Quarantine and stream: The role customer service plays in the Streaming Wars

Written by Emily Cummins  on   Apr 27, 2020

How AI Helps Maintain Binge-Worthy Subscribers

What do we do when a pandemic emerges? We turn to the tube.

Quarantine and stream is the new norm. Per the Economist, “citizens face what could be months of isolation at home, ideal conditions for binge-watching1.

Netflix breakout Tiger King will be remembered by 64 million people as an entertainment highlight during Covid-19.

And, that’s exactly what we’re doing, some of us for over 8 hours a day, binge-watching three shows in the past week2.

Streaming and video on demand (VOD) companies like HBO Now, Netflix, Amazon Prime, Hulu, Disney+ and literally hundreds of others are in a race to capture this content-obsessed audience and turn it into long-term profitability. While content is critical in determining where people tune in and subscribe, the customer experience will pave the way for higher lifetime value.

Binge-worthy streaming rates: Is it enough to hook long-term customers? 

This Covid-19 captive audience has companies scrambling to entice viewers to use their service: HBO is streaming 500 hours of movies and shows for free3; Comcast is making on-demand and content available to select customers for free4; Hulu has added a live news stream; and at least a dozen others streaming companies are offering free trials 5. And these are just a few examples. 

People are hungry for content. As live sports are cancelled and we have more hours in a day to fill, streaming is skyrocketing around the world: Spain (+191%); France (+187%); Italy (+116%); Mexico (+89%); United States (+89%); Germany (+75%) and United Kingdom (+71%) 6

No one should be surprised by this boost. After all, streaming during a crisis is nothing new. During Hurricane Harvey in Houston in 2017 and a New York blizzard in 2016, when people were also homebound, streaming too skyrocketed.

Source: The Economist

In these weather situations, viewership uptick turned out to be a short-term boon1. So will it be different during this time? Will Showtime hold on to its 78% increase in viewership and HBO Now its incredible 90% bump in its subscriber numbers7? What about Netflix and its 15 million new subscribers (more than double what was expected)8? There are mixed thoughts. 

The Economist reported that “although households will be parked on their sofas and looking for entertainment during the pandemic, it is unclear whether this sudden surge in demand will help the likes of Netflix in the long term.1

Conversely, AB Bernstein analyst Todd Juenger, says “the adoption of streaming will be accelerated and further ingrained into the culture.” While Conviva CEO Bill Demas says, “We anticipate streaming providers will retain new viewers long after the coronavirus.9

To help sustain long-term viewers, higher ARPU and engrain new viewing habits, streaming companies need to prioritize the C/X. But, this hasn’t been easy during the Covid-19 pandemic. 

The customer service challenges for streaming companies could have long-term implications for the “now” generation 

There are few things more frustrating than forgetting a password when logging into a new device to stream a show or being interrupted at the climax of a drama with a buffering issue.

Streaming issues always seem to happen at the climax.

When a viewer encounters a problem, (and there’s been a few — just search #NetflixDown on Twitter), customer support has been hard to come by throughout this crisis.  It’s commonplace to hear things from companies like “Thank you for your patience” as we’re experiencing “unusually high call volume” and “longer-than normal wait times.” 

With a growing audience base, it’s not surprising that these companies are seeing a surge in customer service tickets. The situation is only confounded by the fact that agent teams are working remotely, and lacking the right tools, proper work environments and quality control measures to assist customers in the same way as they did before. In a few examples, Roku and Netflix have shut down phone support. Hulu stopped offering 24-hour support, while Netflix limited support hours. Disney+ warns of long wait times on live chat10.

Even when an agent team is facing challenges in an unprecedented environment, companies need to strive to provide convenient, quick and effortless support. Customers still expect resolutions now – to everything from how to troubleshoot buffering issues, reset passwords, sign in to new devices, update payment details and cancel accounts. 

The support provided today will directly impact the lifetime value of a customer. When a person becomes frustrated with a streaming service, 60% of users will take some sort of action, including abandoning the service altogether11

The most important thing to focus on is a speedy response and eliminating long hold-times. Forrester has found that 60% of people say valuing their time is the most important thing that a company can do with customer service. And this is why VOD companies that prioritize the C/X are adopting AI. This is for a few key reasons:  

  • AI automatically resolves everyday tickets instantly, without human intervention. Low resolution times for common troubleshooting, account and payment issues.
  • Always-on, always-available support. There are no “office hours”, breaks or disruptions with a remote workforce. AI can respond to customers around the clock.
  • AI assists agents with recommended responses and data to help them work smarter, not harder. Agents focus on critical and high-risk needs.
  • AI scales up instantly. There is no need to hire new and train new agents when demand and ticket volume surges. AI can manage an infinite number of tickets simultaneously.

The result of bringing AI into the workforce is a good customer experience that results in long-term engagement. 

