Today, many industries rely on technology to operate successfully. More so, our current climate has forced businesses to shift to remote work, making digital reliance more critical than ever. Consumers are expecting great customer service no matter what, even when face-to-face interaction isn’t an option.
That’s where automation comes into play. According to Business News Daily, many business owners are already taking advantage of automation in one form or another, as it can be highly valuable to a company’s bottom line.
“Automation takes a lot of forms,” Fred Townes, co-founder and COO of a real estate tech company shared with Business News Daily. “For small businesses, the most important thing is [repetition]. When you find something you do more than once that adds value … you want to look into automation.”
Automation doesn’t necessarily mean sacrificing the customer experience, rather, it can better equip agents with the information and resources needed to better service customers. Customer service is just one of many factors business owners can automate and see improvements in terms of scalability and overall efficiencies.
Why is Automation Valuable in Customer Service?
AI in customer service has had a good reputation for some time. In fact, last year, research predicted that digital customer service options would increase by 143% this year. When automation handles the repetitive tasks, it frees up time for agents to interact directly with customers. With the help of AI, customer service agents have more time to interact with more consumers, with less strain, and see a major impact on scalability.
Customer expectations are different than they were a decade ago. Consumers understand the capabilities of technology and want to feel that businesses are taking full advantage of it. Businesses that utilize different service types online, such as self-service and full-service options, are providing their customers with flexibility. Giving customers the opportunity to help themselves, through carefully curated content and online chatbots, are a few ways to offer self-service. Since 67% of customers prefer self-service over talking to a company representative, this flexibility makes it easier for customers to get the answers they need as quickly as possible.
How Can Businesses Make the Switch?
If your business has yet to implement customer service automation, you’ve come to the right place. Kustomer strives to make personalized, efficient and effortless customer service a reality for any business. As a multi-channel SaaS platform with powerful AI capabilities, Kustomer enables companies to contextualize all conversations to not only better understand each customer, but also to eliminate the distracting, time-consuming tasks that fall upon agents.
From cross-channel conversations to automated business processes, the Kustomer platform can help your business improve overall efficiencies by automating routine actions and eliminating manual tasks altogether.
Interested in learning more about how automation can change your customer service reputation? Contact Kustomer today to get started.
Now more than ever, artificial intelligence (AI) is becoming a fundamental cornerstone of business operations and is changing the way that companies across the globe work. In fact, the International Data Corporation (IDC) Worldwide Semiannual Artificial Intelligence Systems Spending Guide forecasted spending on AI to reach $79.2 billion in 2022, with a growth rate of 38% between 2018 and 2022.
Speaking to the world of customer service, intelligent automation is a solution that can personalize the interactions between businesses and their customers while making the experience more efficient and streamlined.
Let’s take a closer look at what defines intelligent automation and some of the benefits it can bring to customer service:
What Is Intelligent Automation?
According to Forbes Council Member Vik Renjen, intelligent automation is defined as the combination of AI and Robotic Process Automation (RPA) used to mimic the behavior of the customer by using applications to find and transform data into business processes and workflows. In customer service, intelligent automation can be used to capture valuable data that supports and manages customer interactions automatically.
What are the Benefits of Intelligent Automation in Customer Service?
AI doesn’t have to replace humans in customer service, rather, it can be used as a supplemental tool for providing necessary information and assistance to customers and agents alilke. Here are some of the benefits that come with intelligent automation in customer service:
Chatbots, one form of AI that can be beneficial, help organizations scale their services to more people than they would be able to provide through live agents alone. These bots can be used to collect information from customers, suggest automated responses and assist agents along the customer journey. They can also answer simple questions, such as those around business hours, return policies, order status and more.
Increased Attention to Conversation Wants and Needs
Intelligent automation can be used to pinpoint topical keywords throughout a conversation to better assist the customer. One of the customer service features available in the Kustomer platform is Automated Conversation Classification, which uses intelligent automation to categorize conversations via machine learning, so that the conversation is immediately routed to the most appropriate agent to properly meet his or her specific wants and needs.
Improved Resourcing Processes
AI can also be used to predict conversation volume during certain periods to assist management in delegating work and managing staffing requirements. Additionally, AI can be used to track how well agents are performing; with an understanding of how much “work” AI is handling, and where agents are falling short, management can get insight into the knowledge of the agent and provide training or additional resources based on the information.
How Kustomer Can Help
Intelligent automation is a key factor behind Kustomer’s ability to serve its clients and their customers. Kustomer uses Kustomer IQ to automate time consuming processes and provide teams with valuable insights, so that businesses can provide consistent, personalized customer service in a timely manner.
Interested in learning more about Kustomer’s solution to your current customer service platform? Contact us directly today to get started.
Life has become a series of trade-offs and workarounds in light of the pandemic. Curbside pick-ups are my new norm. My inbox is a litany of order confirmations and estimated delivery times. Last weekend, I drove to a local hardware store and found the following handwritten message on a sign at their front door: “Know what you want. Get in and get out.” At times, my interactions with people feel purely transactional.
The world has changed, and customer service is changing right along with it. Businesses are being challenged with a paradoxical conundrum: how do we retain our humanness? How do we maintain trust in a time of uncertainty? Below are three ways customer service teams must adjust in light of the global pandemic.
Empathy Is #1.
Companies who approach customer service with a deeper level of empathy are more likely to maintain loyalty and win new business. This concept is not a newfound revelation. In fact, The Empathy Business has studied the efficacy of empathy in business for years. And what have they found? Organizations that focus on the “emotional impact” they have on employees, customers, and society are valued higher and earn more than their counterparts.
In the world of COVID-19, empathy is even more desperately needed. Quarantine measures and social isolation mean a rise in loneliness and other mental health issues. Think about it this way: what if your organization delivers the only social interaction an individual will experience for a full day? Armed with that information, how should you change your customer journey?
Start small. Use your data. Study the way your customers use your tools and services. Where are they running into roadblocks? Where are you making your customers’ lives easier? Take note and adjust. Document your FAQs in an accessible location, like a Knowledge Base. Have the patience to clearly explain the nuances of your business and policies. Above all else, practice kindness in all of your communication.
“One-Size-Fits-All” Won’t Succeed.
As the pandemic spreads, we’ve seen a spike in conversations for many of our clients. And according to a recent survey by Kustomer, there has been a 17% increase in inquiries across industries. With this influx in communication, it no longer makes sense to force every customer to call the same number to contact your company. Instead, it’s time to get smart about the channels you employ to manage customer interactions, and it’s time to invest in a fully-fledged omnichannel experience.
But beware: you should avoid blindly adding new service channels without a strategy in place. Dig deep into your customer personas and understand their respective beliefs and behaviors. McKinsey notes that “not all customers are the same, and it’s how they differ in their behavior and preferences—particularly on digital—that should have an outsize influence on how service journeys are designed.” Keep this in mind, too: a small percentage of customers — classified as the “offline society” -— may suddenly be forced into adopting digital communication in light of shelter-in-place orders. Take these different customers into account when adapting your customer service strategies.
Automation Is a Necessity, Not a Luxury.
As we’ve seen an increase in the number of inquiries, we’ve also seen an increase in the need for artificial intelligence and machine learning technologies. Agents can become easily overwhelmed by an onslaught of new messages. AI can automate some of the more tedious tasks that those agents might encounter, thereby freeing their time for more important work.
Consider how you can deflect commonly asked questions and save your agents valuable time. Let’s say you’re an airline in today’s world. With the rise of the pandemic, you’re being flooded with requests for information about your refund policy. Instead of directing your team to answer each inquiry individually, you could use automation to serve up pre-written articles that align with the inquiry’s keywords. Not only do you protect your team’s time, but you also deliver a better customer experience as customers receive near-instant answers to their questions. Beyond that, unsuccessful deflections can provide a treasure trove of data to guide your future content.
Those who adapt and adjust their strategies now can influence their fate in the post-pandemic world. The opportunity is there. We have to be good stewards of our time and resources to capitalize on it.
Save your team time and money with AI for Customer Service
Customer service teams are being asked to do way more with much less, and here at Kustomer we want to ensure that your team has all the tools to be as efficient and effective as possible. It’s impossible for teams to achieve this without eliminating manual, time-consuming work, like sifting through queues, escalating issues, or processing transactions.
Identifying the intent of every conversation might be step one in a service interaction, but automating this process transforms how you operationalize support, driving efficient customer service that keeps customers and agents happy.
Intent Identification, the newest and most powerful feature of Kustomer IQ, analyzes and classifies inbound conversations, and uses those new attributes to trigger process automation that takes work off your team’s plate. In other words, machine learning analyzes your historical data to predict a customer’s intent for contacting customer service. It can also tag spam conversations, as well as automatically flag urgent conversations that need to be prioritized.
Here’s how it works.
STEP ONE: First, users select what they want to predict.
STEP TWO: Once you select what you want to predict, the tool checks to see if there is enough data that can be used to accurately make a prediction.
STEP THREE: If everything checks out, the system is trained to detect specific language in messages.
