Dev meeting – AIs, interplanetary file systems and Nix

We had several lighting talks in the dev meeting this week.

Rich talked about WitAI – a language parsing web service that helps with building chatbots. He’s written a “drinksbot” in it, which we used via Slack before the dev meeting to order drinks. You provide it with a corpus of example sentences, and it learns from that.

Chris spoke on the Interplanetary file system. In typical direct file request, you might retrieve it synchronously from a single location. IPFS allows you to retrieve content from a set of distributed stores – essentially a peer-to-peer, torrent-like filesystem, that can be more robust and potentially faster than a direct file retrieval; because content is stored can be stored in a number of places.

Reece talked about antagonistic attacks on neural networks. Because neural networks (e.g. for image recognition) will typically work by perturb the input image to make the neural network recognize it as something entirely different – even though the change to the human might be imperceptible. For example – researchers recently made a model of a turtle (to human eyes) look like a gun (to neural network recognition) to illustrate this issue. This is a problem with neural networks generally – that the neural network may be using very specific things for its recognition, and it may be hard to identify these.

Stephen talked about teaching (very) young developers – like his daughter – to program. There is a lot of implicit knowledge and setup and background in a typical dev environment, and you want to get to working code as quickly as possible. HTML 5 and JavaScript are good for this. You should optimize for small victories, and concentrate on fast feedback loops.

Tim talked about Vue, the JS framework. You create reusable components that have an HTML component and some connected JavaScript that can control . It’s somewhere in the middle in terms of ease of use – it’s more complicated and powerful than Knockout, but simpler and more usable than React and Angular (and it can be used initially just by including script tags, and then extended to manage things like transpilation). We’re using it in a number of projects at present.

Rodney talked about “Where I’ve been” – specifically about the Nix conference that he recently attended. There is a new Nix tool for handling packaging and building in the Nix system, which has a simpler (command-line) UI. Another talk was about the security developments to the Nix project, such as having specific security experts who can getting security notifications from other projects early on; about automated scanning tools for scanning deployed Nix implementations for security holes. Nix in production was being discussed – e.g. for a company that’s doing cycle dock management in the Netherlands. Using Nix throughout an entire environment, from development through to production, brings many advantages. At Tumblr, Nix is used for testing of live SQL instance replication. Then there was a Hackfest – with spontaneous collaboration, and good work done on Nix cross-compilation.

Facebook Messenger will soon let all businesses send sponsored messages

Facebook today announced that sponsored messages are being rolled out to more businesses. The Messenger ads were initially made available to a small number of businesses but will become available to all businesses in the coming months, the company said in a blog post.

Unlike other Messenger ads, sponsored messages that pop up on your Messenger home screen will not be marked as such. In fact, they look like all other conversations on Facebook Messenger.

As previously defined rules for advertising and marketing on Facebook Messenger state, promotional messages like the kind being rolled out now may only be sent once in a 24-hour period, and they can only be sent to a person who previously chatted with that business’ Facebook Messenger bot or account.

During the company’s third quarter earnings report on Wednesday, Facebook CEO Mark Zuckerberg told investors that more than 20 million businesses use Facebook monthly to communicate with customers.

Advertisers interested in sending sponsored messages must create a Custom Audience, which can be used for retargeting ads at people who have done things like clicked a Messenger bot call to action or deleted a Messenger bot, or simply to target new Messenger bot users.

Advertising and business services through Facebook’s messaging apps have grown steadily in the second half of 2017 following questions about monetization this summer. In July, while onstage at MobileBeat, Facebook Messenger head of product Stan Chudnovsky announced the company will gradually bring ads to the Messenger home screen worldwide. In recent weeks, Facebook opened the Messenger objective for ad campaigns.

At the same time, verified WhatsApp business accounts and a paid WhatsApp customer service app have also opened up.

In August 2016, after initial restrictions on promotional material, Facebook Messenger opened to promotional and marketing material. Ads that appear in the Facebook News Feed and lead to a chat experience with a human or automated bot on Messenger have been available since the launch of Messenger Platform 1.3 in November 2016. Since then, those same ads have expanded to Instagram.

Both WhatsApp and Facebook Messenger garner more than 1 billion monthly active users around the world. They compete in the same space to connect businesses with customers as other chat apps with hundreds of millions of users, such as Skype, Apple’s iMessage, and Twitter.

Silicon Valley may have just began to come around to this form of conversational commerce, but it’s been a big part of business in Asia for a while now.

WeChat and Line have offered advertising in one form or another since 2014 and 2015, respectively.

Advertising has become the highest grossing sector of Line Corp, the company behind the Line app. According to earnings announced in late October, 47 percent of the $371 million in Line revenue in the past quarter came from advertising, up 40 percent compared to the same quarter last year.

Dialogs, buttons, and menus make bots more productive

Conversational interfaces have become a larger part of our day-to-day lives. Many of us wake up in the morning and talk to Alexa or Google Home about the weather, ask Siri to “call mom” during our commute, and engage with several Slack apps and bots throughout the workday. But as the AI and bot industry matures, developers realize that users do not really care about AI or chat. What they really want is a better way to complete tasks. In this article, I will talk about how to optimize your conversational interface to do just that.

In my bot design book, I coined the term of a “conversational funnel.” Like a web or mobile user funnel, a conversational funnel models and measures the engagement, ease of use, and conversion of a user within a conversational interface.

Do you have an AI strategy — or hoping to get one? Check out VB Summit on October 23-24 in Berkeley, a high-level, invite-only AI event for business leaders.

Let’s imagine the conversational funnel for a bot that helps people buy a new laptop:


In each step of the funnel, users get closer to making the purchase, but fewer and fewer of them progress through the entire funnel. Many drop off — confused, distracted, or just tired of the process.

Never underestimate the power of making things easy to use. Companies like Lyft and Uber, and even Google and Amazon, were built on the foundation of providing an easier way to consume a pre-existing service or product. Conversational interfaces are no different. If they can become an easier, more pleasant way to identify a service or a product, they will be successful. Otherwise, they will fail.

Let’s explore ways to optimize a conversational funnel.

Buttons for simple choices

Here is a very common funnel failure we see with conversational interfaces today:

At this point, the user is already frustrated. They would rather go online and view the report on a web page. It is definitely not a pleasant or easy experience. The user isn’t satisfied, and the bot appears dense and unhelpful. But what if we change the interaction just a bit?

Now it is very clear what the user should do. Taking action is quick and contextual, whether interacting on web or mobile.