The churn dilemma and courting customers with AI  

While we don’t yet know how long new subscribers will stick around, the churn rate for streaming companies averages around 18%12. Shrinkages of current subscriber bases should be expected. Netflix’s letter to shareholders notes that “viewing [will] decline and membership growth [will] decelerate as home confinement ends.13

So how can these companies reduce churn as much as possible? AI can identify potential customers at risk of churning to preemptively intervene with an incentive to stick around, such as a free add-on product or bumping up to a premium service. 

With so many new viewers, though, these companies need to be able to prioritize their courting resources. Who is worth an incentive as there is a high potential ARPU? Who plays hot potato with services and simply jumps around?  (In one survey, half of respondents “confessed to starting a free trial of a platform and then canceling it once they finished the show they wanted to see.”2

By analyzing troves of data these companies have at their fingertips, AI can cluster and identify target audience groups based on profile attributes and behavior to spend resources in the right places that will pay off in lifetime value.  

It’s a big business. With big competition.  

The streaming sea is getting full of more and more fish.  In fact, there are over 200 OTT providers in the U.S.14 and newbies are still jumping in, including NBCUniversal’s Peacock, WarnerMedia’s HBO Max and Walmart has sold its video-on-demand service, Vudu, to Fandango. 

The bottom line is people have options. To keep subscribers happy and tuned-in, streaming and VOD companies need to prioritize customer support with a focus on meaningful, quick resolutions whenever and wherever customers want it. While content may be king, the C/X is queen. 

Can we discuss how to use AI to maintain binge-worthy subscriber numbers? Get in touch. We’ll pause our show to talk to you any time. 

References 

  1. The Economist: https://www.economist.com/graphic-detail/2020/03/27/covid-19-is-a-short-term-boon-to-streaming-services
  2. New York Post: https://nypost.com/2020/04/14/average-american-streaming-content-8-hours-a-day-during-covid-19-according-to-new-research/
  3. HBO: https://help.hbogo.com/hc/en-us
  4. MultiChannel.com: https://www.multichannel.com/news/comcast-opens-on-demand-svod-titles-during-covid-19-outbreak
  5. Bustle: https://www.bustle.com/p/12-streaming-services-with-free-trials-amid-the-covid-19-pandemic-22663732
  6. The Observer: https://observer.com/2020/04/netflix-disney-plus-hbo-streaming-ratings-traffic-coronavirus-lockdown/
  7. CultofMac: https://www.cultofmac.com/695697/apple-tv-misses-out-covid-19-surge-video-streaming/
  8. The Verge: https://www.theverge.com/2020/4/21/21229587/netflix-earnings-coronavirus-pandemic-streaming-entertainment
  9. MarketWatch: https://www.marketwatch.com/story/netflix-in-the-age-of-covid-19-streaming-pioneer-may-have-new-edge-on-competition-2020-04-07
  10. Variety: https://variety.com/2020/digital/news/netflix-drops-phone-customer-support-worldwide-coronavirus-1203543349/
  11. ClickZ: https://www.clickz.com/survey-streaming-mobile-video-user-frustrations-worldwide/226092/
  12. Broadcasting Cable: https://www.broadcastingcable.com/blog/as-streaming-wars-intensify-so-does-fight-to-keep-subscribers
  13. Netflix: https://s22.q4cdn.com/959853165/files/doc_financials/2020/q1/FINAL-Q1-20-Shareholder-Letter.pdf
  14. Digiday: https://digiday.com/media/evolution-streaming-video-services-4-charts/

What’s The Difference Between Conversational Chatbot Solutions, Rules Based Chatbots, and Traditional AI?

Written by Can Ozdoruk  on   Apr 23, 2020

The History of Chatbots

As you may already know, chatbots are software that use natural language processing (NLP) to engage in conversations with users. But that doesn’t mean that all types of chatbots are created equal. Below, we are going to demystify three common terms for chatbot that you may be hearing across the industry: conversational chatbots, rules-based chatbots, and AI. 

You can include these bots in mobile applications, messaging apps, websites, email, and even voice platforms like Alexa. Online retailers are integrating their chatbots with Shopify to increase revenue. 

Along with countless benefits, many companies use chatbots for customer service as a way to provide immediate resolutions to common issues.

Conversational chatbot solutions and artificial intelligence have never been more popular than they are today. In fact, data from Google Trends shows that interest in chatbot solutions has increased ten-fold over the last 5 years.

Interest in chatbots over time, from January 1, 2004 through October 2020, according to Google Trends

During this explosion of interest, “chatbot” has evolved into an umbrella term that may inaccurately describe what a chatbot can and cannot do. Chatbots and conversational AI technology are often used interchangeably. In reality, the capabilities between chatbot technology and artificial intelligence are very different. We’ll explore more about what separates some chatbots from others below.

Chatbots vs. AI. What exactly is the difference?

It’s important to understand why modern artificial intelligence chatbots (also known as Conversational AI or AI agents) differ greatly from first-generation (rule-based) chatbots. The first chatbots adopted by companies were based on stringent rules and rigid decision trees that often led to frustrating user experiences. On the other hand, modern chatbots are more forgiving when it comes to following strict rules, enabling users to engage naturally in conversation.

