intriguing developments, barriers to adoption remain. Emotion analytics triggers several privacy and security issues. Are customers willing to have their emotions analyzed? Is consent required? It will take time to select the right use cases and to determine the best data sets to capture while making sure the information can be effectively measured to optimize customer experience. However, in the meantime, these technologies are creating valuable opportunities for companies to connect with customers on an emotional level, making sure they truly have the customer’s best interests at heart.
Analysis of Average Handling Time is deeply entrenched in the customer service field and almost every contact center manager wants to improve AHT. Measuring – and reducing – the duration of each call has long been seen as the most important way to ensure efficiency, plan headcount and reduce operational costs.
AHT = Total Talk Time + Total Hold Time + Total Post-Call Work/Number of Calls Handled
The AHT Debate
The problem with AHT is that it does not differentiate between complex cases that usually take longer to resolve, and simple ones that can be easily resolved within a few minutes. Another problem is that when an agent is measured by speed alone, the priority is to end the call as fast as possible, rather than to ensure that the customer has received the best solution.
Leading management journal Harvard Business Review has called the value of AHT into question, stating that it encourages agents to deprioritize complex issues and keep calls short, even if they could have resolved the enquiry had they invested the time in doing so. Consider this scenario:
Agent: Thanks for calling United TelCo, how can I help you?
Customer: Hi, I’m having a problem with my internet.
Agent: What’s the issue?
Customer: There’s a red light flashing on my router and I can’t access any websites. I’ve tried resetting the router, restarting my computer and I even googled the issue. Nothing worked.
Agent: Hmm. Sounds tricky. Let me schedule a technician to come take a look. Would Tuesday between 10 and 2 work?
While this short call certainly contributed to this agent’s low AHT, it did nothing to resolve the customer’s issue promptly. Worse still, the company must now incur the cost of a tech dispatch and the customer will have to wait days for what could have been an easy fix, had the agent only spent more time trying to help and less on seeking to improve AHT.
In fact, a higher AHT can result in happier, more loyal customers. Consider this second scenario:
Agent: Thanks for calling United TelCo, how can I help you?
Customer: Hi, I just got back from a trip to Hawaii and my internet is down.
Agent: What’s the problem?
Customer: Have you ever been to Hawaii? I waited years to go, it was my dream. I saved up both the funds and vacation days, and let me tell you it wasn’t easy.
Agent: That sounds amazing, sir. Now, what exactly is happening with the internet?
Customer: Are you married? Let me give you some advice, make the most of it. I wish I’d traveled when my Sophie was alive, she would have loved the island. Traveling alone can’t compare with experiencing new things with someone you love, y’know?
Agent: I see what you mean, sir. What’s the model number of your router, please?
Customer: Let me check. Thanks for listening, I know I sometimes ramble on a bit.
If this agent had been overly focused on trying to improve AHT, as soon as he realized the customer was going off-topic, he might have dispatched a technician in order to cut the call short. This would have resulted in a negative emotional experience for the customer – irrespective of the quality of the eventual solution.
Improve AHT with a customized blend of KPIs
While most leading companies still seek to improve AHT as their primary contact center metric, aligning it with other performance-based KPIs such as Mean Time to Resolution (MMTR), First Call Resolution (FCR) and Net Promoter Score (NPS) provides a more holistic view of agent performance, as well as a clearer picture of the customer experience.
How to improve AHT with technology
Technological solutions can be implemented that help to improve AHT, without negatively impacting the quality of your customer service or the customer’s overall experience with your company.
Virtual assistants to collect basic information
Agents can save significant amounts of time by having virtual assistants – or chatbots – cover the basics at the start of a customer interaction. This may include the customer’s name, account or policy number and the general reason for the call. This early collection of data ensures the customer is routed to the correct department and eliminates the need for agents to waste precious call time collecting this information.
Robust self-service options
Customers want to help themselves at a time most convenient to them. Make sure your self-service channels are as robust and easy to use as possible, so customers can help themselves without having to wait for an agent. This could include FAQs, detailed information on the company’s products, shipping information and return/refund policies.
Live chat efficiencies
As live chat continues to grow in popularity, companies should ensure agents are utilizing the technology efficiently and in a manner that will reduce AHT. For example, a single agent can engage in multiple live chat sessions simultaneously, and preconfigured responses can be utilized to save time.