Pull-down lists for more choices

This problem of capturing the right information becomes even more complex when choosing from a large list of items:

Again, the user is frustrated. It is hard to spell out a specific item from a large list. Let’s make a small modification:

Now the experience is much easier. For example, Slack menus are auto-complete enabled, so it’s simple for the user to start typing and select the right item from a list. Again, improvements like these make the interaction more productive than the web or mobile app alternatives.

Forms for collecting structured data

When it comes to real-life examples, many bots must collect more structured information, which can be particularly cumbersome to collect with regular text.

Just reading this correspondence might make you tired. Capturing long structured user input is a nightmare when turned into plain conversation. So what should a bot do? Well, this week my team is introducing dialogs, a new Slack feature that allows developers to build interactive forms.

Once the user has clicked on the button, a well-structured form pops up in the conversation.

After the user fills in the form, the conversational interface can change accordingly, keeping the information captured in the context of the conversation.

As you can see, in this example the bot captured the structured data and brought the results back into the conversation, keeping the core value of bots in messaging apps: retaining a quick and contextual workflow.

Dialogs are new interactive modals that can capture multiple pieces of information and send them directly to your bot. You can use them to build a robust form inside Slack, or simply collect a single line of text.

Traditional conversation is a great hammer, but not everything’s a nail. Using rich interactions like buttons, menus, and dialogs make better performing conversational funnels and helps create a more pleasant and productive way to get things done. With these new emerging experiences, we take a large step toward making our lives simpler, more pleasant, and more productive.

Amir Shevat is head of developer relations at Slack.

What’s With All The Crappy Chatbots?

Underwhelmed by your chatbot? You’re not alone. Filament’s Rory McElearney – an actual human – offers some comforting words

The market is currently seeing a proliferation of poor chatbot implementations. Part of the problem is a lack of understanding of chatbot user experience (UX) and the rigidity of frameworks on which they are being built. Another part is our high expectations of this form of technology. So what’s the current state of the art? And will we all ever truly love our ubiquitous chatbot companions?

Conversational UX – rushing to a new (old) medium

It seems you can’t go a week without running into a new way to make chatbots. Whether it’s an open source code library for building the bot engine [1], big players such as Google’s or IBM Watson that manage conversation flows on top of their Natural Language Parsing (NLP) services, or a fully-fledged drag-and-drop platform for ‘launching a bot in seven minutes’ [2], there can be little doubt that the massive appetite for bots is being served. But since their dramatic launch a few years ago, it does feel as though remarkably little progress has been made. The economic case for chatbots remains strong, as evidenced by the flow of money into the sector, but many customers have been decidedly underwhelmed by their first contact.

The chat interface is nothing new, since giving orders via the command line was the original way of communicating with a computer. In many ways, all we’ve done is come full circle. However, since those early days, we have all become accustomed to navigating by screen, mouse and touchpad. All the factors that made a graphic interface preferable for the majority of people haven’t suddenly gone away, so what has changed to fuel this rise in demand for chatbots?

Two things stand out. Firstly, users are choosing to spend more time in smartphone messenger apps at the expense of their desktop devices. Since it’s almost impossible to entice users to download a new app, the old adage stands that companies must go where their customers are – into the chat space.

Secondly, access to advanced machine learning algorithms has become increasingly straightforward. An Application Programming Interface (API) economy which grants affordable access, one drip at a time, to a fortune of AI and machine learning IP is being used by developers who want to build machine learning into their products. The APIs powering the chatbot boom come from the field of Natural Language Parsing (NLP) or Natural Language Understanding (NLU). These allow bots to take the words that you type and detect what you really mean from this natural language. They can even deduce your mood as you’re typing or how you feel about specific topics based on your choice of phrasing. This is an exciting field, with breakthroughs being made all the time. Even so, turning this potential into an engaging chatbot has proved to be a difficult task.

Realistic expectations?

It is possible to build chatbots whose responses are generated programmatically but beyond simple chit-chat, anything remotely complicated will require the bot to make a call to a database or some other external source. If there’s a button on a website that requires a call to a database, a developer needs to write code for it. The same is true for bots, as you can no more ask a bot to perform an unprogrammed action than you can ask an interface for a non-existent button. Chatbots present a unique challenge because unlike the graphic interface, where the aim is to clearly display all options, part of the allure of chatbots is the illusion that it understands you. Spelling out what you can and can’t do risks breaking that spell.

The best bots are the ones that naturally draw users into asking them about what the bot knows best. Consider the perceived effectiveness of Amazon Echo’s Alexa versus Apple’s Siri. Most of the time, Alexa only has to deal with changing songs or reordering loo roll, yet Siri is expected to deal with all manner of requests. Creating a bot that maintains the illusion of understanding becomes more difficult as users ask more of it because the bot can be confused by requests across a wider range of topics.

And that’s when human users are actually making our wishes very clear. Alexa’s success is, in part, due to her plugged-in-at-the-wall microphone’s ability to hear and understand more clearly than Siri. Up until now, the instructions humans have given to computers have been pretty unambiguous but on top of that, instead of a scroll and click, they now also have to try and interpret misspelled sarcasm.

Deciding which bucket a word or phrase should go into – which is what your bot must do in order to figure out what you’re talking about – is known in machine learning as a ‘classification’ problem. Deciding whose face is in a picture or making a film recommendation fall into the same category. The machine is making decisions based on training from humans, so if even a human would have a hard time differentiating between buckets, the machine has no chance!

“It’s not you, it’s me”

Even if what you’ve said has been understood, a bot still has to deal with the fact that humans are fickle creatures. They might not really know what they want, then they often change their minds. Then the chat interface challenges some of our most basic assumptions about design and UX by denying the user the ability to see where they are, or where they can go next. It removes the ability to easily go back to where they have been.

We have all become experts at navigating the flat space of a screen. Our eyes instantly detect navbars and dropdowns as if detecting the walls of a room we’ve just entered. These familiar web design conventions give this space a multidimensional quality. We scroll down to read more, up to read it again, left and right to go backwards and forwards in time like we’re reading a book.

Yet having a conversation with a bot can be unsettling because these conventions are of no use. If you make a mistake and want to go back, it’s not always immediately apparent how to do so. This can generate a momentary panic and loss of trust that will have a massive impact on the quality of your experience.

Another limitation of the chat interface is that it denies the user the ability to quickly browse results. The front page of a Google result is a dense, easy to scan block of information that would be far less accessible in a chat interface. Web pages ease the pain by presenting large amounts of information using a consistent layout. You know what part of the screen to look at, that the headers are in a different text style from the content. You can quickly scan in the knowledge that you don’t need to click on any result unless it catches your eye.