More companies are looking to virtual assistants and conversational interfaces to provide anytime, anywhere customer support. So, it’s important to have a clear understanding of different technologies. That’s because the scope of a conversational agent initiative and the end-user experience is vastly different. Rules-based chatbots are limited to very basic scenarios. On the other hand, AI-powered virtual assistants are capable of engaging in natural language understanding, participating in 1:1 conversations due to machine learning, deep learning, and conversational experience.

Companies must ensure that they are adopting the right technology for their business and their customers. This is because the customer experience plays a critical role in consumer buying decisions and loyalty. Here is an example between modern conversational AI and basic bot technology:

If a person asks a question that a chatbot has not explicitly been trained to handle, it is easily confused. Conversational virtual assistants enable users to engage in natural, human-like conversation.

What Are Rules-Based Chatbots?

Rules-based chatbots can automate customer service in very specific scenarios. For example, looking up an order status or browsing through a product catalog. Basic chatbot technology moves the conversation forward via bot-prompted keywords or UX features like Facebook Messenger’s suggested responses. (As compared to typing in a question in free-form, using slang and engaging naturally in a conversation). 

Basic chatbot platforms have limited, if any, natural language processing. Typically, the bot will ask a user a question and display a few responses in which a person can select from or it will identify a specific keyword in a user’s question. Based on a person’s input, the conversation moves forward on a specific path. With pattern-based bots, what a user says must explicitly match with how a bot was pre-trained in order for it to understand and move the conversation forward.

In regards to this, variations of a question must be pre-trained for a chatbot to accurately understand what a person is trying to say. For instance, a virtual assistant is trained to understand “Where’s my order?” If a customer asks the same question slightly differently, “Is my package arriving today?”, the bot will not accurately understand the intent of the question is “order status.”

That is, unless it has been explicitly trained to do so within the labeling and learning provided in its training data.

Rules-Based Bots And The User Experience 

Chatbots lack semantics and advanced Natural Language Processing to understand the context of a message.

The user experience with rules-based bots is often alinear. If a person says something that is not preempted, a chatbot will get confused. The virtual assistant will most likely repeat the same question until it understands a response. For example, a chatbot designed to help people order a pizza will not know how to respond to a customer asking for nutritional facts as they are selecting toppings. 

How to Train Rules-Based Bots

Chatbot training is a manual process and requires programming every flow and utterance of a question. A human workforce also identifies and implements ongoing improvements. 

If you’re deploying a rules-based bot, make sure that you select a very specific use case. Fandango, for instance, has a bot that asks people for their zip code and pulls up movies playing locally. In another example, The Wall Street Journal lets users type in a stock symbol to get live stock quotes. These use cases are very specific and well defined and work well for bots. 

Be upfront with your customers on a chatbot’s capabilities. You’ll need to provide an alternative method of getting support for other matters (i.e. I’m the Order Tracking bot. To find your order, type in your confirmation number below. If you need something else, please call ….). 

Conversational Chatbots

Conversational chatbot solutions are AI-powered virtual agents that provide a more human-like experience. In opposition to rules-based chatbots, they are capable of:

  • carrying on a natural conversation
  • understanding the meanings of words
  • understanding misspellings
  • continuously improving over time

Because of these important differentiating features, conversational chatbots provide a greater user experience through the use of natural language processing and leverage semantics to understand the context of what a person is saying.


Discover the key questions to ask when scheduling a chatbot demo.


Conversational AI Examples

Here’s a quick example scenario of how conversational AI works: “I got the side table delivered yesterday but it looks like it might have been broken while in route. There’s a crack in the front. Can you help me? I would like my money back.”  An AI-powered virtual agent would be able to decipher that a person is looking to return an item and receive a refund.  An AI thinks like a human, not a robot, and is able to maintain a conversational flow.

AI-based chatbots that are conversational use machine learning technologies to understand, contextualize, and predict to accurately respond to user inputs. They enable companies to provide hyper-relevant personalized engagement, not generalized support. This can be done by training algorithms used in these chatbots with historical data from real user responses and can be optimized with ongoing user feedback (reinforcement learning). Like humans, AI virtual agents are able to decide the next best action based on a variety of things including contextual-factors, customer profiles, sentiment, or business policies. Furthermore, it can alter how it responds based on real-time sentiment analysis. For instance, an AI Agent treats a person who checks the status of their (on-time) flight differently based on how they react. A virtual agent would presume that a person who responds with “Oh no!!”  that they are likely to miss their flight. 

Two Different Types of Conversational Chatbots: Generative vs Retrieval

Continuing, there are two subclasses of learning-based chatbots: generative chatbots and retrieval chatbots. Generative chatbots can dynamically create responses in real-time, and retrieval chatbots select from a pool of responses based on the person’s message to the bot.

AI-based chatbots leverage semantics to understand the context of what a person is saying. Therefore, these bots can engage more naturally in conversation, and respond to more inputs without being explicitly trained on every single way a person might phrase their question, like the flight example above. Traditionally, these bots may not have been as accurate as pattern-based methods and used to take a long time to train. However, there are a few companies, like Netomi, that have built robust NLP engines that accurately understand user inputs up to 95% of the time, which means scaling and training are now exponentially easier and the end-user experience is much better than pattern-based bots.

