Here at Kustomer, we believe artificial intelligence plays an essential role in helping companies scale customer service and efficiently deliver exceptional results. And we aren’t alone. Gartner predicts that 72% of customer interactions will involve technology such as machine learning and chatbots by 2022. That’s why we are excited to share that Kustomer is acquiring Reply.ai to deliver even deeper intelligent self-service and agent assistance to our customers.
As a long-time partner of Kustomer, Reply is able to seamlessly integrate their tools into the Kustomer platform and help brands efficiently scale without compromising quality of service. The partnership furthers our commitment to integrating Kustomer IQ, our artificial intelligence engine, throughout the customer journey, while providing some powerful benefits to businesses and customers.
Reply is ranked as one of Forrester’s Top 10 AI Providers for Customer Service Automation, leveraging sophisticated machine learning models to power incredibly accurate self-service chatbots and deflection tools. With an astounding 40% average deflection rate, Reply, now a part of Kustomer IQ, can successfully resolve nearly half of all initial customer communications without the need for live interaction with a service agent. And with 67% of customers preferring self-service over talking to a company representative, deflection tools are not only a win for businesses, but also for customers.
With Reply now a part of Kustomer IQ, our customers will save thousands of hours spent answering simple questions, so they can focus on the most important cases that have a much larger impact on business and loyalty. At a time when customer service teams are being asked to do more with less, our suite of AI tools can tackle your growing queue of inquiries around the clock, while drastically minimizing costs.
We are so excited to welcome co-founders Omar and Pablo Pera, and the entire Reply team of world class data scientists and engineers to the Kustomer Krew. With Reply’s engineering offices in Madrid, this acquisition will also expand our presence in Europe and accelerate our growth in the region.
It goes without saying that we’ve always been committed to revolutionizing customer service. Today’s acquisition of Reply marks one more step in that journey.
What Customers Can Now Expect From Kustomer IQ
Built within Reply’s natural language processing engine, these best-in-class chatbots feature visual flow builders and templates for easy one-time creation and deployment across multiple channels and languages, providing effortless experiences by connecting customers to the right information.
Knowledge Base Deflection
An enhanced deflection widget can be embedded in forms, email, and chat, and features a powerful information retrieval system in which a semantic search engine and answer extractor not only provide relevant articles and content, but the exact answer to a question.
The platform can plug into third-party APIs to leverage a company’s most critical customer data points when deflecting. For example: a chatbot can answer the question “where is my order” or “when does my policy expire”.
Relevant answers and subsequent actions can be suggested to agents based on historical behavior, user text and conversation context. Actions include routing or auto-tagging conversations, as well as responding with relevant templated content.
Deflection success rates are measured and chatbot behavior can be evaluated, with functionality to support custom events, set conversion goals and segment audiences.
Reply is the first acquisition for Kustomer, reinforcing Kustomer’s commitment to AI and machine learning capabilities throughout the customer journey. With Reply, Kustomer will now offer enhanced chatbot and deflection capabilities through its customer service platform.
New York, NY – May 14, 2020 — Kustomer, the omnichannel SaaS platform reimagining enterprise customer service to deliver standout experiences, announced today it has signed an agreement to acquire Reply.ai, a customer service automation company founded in 2016 that helps companies scale intelligent customer service without compromising experience. Reply leverages artificial intelligence and machine learning models to improve agent efficiency through self-service chatbot and deflection capabilities. This announcement comes on the heels of the expanded roll-out of Kustomer IQ, the artificial intelligence engine embedded across Kustomer’s CRM platform. With Reply, Kustomer can provide even deeper intelligent self-service and assistance via Natural Language Processing (NLP) based chatbots, enhanced omnichannel customer deflection and machine learning based response suggestions. Madrid based Reply will also accelerate Kustomer’s European growth by significantly increasing its presence in the region.
“We believe artificial intelligence is essential to helping today’s enterprises scale customer service and efficiently deliver exceptional results. We recently rolled out Kustomer IQ to meet the growing need for companies to have access to the power of AI, and with today’s acquisition, we continue our investment in bringing self-service tools and intelligence capabilities to our clients,” said Brad Birnbaum, CEO and Co-Founder of Kustomer. “Reply has built deflection and self-service chatbots that help companies effectively deflect initial customer communications at an astounding rate of 40 percent. This means that almost half of all initial customer communications can be successfully resolved without requiring live interaction with a service agent, bringing greater efficiency to the entire customer service function. We are excited to welcome co-founders Omar and Pablo Pera and the entire Reply team of world class data scientists and engineers to the Kustomer Krew.”
The Reply suite of tools include deflection capabilities that look at historical and contextual data, continuously improving over time, as well as deflection widgets that can be embedded in forms and email, and features a powerful information retrieval system that extracts relevant answers to customer questions from a company knowledge base. Reply also features a platform to build chatbots that can be deployed across multiple channels and languages, and agent-assist tools that suggest relevant answers to messages and subsequent actions, such as routing or auto-tagging conversations.
“We are excited for Reply to join Kustomer and share its mission to make customer service more efficient, effective and personalized. As a long-time partner of Kustomer, we are able to seamlessly integrate our deflection and chatbots technologies into Kustomer’s platform and help brands more cost-effectively increase efficiency. We look forward to working with Brad and the entire team,” said Omar Pera, Co-Founder of Reply.
“By leveraging advanced AI capabilities and Kustomer’s robust CRM platform, combined with self-service deflection tools, Kustomer is uniquely built for the needs of today’s enterprise companies,” adds Birnbaum. “Since 2015, we have been committed to revolutionizing customer service and today’s acquisition of Reply marks one more step in our journey.”
Kustomer is the omnichannel SaaS CRM platform reimagining enterprise customer service to deliver standout experiences. 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, Glovo, Glossier and Sweetgreen. Headquartered in NYC, Kustomer was founded in 2015 by serial entrepreneurs Brad Birnbaum and Jeremy Suriel, has raised over $174M 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.
Reply.ai helps brands scale customer service by providing AI-powered solutions that instantly resolve common customer questions on chat and ticketing channels. The two core products, Deflect for Ticketing and Deflect for Chat, reduce consumer frustration and deliver 24/7 personalized service, ultimately increasing self-service effectiveness and support team capacity. Reply’s customers, like The Cosmopolitan Of Las Vegas, Vail Resorts, Honeywell, Glovo and Paula’s Choice, rely on Reply for innovative and industry-focused solutions to customer service problems. Reply was founded in 2016 by former Google and CERN engineers and is headquartered in New York City and Madrid, Spain.
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.