But in a chat console, there is no opportunity to lower the cost of scanning with clever design and layouts. Each line of text is as important as the next and the total number of words on screen at any time is limited by both the chat window and the user’s ability to digest them.

Building bots remains a fledgling art form

Despite all of these challenges, the forces driving the move towards chatbots are only going to grow stronger. Natural Language Parsing is going to grow better and cheaper, while lessons will be learned from the bots currently in production. Even users will help by slowly becoming accustomed to dealing with chatbots.

The tools the chatbot developer can employ will get more sophisticated too, such as bots’ ability to track the context of a conversation in a way that mimics human short-term memory. A skilled bot architect can also draw upon under-the-hood machine learning assets such as trained classifiers and knowledge graphs, empowering the bot with the language of its domain, then bottling that ‘expertise’ into a set of entities and relationships.

One of the most important things a bot builder can do is to be aware of the early stage of this industry, both in terms of the tooling available and user acceptance. When deploying a bot, it’s unlikely to be perfect out of the box. Every bot will eventually be asked a question out of its scope because how a developer thinks users will behave and how they actually will are two completely different things!

There are two key design principles to follow. One, the conversation must be designed with fallbacks and to fail gracefully. Two, a developer will need supporting analytics and tooling in order to iterate and retrain their bot based on this feedback.

Dating the Martian

Will we ever fall in love with chatbots?  For many users, talking to bots is like dating a Martian and at the moment, we’re all still on our awkward first date, with both sides talking past each other in between plenty of awkward silences. But as time goes on, we will adjust to the other’s foibles, learn each other’s language and, eventually, start having flowing conversations.

We would urge you all to stick with it because without question, the relationship between user and computing power is evolving. These first awkward bots and our stilted conversations with them are just the start of an ongoing relationship. Within the next decade, we will be absorbing computing power in whole new ways and augmented realities will touch every aspect of our lives.

Rory McElearney is a developer and chatbot UX expert at Filament. He has spent 18 months specialising in this emerging field, designing and deploying elegant chatbot solutions

The post What’s With All The Crappy Chatbots? appeared first on Disruption Hub.

Digiday Research: Attack of the chatbots

This is the third part of a research series on a set of emerging technologies in media and marketing. Read our other reports on virtual reality and augmented reality.

The 2016 election was a hotbed of media experimentation, and The New York Times was no wallflower. To cover the last 19 days of the race, it built a Facebook Messenger bot engineered to deliver insight from political reporter Nick Confessore.

Digiday Research surveyed 172 executives from media and marketing companies to uncover their approaches to chatbots — and the development of the market. Key findings:

  • About 47 percent of publishers and 31 percent of marketers use chatbots.
  • Fifty-two percent of publishers and marketers say chatbots are mostly deployed as information resources.
  • Facebook Messenger is the chatbot’s natural habitat, with 88 percent of both publishers and marketers deploying them on that platform.
  • According to marketers, chatbots conduct less than 20 percent of consumer interaction before passing the conversations off to human support.
  • The proportion of chat requests fulfilled and the number of chats initiated are the dominant ways chatbot performance is measured now.

The Times editorial team wrote a “choose your own adventure”-style script each day to simulate conversation with Confessore. Text prompts guided users through a pre-structured dialogue. “It was fully automated, but originally created by a human,” said Andrew Phelps, a member of Story[X], the Times’ innovation division. “So people still really identified with Nick the reporter, and it felt like a personal experience.”

Chatbots like these dot the publishing landscape: About 47 percent of publishers surveyed have used them. But they’re still far from intelligent.

“The illusion that HAL is out there, and the machine is alive is just that: an illusion,” said Derek Fridman, global executive experience director at Huge. “There’s machine learning taking place and algorithms making decisions, but in most cases, we’re scripting sequences.”

That’s fine for now, since 52 percent of media and marketing professionals say chatbots are predominantly employed as information resources.

“We’re really pushing brands to focus on utility, things that consumers are going to want to use on a regular basis,” said Adam Simon, director of strategy at IPG Media Lab.

“Anything that makes my life more convenient, I’m willing to give you everything up to my fingerprint for,” agreed Fridman. “With chatbots, it’s all, ‘Give me, give me, give me.’ I want to make a request. I want something back right away.”

When FT Labs created a chatbot integrated with the site search capability, they were doing just that — two years too soon. “We had to explain to users what [Slack] was,” said Chris Gathercole, head of the Financial Times’ FT Labs, of the now-ubiquitous workplace chat platform that housed the bot. “The project ran out of steam; we tried too early.”

But Gathercole is more optimistic about the future, especially around emerging voice environments: “That’s a good back end to support conversations.”

Educating users about new platforms is a persistent, chronic challenge both media and marketing professionals feel when pushing emerging tech. That’s why an overwhelming proportion of both publishers and marketers (88 percent) deploy chatbots on the most familiar platform of all: Facebook Messenger.

“We tend to make bets where we see opportunities to provide the best value for users in a space that’s familiar to them, but can be made new through innovation and technology,” said Brian Dell, director of Quartz Creative.

There are other clear reasons to employ chatbots in closed, private-message settings, especially given Microsoft Tay’s highly public, highly toxic Twitter malfunction. “A chatbot makes a lot more sense as a closed, one-to-one session, rather than broadcasting all of the problems consumers are having to everyone at all times,” said Michael Lebowitz, CEO of Big Spaceship.

This potential for public failure is vital to grasp, given that 38 percent of marketers who use chatbots use them for customer support purposes.

Even then, humans step in when the situation calls for a true conversationalist. Most marketers (91 percent) say the chatbots themselves conduct less than 20 percent of consumer interaction, passing the conversations off to human support when things go off-script.

The technology simply isn’t far along enough to absorb the brunt of customer interaction. Too many conversational dead ends (or misunderstandings) cause most unsupervised chatbot chats to spectacularly fail the Turing test.

This is fine, as long as marketers acknowledge the problem (and keep the conversations fairly private). “That would not be an error of the technology, but of putting too much trust in the system,” said Lebowitz.

Still, the ability to at least initiate many conversations points back to chatbots’ main strength: scaling one-to-one interactions. Forty-one percent of media and marketing professionals say chatbots have high or the highest potential in this area.

This is also reflected in how chatbot success is currently measured: 38 percent of marketers say the proportion of chat requests fulfilled, while 33 percent say it’s the number of chats initiated. Publishers aren’t so split: 66 percent say it comes down to the number of chats initiated. The focus, so far, is on connections, not engagement, reflecting a still-dominant industry preference for impressions.