Conversational chatbot solutions powered by AI also support multi-turn dialogue. This is the ability to switch between various user questions within a single conversation. This is what sets apart a human-like AI versus building chatbots. An AI-powered virtual agent responds without getting confused if a person pivots the conversation. For instance, a person can ask about the price of checking a bag in the midst of checking flight status. In conclusion, AI can also understand more short-form and slang than chatbots. 

How to Train Conversational Chatbot Solutions

AI training is a combination of supervised and unsupervised learning. AI can learn from historic data. With customer service, this includes customer support email, chat and messaging logs, to identify and group together similar questions and scenarios. Training is on auto-pilot. An AI learns how a situation has been handled and teaches itself to act in the same way. 

AI also uses deep reinforcement learning to improve over-time based on real-life interactions. AI-powered virtual agents are able to determine patterns based on how end users are responding in various circumstances. This is based on things like customer segmentation and contextual factors. For instance, if meal-delivery customers have issues with changing their subscription day, an AI would learn to proactively offer this information. 

The richness of the technology has Gartner predicting that by 2021, 15% of all customer service interactions will be completely handled by AI1. The best AI chatbots tend to be the most self-sufficient when it comes to adapting. When you hear about terrible chatbot fails, those are likely stemming from less-sophisticated bots and/or an improper way to set them up – basically launching a bot without enough training.

Key Trends in Chatbot Technology 

There are a few emerging trends that are propelling the sharp rise in the adoption of conversational chatbots. Take a look at these key chatbot trends:

  • Personalization – personalization involves chatbots tailoring the interaction based on customer profile and behavior. For example, an AI bot could provide a hyper-relevant cross-sell recommendation by learning that a customer prefers certain brands or types of products. By incorporating customer experience personalization, chatbots respond on an individual level, providing more meaningful interactions.
  • Voice recognition – voice recognition enables faster, hands-free interactions for users, making AI bots even more convenient. Examples of voice recognition can be found in the array of personal assistants, including Google Assistant, Siri and Alexa. Companies, including WestJet, are also launching skills on voice platforms to provide yet even more choice with how customers receive support.
  • Machine learning operations (MLOps) MLOps is a strategy used to automate and operationalize machine learning workflows. This strategy plays a role in chatbots by improving the speed and ease with which bots can be trained and improved. With such automation, bots are ready for market faster and can be more frequent, and easily updated.
  • Memory and context – many brands store customer information in customer relationship management systems (CRMs). When integrated into chatbots, CRMs can provide valuable information that enables chatbots to continue previous conversations with customers or look up specific details about the user. While this is often limited to profile details for privacy, chatbot engineers are working on ways to make queries more secure to enable broader interactions.

Exploring a new CRM solution? Learn more about two of the industry leaders in our Intercom vs. Zendesk review.


What To Keep In Mind As The Differences Between Chatbots vs. Conversational Chatbots With AI

Conversational chatbot and AI adoption is skyrocketing. In fact, according to Accenture, 60% of surveyed executives plan to implement conversational bots for after-sales, customer service, and social media. Accenture isn’t the only organization projecting big movement within the chatbot space – just take a look at these very telling chatbot statistics. At first glance, chatbot technology and AI-powered conversational interfaces appear very similar. When you go below the surface, though, the technology could not be more different. The initial training, the ongoing improvement, and the end-customer experience are not even close to being in the same league. 

Interested in learning more about artificial intelligence and chatbot technology? We’d love to discuss how our powerful AI chatbot platform provides the frustration-free experience your customers expect. Don’t use a robotic, limited chatbot solution that plummets your CSAT. Let’s chat. 

References

A Customer Service Crash Course for eLearning Companies That can Turn Short-term Demand into a Long-term Boon

Written by Can Ozdoruk  on   Apr 22, 2020

AI helps online learning, MOOC and learning management technology companies provide A+ support

Face-to-face classes at K-12 schools, colleges and universities are cancelled. Online classes, once sought out by only those who required more flexible schedules, have now become the only means for completing course requirements and degrees. Stay-at-home orders mean people across the globe are looking to better themselves or quell boredom by upskilling or learning a new hobby.

eLearning companies are seizing the opportunity. Coursera is offering 100 free online courses. Udemy, meanwhile, is touting its risk-free 30-day money-back guarantee. Others are offering Covid-19-specific courses to drum up new students1.

And it’s working. 

The result is unprecedented demand for online courses and learning management software (LMS). In one example, Class Central’s CEO tweeted about the mind-blowing new demand for the company, reporting that in 48 hours, 1M+ people found classes on the site. Across the board, search volume for “online classes” peaked at 2.5X the search rate over the same period last year. 

An often overlooked part of a business during a period of unprecedented and unexpected growth is customer service. The support that companies provide, though, is what can turn short-term demand into long-term value.