STEP FOUR: Users review the model for expected accuracy.
STEP FIVE: Once the training is complete, the model is ready for use and deployed. Intent Identification will tag inbound messages which can then trigger process automation.
Emily Marcogliese, Head of Customer Service from our partner at thredUp recently shared, “Kustomer IQ has had a tremendous impact on my team’s efficiency. Machine learning instantly identifies the purpose of every inbound conversation, then intelligently routes each customer to a specific team based on their contact reason, such as orders, returns, or clean out. Rather than spend time manually routing conversations, my team can focus on delivering personalized service and resolving issues quickly to decrease customer effort.”
Intent Identification in action.
Once conversations are analyzed and classified, Intent Identification can unlock powerful automation. Here are a few of the ways it can be put into action:
Automate Rules: Our rules engines can automate any process, like escalating unhappy customers to more knowledgeable teams, or execute transactional interactions like refunds, returns or status updates.
Route Conversations: Instantly and accurately route conversations to specialized teams based on how you classify customer outreach, such as by contact reason or product line.
Send Auto-Responses: Responses to your most common questions can be automated, freeing up valuable time and energy.
Read more about Intent Identification in our Help Center, and check out our pricing page to view our Kustomer IQ packages.
In this episode of Customer Service Secrets, Vikas Bhambri, Senior Vice President of Sales and CX at Kustomer, joins Gabe Larsen in discussing how both human customer service agents and artificial intelligence (AI) are mutually beneficial in the development of real and positive customer experiences.
AI Bots Alone Cannot Solve Customers’ Problems
The artificial intelligence bots of today’s world are not only growing in popularity, but they are also growing in capability. They are the focus of various customer service conferences around the globe; but are they being utilized in the correct way? Vikas Bhambri, with his 20 years of experience in customer service, claims that even though they are receiving growing amounts of attention, people do not seem to understand where AI bots show their strengths.
Bhambri discusses how everyone is “hyper fixated… it’s getting kind of buzzwordy… [but] the key to me is, let’s think about the customer. Let’s start with the customer and the experience that they want, whatever you want to offer them, and then let’s figure out where you appropriately position the bot versus the human being.” Only in the future, when innovations permit even further data for both bot and human, can they coexist in beneficial harmony.
Knowing Your Customer
Human reps and bots will more successfully coexist when the bots are able to recall previous data from individual customers. Doing so will enable customer service branches to personally help clients, rather than run everyone that calls for assistance through the same AI loop. Vikas goes on about the necessity of “AI machine learning… [bots should be] looking at the results of anybody who’s ever asked a similar question and what has been offered to them and what actually resolved their issue… that’s where it gets… more in depth.”
He also gives an example of treating customers differently by comparing clients that have different demographics. Your company will have “all [of] these different issues that have been resolved across [your] entire customer base and now a multimillion dollar customer comes to [your] website and asks a question.” Your bots of today are “probably going to offer them the same solution as [it] did to the last 20 people that asked that question… they’re only looking at the question, and they’re not looking at who [they] are.”
Where the Bot and Agent Best Work Together
Once the customer has been personally identified, it is vital that both the bots and reps focus on the customer’s needs. Not only is the client’s problem important, but the means, or channel, that your company uses to resolve it should be an additional focus. It is essential to understand the customer’s situation, and realize whether a personal interaction with a rep or an automated conversation would be more efficient.
Vikas and Gabe talk about the idea of whether the customer wants “to speak to somebody [or] if [they] don’t, and want to do it [themselves].” Vikas gives the potential example of using new tech-advances like geolocation to aid in the customer experience as well. He says, for example, “I’m an airline, and when you’re reaching out to me from an airport the moment I kind of initiate that, [I] should be like, ‘Oh Mr. Barry, I see you’re booked on the flight from Orlando to New York because — and I know you’re in the airport right now. You know what, we’ve already rebooked you. Just head over to gate 43.’” The future holds great potential for the merger of AI and human reps, but the customer experience can only really be elevated when the customer’s needs and situations are understood by both.
Do you want to enjoy more in depth ideas about how to better the customer experience? Listen to Customer Service Secrets episode “Bots Vs Human: How to be Successful in AI Customer Experience” to hear it directly from the experts.
You can also listen and subscribe to our podcast here:
Full Episode Transcript:
How to Combine the Best of Both Human and Artificial Intelligence to Kindle a Successful Customer Experience
Intro Voice: (00:04)
You’re listening to the Customer Service Secrets podcast by Kustomer.
Gabe Larsen: (00:11)
Hi, welcome everybody to today’s show. Today we’re going to be talking about bots versus humans, all things customer experiences. We’ve brought on Vikas Bhambri where he currently is the SVP of sales and customer experience over here at Kustomer. Vikas thanks for joining man, how are you?
Vikas Bhambri: (00:26)
Glad to be here man. My partner in crime. Guest number what, 56 on the podcast?
Gabe Larsen: (00:32)
No, when this comes out man, this is, you’re going to be earlier than that.
Vikas Bhambri: (00:37)
I asked to be number one. I would like to quickly dismiss it… Now it’s like 56, 57, somewhere along those lines.
Gabe Larsen: (00:45)
If you could see me right now, my face is red. I did tell him that but I’m not going to fulfill that promise. Well you’ve been on vacation for like a whole four days. So what do you expect me to do, wait?
Vikas Bhambri: (00:55)
I’m glad the place is still intact, you know?
Gabe Larsen: (00:59)
So I probably didn’t do justice introducing you. Tell us just a little more about your background, some things you do over here at Kustomer, etc.
Vikas Bhambri: (01:04)
Sure, I’ll give you the short version. You can tell me if it’s not short enough or if you want me to go into more detail. Twenty years, CRM, contact center veteran. A lot of people don’t actually know this about me, but I started my journey, or my career in the contact center. I was a guy who carried a pager around, got the call — the page at two in the morning that something was wrong with my application. So back in the day, you have to actually be the coder and the QA, and the help desk for your product. So I did that, but then found myself actually implementing contact center technology. My first client was CSX transportation. If you don’t know them, they’re a big commercial railway on the East coast. And I actually implemented the contact center solution where if you were at a railroad crossing and the crossing was down or broken or the gate was smashed, you call the 1-800 number, it would route into the platform that I implemented with the agent.
Gabe Larsen: (02:10)
What was this 1970, 1960?
Vikas Bhambri: (02:15)
I’m not that old. It was probably just around the .com, so 2000, 2001. I went from there to implementing contact centers, like Bank of America, UPS. So I’ve been on this like CRM contact center journey since its inception.
Gabe Larsen: (02:31)
Was that on purpose or was that just by accident? I mean, are you that passionate about the space?
Vikas Bhambri: (02:36)
You know what, I’ve become passionate about it. I mean, you know, initially it was a job. Oh this is interesting. And you know, for me it was more around I love technology. So it was the perfect role to be a business analyst or project manager working with technology. But then as I got into it more and more and spent more time in the contact center, in the trenches, and then in the CRM world, which now encompasses sales, marketing, etc. And just seeing that evolution. So it’s been fun. I’ve worked across the globe, I spent five years in Europe. I’ve done CRM sales service marketing, you name the industry: retail, TELCO, financial services, insurance, healthcare… so it’s really been a great run over 20 years.
Gabe Larsen: (03:23)
I love it, man, that’s right. It’s funny, you and I have known each other for a few months now, but I forget that history, that’s a pretty rich history.
Vikas Bhambri: (03:31)
Yeah, a lot of people, they get caught up in the title, the most recent title, right. Like, oh you’re sales and CX leader and reality is, I actually started my career as a developer. It’s been a wild ride.
Gabe Larsen: (03:42)
Yeah, that’s a little bit of a change, right? Board room to dev room. So let’s dive in: Talk bots and humans for a minute. So obviously it’s a little controversial, isn’t it?
Vikas Bhambri: (03:55)
It is, because I think, of late, everybody is hyper fixated. You go to any conference, you go to any meeting and everybody wants to talk about bots. It’s getting kind of buzzwordy right? And everybody now says they do it. Everybody says they’ve got one. The key to me is, let’s think about the customer. Let’s start with the customer and the experience that they want, whatever you want to offer them, and then let’s figure out where you appropriately position the bot versus the human being. And I think ideally, and I think that the future will actually be where, they coexist. And so we can stop having this…
Gabe Larsen: (04:37)
One eliminates the other, one pushes the other out.
Vikas Bhambri: (04:42)
Right? And even the way some people talk about bots is they’re like, “look, we’re going to — we’re going to implement the bot and they’re going to solve the problem.” And then what happens when they don’t? Now the customer’s frustrated, right? Now, they pick up the phone or they called the agent and the agent has no idea that they just went through an eight step process with a bot and it failed. So even understanding like how do I take a journey that may start out with a bot, and actually escalate it to a human experience.
Vikas Bhambri: (05:11)
And what nobody talks about is, when did it start out in a human experience and then maybe kind of escalate to a bot, right? So you actually empower the human agent with more information, more data, more automation for them to give intelligent solutions back.