But that doesn’t mean marketers are going all in on utility at the expense of entertainment with their bots. “Where it gets more interesting is how chatbots could become part of larger narratives,” said Lebowitz. “Years ago, we took over digital for the Skittles brand, and the conceit of our [Facebook] presence was that all of the posts would be issued from the character of The Rainbow. Could the brand be issuing funny-enough content in a one-to-one or one-to-few setup that it would be worth bringing that personification to a chatbot?”

According to IPG Media Lab, yes. The lab worked with ‘90s fizzy alcoholic hit Zima for its 2017 comeback, programming a bot emerged freshly from millennials’ halcyon days with no awareness of contemporary trends or current events. To assess its success, IPG Media Labs is looking past impressions to compare how the chatbot performs as a channel relative to other campaign channels.

“How does this perform on a cost basis against your mobile site or your Instagram feed,” said Simon. As for why consumers would even be interested in a branded chatbot without much utility, “it’s the same reason people are on Facebook in the first place: They’re killing time in line at the grocery store.”

Both media and marketing professionals still have a lot to learn about this new set of conversational interfaces, but all of the information they’re capturing through these early experiments will help accelerate the process.

“At the end of the day, we’re seeing our clients just want to collect as much data as possible about what their consumers’ wants, dreams and needs are,” said Fridman. “As the machine becomes more aware, we can begin to leverage all that data to be a hell of a lot smarter in the next generation from an AI standpoint.”

The post Digiday Research: Attack of the chatbots appeared first on Digiday.

Intelligent assistants vs. chatbots: Which is best for your biz? (VB Live)

Looking to implement an intelligent assistant or chatbot? Don’t miss our latest VB Live event, where we tap a panel of developers with long-term, hands-on experience in selecting the right digital engagement solution, planning a strategy, and seeing results. Register now!

Register for free right here.

The future is here; it’s just not evenly distributed – yet. As chatbots and intelligent assistants get more sophisticated and use cases start piling up, they’re finally moving out of the early adopter phase and into the need-to-have territory for businesses.

A recent study shows that consumers are ready for them, if they’re done right. It revealed that 40 percent of consumers would make a purchase from a chatbot interface, and nearly 60 percent of would engage with a chatbot especially if it meant receiving coupons or special offers.

But the buzz surrounding these tools has done a lot to obscure what they actually are, what they can do for your company, and how you can implement one successfully, and not egregiously.

The difference

Today’s chatbots and virtual assistants have evolved past basic logic with the integration of back-end artificial intelligence. It helps to create experiences that are more conversational while providing a lot more utility for the end user.

Chatbots are generally focused on on a single purpose, whether it’s in ecommerce as a shopping agent, first-level customer service, or customer engagement and entertainment. With less complicated machine learning algorithms and leaner architecture, they require less infrastructure and are far quicker to build, deploy, and implement than an AI-powered virtual assistant, letting you automate a single business function with a smaller investment.

Intelligent assistants can technically be chatbots if they interact with you through a conversational interface such as Slack or Facebook Messenger, but they’re powered by more advanced cognitive computing technologies such as advancements in natural language processing, complex machine learning, and AI. They can continuously learn from consumer interaction to become better at predicting end users’ needs, and can potentially understand and carry out multi-step requests and perform more complex tasks such as making a hotel or plane reservation.

The measure of success

The key measure of success of either is how much value the chatbot or assistant adds. No user is impressed by a shiny new feature that doesn’t do anything to add value to their experience — which makes your company breathlessly bandwagony, rather than technologically sophisticated.

A chatbot or virtual assistant should either be performing a task a person would find hard to do themselves, or saving your user time by performing tasks that would take them a long while to accomplish. Just think about the times you’ve been forced into a voice recognition maze with no “hit zero for an operator” option. And how very, very close you probably came to throwing your phone out the window.

Do you really need one?

You can actually hurt your business if you don’t think critically about if your brand really will benefit from the power of chatbots or virtual assistants — or if you’re just riding a trend. Plus, if it’s implemented without a plan, the execution is going to provide actual pain and frustration to your consumer (think about that endless voice tree).

So learn how to get those customers on board, plus which platform you need and how to launch, when you join our latest VB Live event!

Don’t miss out!

Register now.

In this webinar, you’ll:

  • Understand the messaging platforms of the future
  • Learn which platforms people are using — and why
  • Measure the success of your chatbot through best-practice KPIs
  • Create personalized interaction between your organization and your customers


  • Amir Shevat, Director of Developer Relations, Slack
  • Stewart Rogers, Director of Marketing Technology, VB
  • Rachael Brownell, Moderator, VB

Chatbots on Facebook Messenger linked to increased sales

“People prefer to use Messenger to interact with companies,” Facebook VP of messaging products David Marcus said during his keynote at this year’s F8 conference. With over 65 million businesses active on Facebook, this shouldn’t come as a surprise. An estimated 80 percent of these companies use messaging to reach Facebook Messenger’s 1.2 billion monthly active users. When Messenger opened its doors to developers in April 2016, 78 percent of adults were still unaware that chatbots even existed. After a year of growing pains for chatbots, though, new features for Facebook Messenger 2.0 were announced in April. These new elements were designed specifically to scale up the business-and-bot side of its service.

During this year’s F8 conference, Marcus noted the company is working on convincing consumers to open Messenger rather than pick up the phone or send an email. Facebook’s Messenger Platform 2.0 is making this possible with recently added features designed specifically for business. Features like the Discover Tab, Chat Extensions, Smart Replies, and M Suggestions are all designed to help companies gain visibility and reach a larger audience.

“Messaging with businesses is on the rise, with over 2 billion messages sent between people and businesses — including automated conversations — each month,” said Kemal El Moujahid, the product manager for Messenger. “Messenger offers businesses new ways to engage with their community and potential customers. Whether it’s raising awareness for a brand, enabling transactions, acquiring new customers, or delivering superior customer service, Messenger can be a core part of business solutions.”

The success of Facebook Messenger bots for businesses has become especially apparent to Bobby Mukherjee, the CEO of Loka. Since the launch of Facebook’s Messenger Platform 2.0, Loka has helped businesses create Facebook Messenger bots to increase sales and improve customer service.

“Bots are clearly the next wave in customer focused innovation, taking over the mantle from mobile apps. It’s exciting to see a new wave cresting, and helping companies maximize the opportunity,” said Mukherjee.

Rise of bots, rise in sales

During his keynote, Marcus mentioned a number of companies that have seen an increase in sales, bookings, and productivity as a result of Facebook Messenger bots.