If you look at the search volume for “online classes” over the past five years, there are noticeable peaks occurring in early January, potentially as a result of New Year’s Resolutions, and again in August. During Covid-19, the search quantities have doubled that of the busiest times of the year – 2.5X the search rate over the same time last year.

The ‘Black Swan’ moment impacts eLearning and Distance Learning companies 

When discussing the new normal of online learning from home, The Chronicle of Higher Education’s Goldie Blumenstyk says, “it seems safe to say that this will be not only enormously disruptive but also paradigm changing 2. The ‘black swan,’ that unforeseen event that changes everything, is upon us.” 

With a new, digitally-connected audience of learners, the future of education may be transformed forever. Companies offering self-paced online classes like Coursera and Udemy have the opportunity to earn life-long fans and students. At the same time, LMS companies catering to K-12 and university audiences like Schoology, Instructure’s Canvas, Blackboard and Google Classroom have already changed the perception of learning from home, and potentially, could lay the groundwork for broader remote learning in the future. 

This era of online learning could be boon or bane for these companies, As one publication points out, beyond the content and usefulness of self-directed classes or the ease of use of the platform itself, it’s the user experience that has the potential to rewrite the future and drive the education boon that has been lurking on the horizon for years 3. To satisfy the demand for online courses while providing a top-notch customer experience, companies need to be available and support customers when they need them. Providing resolutions to questions and issues quickly, effortlessly and conveniently is the key to building customer trust that can lead to long-term relationships, advocacy and engagement. 

New customers lead to a strained customer service department  

For most, logging in from home to take a class is a new experience. With any new behavior and technology, questions arise: How do I submit a project? Am I eligible to earn a certificate? Can you help me log in to stream my class?

As a result of the enormous customer surge, customer service teams and call centers for eLearning and LMS providers are overwhelmed with tickets, resulting in hours- or days-long response times, and desperate and frustrated customers who aren’t afraid to air their grievances publicly.

Hilarious tweets about home-schooling have been followed by complaints about the platforms schools have adopted, marred with technical difficulties, straining parents and students in these difficult times.

A look at the impact of AI for Customer Service: Proactive, Revenue-Generating and Immediate

It’s unrealistic for companies to hire a customer service team to provide the immediate, effortless support that customers need right now. Many of the issues that arise are time-sensitive – coming up when someone is set to stream a class, take a test, or submit an assignment. 

To respond quickly and offer high-quality resolutions, companies would have to hire an army of people to answer questions around-the-clock, 24/7, across multiple channels (email, chat, social). It’s simply cost-prohibitive for these companies as a customer base swells.

Instead, eLearning companies are turning to AI to scale customer service across channels, reduce costs and assist agents. The result is happier customers that turn into advocates and long-term users. Here are the three biggest opportunities for which AI can help customer service groups of online learning and LMS companies:

1. Immediate Resolutions

First and foremost, AI helps customer service teams resolve issues faster, and deliver solutions when customers need them. Repeatable, simple queries like streaming issues, obtaining a certificate, billing and payment issues, and usability questions can be resolved in seconds, without any human intervention. 

This offloads mundane work from human agents teams, who can now focus on complex queries or specific audiences. LMS providers, for instance, might want to always have human-led support for administrators, while questions from students and parents are more likely predictable and therefore, can be automatically resolved. 

By carving out frequently asked questions from their queue, human agents can provide better support to these VIP audiences while resolution time is decreased across the board. In this way, automated customer service results in happier customers and more-productive agents.

2. Proactive Support

AI can enable companies to anticipate issues before they happen, such as common snafus that arise during the lifecycle and even when a customer is at risk of churning.

By analyzing historical data, AI can understand in which circumstances a person gets stuck and needs assistance, or when the customer journey specific questions tend to come up. This could be a teacher creating a first assignment who has missed a crucial step or a student taking a test for the first time. By integrating with a CRM, AI can be trained to trigger messages or emails at the exact time of relevance to the customer, providing critical information and resulting in deflection from expensive support channels. 

For online course providers, AI can also anticipate when a student is at risk of churning and not finishing a course. A message of encouragement, a prompt from the instructor, or a reminder on how far she’s come can keep people engaged enough to complete a course. 

Offering proactive care can also significantly cut costs: over a 12 month period, proactive customer service can lead to a 20-30% reduction in call center calls — lowering call center operating costs by as much as 25%4.

3. Revenue-Generating 

Targeted upsell can also be initiated at the exact moment of relevance. Think of it like the Netflix Recommendation Engine for eLearning. An AI can upsell to a premium plan, recommend add-on products or new courses. This can all be done by analyzing the potential return, as well as user profiles, interests and usage of a product. Targeted incentives can be provided to hook high-revenue customers, while skipping those customers who are not likely to be profitable in the future. 

Boon or bust? The time for AI in eLearning is now

The customer experience provided today can encourage people to continue to use and recommend your service when things settle down. Excellent customer support is the foundation of long-lasting relationships and profitable customer lifetime value (CLV). Delayed and frustrating customer support will be sure to beget the bust, while effortless, convenient and immediate service is sure to bring on the boon. 