Gabe Larsen: (05:27)
Let’s go back to it. So maybe take one step back real quick because I want to dive into those two use cases. But when you say bot, how is that different than chat versus AI versus… give us a little click on that.
Vikas Bhambri: (05:43)
Sure. I think for me, when you think about bot, I kind of liken it to just robots, right? It’s technology that does a task. Now when you get in a chat box that’s just serving technology through a medium, which happens to be chat. But I would argue chatbots are already outdated because chat is only one digital interaction. Why wouldn’t you do the same on Facebook messenger? Why wouldn’t you be the same on WhatsApp or SMS? So even the whole nomenclature now it’s already outdated.
Gabe Larsen: (06:13)
Well and it did feel like chat — chat’s been around for so long. It’s like wow, is this really something that new, adding a little more of a bot or a push notification in a bot? But it seems like maybe as we take it to different channels, that would be one thing that would certainly be different. It’s this automated interaction in a channel, chat particularly, that allows you to potentially deflect or get rid of some of the human interactions.
Vikas Bhambri: (06:39)
Chat was rightfully kind of the first kind of place to offer it. Because at the end of the day, people are already used to doing pre-chat surveys and asking certain questions. So it kind of made sense to offer it there, right? And you know, you’ve got companies like Drift and others that are doing it in different styles. So it makes sense. But why wouldn’t you offer some sort of automation when somebody goes to your knowledge base? So now we call that — now we’ve kind of pocketed that into self-service deflection. At the end of the day, it’s still a bot. It’s still technology that is looking to the customer to answer certain questions or make some self identifiers and then offer them a solution.
Gabe Larsen: (07:21)
Got it. Got it. Okay, perfect. That’s great to just get the fundamentals. And then one step above that, where do you feel like it’s, maybe it’s where we are currently or where we should be going, but there’s stuff that’s like pre-programmed, like branching stuff you could put in. So they like press a button or they answer yes and then it delivers them a message versus true intelligence, like they write something, the bot reads it and actually responds back in an intelligent way. Are both of those happening? Is just one of those happening? Where are we in this evolution of the bot, so to say?
Vikas Bhambri: (07:57)
Sure. So, to me it’s kind of the if, then, else, right? Like the choose your own adventure. For those of us that are old enough to remember those.
Gabe Larsen: (08:04)
Those books were good. I should get one of those for my eight year old, actually.
Vikas Bhambri: (08:13)
But here’s the thing: So the if, then, else, the branchable logic that’s there. It’s been done. I think you see that quite often now.
Gabe Larsen: (08:22)
That’s pretty table stakes now.
Vikas Bhambri: (08:24)
Right? That’s table stakes. I think true AI, where you’re looking at the question the person’s asking, analyzing it, then comparing it to other questions… When we talk about true AI, machine learning, it’s now looking at the result set of anybody who’s ever asked a similar question and what has been offered to them and what actually resolved their issue. So that’s where it gets a whole much more in-depth. Now, I still think the problem with even that concept and why I’m excited about some of the things we’re working on, is that it’s still very limited to all the problems that people have asked and answered. It still doesn’t really take into account who that customer is. I think that’s still one of the things when we talk about bots and you’re only as good as your data. So what I describe to people is… look, imagine you bought a robot to clean your house and you only put it in one room of your house and said learn and then you unleashed it on the whole house. You’d probably end up with a wreck because the dimensions of your one room are not all of the rooms. And I think that’s when people create these algorithms, they’re only thinking about one problem area and then all of a sudden they unleash it. For example, I’ve got all these different issues that have been resolved across my entire customer base and now a multi-million dollar customer comes to my website and asks a question. I’m probably going to offer them the same solution as I did to the last 20 people that asked that question. Now taking into account that they’re a $5 million customers, now I’m going to wreck my house.
Gabe Larsen: (10:00)
Oh, interesting. Almost like a tiered… you know, we talk about like tiered support where if I’m a gold member, I call in and I’m treated different. But you’re not really treated different with a bot because they don’t know a lot about you, and they’re only looking at the questions.
Vikas Bhambri: (10:14)
They’re only looking at the question, and they’re not looking at who are you.
Gabe Larsen: (10:16)
So that might be one of the future trends, as you think about bots and how they… I can’t think of anybody doing that, that’s pretty… wow, that’s different.
Vikas Bhambri: (10:27)
That’s it. The more data you can feed this, the more intelligent it’s going to be. I think the problem is when people are thinking about it, they’re not thinking about what data am I going to keep using with the robot? Because that’s easy for people to say, “what information you’ve giving the robot?” And if you’re not giving them all the details, they’re going to make foolish decisions.
Gabe Larsen: (10:47)
What else do you have? If you had to kind of say a couple of years from now, I mean I just thought that was interesting. Kind of the personalization of the bot around the individual, the company, whoever it may be, and then treat them slightly different. Any other things you see in a couple of years from now, where the bot is going to that might be a little outside of the norm?
Vikas Bhambri: (11:06)
I think the big thing — well, before we even get there is I think there’s going to have to be this harmony between the bot and the human experience, which I don’t think exists today.
Gabe Larsen: (11:17)
So lets click into that, and then we’ll come back to the trends. So right now people are kind of thinking about it: as I interact with the bot and then there is a chance I would maybe escalate to human.
Vikas Bhambri: (11:31)
It’s still clunky. The hand off is clunky because a lot of times, well we’ve all experienced that as consumers. I get asked a bunch of questions by the bot, I get served up to human agent because the bot can’t actually answer my issue. And the agent actually asks me all the questions again. That’s like the most fundamental failure of the hand off because they have no visibility. They may know that you did communicate. A lot of brands won’t give their bots names. So like you, you asked Jeeves or you asked Elsa, right?
Gabe Larsen: (12:06)
Elsa is a Frozen reference in case anybody’s wondering.
Vikas Bhambri: (12:11)
Right, anybody whose kids are listening, they got it. All of a sudden, they talked to Elsa before me, but you don’t know what they asked and answered. So that’s a very fundamental flaw, but people are getting better at that. They’ll at least give you the tree, showing everything that the person went through with the bot, right?
Gabe Larsen: (12:27)
Do they? I sometimes question if they’re even getting that, but fair. Yeah, they could get that far.
Vikas Bhambri: (12:34)
There are some people who are a bit further along, if you look on a maturity index, so now I know what questions you asked the bot or answers you gave, and why they can’t resolve it. But to a degree, I almost have to still go and do my own due diligence and figure things out. So I think that’s step one. Now the other thing is, how do I take that data that I did get, plus what the agent captures and now offer up intelligent suggestions to the agent to resolve? That’s where I think you start getting true harmony is automation on the front end, smooth pass off, but then also helping the agent be smart by giving them smarter answers.
Gabe Larsen: (13:16)
But help me visualize that a little bit. So what would that look like? The first part I get, so you get the automation. I like the second part, the smooth transition, because that just feels clunky in my own experience with bots. But that third part. It’s like, ooh, how can we enable the effectiveness of the agents so they are responding back smarter? Any examples, like tactical examples that may come to mind?
Vikas Bhambri: (13:38)
Think about this and let’s just use your cable box provider. You just went through an eight step troubleshooting process with the bot. It failed. You’re on the phone with the human agent and the technician is saying, “Ah, okay I see that you went through this process.” Maybe the agent gathers one or two more details from you. You know what I mean? You check the remote, you know the batteries in your remote or whatever. Now, the intelligence to the agent says I’m going to take all the steps that the customer did with the bot, plus what you gathered and now I’m going to offer up a solution. Take both sides of that discussion and then offer up a solution.
Gabe Larsen: (14:20)
Cool! Interesting. So now flip it, because that’s kind of the standard idea, that can we deflect –? Well, do one more quick double clickback on that. So I liked your three step process. You have automation. If you need to escalate, you pass it off smoothly and then you kind of provide real intelligence or a recommendation. A lot of people are wondering how far you can go with a bot before you have to escalate. That’s, I guess, the elimination conversation. Where do you kind of recommend companies who aren’t thinking about that? Try to get rid of the small stuff, focus on the return? How far can you automate that bottom part of customer service?
Vikas Bhambri: (14:58)
I think the two factors you have to look at are one, what can the customer do themselves or can you kind of use the bot to guide them through to conclusion? So that to me is number one, because at the end of the day, as much as brands don’t want to talk to customers, customers don’t want to talk to brands either. Right?
Gabe Larsen: (15:20)
Do you think that’s true? I mean is that kind of where we are? I mean people don’t want to really do it.
Vikas Bhambri: (15:25)
They don’t want to. It’s not a bad thing. And I think we need to get away from that. If I’m booking a round trip flight from New York to LA, I don’t want to talk to anybody, I want to go to a website, I want to go to a mobile app, I want to book the ticket, get a reasonable fare, select my seat and I’m done. It’s paid for it and everything, right? I don’t want to ever speak to a human being and the airline doesn’t want to speak to you either.