Sephora, for example, has seen an 11 percent increase in booking rates through the Sephora Reservation Assistant. Sephora’s goal was to encourage more clients to visit its stores by offering an easy way to book makeovers. There are five fewer steps involved for booking makeovers through Sephora’s Facebook Messenger bot than through its online app, enabling clients to schedule appointments in a matter of minutes.

In as few as three steps, Messenger’s conversational interface allows Sephora customers to initiate a conversation by typing the city in which they wish to book a makeover. The bot immediately shares a list of Sephora locations in that city. The user then selects a store, and the bot instantly displays available dates and times. Appointments are booked in a matter of minutes.

Sephora also reported that its Messenger bot has helped increase in-store sales. The retailer sees an average spend of over $50 from clients who have booked an in-store service via its Messenger assistant.

SnapTravel is another company that has found success with its Facebook Messenger bot. Since using Facebook Messenger, SnapTravel has seen $1 million in hotel bookings in less than a year.

Accessing SnapTravel through Facebook Messenger is easy because you don’t need to download an app. Users can directly message SnapTravel via Messenger to find a desirable hotel to book. All you have to do is send the bot a private message with your basic travel information, such as your destination, dates, and budget. The bot will then respond immediately with a number of options.

The hotel rates quoted by SnapTravel are similar to what you’ll find on any of the big metasearch sites, such as Kayak, Priceline, or Expedia. This is because SnapTravel is connected to dozens of online travel agents and can find the best possible price currently available on the market. When it quotes a rate, it will also show you the best available rate on for comparison.

Tommy Hilfiger is yet another company finding success with its Facebook Messenger bot. The fashion empire developed an experience where they used a bot for Messenger to help people buy fashion directly from the runway during Fashion Week in NYC. There was an 87 percent return rate for people coming back again to use the Messenger experience, with over 60,000 messages being exchanged. The company noted that 3.5 times more was spent through Messenger than any other digital channels.

New features, new value

Yet Facebook Messenger bots for businesses have only recently been gaining traction. It has only been since the launch of Facebook Messenger 2.0 that organizations are finding real value in Messenger bots.

For instance, since the implementation of new features like the Discover Tab, businesses are able to have more visibility and combine automated and human chat experiences in a single stream.

“The new capabilities of Facebook Messenger 2.0 greatly increased the customer user experience potential. Companies that can launch a Facebook Messenger bot on the new platform are poised to lock in a first mover advantage,” said Mukherjee.

There are now 100,000 bot developers who have built 100,000 active bots on the Messenger Platform. That’s up 233 percent from the 30,000 bots on Messenger six months after the feature’s launch in April last year. And as more bots continue to be developed, more businesses are sure to see an increase in sales and productivity.

With 1.2 billion active monthly users on Facebook Messenger, it’s easy to see the value businesses can gain by having a Messenger bot. Brands are now capable of reaching the entire Facebook world.

Rachel Wolfson is a content marketing consultant, tech blogger, and fitness buff.

Bot analytics platform releases new chatbot landscape

Since we started building bots at KeyReply more than two years ago, the industry has seen massive interest and change. This makes it hard for companies and customers to figure out what’s really happening — so we hope to throw some light on this industry by creating a landscape of chatbot-related businesses. There’s no way to put everyone into this landscape, so we have selected examples that give readers an overview of the industry, such as notable or dominant providers and tools widely used to develop bots.

To put everything into a coherent structure, we arranged companies along the axes according to the functions of their bots and how they built them.

On the horizontal axis, the “marketing” function refers to a bot’s ability to drive exposure, reach, and interaction with the brand or product for potential and current customers. The “support” function refers to a bot’s ability to assist current customers with problems and to resolve those problems for them.

On the vertical axis, “managed” refers to companies outsourcing the development of bots to external vendors, whereas “self-serve” refers to them building their bots in-house or with an off-the-shelf tool.

Spinning out concentric circles

From the inside out, the concentric circles represent:

  • Platforms: The messaging platforms that enable the existence of bots through robust send-and-receive APIs, frameworks, and ecosystems.
  • Brands: Companies that have launched and experimented with bots in that quadrant (for example, Managed x Support).
  • Providers: Companies that have the capabilities to deliver exceptional work in that quadrant.
  • Tools: The supporting tools used by providers, brands, or developers to deliver bot experiences.

Here are some of our observations about each of the concentric circles.


In this study, text is the main interaction mode we explore (we might explore voice bots in another study).

Facebook Messenger is one of the leading chat platforms, with over 1 billion daily active users worldwide. With a strong push internally within Facebook for Messenger bots, lots of companies and developers alike have been heavily investing in such bots.

SMS remains a baseline option, and companies continue to use it to send automated reminders and information. As different messaging apps gain a foothold, such as Line and Telegram, their bot platforms will become more attractive for companies to invest in. (WhatsApp as a bot platform is still conjecture at this stage.)

By platforms, the type of bot content and interaction paradigm also differs. Line and Kik bots tend to be more brand engagement focused, and are more likely to be “loudhailer” type bots (mostly announcements and promotions) than SMS or Messenger bots, which tend to be more varied across support and brand engagement.


Brands are companies that have launched their own bots, split by bot type in specific quadrants as defined above.

Marketing bots tend to be largely campaign-driven, where they can be used effectively for driving engagement in short bursts. Longer-term marketing or sales efforts in the market are still mostly experimental, as it may be hard to define metrics for success without a strong indicator from the proof-of-concepts.

Support bots, however, have been around for much longer, and customers are already used to them. Metrics for deflection and customer satisfaction may also be more well-defined; hence there will be a “flight to quality” in this space to those providers that genuinely can deliver on their promises to answer customers well, not piss them off, and elevate the support experience.


These are supporting tools used by the providers and brands or by bot developers.

This is a section that is pretty hard to characterize, because many companies won’t just reveal their tech stack where you can find them. Just based on our own exploration and interviews with other bot companies, these are some of the more useful tools that contribute greatly to the bot-building endeavor.

Using this landscape

What does your business need: marketing or support?

  • If you require constant interaction with consumers to drive engagement or sales (whether companies such as Victoria’s Secret or even governments such as, consider a marketing chatbot.
  • If your products require heavy customer support or assistance (electronics companies such as Apple), you should focus on a customer support bot.

How do you do it: buy or build?

  • Does your company have the resources and capabilities to build software in-house? For example, Skyscanner has a strong existing tech team and robust algorithms; hence, they can apply that to their flight search bot.
  • Does your company have the resources to cope with complex NLP and data science issues that might arise? If no, then you might be better off outsourcing the development of bots, which is not your core business.