Interested in learning more about how to earn good marks for customer service with AI? We can have you up and running in as little as two weeks. Let’s chat

References

Meal-Kit & Grocery Delivery Companies Need AI More than ever to Serve Customer Care as Opportunity and Challenges Mount

Written by Can Ozdoruk  on   Apr 9, 2020

Leveraging AI to automate and augment customer service can keep meal-kit and grocery delivery customers who have discovered the value of dinner at your doorstep happy and entice trial users to stick around when better times return 

Stay-at-home orders and social distancing recommendations in Covid-19’s wake has created a huge demand for meal-kit and grocery delivery companies. These companies are dealing with growing pains associated with ballooning customer bases, as well as supply chain and delivery issues. The result is customer service teams underwater with tickets and customer requests, with resolutions taking days, or weeks. 

When the customer service teams of meal and grocery delivery services are stretched, AI can automate and augment customer care to keep customers satisfied and coming back for more. 

Practically overnight, the meal kit and grocery delivery industry changed 

Consumers like myself agree. Right now, meal-kits make perfect sense: I can no longer go eat in a restaurant; it’s exciting to get dinner at my door that the whole family can help in preparing;  I’m bored and need at-home activities; and limiting grocery store visits is very appealing. And this is exactly why I signed up for one of these companies after years of consideration and tempting promo codes showing up in my mailbox. 

And, I’m not alone. 

With COVID-19 changing consumer behavior, companies like Purple Carrot1 and BlueApron2 are citing a “sharp increase” in demand, while Sunbasket CEO has referred to it as “dramatic”. HomeChef talks about an “unprecedented increase in orders1.”

 On the surface, this surge in demand looks incredible for these companies, as evident in stock prices springing up while most other industries rollercoaster at best. Hello Fresh, in one example, is “set to make about three quarters of the profit that analysts had anticipated for the full year” in just three months3

Meal kit companies, however, are facing incredible challenges as they race to meet this growing demand, including: 

1. A strained, disparate supply chain

The supply chain issue is twofold –  “unforeseen disruptions” and difficult ingredient sourcing as well as new demand. This results in last-minute cancellations, unexpected ingredient swaps, limited menu options and companies quickly selling out of popular items. Customers are taking to social media to complain about recipes being sold out on the day they were released, or extra charges for certain items.

2. Shipping delays

The logistics infrastructure in the United States is under strain as workers become sick and hours are limited. Just look at the golden standard of reliable and fast delivery: Amazon Prime, which previously delivered in one or two days, is now as long as a month4. In another example, Thrive Market, an online retailer of dry goods, frozen meat and seafood and wine, reported an up to two-week delay in their deliveries during March 2020. 

The uncertain environment has even led to companies like UPS and FedEx suspending Money Back Guarantee for all shipments to any destination.  

As a personal example, on March 26th, I have scheduled a delivery from Farmstead to be delivered by April 9th. On April 8th, while I was making room in my pantry for fresh fruits and vegetables, I learned it’s not coming the next day. It’s arriving… wait for it…. on May 10th! 

3. Staffing challenges

Scaling a team to manage distribution for increased demand does not happen overnight. In a Facebook post, Linda Findley Kozlowski, president and CEO of Blue Apron, said: “Within the span of 48 hours, we saw an increase in orders that surpassed the staff we had in place to fulfill this higher-than-expected demand, and this caused some challenges in our fulfillment process.” 

While demand is great, these challenges are leading to companies like Sun Basket and Gusto not currently accepting new customers, instead putting people on a waiting list. [As seen on their website April 7, 2020]. 

How AI can enable meal-kit and grocery delivery companies to provide exceptional customer service

Meal-kits and grocery delivery companies need to have a healthy balance between keeping their loyal, long-term customers happy and delighting new customers.  

Loyal customers are becoming frustrated with delays, limited options, partially fulfilled orders, missed delivery windows and slow responses. New customers, having not experienced service under normal circumstances, are less likely to stick around if they don’t get the experience that they expect. 

In an industry where churn is already high, customer service now is more critical than ever. When things go wrong with distribution and supply, often outside of a company’s control, the way to build brand love is by being always-available, ultra-responsive and helpful in moments of need.   

Think about the circumstances that people are reaching out today. Customers who were expecting orders are getting last-minute cancellations. They have requests to update addresses and meals. Orders are being delivered with only a fraction of items on a list. Time is of the essence in a lot of these scenarios.  Companies must get back to customers quickly with a personalized and meaningful resolution. 

It’s cost-prohibitive to hire a human-only agent team to scale customer service operations, 24/7, across channels and provide the timely support people expect. By adopting AI, companies can automate, augment and scale excellent customer care. Companies that use AI alleviate stress on customer support teams and provide superior C/X in these primary ways:  

AUTOMATE: Full resolution of repeatable tickets in seconds 
As Gartner suggests, during Covid-19, companies need to “use chatbots in digital channels to address the most commonly asked questions to offload volumes to service agents5.”