Gabe Larsen: (15:53)
It just sounds bad.
Vikas Bhambri: (15:54)
But quote unquote, we’re talking, right? Because we’re obviously transacting business, but we don’t have to have an elongated discussion. Right? So that’s number one. Number two is when do you want to get that human being involved? Because now I’m booking New York to San Francisco, to LA, to Portland.
Vikas Bhambri: (16:19)
It gets more complicated. I want to be able to speak to somebody if I don’t want to do it myself. Number two is there’s an adverse event, right? My flight to San Francisco gets canceled. Now everything is going to be botched. I want somebody to jump in. And third, you have customers whether it’s ato demographic, whether it’s a high end customer, that you want to offer, that additional level of service, if they choose to use it. And that’s why I said bots can be a one size fits all because if you’ve got a premiere business traveler, you want to be able to say, look, if you want to go and book that round trip ticket yourself, go for it. But by the way, we’re here for you.
Gabe Larsen: (17:00)
And maybe it’s just where I am. Don’t get me wrong, I like to book stuff on my app and things like that, but I’m in like a Delta premiere or whatever that is, gold or medallion, and maybe I’m driving, you know, I’m just gonna call them up and have them walked me through it and book my roundtrip ticket or something. I like that sometimes, so I do like that option. I like the complication. The emergency totally resonates, right? It’s like when your flights booked and you’re trying to go home for Christmas, the last thing you want to deal with is a bot. Is my flight canceled? Just help me, I need to talk to someone.
Vikas Bhambri: (17:35)
So that’s why I think brands need to look at where is that inflection? Where does that point where the customer is going to yell? Now there might be some customers, they don’t care if they’re yelling all day long, right? But certain segments of customers, we care, the brand absolutely does care because they want your repeat business. Right?
Gabe Larsen: (17:54)
So do you feel like if you implement some sort of automated bot program, are you affecting negatively or impacting negatively the customer experience?
Vikas Bhambri: (18:02)
No. If it’s done thoughtfully where you’re thinking about that journey and go back to the customer journey, or customer map. It may actually benefit the customer. If I want to change my address, do I want to speak to somebody by changing my address? No I want to go in, I want to punch it in and I want to hit submit and let it go.
Gabe Larsen: (18:25)
I think the problem people are running into is because it’s such a trendy word now, I think I’ve run into this in the past a little bit is you’re like, well, let’s throw a bot on our website or let’s throw a bot somewhere and you don’t watch the rest of that customer journey and that’s where you drop off on kind of points two and three. We have a bot, but the experience actually got worse because we didn’t help them.
Vikas Bhambri: (18:42)
I think like anything, A, you need to AB test, and B, you need to do the what if scenario. What if a customer wants to do this? What if they do that, and you need to really think it through. But the easy thing to do is just… you almost need like a program management around iit.
Gabe Larsen: (18:59)
You really do. What I learned in my last gig is, we got a bot and it was cool. We threw it up there and pretty soon I was like, I need someone to own this and own the journey. It’s not just a side gig that someone else can do by themselves..
Vikas Bhambri: (19:16)
Like in marketing, right? You have somebody who does your search engine optimization. You need a bot optimizer.
Gabe Larsen: (19:28)
So flip the other way then. Is there a reason or a method to go to a human, then to a bot? Is that in our future, that certainly would be kind of a side scenario or side use case. But is anybody doing that? No.
Vikas Bhambri: (19:47)
No. I don’t think anybody’s doing that. I think right now it’s about bot to human. But where I think is a missed opportunity in the near term is to empower that agent with more choice for automation, where they can do things. And you’re seeing in some industries I think TELCO is actually ahead of this where your agent will take action on your behalf, like so you don’t have to get up and reboot your cable box. There’ll be like, we can do it from our side.
Vikas Bhambri: (20:19)
It’s things that financial service institutions are able to do, where the agent can initiate fraud detection and things like that. So I do think there is things happening on that side, but I’d like to see more of that across the board.
Gabe Larsen: (20:31)
See if we can’t bring that together. Okay, last two questions and I’ll let you get back to your day job. So one is for people who are starting to go down this journey, human versus bot, I think you’ve given them a lot of material. Where would you kind of say, if you’re starting this journey, here’s a couple things I think about or if you start, here’s the baby step you could do now, what’s kind of that easy step that you could take starting the journey of maybe getting a bot into your program and your customer service journey?
Vikas Bhambri: (20:59)
I would start with my knowledge base, your FAQ’s. The reason I think people should be putting FAQ’s or their knowledge base together is they’re like, oh this stuff is so darn easy that I expect my customers can do it themselves. So start there and start putting that into your initial bot journey. Where you’re basically pointing them to existing artifacts, things that exist. And then as you start triaging through those, then it’s like what are the next level of… let’s actually sit down with the agents or the reporting, and look at what are people reaching out to us about. And ideally you want to look at the end of the day, you want to fix the end solution. But if you can’t do that in the near term, what can we do that can automate the solution.
Gabe Larsen: (21:54)
I love that, I love that. That’s a great place to start. Okay. Last question is, we touched on it a little bit before, but there’s a lot of movement in the space. A lot of new technology is coming out, all different languages. Obviously some buzzwords. Any kind of predictions as you move into the future, thinking about humans, bots, anything kind of on your mind that says, I think it’d be fascinating if we saw X or Y in the future as bots evolve and iterate?
Vikas Bhambri: (22:20)
I think the biggest thing is, how much data can we feed? What I mean by that is, look, if I’m on my mobile device and you’ve got so much information, whether we believe it or not, the brand potentially has access to my geolocation. They have access to certain data about me on my phone. They have a profile on me. They understand the question I may be asking. Where to me almost get to the point where you’re doing predictive analysis. Before I even ask you my question, you know the question I’m going to ask because we have so much data about you. So we’re like, wait a minute, most of the time when somebody is reaching out to us, I’m an airline, and when you’re reaching out to me from an airport. The moment I kind of initiate that, you should be like, “Oh Mr. Barry, I see you’re booked on the flight from Orlando to New York because, and I know you’re in the airport right now. You know what we’ve already rebooked you, just head over to gate 43.” That’s the Nirvana.
Gabe Larsen: (23:28)
You know the funny thing is I used to be nervous a little bit about the data thing and giving too much data, but now I’m like, I want to give, and I think there’s people like me in this world who are willing to give up less privacy. They’ll have less privacy to get better service, to get more personalization. I’m like, dude take my social security number and take whatever you want, but give me that type of service.
Vikas Bhambri: (23:51)
I think that’s ultimately it. Like look, GDPR, you’ve seen the California Consumer Privacy Act, all of this stuff, people are still hitting every website. You know, I was in Europe, and every website comes up with a pop up and everybody hits accept. Why? Because I’m giving you data because at the end of the day I’m hoping you’re going to market to me better, sell to me more intelligently or are you going to give me better service. There will always be people that will opt out. Most people think, “if you’re going to offer me more value, I’m willing to give that.” And there is so much you can do with it to benefit the consumer.
Gabe Larsen: (24:31)
Could you be proactive? You’ve heard some of those stories where you know people are the target. Did you hear the target story where they were buying this family was buying different things and then they sent them like a gift card or a coupon for… I won’t get into the details, but basically send them a coupon and they were like, “Hey, we’re not, we’re not actually experiencing that. We’re not doing it.” Well they went and asked their daughter, and it sounded like that person is sick or is not working. But based on the behavior, their AI triggered, and sent a coupon for something, this father got it so it can get a little bit out of hand. But my goodness, the stuff you can do with data, wow. You can take this pretty far.
Gabe Larsen: (25:17)
Cool man. Well, I appreciate it. So if someone wants to get in touch with you, learn a little bit more about what you do, you know, continue the dialogue, what’s the best way to do that?
Vikas Bhambri: (25:26)
LinkedIn is always a great place to hit me up. You can hit me up at kustomer.com as well, either or.
Gabe Larsen: (25:34)
Love it, man. Well, I appreciate you joining. Great times. Audience, have a fantastic day.
Exit Voice: (25:47)
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Kustomer IQ leverages advanced artificial intelligence to help agents more efficiently analyze and take action on customer requests. Machine learning models are deployed to allow support experiences to be more rapid and accurate.
NEW YORK, April 15, 2020 /PRNewswire/ — Kustomer, the SaaS platform reimagining enterprise customer service, announced the broad rollout of Kustomer IQ, its artificial intelligence engine embedded across the Kustomer CRM platform. Kustomer IQ is empowering companies to do more with less, and operate as efficiently as possible in the face of a reduction of resources. The solution leverages the power of machine learning to get to the root of customer needs, helping achieve effective customer experiences by eliminating manual guesswork and arming agents with the tools and insights that drive results across responses, routing, and analytics. Kustomer IQ also offers customer deflection tools across web, chat, and email channels, which automate the communication of initial and routine customer inquiries.