Should you do it: strategic or faddish?

  • If your value proposition involves providing convenient and fast service to your customers, then developing bots in-house may enhance your value proposition and strengthen your company’s competitive advantage in the long run.

What do you want to invest: all-in or experimental?

  • Do you want or need to have a full control of conversations or customer data and already have a good idea what a bot should achieve? If yes, then find an enterprise-grade provider or build it in-house with a specific team dedicated to the project for at least 3-6 months.
  • If you’re simply experimenting with bots, then it’s fine if you sandbox some data and build a small use case on the cloud, working with a provider for a proof-of-concept or hacking together a bot internally.

What’s next

We’ll continue updating this map as we go along. If you think there’s a brand, provider, or tool that should be added, please let us know! If you’d like to argue for or against any classification, tell us that as well, and we can have a healthy debate.

Thanks for reading, and don’t forget to share if you found this useful!

More examples for each quadrant can be found on the KeyReply blog.

Carylyne Chan is the COO at KeyReply, a bot analytics platform.

Forget chatbots — you should create a workbot instead

It’s no secret that chatbots are growing in popularity. From Facebook’s ecommerce bots for consumers to a plethora of customer service tools that now rely on chatbots to interface with customers, it’s clear that consumer chatbots have hit mainstream. Even Apple is expanding its commitment to chat technology with the release of Business Chat at a recent WWDC, allowing consumers to interact with businesses through iMessage.

Because chatbots can automate across a variety of repeatable tasks such sales, customer service, and online banking, they are also ideal candidates for the workplace. In fact, 80 percent of businesses are already leveraging chatbot technology. But what about employee-to-company interaction through bots? Chatbots designed for the work environment, or workbots, could become the next step function in work productivity.

Automation can become a distraction

One of the biggest challenges for the modern corporate citizen is the variety of complex systems that are needed in order to get things done. Organizations have spent a fair amount of effort in the past three decades automating nearly every aspect of their business — from inventory management to quote-to-cash to order fulfillment. The problem with these applications is that they are too complex for typical employees and were designed to be used on a desktop, and interacted with via a mouse and keyboard. Furthermore, in order to get their job done and fulfill their commitments, a typical employee must interact with multiple applications, each with its own learning curve.

Companies have been struggling to simplify the interfaces to these applications as well. It started in the late 1990s and early 2000s with the web, when most of the application front-ends were browser-enabled to allow client-free access. This was followed by employee portals, which consolidated all the useful applications into a central place, and is now continuing with enterprise mobility, which is attempting to expose some of the most useful business workflows as mobile apps.

While enterprise apps make sense for tasks that are performed frequently or on a regular basis (like submitting your weekly time sheet or approving expenses), the valuable real estate they occupy on the already cluttered home screen makes less sense for the long-tail of tasks that are accessed less frequently.

So yes, there might be “an app for that,” but … do I even want an app?

Workbots to the rescue

Workbots could be the cure for what’s often called “app fatigue.”

They work within the corporate messenger environment (such as Jabber, Skype for Business, Slack, and others) and respond to commands and questions in natural language, whether typed or dictated. They have access to all the corporate information needed to get the job done and can perform complex tasks across multiple systems. The workbot knows what tasks are executed in which back-end system, so the user doesn’t have to know. Because bots rely on natural language processing (NLP) — the ability of humans to interact with computers using free-form language — workbots can help an employee get to the starting point quickly and without any training, in the same way a search engine would, and then help guide the user through the task in a step-by-step fashion.

Chat is no longer just about communication, it’s about bringing the user information. For instance, in the workplace, people could use workbots to put together a schedule, get assignments or basic information, find other employees, and approve or deny requests. The airline industry is a good example, ripe to take advantage of workbots for workers at the edge of the enterprise: baggage handlers, warehouse employees, and truck drivers. Rather than investing in additional IT equipment, those workers can use workbots to gather information, report sick days, or check on the status of baggage.

Natural language processing is here to stay, and it is already transforming the way people work. Generation Y and millennials already prefer to communicate via chat conversation, so workbots fit with their expected mental model of getting things done in the context of a chat conversation. This by no means is a replacement for mobile apps, as there are perfectly good scenarios where apps make lots of sense. Rather, workbots are an augmentation that can extend the reach of back-office applications to the very edges of the enterprise and result in a significant productivity gain, faster response time, and better data accuracy, which in turn leads to better decision making.

Oren Ariel is the cofounder and CTO of Capriza, a mobile app platform.

How voicebots lighten our cognitive load

The announcement of Apple’s HomePod smart speaker at last week’s WWDC event marks the latest entrant to a growing market for voice assistants, currently led by tech giants Amazon and Google.

Each new launch brings the promise of a slicker user experience and a more efficient use of our time. But it’s also driving the formation of a new kind of relationship between the user, the tech, and the company or brand behind it.

It might take several iterations of these voice assistants to integrate seamlessly into our daily routines, but the first stage for any company looking to take advantage of this new tech will be to ask: How do users feel about it all?

We recently partnered with Mindshare Futures and J. Walter Thompson Innovation Group for their Speak Easy research project to answer that very question. Our portion of the study involved observing 102 smartphone users as they carried out a selection of tasks using Amazon’s Alexa, Google Assistant, text-based search, and questions directed to a real person. While the users performed these tasks, we monitored their neurological responses to the experience using steady state topography.

The findings combine user brain data with survey and interview responses to get a broad picture of our developing relationships with voice technology.

Lightening the load

We’ve all heard about Siri, Alexa, Google Home, and Cortana, but many of us don’t naturally warm up to voice assistants. In fact, our neuro-research shows a significant lack of emotional response to interactions with an assistant compared to face-to-face human interaction. However, it should be noted that even in the relatively short period of time people were taking part in the study (about 30 minutes), these responses improved as users become more at ease with the technology.

Getting used to voice assistants should come quite naturally to us. Our research found that the likes of Alexa demand far less of users than text-based interactions. This lighter cognitive load is probably because speaking comes more naturally to us than text-based interaction does. That, of course, makes the whole process quicker, too — we speak at a rate of about 150 words a minute, three times faster than most typing.

That’s encouraging news for companies investing in new voice applications to give users a more convenient experience. As for developing a more emotional connection between users and this new technology — that’s a fine line to walk.

Uncanny territory

Voice assistants can evoke a surprising range of responses from users that we might normally expect to be reserved for humans.

