I’m working on a marketing discussion question and need an explanation and answer to help me learn.
After reading the HubSpot case (attached), please answer the following questions:
What do you think of adopting chatbot in general? Do you see any potential of chatbot in marketing, or not at all? If yes, why? What are the pros of chatbot that can outweigh its cons? If no, why? What are the cons that can outweigh the pros? Be specific as to why. You can support your argument using details from the case if needed.
Do you think if a company should disclose that customers are interacting with AI when they are using the chatbot? If yes, why? If no, why? Be specific as to why. Provide your reasoning focusing on its impact on user experience and customer relationship building. What are the potential risks if you disclose (if yes) or don’t disclose (if no) it? You can support your argument using details from the case if needed.
In which stage in the customer’s purchase funnel do you think the chatbot can provide the most value? Why? Be specific as to why. You can use the details from the case if needed.For the exclusive use of A. Kliuchyk, 2023.
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REV: OCTOBER 22, 2019
JILL AVERY
THOMAS STEENBURGH
HubSpot and Motion AI: Chatbot-Enabled CRM
On September 20, 2017, HubSpot, an inbound marketing, sales, and customer relationship
management (CRM) software provider, announced that it had acquired Motion AI, a software platform
that enabled companies to easily build and deploy chatbots to interact with their customers. Chatbots
were pieces of conversational software powered by artificial intelligence that had the capability to
engage in one-to-one chats with customers on their preferred chat platform, such as Facebook
Messenger or WeChat. Fueled by pre-programmed algorithms, natural language processing, and/or
machine learning, chatbots conversed in ways that mimicked actual human communication.
Since its founding in November 2015, Motion AI had facilitated the building of 80,000 bots for
brands including T-Mobile, Kia, Sony, and Wix, which were busy conversing with customers via 40
million total chat messages sent to date. The software was simple to use and enabled anyone, regardless
of their level of technical knowledge, to build and manage a chatbot. The entire Motion AI team,
including founder and CEO David Nelson, joined HubSpot following the acquisition.
HubSpot saw great potential for chatbots for its business-to-business (B2B) customers, who could
use them to automate many of their customer interactions that were staffed by humans at the time of
the acquisition. Unlike other automated customer service solutions, such as interactive voice telephone
response (IVR) systems that were almost universally disliked for their robotic nature, chatbots were
getting closer to passing the Turing Test, simulating a human conversational partner so well that it was
difficult to sense when one was chatting with a machine. Thus, chatbots had the potential to enable a
company to nurture and manage one-to-one customized relationships with prospects and customers
efficiently at scale by making artificial intelligence the new frontline face of their brands.
Chief Strategy Officer Brad Coffey and Chief Marketing Officer Kipp Bodnar were responsible for
working with Nelson to bring Motion AI’s technology into the HubSpot family of products. Before
unleashing bot-building technology to its customers, HubSpot first needed to develop some best
practices for the use of chatbots for CRM. Without proper instruction, Coffey worried that companies,
in their rush to incorporate the newest marketing technology, would build bots that would do more
harm to their brands than good. He prognosticated:
In the not-so-distant future, there’s a bleak, forsaken landscape. Civilization, absent.
Communication channels, silent. All of the people have fled, terrorized by never-ending
notifications and antagonizing messages. What could cause such a desolate scene? Bad
HBS Senior Lecturer Jill Avery and Professor Thomas Steenburgh (University of Virginia) prepared this case. It was reviewed and approved before
publication by a company designate. Funding for the development of this case was provided by Harvard Business School and not by the company.
Jill Avery has served as a paid consultant to HubSpot. HBS cases are developed solely as the basis for class discussion. Cases are not intended to
serve as endorsements, sources of primary data, or illustrations of effective or ineffective management.
Copyright © 2018, 2019 President and Fellows of Harvard College. To order copies or request permission to reproduce materials, call 1-800-5457685, write Harvard Business School Publishing, Boston, MA 02163, or go to www.hbsp.harvard.edu. This publication may not be digitized,
photocopied, or otherwise reproduced, posted, or transmitted, without the permission of Harvard Business School.
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HubSpot and Motion AI: Chatbot-Enabled CRM
bots. Okay, maybe that sounds a bit too much like the next superhero blockbuster. But it
wouldn’t be the first time that brands abused a new technology until people were buried
in spam up to their eyeballs.
He continued, “Five percent of companies worldwide say they are using chatbots regularly in 2016,
20% are piloting them, and 32% are planning to use or test them in 2017. As more and more brands join
the race, we’re in desperate need of a framework around doing bots the right way—one that reflects
the way consumers have changed.”
The Motion AI technology would be incorporated into HubSpot’s product over the next few
months, so the team had little time to make some important decisions. First, they had to clearly assess
the implications associated with the use of bots versus humans to create, nurture, and manage
customer relationships, and determine whether and where bots were appropriate for use during
marketing and selling processes. Second, they had to decide to what extent to anthropomorphize
chatbots. How human-like should they be? Was a conversational user interface (UI) the desired
solution, or would a more functional UI produce more efficiency for customers? How much should the
bot embody the brand’s personality or mimic the conversational style of an individual user? Should
users know when they were interacting with a bot, or could human-like bots create stronger
relationships?