“Kustomer IQ delivers quicker and more accurate results to customer service inquiries by leveraging sophisticated machine learning models. Our intelligence tools help companies efficiently automate and scale communication without compromising accuracy, whether it’s routing inbound requests to the right team or measuring a customer’s sentiment,” said Brad Birnbaum, CEO and Co-Founder of Kustomer. “This increase in speed and overall quality experiences translates into more satisfied and loyal customers, which every business needs right now.”
With Kustomer IQ, companies can access AI-powered tools to contextualize every conversation and leverage that data to save valuable agent time for more meaningful and essential customer service. Its highly accurate machine learning models are easily trained with a few simple clicks allowing any company to harness the power of modern AI.
“Kustomer IQ has had a tremendous impact on my team’s efficiency. Machine learning instantly identifies the purpose of every inbound conversation, then intelligently routes each customer to a specific team based on their contact reason, such as orders, returns, or clean out. Rather than spend time manually routing conversations, my team can focus on delivering personalized service and resolving issues quickly to decrease customer effort,” says Emily Marcogliese, Head of Customer Service at ThredUp.
Kustomer IQ includes new features such as:
Automated Self-Service: Native omnichannel deflection capabilities provide relevant and accurate content to customers from an organization’s Knowledge Base prior to agent intervention.
Intent Identification: Machine learning analyzes and classifies inbound conversations, triggering smarter processes that expedite customer experiences.
Agent Recommendations: Machine learning analyzes customer messages and suggests relevant help articles and policy content to resolve issues faster.
Global Language Detection: Featuring natural language processing and powered by Amazon Comprehend, language detection helps companies deliver consistent experiences to all customers.
Sentiment Analysis: Machine learning analyzes messages and recognizes exactly how customers are feeling, assigning a sentiment score to help agents mirror emotions, and calm frustrations.
NLP (Natural Language Processing) Chatbot (coming soon): By leveraging NLP and the advanced CRM data model in the Kustomer platform, its chatbot will be able to offer automated information gathering and human-like levels of customer service to company conversations
Kustomer IQ is being offered in three tiers: Kustomer IQ Lite, Kustomer IQ and Kustomer IQ+. Kustomer IQ includes all of the features above, enabling companies to provide more efficient experiences through sophisticated automation and accurate predictive insights. Kustomer IQ+, coming soon, will provide end-customers a new way to interact with brands, featuring an AI-powered chatbot functionality. The chatbot will be capable of conducting two-way dialogue via live chat and be able to recognize customers based on custom object data, helping brands accurately deflect inquiries around orders, shipping and tracking.
To provide all customers with powerful AI capabilities, Kustomer has included a complimentary Lite version of Kustomer IQ as part of their existing pricing plans. Kustomer IQ Lite includes global language detection and sentiment analysis, so all customers can provide empathetic service and support around the world.
“We’re proud to announce Kustomer IQ Lite includes automated self-service, so all companies can take advantage of deflection and conversational assistant support capabilities,” adds Birnbaum. “Now, more than ever, when companies are struggling to do more with less, we believe that deflection will serve as an added leg up in delivering an exceptional support experience. We are committed to our client’s success and are proud to offer the enhanced AI capabilities that we believe are crucial.”
Kustomer is the omnichannel SaaS platform reimagining enterprise customer service to deliver standout experiences – not resolve tickets. Built with intelligent automation, Kustomer scales to meet the needs of any contact center and business by unifying data from multiple sources and enabling companies to deliver effortless, consistent and personalized service and support through a single timeline view. Today, Kustomer is the core platform of some of the leading customer service brands like Ring, Glossier, Away, Glovo, Slice and UNTUCKit. Headquartered in NYC, Kustomer was founded in 2015 by serial entrepreneurs Brad Birnbaum and Jeremy Suriel, has raised over $173M in venture funding, and is backed by leading VCs including: Coatue, Tiger Global Management, Battery Ventures, Redpoint Ventures, Cisco Investments, Canaan Partners, Boldstart Ventures and Social Leverage.
Announcing AI for Customer Service in your CRM Platform
While these are extraordinary times, your organization’s top priority is unwavering—to provide customers with exceptional, convenient customer service. For many, customer conversations are only increasing, and Kustomer has stuck its course to build a CRM platform packed with the tools and capabilities that help you connect with customers, keep them engaged, and manage processes to perform at max capacity and grow.
We also think our platform should eliminate any barriers between your agents and your customers because time is of the essence—now more than ever. That’s where we think AI comes in, not to take on the role of the agent, but to act as a partner in getting to the root of customer needs in order to handle simple responses or immediately place them in touch with their best, most expedient resource.
We’re excited to announce our suite of AI-enabled capabilities is now available to customers. Called Kustomer IQ, it enables smarter processes that eliminate busywork and help teams better understand what customers need.
“Customer service is one of the first touch points that a consumer has with a brand, and modern companies need the tools to scale automation without compromising the exceptional brand experience customers have come to expect,” said Brad Birnbaum, CEO and Co-Founder of Kustomer. “Kustomer IQ will help companies further serve the needs of their customers by providing self-service tools to bring even more efficiencies to customer service. We are especially proud to broadly roll this out right now.”
AI Makes Every Conversation More Convenient
Kustomer IQ Lite is available to all customers on our Enterprise and Ultimate plans, and features self-service tools and predictive capabilities that help gauge emotions and equip agents with knowledge, content, and empathy to get the job done quickly.
We See Automated Self-Service Differently
Kustomer IQ works alongside your team to tackle easy questions, and elicit details from your customers so agents can get a head start, or possibly deflect simple inquiries.
Kustomer IQ is omnichannel, using content from an organization’s knowledge base to suggest relevant articles when customers reach out via email, live chat, or form submission. Watch our tutorial below on how to get started with deflection in chat today.
We’ve added deflection to our reporting stack, providing real-time analysis so you can verify success, quantify time saved for customers and agents, and understand how content is performing, with insights into what customers are seeking to help plan future articles and deflect more conversations.
Our smart Conversational Assistant prompts customers to provide additional information around their needs, and in the coming weeks, we plan on adding branchable logic to the Kustomer IQ suite, which will extend your ability to gather finer details and help decide the most appropriate service path.
Deliver Empathetic Global Support
Kustomer is proud to partner with organizations representing six continents around the world—we’re a global solution available in more than 60 languages.
For those managing global customer service, Kustomer IQ leverages Amazon Comprehend machine learning capabilities to detect language upon contact, then route conversations to native-speaking agents or enable multilingual translated content if needed. It also analyzes messages to understand how customers are feeling, and assigns a sentiment score to help agents get on even footing as soon as they respond.
Get to the Root of Every Conversation
Our basic Kustomer IQ package features more sophisticated predictive and automation capabilities powered by machine learning, which work by analyzing data and content to accurately assess customer needs and activate smarter processes in the Kustomer platform. Check out our pricing page for more information.
Impeccable Customer Service, Fast
Intent Identification, the newest, most powerful feature of Kustomer IQ, analyzes and classifies inbound conversations, and uses those new attributes or conversation tags to trigger process automation that takes work off your team’s plate. This includes intelligent conversation routing, all based on how you classify customer outreach, such as by contact reason or product line. In addition, automated workflows can be triggered to manage communications, initiate greetings, or provide temporary air cover.
Emily Marcogliese, Head of Customer Service from our partner at thredUp recently shared, “Kustomer IQ has had a tremendous impact on my team’s efficiency. Machine learning instantly identifies the purpose of every inbound conversation, then intelligently routes each customer to a specific team based on their contact reason, such as orders, returns, or clean out. Rather than spend time manually routing conversations, my team can focus on delivering personalized service and resolving issues quickly to decrease customer effort.”
Agent Recommendations (coming soon to Kustomer IQ) use internal and external content to generate content suggestions in real time, which will come in handy for agents-in-training who may be new to policies and brand knowledge. Machine learning analyzes customer messages to accurately offer information for faster conversations and precise resolution.
Get Started Now
We’re excited to announce that Enterprise and Ultimate plans come equipped with a Lite version of Kustomer IQ, so all our customers can take advantage of these awesome capabilities. Kustomer IQ Lite includes Automated Self-Service with our Conversational Assistant, Global Language Detection, and Sentiment Analysis.
Our base package for Kustomer IQ will be available on April 30, featuring enhanced AI capabilities to automate more conversations, including conversation classification, agent recommendations, and a more advanced Conversational Assistant.
And coming later this year, Kustomer IQ+ will open up new ways for customers to interact with brands. Featuring enhanced AI functionality, Kustomer IQ+ will automate simple, contextualized two-way dialogue on live chat with custom object recognition, so you can leverage and update information regarding orders, purchases, shipping, tracking, and much more.
The global work environment is undergoing a massive shift, and with recent events forcing the acceleration of remote work, leaders everywhere are scrambling to find ways to maintain and continue building a strong team culture within their organizations during this abrupt transition. Luckily, thanks to modern technology, there are many ways to create an environment of positive behavior, togetherness and productivity even in a remote team.