Mindshare and JWT’s study showed over one third (37 percent) of participants claiming they loved their assistants so much they wished it was a real person. Perhaps the most alarming find was that one quarter of users said they fantasized about their voice assistant!

While these claims may sound extreme, we can understand how these feelings arise.

Have you ever spotted a surprising arrangement of wall fittings, an electrical plug, or a bathroom sink that clearly resembles a face? It’s a side effect of our tendency to understand our surroundings in easily relatable terms — and what’s more relatable to humans than a human face?

Likewise, we may find ourselves attributing thoughts and feelings to objects or animals.

So as assistants become more humanlike in terms of their services and responses to us, we will in turn attribute a greater degree of human personality to them. This could, to some extent, result in strong feelings that we’d normally have toward a fellow person. Strong feelings of love, anger, and frustration can all be felt towards Alexa or Siri, and by extension the brands behind them, simply because of the way we relate to the world around us.

So there is a dilemma for voice tech developers — do you pursue humanlike personality attributes and navigate the emotional side effects that may produce or keep your assistant firmly in robot territory?

The best strategy seems to be pursue more humanlike connections, but only to a point.

A more natural, human experience encourages more natural interactions that are easier for us to manage. As a result, we’ll use those assistants more. However, there is a point at which the human-technology spectrum falls into “uncanny valley” territory.

This is the phenomenon of encountering a robot, or in this case a computerized persona, that falls between the “human” and “robotic” categories — almost human, but with something not quite right. Things that fall into the uncanny valley tend to trigger strong feelings of unease and even revulsion that turn us away from interacting with the offending object in future.

Research and deliver

The uncanny valley can make developing voice assistant tools seem like a precarious balancing act, but thorough user testing in these formative stages will help to highlight the aspects that might turn a potential user off of the technology.

Otherwise, the most important consideration for future progress in voice interfaces is simple utility. Eighty-seven percent of Mindshare and JWT’s respondents agreed that “when technology works properly, it really simplifies my life.”

This is the promise that years of sci-fi has set up for voice developers — efficiency and simplicity, to the point of preemptive service. Once the emotional nuances are ironed out, the biggest barrier to adoption for Apple, Google, and Amazon’s tech will be if their assistants don’t deliver on that promise.

Heather Andrew is the UK CEO at Neuro-Insight, a neuroscience-related market research company.

One year later, here’s the state of the chatbot economy

“What’s the state of the global chatbot economy today?”

That’s a question that I’ve been asked a lot lately, and one that I’m hoping to get more answers to by attending the second edition of Chatbot Summit in Berlin at the end of the month.  Since bot mania took over the tech world nearly a year and a half ago, not a single month has gone by without significant news coming from both startups and the usual suspects (i.e. Facebook, Google, Amazon, Microsoft, etc.). Needless to say the space has been hyperactive since the beginning, and it doesn’t show any sign of slowing down.

That said, a lot of brand managers and chief digital officers are still new to the bot world. As such, I thought it would be handy to go back in time and take a snapshot of where the bot ecosystem stands today.

By the numbers

As of mid-2016, more than 11,000 Facebook Messenger bots and 20,000 Kik bots had been launched. Over the last year, you could’ve shopped New York Fashion Week pieces from the Burberry bot, asked the Starbuck’s Pumpkin Spice Latte bot its favorite book, or sent emojis to the British Airways bot to get vacation recommendations. Fast forward to this stat from April 2017: 100,000 bots were created for Messenger alone in the first year of the Messenger platform.

VCs have started paying attention to chatbots too. In the first six months of 2016 alone, $58M was invested in chatbots and 29 new bot startups were founded. Slack launched an $80M fund last year to invest specifically in bots running on its platform. There is, in fact, even a website that tracks all of the chatbot financings as they happen.

Race between the tech giants

For the past few years, the industry giants have been competing for the top spot in the race to build the best chatbot and the best bot platform.

“Bots are the new apps,” Microsoft CEO Satya Nadella said in March 2016, setting off the chatbot revolution. But the company took to the movement with a bit of a rocky start last year. An AI chatbot made for Twitter, Tay got taken down about 24 hours after its launch when its tweets became racist and hate-filled. After that, Microsoft launched Zo, a bot for Kik , in December 2016 and then Ruuh, an AI bot for the Indian market, this March.

Facebook’s exclusive AI butler bot, named M, was released in 2015 to an exclusive group of 10,000 users. It’s one of the few bots that isn’t rules-based; instead, this virtual assistant isbuilt on algorithms and, when faced with complex requests that those algorithms can’t handle, powered by humans.

Google, on the other hand, has been busy amassing tools to support chatbots. For example, the company acquired, a conversational user interface platform used by over 60,000 developers, last September for an undisclosed amount. And in May 2017, Google debuted a chatbot analytics platform called Chatbase that can make suggestions on how to improve bots based on user analytics.

This last April, Amazon opened Amazon Lex to the public. Lex is a platform that gives developers access to the same tools that power its digital assistant Alexa, and it bundles speech recognition, text recognition, and conversational interfaces. In the same week, Amazon also announced its AWS Chatbot Challenge–a competition to create a lifelike user experience.

Samsung bought Viv, billed as a more powerful Siri, last October for more than $200 million and launched Nexshop Training in February, a virtual assistant chatbot built for training retail personnel.

Apple’s response to the chatbot movement, in a very Apple-like move, didn’t involve any chatbots. Known as iMessage Apps, developers can build app extensions that live inside an “app drawer” in iMessage. For instance, in a Square Cash iMessage app, there would be a green widget instead of a keyboard so you can input the amount of money to deposit.

Brands are joining the conversation

Following the leadership of tech giants, brands are joining on the fun. Last December, 80% of brands said in a survey that they were using or plan to implement a bot by 2020.

Hundreds of brands have created their own bots across verticals as varied as Casper’s text-based Insomnobot, which will stay up with you during your sleep-deprived nights, to Sephora’s beauty bot that will recommend makeup tutorial videos via Kik and Messenger. You can also find news bots that update you daily, sports bots that tell you the score the second the game ends, and finance bots to check your account balance or send money.

The next frontier

This all happened in the last year or so. Imagine where we’ll stand in 2018, as interest in bots starts to transcend Silicon Valley and continues to spread across Madison Avenue, where ad agencies increasingly evangelizing brands to the virtues of chatbots.

Chatbots themselves are getting better and more ubiquitous, and the tools for building and analyzing them are becoming more plentiful. So what’s next?

First, bots will need to be interoperable across platforms. You should be able to find the same bots on Facebook, Kik, Alexa, Google Home–any messaging platform you choose–and they should all be able to remember your data across platforms to create a seamless user experience.