Visual Assistance for faster issue resolution
Visual Assistance allows an agent to see a customer’s issue via their smart phone camera or by sharing their smart phone screen. This enables the agent to quickly diagnose the problem and visually guide the customer towards a solution. Visual Assistance eliminates the long lists of questions and answers that agents traditionally used to get to the root of a problem, enabling them to dramatically improve AHT.
Click here to access a new Data Sheet about the benefits of Visual Assistance and the huge impact it can have on reducing AHT.
More consumers than ever are sending back fully functioning electronic products. This No Fault Found phenomenon is costing companies a fortune – how can they address the issue of NFF returns?
Sara Turner was looking forward to receiving her new espresso machine. But when she opened the box, she was confused and dismayed to see that the machine was different than expected – there was no milk frother. She checked her order and realized that the online image of a nice frothy cappuccino was for illustration only. Sigh. No coffee today.
Her husband Mike was just as excited to open his package. He was looking forward to saving money with his new smart thermostat. However, he immediately felt overwhelmed by the multi-step installation instructions. He called customer services, who repeated the same instructions he’d just read in the manual – and he still couldn’t make heads or tails of them. Oh well. Back to the store.
2019 Survey: NFF Returns
TechSee, a global leader in visual customer assistance powered by AI and Augmented Reality, has released the results of a study on consumer behavior when returning non-defective electronic devices, revealing some eye-opening findings.
The survey demonstrates that 41 percent of us have returned a non-defective item in the past 12 months. Global professional service consultants estimate that such returns account for more than $17 billion in lost annual revenues for brick-and-mortar and online retailers.
The study also revealed that 65 percent of respondents decided to return non-defective electronics early on, citing frustration or confusion during product unboxing, installation, and first use.
Consumers most often returned small home appliances, such as blenders and coffee machines (28.5 percent) followed by entertainment products, including speakers, TVs, and gaming consoles (25.2 percent), small gadgets (20.3 percent), phones and tablets (15 percent), major appliances such as washing machines and refrigerators (5.7 percent), and home office products (4.8 percent).
NFF returns are rampant
The prevalence of consumer returns of non-defective electronic devices represents a massive pain point for brands and retailers, with Accenture reporting back in 2011 that 68% of all consumer electronics (CE) returns fall under the umbrella of No Fault Found (NFF), situations when an item is returned despite functioning properly.
Good customer service can prevent returns
54% of consumers agreed that they would return a product if they found it difficult to install, while nearly 70% said they would also return it if they found it hard to operate, suggesting customer support is a critical component in lowering NFF returns.
In fact, 72 percent of those polled stated that good customer service would dissuade them from returning a product. This underscores that more positive unboxing and customer service experiences – both immediately post-purchase and on an ongoing basis – have the power to influence whether the device will be returned.
Visual experiences are vital
Many participants expressing a propensity to return products also stated they had never seen or interacted with a product they’d returned – physically or virtually via video or augmented reality – prior to purchasing; only 16% claimed they’d had the ability to “try before they buy.”
When seeing or interacting live with a product is not possible or convenient, video has emerged as an effective alternative. With the right features and capabilities, video can offer users a more engaging and interactive experience, during initial setup, configuration, troubleshooting or for regular maintenance, as this short video demonstrates.
Video and augmented reality are effective technologies to help prevent returns
45% of consumers state that watching a product video will dissuade them from returning a product and 44% say the same for a live video streaming session with an expert.
Visual Assistance powered by augmented reality (AR) can make the unboxing, installation and activation process even more intuitive by delivering a new level of visual customer assistance. This technology is considered more effective than product videos, as it is more personalized and applies to the customer’s actual environment, incorporating interactive feedback and the ability to correct the customer when needed.
With Gartner predicting that by 2020, 85% of consumers will manage their brand interactions without the input of a human agent, advanced customer self-service solutions – such as AI-driven mobile visual guidance – are more important than ever.
Combining video and augmented reality with customer service excellence, by empowering customers to visualize their products before purchase and receive assistance during the post-purchase period, will go a long way towards reducing NFF returns.
TechSee polled more than 3,000 U.S. consumers for the census-weighted study, interviewing men and women aged 18 to 60 of varying incomes, education levels, and geographic locations.
Download the full results of this groundbreaking survey here, and get your hands on a stunning infographic presenting the headline findings here.