Of course, with any types of changes, there are a few adjustments that need to be made. Here are some of the ways to not only maintain, but to build a strong culture while transitioning to a remote team:
1. Ensure that your team is equipped with the right tools that match your culture and encourage collaboration
The concept of “the path of least resistance” comes into play in all aspects of life, and building a strong team culture in a remote environment is no different. When I think about some of the work friendships I’ve made in my career, many of those friendships were forged with people who were in the same “new-hire onboarding” class as I was. Those friendships were strengthened if they happened to be on the same team, and even more so if we became deskmates. The same concept applies to remote work. Work relationships are built with those we communicate with often.
When it comes to building culture in the context of a remote environment, the easier it is to communicate and collaborate, the more those behaviors will be reinforced. It is especially important in a remote setting to err on the side of over-communication as opposed to under-communication, as rampant miscommunication and missing information can dismantle trust and culture fast. With a wide variety of instant messaging and video conferencing platforms, along with the internal notes and comments sections of your customer management platform, an environment of open communication and collaboration in remote teams is no longer just a dream, it is a very achievable reality.
2. Create opportunities for remote social interactions
In a remote work environment it can feel as if you should only reach out to a colleague when problems arise or help is needed. During those times, stress levels are high and there can be a buildup of negative emotions towards an individual, especially when all interactions with them are stressful, demanding and require deep thought. Without a foundation of trust and camaraderie, it’s much easier to misinterpret the intention of an e-mail or message.
This problem is often alleviated in an in-office environment since colleagues will inevitably bump into each other during coffee or lunch breaks. In a remote work environment, not so much. This is why it’s smart to have fully optional, but regularly scheduled, virtual coffee and lunch breaks. By encouraging remote team members to bond virtually, and foster a “remote office social life”, teammates can feel much more comfortable asking each other questions and giving honest feedback when it comes to business.
3. Setting clear goals and expectations
While setting clear goals and expectations is important in any environment, dysfunction from a lack of direction becomes more apparent in a remote team. While some remote employees may disappear into the abyss when there is a lack of direction, others may overcompensate and overwork to appear productive, which could potentially lead to burnout. Neither of these scenarios are beneficial for the employee or the employer. It is up to leadership and the managers to set SMART goals (Specific, Measurable, Achievable, Relevant, Time Bound) and hold employees accountable, giving direct feedback if expectations aren’t being met. This allows remote employees to stay connected to the overall mission and goals of the company as well as empower the employee to engage in their work. The happiest employees have a deeper sense of meaning to their work than to simply clock in and clock out for a paycheck.
4. Foster an environment that celebrates wins
While it is important to see reality for what it is and to find gaps and weaknesses in the business, it is equally, if not more important, to find strengths and reasons to celebrate. In an in-office environment, it’s easy to celebrate all sorts of “wins”. Whether you just brought a promising new hire on board, ran a smooth implementation of new software, or helped turn an angry customer into a happy one, news will get around. In a remote environment, employees may often feel isolated and lonely. Negative and urgent news may travel faster than the small wins, but it is crucial to to emphasize the wins. By fostering an environment that celebrates all the wins and allows the cheers to reverberate across communication channels, you encourage a culture of positivity that lifts employees up.
Want more practical tips for working remotely? Check out our latest infographic on how to stay sane and productive while working from home.
As much as we may not want to admit it, we are living in a whole new world, and customer service leaders are having to learn new ways of being successful, from the way they treat their customers to the way they manage their employees.
As your organization makes necessary changes to stay connected and responsive during this trying time, here are some additional ways you can leverage capabilities within Kustomer to stay productive and collaborative:
1. Tap Into Unlimited Collaboration
As part of Kustomer’s Ultimate Package, now available to all customers, Unlimited Collaboration allows you to loop in anyone from any department within your organization to help resolve inquiries more efficiently in a remote environment. Features like Notes, Following and @Mentions let cross-functional teams conduct internal communications and ensure customers get the expedited service they need right now, no matter where they are in the world.
2. Manage With Team Pulse
Another Ultimate Package tool, Team Pulse allows you to see what your agents are working on in real time, enabling teams to manage performance and effectiveness seamlessly. Supervisors can quickly jump to the customers and searches that agents are viewing in real-time as well as adjust queue assignments and availability, all from the Team Pulse dashboard.
3. Expand Your Shortcut Library
Companies are updating policies to accommodate for the coronavirus, and your agents should be armed with the correct information to share with customers. Add in any new policies or FAQs to your shortcut library to ensure your agents have everything they need at their fingertips.
4. Introduce Users to their Performance Dashboard
Your teams may be experiencing an influx of conversations due to customer concerns. Ensure your agents understand their traffic volume, performance, satisfaction, and peak times of interaction, so they can anticipate busier times of day and easily keep tabs on how customers are feeling.
5. Activate Your Social Media Channels
As customers contact you across an array of channels, make sure you’re ensuring seamless communication by having all channels in one holistic view. You can quickly install Facebook or Twitter DMs directly from the Kustomer App Directory, and customers can get consistent attention if they reach out over social media.
6. Set up These Useful Business Rules
Your team doesn’t have to get bogged down trying to keep conversation traffic organized. Business Rules are a great way to automate routine tasks. Here are a few you can set up right now to drive more efficiency:
Send Messages:Watch our video and start sending automated messages whenever you need.
Auto-Mark Auto Responses ‘Done’: A single rule can cover a lot of ground. These conversations may contain a variety of subject lines that all mean the same thing. Create a rule that can automatically mark any conversation that’s titled: automatic response, automated response, auto response, etc. as ‘Done’.
Assign a Specific Team to Multiple Channels: During these rapidly changing times, you may need to shift priorities quickly. Create a business rule that automatically assigns any conversation from specific channels to a designated team, to make sure all customers are covered.
Automatically Tag Conversations: Business Rules can automatically tag conversations based on context, such as any conversations related to the novel coronavirus. Just make sure you’ve added any tags you need to your library, and build rules to apply them.
7. Route Conversations based on Customer Attributes
Cut down on unnecessary busy work by intelligently and automatically routing customers to the most appropriate agent, based on information like language, sentiment or customer history.
Remember, the Kustomer platform is accessible from anywhere—requiring nothing more than standard WiFi and an internet browser. No downloads. No plugins. No premium internet connection needed.
Want more practical tips for working remotely? Check out our latest infographic on how to stay sane and productive while working from home.
The final days of 2019 are drawing to a close, and that means it’s time for Kustomer’s year-end wrap up. It has been an exciting year, with much growth and development not only for Kustomer, but for the customer service space as a whole. Here are some of the most memorable highlights:
Top Customer Service Trends of 2019
The throughline of all developments in customer service this past year have stemmed from one fact: customer expectations are growing and brands are struggling to keep up. Consumers demand instantaneous communications on every channel, while simultaneously expecting personalized connections with the brands they do business with. Here is how customer service has changed as a result.
Omnichannel Not Multichannel
Just as companies finally began to feel comfortable achieving multichannel support, customers began to demand more: they now expect TRUE omnichannel support. Unfortunately, the two terms have almost become interchangeable, with many companies and technology providers conflating them. Multichannel support simply means offering customers more than one method for contacting customer service.
Omnichannel support, by comparison, shifts perspective from ticket resolution to customer relationship building. Customers have the freedom to move between channels throughout their engagement, and are guaranteed consistency, so each conversation starts where the last ended. Achieving true omnichannel support was a focus of many organizations in 2019.
The Rise of AI and ML
Artificial intelligence (AI) and machine learning (ML) have always been trendy buzzwords in the customer service space, but now they are actually impacting customer service. It’s predicted that the use of AI in customer service will increase by 143% by late 2020, so many organizations have spent this year implementing initial automation and preparing their organizations for change.
AI, ML and automation can enable customer service teams to work more efficiently and focus on the customers who need the most help. In 2020 and beyond, AI will largely take over the tedious tasks, while agents can help solve the harder problems, nurture customer relationships and engage in proactive outreach.
The Changing Face of the Support Agent
As a result of this increased adoption of AI and automation, the role of the support agent has also begun to shift. Customer service agents now spend more time building brand equity and customer relationships. The agent’s job is to reflect the company’s mission and values, and act as a trusted partner for customers. The changing expectations of consumers means that customers want to do business with companies they believe in, feeling as though they are a part of the brand. Customer service agents can help do just that, through both proactive and reactive support.
Customer service will continue to become more of an “escalation channel”, with agents spending less time responding to inquiries and answering simple questions, and more time tackling complex or difficult problems.
Kustomer 2019 Developments
Not only was this a huge year for the customer service space, it was also a momentous year for Kustomer as an organization. In 2019 we:
Raised series C, D & E funding, totaling $135M
Achieved company growth of 350%
Opened a brand new office in Durham, North Carolina
We’ve also witnessed tremendous product developments in the past year, including the official announcement of KustomerIQ, which embeds artificial intelligence and machine learning across the platform to enable companies to provide smarter, automated, and more personalized customer experiences. We launched our first EU data center in Dublin to better serve our growing international client base, and achieved SOC 2 Type I and HIPAA compliance to address our client’s regulatory requirements.