Leveraging historical data, AI can be trained to automatically resolve everyday, repeatable customer tickets. By integrating with a CRM, AI can offer full resolution, just like a human would, for things like address, timing and order size updates. This offloads mundane work from human agents, who can focus solely on critical, high-touch customer needs. 

ESCALATE: Flawless hand-off to human agents based on issue, sentiment and customer
When a complex, new or unique case arises, AI can immediately route a customer to a human agent within the original thread or interface. AI can also quickly hand-off conversations based on sentiment (i.e. the customer is angry or upset) and based on customer profile (i.e. always routing VIP, loyal or high-value customers to a human agent). 

PERSONALIZE: Tailor every conversation to the individual 
Integrating with back-end systems enables AI to curate data to help agents work faster and smarter. In real-time, AI can pull information to help resolve issues on an individual level – whether it’s previous orders, diet restrictions, ingredients, payments or credits.

REPLY: Engage beyond automated emails
Throughout the customer journey, companies send numerous automated emails, whether it’s a sign-up or confirmation or prompt to make any last-minute changes an order.  These emails, though, are often one-way communication. If a customer responds, there is often a substantial lag in response time. By leveraging an email AI agent, companies can automatically respond to questions asked in reply to these emails beyond a canned auto-reply. 

CANCEL and COURT: Seamless cancellations and targeted incentives
Traditionally, the industry has high churn rates: reaching double digits every quarter6. Churn is inevitable and companies need to make it a seamless, easy process to keep the door open for potential future business. If a cancel request comes in, AI agents can integrate with back-end systems to fully resolve cancellation requests. AI can also recognize which customers are loyal and profitable and offer an incentive in an effort to keep a customer, or route to a human agent for VIP treatment.  

ENGAGE: Be available across every channel 
Customers don’t just seek resolution on private emails, 1:1 messaging or website chat. Often, they take to  complaint channels like Facebook and Twitter to seek help, commenting on posts or tweets with an issue. A single AI can be deployed across every care and complaint channel to help agents respond quickly, apologize if needed, and move a conversation to a private channel. 

UNDERSTAND: Identify new issues quickly 
AI can identify trending issues and alert customer service managers when something new arises. For instance, if, out of the blue, a lot of customers are asking about coupon and promo code issues, AI can alert CS managers who can see if there is a technical issue or develop a response that can be widely distributed as other customers inevitably reach out. 

ANTICIPATE: Scale proactive communication
87% of customers want to be proactively reached out to by a company for customer service related issues7. If you get out in front of issues as soon as you become aware, you are able to give customers time to modify or cancel orders, or simply adjust expectations. Reaching out to customers immediately using human agents, though, is time consuming and often results in delayed communication. AI can automate messages and alerts, while also responding immediately to follow up questions. Not convinced yet?  Over a 12 month period, proactive customer service can lead to a 20-30% reduction in call center calls — lowering call center operating costs by as much as 25% 7.

Customer service issues are not as scarce as grocery store shelves these days. Meal switches, missing ingredients and items, delayed deliveries, reduced selection and issues with coupons are just a few.  While there are many factors outside of a company’s control, meal-kits and grocery delivery companies have 100% control over the customer service they provide. 

Let AI turn short-term demand into long-term relationships

Many speculate that the surge in demand will die down as social distancing guidelines loosen and restaurants start running in full capacity. If meal-kit and grocery delivery companies focus on the overall customer experience during this time, they have the opportunity to rewrite the future of their business.

AI can help you address today’s challenges and scale as your customer needs and business evolve. 

Let’s get started today to respond to your customers immediately with effortless resolutions. After-all, these are the key ingredients to happy, healthy, long-term customer relationships.  

Want to give our demo meal-kit bot a try? Sample it here.

References

  1. The Spoon: https://thespoon.tech/kroger-begins-roll-out-of-home-chef-meal-kits/
  2. Food Navigator: https://www.foodnavigator-usa.com/Article/2020/03/20/COVID-19-Mealkit-brands-see-sharp-increase-in-demand-although-longer-term-impact-harder-to-predict#
  3. Business Times: https://www.businesstimes.com.sg/garage/meal-kits-are-the-next-best-thing-in-covid-19-pandemic
  4. Vox: https://www.vox.com/recode/2020/3/22/21190372/amazon-prime-delivery-delays-april-21-coronavirus-covid-19
  5. Gartner: https://www.gartner.com/webinar/3981643
  6. Food Truck Empire: https://foodtruckempire.com/news/meal-kit-industry/
  7. MyCustomer: https://www.mycustomer.com/service/management/infographic-five-stats-that-prove-proactive-customer-service-can-make-you-a

Getting Started with Netomi on Zendesk

Written by Emily Cummins  on   Apr 6, 2020

Bringing AI and customer service into your workforce has never been easier, and implementing chatbots can help your business to save up to 30% on your customer service costs, according to Gartner.