Second, we’ll need to find ways to make chatbots more easily accessible. Discoverability is still a major problem in the bot ecosystem that needs to be addressed in order for chatbots to become mainstream and democratized.

One thing is sure: we are living in exponential times and I cannot wait to see where we’ll be in twelve months. Probably light-years from where we are now…we’ll see. But this is clearly just the beginning of the chatbot revolution.

Étienne Mérineau is the cofounder and Head of Conversation Design at, a chatbot development platform for brands.

Are chatbots just another fad?

In April 2016, Facebook launched an open version of Messenger which invited developers to create artificial intelligence chatbots that would interact with Facebook users. The excitement that followed was immediate, and by June 2016, 11,000 bots had been made available.

Chatbots were predicted to change the way consumers and brands interacted. Interactions like calling a company’s customer service line and punching in a number to select a menu option, or locating a confirmation email and printing the confirmation code, were going to be replaced by the help of brand chatbots. All of our online purchases, dinner reservations, trip details, etc. were going to be handled and taken care of all within our messaging apps.

The most exciting part of all this was that chatbots were going to understand the nuances in human language, i.e., context, tonality, and cultural references, and be able to respond accurately, in kind, to customers. However exciting this may have seemed, it was only going to work if these chatbots could properly understand you, which has always been hit or miss.

Straight away, the bots appeared to be a disappointment, with many early adopters comparing the bots’ intelligence with that of the Microsoft Office wizard from the 90’s. Now, as we reach the middle of 2017, we still use emails, phone calls, and mobile apps to interact with brands. In fact, few people have ever tried to use a chatbot, and even fewer companies have spent the time and money to deploy them.

According to a recent Forrester Research executive survey, just 4% of companies have deployed chatbots, but 31% are testing them or plan to roll them out. The percentage of companies using human-manned chat services is still higher.

All of these facts and negative opinions have many people wondering, are there any chatbots that users have actually found useful? And if not, is this evidence that chatbots are nothing more than a fad, ala Facebook’s “poke feature” or Pokemon Go? Let’s find out by first understanding what exactly it is that, up until now, has made chatbots so disappointing.

What are chatbots missing?

There is one traditional argument against chatbots that everyone can seem to agree on and that is that their interactions with clients can seem canned or automated, rather than human-like. Early adopters claim chatbots have the same variety of conversational skills as automated call menus. Users can ask very general questions to a chatbot to get a very standard, short response.

Ted Carmichael’s experience provides a perfect example of a chatbot lacking language capabilities. He tried to buy a pair of pants using the retail shop, H&M’s, chatbot.

Two messages into the conversation with the H&M chatbot and Ted’s already made an error. He has hastily written “Women’s” instead of “Men’s” when asked to choose between a type of clothing. Ted attempts to backpedal to correct this mistake, but the chatbot isn’t equipped to handle any responses other than the ones it has presented Ted.

The chatbot offers back the same canned response to Ted’s numerous attempts at crafting the magical message that will make the chatbot understand that he wants to go back. Eventually, Ted cuts his losses and moves on to try his luck with a chatbot from a different company.

While this mistake was the consumer’s fault, customers change their minds all the time and wanting to go back or choose a different item is part and parcel of the customer service industry. If chatbots are going to take over as an alternative to emailing and calling brands, they’ll need to be able to help consumers through the whole buyer journey, mistakes and all.

Have any useful chatbots been deployed?

Chatbots that have been designed to be a bit less ambitious can operate successfully thanks to a simple set of actions, narrowing the possible variations in commands and responses.

One of these chatbots is the Shopify Facebook Messenger bot. This bot will collect all the web locations of items that you’d normally bookmark and place them together in one list inside your Facebook Messenger.

Now, users can forget about saving links and bookmarking pages. Their shopping wish list will be waiting for them inside Facebook Messenger. Also, for items that are out-of-stock, users can save those pages too, and the Shopify bot will notify the user when the product is in stock again. It’s a nifty app, and it works because it depends on simple Natural Language Processing (NLP) and Machine Learning (ML) technologies. 

Because of the lack of advancement, thus far, in NLP and ML technologies, chatbots are only able to handle basic interactions for now. These technologies help chatbots recognize the many nuances in human conversation and respond in kind. This technology is still very much in its infancy, which is why chatbots currently seem like a feature from the 90’s. Tech companies Google, IBM, and Amazon, are making substantial investments to advance the functionality of AI chatbots.

While these advancements progress slowly and chatbots are resigned to a mere level of functionality, this level of functionality is still not giving users any reason to choose to interact with a chatbot over emailing, calling, or mobile apps. But while the first year with AI bots was disappointing, tech experts are not ready to dismiss chatbots just yet.

Facebook’s Messenger app is growing at a faster rate than the Facebook platform itself. Messaging apps continue to be where people spend the most time when on their phones, which leaves a lot of potential for chatbots, especially to infiltrate the e-commerce markets. For the F8 conference in 2017, chatbots and e-commerce were still a big part of Facebook’s conversation. Currently, Messenger has added a Discover tab to make it easier to locate particular bots.

With chatbots in messaging apps, brands could learn more about their customers’ preferences and build user engagement. Needless to say, the desire for chatbots in the market hasn’t gone anywhere.

So what do we do while we wait for chatbots to advance?

The technology isn’t quite there yet, so until then, the best option for brands to use chatbots successfully is to offer a human-bot hybrid. Hybrids incorporate both chatbots and customer support from an actual representative in one experience. Chatbots engage with customers fielding the easy questions, and then at a certain stage the bot directs the consumer to a support person who can finalize the process and answer detailed questions. This way, if the customer gets stuck in a confusing conversation with a chatbot, the support person is just moments away from intervening to make sure the process goes smoothly.

Ted Carmichael, who was mentioned earlier having a bad experience with an H&M bot, found success when working with Burberry’s bot. He requested the bot show the trousers they had in stock, but when it came to asking specifics, like pant leg length or type of fabric, a customer support person took over to make sure all of Ted’s questions were answered adequately.

In conclusion, they’re not a fad — not yet

So, chatbots may not have had the impact that we eagerly anticipated just yet, but this doesn’t mean that they won’t. Rome wasn’t built in a day, and developers will have to go a few rounds with chatbots before they reach their full potential in becoming our own personal AI assistants. Let’s just hope they reach their full potential sooner rather than later, before another advancement in technology steers us in another direction.

Albizu Garcia is the CEO and cofounder of Gain, a marketing technology company .

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