Spotlight on Events
In 2019 we held our first annual user conference, Kustomer Today, which gathered a group of leading experts in customer service for thought provoking and informative discussions. The free, all day event for Kustomer clients explored the Kustomer platform, showcased new product releases, and facilitated networking with innovators shaping the future of customer service. Kustomer’s Bosses Unite event gathered an intimate group of leading women in customer experience (CX), in a Gatsby-like atmosphere, to eat, drink, and share the successes and challenges that come with being a woman in the CX industry.
Kustomer attended industry-leading events such as the TaskUs Summit in San Francisco, where executives from the world’s most innovative and disruptive brands debated the future of customer experience, and explored the changing CX landscape. Kustomer also broke into the European market at the Call and Contact Center Expo UK, where we showcased the innovations of the Kustomer platform and met industry professionals looking to excel in the customer engagement space.
Kustomer Top Content
In case you missed it, check out some of Kustomer’s top content from the past year, where we explore consumer expectations and showcase the brands that are delivering superior customer service:
NEW YORK, NY — October 3, 2019 — Kustomer, the SaaS platform that is reimagining enterprise customer service, today introduced KustomerIQ, embedding Artificial Intelligence and Machine Learning across the Kustomer platform to enhance the customer service experience of companies competing in today’s on-demand world. KustomerIQ uniquely integrates Machine Learning models and other advanced AI capabilities with the Kustomer platform’s powerful data, workflow, and rules engines to enable companies to provide smarter, automated customer experiences that are more personalized, efficient, and effortless. The Kustomer platform stands out among customer service solutions for the comprehensiveness of available customer data and its business process automation that is driven by branchable, multi-step workflows and custom business logic.
“In today’s crowded market, excellent customer service is often the differentiator that builds loyalty and trust between one brand to another,” said Brad Birnbaum, Co-Founder and CEO of Kustomer. “With KustomerIQ and the inclusion of Artificial Intelligence and Machine Learning into our omnichannel platform, Kustomer will now go even further in helping brands automate their business processes, while making it easier for their agents to take action on customer information, ultimately developing a stronger and more profitable customer relationship.”
KustomerIQ brings together a wide breadth of Artificial Intelligence methods such as Machine Learning, Natural Language Processing (NLP), Predictive Analytics, Deep Learning, and Multi-dimensional Neural Network Mappings. Companies adopting KustomerIQ use their own data to train the predictive Machine Learning models, automatically customizing them to address their own business needs. With each new interaction and piece of data, these models learn and self-tune increasing their predictive accuracy and improving the decision making of both the models themselves and the customer service organizations using Kustomer.
Through KustomerIQ, companies will be able to automate manual, repetitive tasks and essential processes of their customer service experiences. In addition, KustomerIQ changes the way companies manage knowledge during a service inquiry by surfacing relevant insights and predicting future outcomes to enhance customer self-service, facilitate real time intervention through recommendations, and streamline proactive outreach. By automating everything and providing the right information at the right time, KustomerIQ frees up agents to focus on more complex and emotional customer interactions, resulting in reduced costs and faster resolution of calls.
KustomerIQ is bringing new smart customer service features to the Kustomer platform, including:
Automated Conversation Classification: Intelligently categorizes and classifies conversations using Machine Learning and attributes of the conversation and customer.
Queues and Routing: Routes customers to the most appropriate agent using conversation classification, agent skills, and overall team capacity to drive the machine learning model. As a result, KustomerIQ helps companies improve customer satisfaction, increase first call resolution, and reduce wait and handle times.
Customer Sentiment Analysis: Using Natural Language Processing, the Kustomer platform can read messages between customers and agents and quantify how a customer feels about a brand in real-time. Seeing customers’ sentiment helps agents empathize with customers in a digital medium, and thus determine the best way to communicate with them.
Automatic Language Detection: Using Natural Language Processing, the Kustomer platform can automatically identify the language being used in a conversation and then route the customer to an agent that speaks the language. In addition, if a company has pre-written responses (shortcuts) set up in multiple languages, those responses will automatically switch to the language used by the customer to ensure a better experience for both customer and agent.
Suggested Agent Shortcuts: Provide recommended pre-written responses to agents based on conversation and customer attributes to help agents immediately access the knowledge they need to resolve customer issues faster.
Customer Self-Service: Automatically suggests help articles from a company’s knowledge base providing an immediate answer to a simple customer questions without interacting with an agent, so customers get answers faster and agents can focus time on more complex customer inquiries. By giving a customer more self-service options it also lowers agent volumes and improves resolution and handle times.
Workforce Management: Helps to predict future conversation volume and staffing needs based on historical and trend data of items, such as SLA attainment and seasonality. Can also assist in identifying training needs by providing insights into areas where an agent or agents are deficient.
To further increase its rapid pace of innovation, Kustomer will triple the size of its development team in 2020. The team will focus on ensuring the continuous improvement of KustomerIQ’s machine learning models and further expansion and integration of innovative Artificial Intelligence capabilities throughout the platform.
Kustomer is the omnichannel SaaS platform reimagining enterprise customer service to deliver standout experiences – not resolve tickets. Built with intelligent automation, Kustomer scales to meet the needs of any contact center and business by unifying data from multiple sources and enabling companies to deliver effortless, consistent and personalized service and support through a single timeline view. Today, Kustomer is the core platform of some of the leading customer service brands like Ring, Rent the Runway, Glossier, Away, Glovo, Slice and UNTUCKit. Headquartered in NYC, Kustomer was founded in 2015 by serial entrepreneurs Brad Birnbaum and Jeremy Suriel, has raised over $113.5M in venture funding, and is backed by leading VCs including: Tiger Global Management, Battery Ventures, Redpoint Ventures, Cisco Investments, Canaan Partners, Boldstart Ventures and Social Leverage.
Customers have high expectations when it comes to the level of service they demand from brands. While the American Express Customer Service Barometer found that Americans are willing to spend up to 17% more on businesses with excellent customer service, the top reason most customers switch products or services is because they feel unappreciated by the brand. In fact, 33% of Americans are inclined to switch to a different company after a bad experience.
Unfortunately for companies, the cost of human support is high. Introducing artificial intelligence (AI) into operations is one way companies can control costs while improving their service abilities and maintaining the human touch that makes customers feel appreciated and valued.
What Is AI Customer Service?
While AI and machine learning may at first appear to threaten the customer service industry, they actually have the power to make customer service agents’ jobs less time-consuming and more fulfilling.
Integrated AI can instantaneously retrieve the data an agent needs, while the agent or support team deals directly with the human side of customer service. This eliminates the need for human agents to run multiple systems simultaneously to address customer inquiries. Rather than employ agents to work 24/7 in a call center, AI can be used to field and classify calls and messages so human agents are then able to work more reasonable shifts with increased efficiency and reduced physical and mental stress.
Through intuitive machine learning that constantly works to improve itself, AI allows companies to be present to the very best of their abilities along every step of the customer journey.
How Are AI and Machine Learning Being Used in Customer Service?
There are plenty of reasons why AI and automation should be loved, especially when it comes to customer service capabilities. Here are a few ways the technology is already being used:
Everyone has had the experience of needing a simple question answered by a brand, only to dread having to jump through customer service hoops just to get someone on the phone who may or may not have the answer. Conversational chatbots can make these conversations more seamless. Not only do conversational platforms help cut costs, they also can help your customer service scale and enable your agents to have more meaningful and productive conversations. By using chatbots to aid your live chat operations, your business will be able to engage customers in real time without the need for an around-the-clock staff.
Amazon, for instance, uses chatbots that leverage the data the company collects on all of its customers and their past orders. By allowing chatbots to access information about the customer’s past preferences, you can have the chatbot interact with customers up to the point where an agent is needed. Once the conversation is transferred to an agent, they can pick up where the chatbot left off.
Eventually, you can train your chatbot to not only acquire customer information, but also recommend the actions customers and agents should take next. If a customer simply needs a common question answered about a product they already purchased, the chatbot can direct them to a FAQ rather than contact an agent. This saves the human agent’s time and allows them to make better use of it dealing with more complex customer queries. All chatbot interactions can be automatically tagged in your AI system so they’re easy to track and reference, and can be used to improve future recommendations.
Robotic Process Automation
Robotic process automation (RPA) can be used to handle the necessary, but routine tasks that keep support agents from interacting with customers in meaningful ways. By taking care of low-priority, mundane tasks, RPA helps customer service agents reclaim time in their days that would be better spent handling high-value customers or fully addressing complex questions without feeling rushed.
RPA works across multiple systems to track user actions within an application to complete and perform tasks ranging from automatically replying to emails to routing conversations. The improved efficiency from saved time on menial tasks also saves companies money. Aside from cutting costs, RPA has the power to increase revenue by speeding up the rate at which customers are able to make purchases through your company.