With our out-of-the-box, Zendesk chatbot native integration, you can quickly launch a sophisticated and highly accurate chatbot that works alongside your human agents

When you choose to work with Netomi, you don’t have to worry about code and nothing changes for your customer service team. Based on the end user experience that you want to provide and the level of customization you want to have for each use case, you can choose between two options: 

  • AI premium service: This is our best-in-class solution that comes with a dedicated customer success team that provides white-glove support every step of the way. We’ll create a really engaging, natural chat experience. You will have complete customization on the conversational design and the ability to integrate with multiple back-end systems. 
  • Zendesk Knowledge Base service: A self-serve option that links to your knowledge base and will be up and running immediately. 

Whichever option you choose, launching Netomi’s AI on Zendesk chat is quick and simple.

Zendesk Chatbot: Netomi’s Premium Service   

When you select our conversational customization option, you’ll create a highly-tailored experience that is expertly designed for the chat interface. The below six steps are completed in as little as two weeks. 

Step 1: Gather historical data 

Historical data is the best place to start when you’re launching a new conversational AI experience for customer service. Pull historical customer service tickets from your Zendesk account. You’ll send an export to your dedicated Netomi customer success team for analysis. 

Step 2: Analyze data to identify use cases 

It’s critical that each ticket is managed properly. By leveraging historical tickets, you’re taking the guesswork out of which tickets to delegate to a chatbot tool, and which should be always routed to a human agent. 

High-risk, critical or rare issues should always be routed to a human agent. Inquiries that are highly-repeatable, pose low-to-medium business risk and require low-to-medium exception management are the ideal customer service tickets to automate

During our clustering process, we’ll identify the right use cases. Essentially, our algorithms will group together historical tickets that have the same intent, making it really easy to determine which use cases account for a high volume of tickets and uncover trends in how human agents have responded in the past. During this step, you’ll select the tickets you will delegate to your AI.

AI can work for almost any industry from eLearning to eCommerce. For instance, a subscription company will likely see that order modifications, skipping a delivery, or address updates, account for a lot of tickets and therefore would make great candidates for AI. Edge cases, such as specific questions regarding a recipe or preparation, would likely account for fewer tickets and therefore would yield a lower benefit when automated. 

This clustering process also helps jumpstart utterance training, giving your AI the ability to understand how people ask things in real-life, not simply the wording on your online wiki or FAQ page. This extensive utterance training helps your AI more accurately classify your customers intent from launch, ensuring a positive customer experience

Step 3: Conversational design 

Once the use cases that will be delegated to AI have been selected, the conversational design process begins where you bring your virtual agent to life. This consists of a few key things: 

  • Bot Personality: Within Netomi’s AI Studio, you’ll bring your bot to life through the tone of your bot responses, making sure it aligns with your brand.
  • Tailoring Use Case Journeys: Create and tailor your responses for each use case within AI Studio. Don’t forget to add emojis and other conversational U/X features like buttons, suggested responses and more. 
  • Conversational Elements: You will also create responses for conversational copy (greetings, common questions), create menus and bridges back to core functionality. 

Step 4: Connect your Netomi bot with your Zendesk Chat 

It’s really easy to integrate a bot with your Zendesk chat account. You’ll simply need to create a dedicated Netomi API client for your particular Zendesk subdomain which will have the ability to access Zendesk, just like any human agent would. 

To link Zendesk to a bot on AI studio, your dedicated customer success team at Netomi will create a simple social configuration on our end. 

Step 5: Connect to your knowledge base

Do you want your bot to provide recommended, relevant articles to use cases that you haven’t trained? You can integrate with your Zendesk help center by linking to your Zendesk knowledge base in AI Studio. This way, even if your AI isn’t providing a full resolution within the chat interface, it can offer the potential for customer self service and subsequently deflect tickets from other channels. 

Step 6: Launch, measure and optimize  

All that’s left now is to embed the Zendesk chat Web widget on your website and we’ll activate your bot. You’ll want to watch performance and review how the AI is interacting with your customers to identify opportunities to enhance the training, add additional use cases or adjust the conversational flow and responses. 

You should start seeing increases in customer satisfaction and agent productivity within days. Check for all the most critical customer service KPIs within the Netomi AI Studio. 

Zendesk Chatbot: Netomi’s Knowledge Base Service

Do you want to get up and running immediately? Our quick and easy Knowledge Base integration is for you. Just enter the URL of your knowledge base into AI Studio and you’re up and running. When your customers ask your bot a question, they will be sent relevant articles to supercharge self-service. 

Step 1: Train the AI by integrating with your knowledge base 

Are you looking to launch immediately and have a robust knowledge base on your website? You can train our AI by simply providing a link to your Knowledge Base. Our AI will immediately get to work analyzing your information so that it can serve up the most relevant ones when users ask a question.

Step 2: Create conversational and human handoff copy

Within Netomi’s AI Studio, you will have the ability to customize how your bot responds to  customers with greetings, hand-offs to human agents and other conversational elements.

Step 3: Launch 

Press “Go Live” and you’re all set!

Step 4: Measure  

See trending topics, customer engagement and other key customer service metrics within AI studio. 

It’s never been easier to launch a next-generation AI on Zendesk. Following these simple steps, you’ll get up and running quickly. What are you waiting for? Let’s get started

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