In the past, automated phone systems performed data dips, moving customers through a phone tree where they were asked to “press 1 for a current reservation,” “press 2 for reception,” “press 3 to make a new appointment” or something similar. The flaw in this system is that the information collected was never handed off to the agent, and the customer would have to repeat themself once they were connected with a human. AI eliminates this unnecessary process — if a customer is calling about a product that’s discontinued, for example, there might not be a need for a human agent to talk to the customer only to relay that same information. This saves time for both parties by supporting your human customer service agent and saving the customer from exasperation.
Using AI to capture information about the customers and pass along only the absolutely necessary parts of that information allows agents to have more meaningful conversations and become more knowledgeable about the areas of the business that matter.
If a customer still wants to talk to a human even after discovering their product is discontinued, the agent can immediately begin the conversation by offering recommendations for other products the customer may like. AI doesn’t eliminate the need for humans, as many people incorrectly assume when they hear talk of using AI in customer service. Instead, it augments the human team and allows them to be better at their jobs.
Monitor Support Operations
When you use AI to monitor support operations, you can predict when conversations may start to turn from positive to negative. This insight allows managers to intercede accordingly, and no longer requires them to randomly audit customer service calls to regulate quality.
AI can also help monitor which responses result in reopened tickets. If response A, for instance, tends to resolve inquiries quickly, but response B results in the ticket repeatedly being opened, the system can recommend you eliminate response B in order to set your agents up for success. Managers and executives can use the data generated by AI to oversee customer service operations in a more clear, efficient way, improving day to day operations for everyone involved.
What Are the Advantages of Automated Customer Service?
Customer satisfaction is directly linked to the service experience, and so it’s important to make sure the customer journey is as seamless as possible. Integrating AI into your customer service isn’t about replacing humans. Rather, it is about arming your customer service agents with the information they need to have purposeful conversations with your customers, and using data to personalize your customers’ experience with your brand.
Incorporating AI customer service not only improves your relationships with your customers, it builds trust and increases brand loyalty. This means more repeat customers, and more word of mouth referrals for your business.
When you build an incremental strategy to roll out AI in your organization and optimize according to data collected, success is sure to follow. Using AI to build a more complete view of a customer’s relationship with the brand helps companies meet high expectations for exemplary service, and come across as anything but artificial.
Kustomer Offers AI Business Solutions
The Kustomer platform stands out among customer service solutions for the comprehensiveness of available customer data and its business process automation that is driven by branchable, multi-step workflows and custom business logic. Kustomer IQ is a groundbreaking new service that integrates machine learning models and other advanced AI capabilities with the Kustomer platform’s powerful data, workflow and rules engines to enable companies to provide smarter, more personalized, automated customer experiences with increased efficiency.
Kustomer IQ integrates machine learning, natural language processing, predictive analytics, deep learning and multi-dimensional neural network mappings as a part of its AI suite. Natural language processing involves the interactions between computer and human language, and dictates the extent to which computers are able to process and analyze large amounts of natural language data. Natural language processing is used along with text analysis, computational linguistics, and biometrics in sentiment analysis, also known as opinion mining, which helps companies keep a finger on the pulse of their target audience’s interests and values.
Companies that employ the AI suite are then able to use their own data to train Kustomer IQ’s predictive machine learning models, automatically customizing them to address their own business needs. With each new interaction and piece of data, these models learn and self-tune increasing their predictive accuracy and improving the decision making of both the models themselves and the customer service organizations using Kustomer.
Through Kustomer IQ, companies will be able to automate manual, repetitive tasks and essential processes of their customer service experiences. In addition, Kustomer IQ changes the way companies manage knowledge during a service inquiry by surfacing relevant insights and predicting future outcomes to enhance customer self-service, facilitate real time intervention through recommendations, and streamline proactive outreach. By automating everything and providing the right information at the right time, Kustomer IQ frees up agents to focus on more complex and emotional customer interactions, resulting in reduced costs and faster resolution of calls.
Features of Kustomer IQ include automated conversation classification, queues and routing, customer sentiment analysis, automatic language detection, suggested agent shortcuts, customer self-service, conversation deflection and workforce management. If you’re interested in learning more about Kustomer IQ and how it can help elevate your business’s customer service capabilities, download our ebook, explore our website and get in touch today.
Kustomer offers real-time, actionable views of customers, continuous omnichannel conversations, and intelligence that automates repetitive, manual tasks to make personalized, efficient and effortless customer service a reality.
Our engineering and product teams have been busy launching new features, improving integrations, and making your top requests a reality. Here are the highlights of what we’ve added to Kustomer over the past few weeks:
Added ability to use custom message attributes when building workflow rules and searches.
Added ability to use queue assignments when building SLA rules.
Ensured previously-assigned conversations are allocated to an agent’s capacity when they make themselves available.
Added exporting of “Conversations Viewed” events data to better understand agents productivity and occupancy within the platform.
Added support for Twi language in Global Languages, Snippets, and web chat.
Added support for creating Snippets in Belarusian and Kazakh.
Eliminated a customer’s need to refresh the chat window to immediately view a message sent by a Proactive Messaging campaign.
Improved syncing of multi-level list options in Conversational Assistant flows, so that options deleted by the admin are removed from selection in the customer view.
Improved translation accuracy for the “End Chat” prompt in chat window.
Added a new notification for agents when sending a Twitter Direct Message (DM) that is not delivered due to customer privacy settings that block DMs from sources they are not following and have not DMed.
Ensured that the unread message count indicator will update properly when a customer leaves their chat window open and idle.
Doug Jarvis is the Director of Product Marketing at Kustomer.
When the conversation turns to AI, there’s often a Sci-Fi novel’s worth of terminology and jargon that the uninitiated reader has to decode. If you’re looking at using automation for service, then here’s a quick guide to the difference between AI, Machine Learning, and Deep Learning.
Artificial Intelligence as a concept has been around since at least the ancient Greeks, who designed some mechanical devices that could be loosely-termed as intelligent. However the term itself is around 60 years old, and the first applicable AI technologies have only just started coming to market in the last few years.
Machine Learning is a more specific subset of AI. It describes machines’ ability to learn from their mistakes and improve over time. A good example of Machine Learning in practice example is the recent Google AI that beat a world champion at Go. The more the AI plays, the better it becomes at spotting patterns and predicting its opponents’ moves.
Deep Learning is a further iteration of machine learning. It describes machine learning algorithms that run on multiple layers, mirroring how our own neurons function. A now common example of deep learning is the way that smart assistants like Alexa or Siri process speech.
Also important is Natural Language Processing. NLP is the ability for a computer program to understand human speech, regardless of slang or dialect. By being able to make sense of written or spoken language in the messy and error-filled ways humans normally express it, AI capabilities become much more applicable to everyday life.
What does this mean for service? Artificial intelligence and intelligent automation can take over existing tasks and create new efficiencies that your organization couldn’t dream of previously. Machine Learning is just one example. By suggesting responses agents can use to common customer queries, a partially-automated system could learn the most effective replies and language for your customer base. Deep learning capabilities should extend to IVR trees, and put an end to the common “Sorry, I didn’t get” response from many systems that currently rely on processing speech. And NLP is crucial for chatbots, and for analytics that look at all of the conversations your agents have across chat, social, and any other text-driven medium.
It’s important to build a solid understanding of these exciting technologies as they become more prevalent and relevant to the service and customer experience sphere. To learn more, listen to our webinar with Solvvy: The Truth About Bots and Intelligent Automation.
Customer Support technology is evolving fast and can be tough to keep up with. Lots of you have asked us about new technology like AI, bots and more. Today we’re announcing our two first entrants to Kustomer Labs that expands our artificial and machine learning offerings. We’ve been working with both for several months and love what they are doing.
What Is Kustomer Labs?
Kustomer Labs is an internal group here at Kustomer that evaluates the newest cutting edge Support technologies for you. We pick the best ones and offer them as integrations/plugins to the Kustomer platform.
Our goal is to offer our more visionary and adventurous users access to the coolest new things we see. We will collect feedback and determine how we will integrate the Kustomer Labs companies more deeply with Kustomer.
Training new and existing Support staff is time consuming and expensive. This NY-based company uses cutting edge AI to provide suggested responses, automate routine conversations and analyze interactions at scale for actionable insights.
We introduced them to the team at Sticker Mule who have already signed up and are preparing to go live with them! Team members at Sticker Mule rely on Init.ai to recommend a response or suggest an action that they can then personalize to deliver a consistent, friendly response to their customers.
“Thanks to the Init.ai integration, our team will be able to resolve issues, with fewer back-and-forth questions to the customer,” said Anthony Constantino, CEO, Sticker Mule. “The combination of Init.ai and Kustomer allows us to have a glue between those customer conversations and the data in our CRM.”
We’ve spent hundreds of hours with Support teams. One of the most common complaints we get is that they want to spend more time interacting with customers and less time on menial tasks like basic response.
Abot Labs is newer to Kustomer Labs. Abot helps businesses and customers save time by making automated help more human. Their AI-powered agent enables companies to meet customer expectations and scale more quickly.
Their technology enables your team to spend less time on the painfully boring questions like Password resets and more time building high quality relationships with customers.