Historically, HubSpot had “practiced what it preached,” using its own products to build its
business. Coffey and his team had to consider whether to use chatbots to nurture and service its own
customer relationships. Currently, a team of chat representatives worked to engage, nurture, and prime
prospects for HubSpot’s sales team. Could they and should they be replaced with chatbots? Was
HubSpot ready for bots to become the face of its brand to prospective customers?
HubSpot’s Acquisition of Motion AI
HubSpot was founded in 2006 as an inbound marketing software-as-a-service (SaaS) a solutions
provider that helped primarily business-to-business (B2B) companies develop online content, attract
visitors to the content, convert the visitors into sales leads, and finally acquire the visitors as customers.
HubSpot’s software helped companies develop, host, disseminate, and analyze digital content to
execute inbound marketing programs, a collection of marketing strategies and techniques focused on
pulling relevant prospects toward a business and its products during a time when these prospects were
actively searching for solutions.
In 2016, HubSpot’s revenues were up 49% to $271 million and were derived from 23,226 small and
medium-sized business (SMB) customers (see Exhibit 1 for the company’s financials). The company
was excited to expand its value proposition and reposition itself as a robust, multi-product growth
stack platform that helped SMBs combine all of their marketing, sales, and customer success software
solutions into one convenient and easy-to-use platform. The growth stack platform was premised on
delivering a promise “to fuel your growth and build deeper relationships, from first hello to happy
customer and beyond,” and included three product solutions:
•
Marketing Hub: Grow your traffic and convert more visitors into customers. Prices ranged from
$50/month for a starter package to $2,400/month for an enterprise solution.
a HubSpot’s software was sold via a software-as-a-service (SaaS) model, where users paid a recurring monthly fee to access the
software.
2
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HubSpot and Motion AI: Chatbot-Enabled CRM
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•
Sales Hub: Drive productivity and close more deals with less work. Prices ranged from
$50/month for a starter package to $400/month for a higher-end, professional solution.
•
Customer Hub: Connect with your customers on their terms and help them succeed. As of
September 2017, HubSpot was offering this product free with its other products.
At the heart of the new platform was the free CRM system that allowed companies to collect and
analyze deeper insights on every contact, lead, and customer. A feature called “Conversations”
empowered the CRM tool to collect customer conversations from Facebook Messenger, web chat, social
media, email, and other messaging outlets into one cross-team inbox to help marketing and sales teams
manage, scale, and leverage one-to-one communications with their customers across all conversation
channels. With its acquisition of Motion AI, HubSpot was hoping to further power efficient and
effective customer conversations for its clients by introducing chatbots that would better engage,
convert, close, and delight their customers at scale. Said Bodnar:
Today’s buyers expect that conversations with a business happen where they are. That
might be the website, but it could also be social media, Skype, Slack, or any messaging
app. They expect that conversations are portable. Regardless of where a conversation gets
started, it should be able to be transferred to any other channel seamlessly. A thread
kicked off on live chat should be able to be passed to Facebook Messenger or email
without data loss or crossed wires. And, they expect that conversations have context.
Context shouldn’t leave with the person who fielded the initial inquiry. All of a customer’s
historical interactions and information should be attached to a common record which
populates instantaneously. We need new technology paired with automation to live up
to our buyers’ expectations and make these types of conversations a reality.
The Market for Chatbots
Chatbots were part of a wave of new artificial intelligence tools that were changing the way people
interacted with technology. Digital virtual assistants housed in a smartphone, desktop, or laptop
computer, such as Apple’s Siri and Microsoft’s Cortana, had paved the way for person-bot
communication. More recently, Amazon’s Alexa, which could be awakened at any time by a voice
prompt that spoke her name, provided ambient virtual assistance to consumers in their home.
Unlike these virtual assistants, chatbots were less sophisticated and tended to specialize in
executing simple tasks rather than providing omnipresent and wide-ranging functionality (see Exhibit
2). While the most advanced virtual assistants were powered by artificial intelligence, which enabled
them to understand complex requests, personalize responses, and improve interactions over time, most
bots in 2017 followed a simple set of rules programmed by a human coder who simulated a typical
conversation. The coder programmed the bot to prompt a conversation by delivering a series of queries
to a customer and then to answer the customer with canned responses triggered by simple if-then
statements. Explained Derek Fridman, Global Experience Director at Huge, a digital agency that helped
its clients build chatbots, “The illusion that HAL [the computer from the movie 2001: A Space Odyssey]
is out there, and the machine is alive is just that: an illusion. There’s machine learning taking place and
algorithms making decisions, but in most cases, we’re scripting sequences.” 1
According to McKinsey & Company, 2 technology companies spent between $20–$30 billion on
artificial intelligence in 2016. The market for chatbots was estimated to be $1 billion and was expected
to nearly double by 2020 and triple within a decade. A 2017 Forrester study 3 claimed that worldwide,
57% of firms were already using chatbots or planned to begin doing so shortly, and 80% of businesses
3
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HubSpot and Motion AI: Chatbot-Enabled CRM
wanted chatbots in place by 2020. In the U.S., 31% of marketers already used chatbots to communicate
with consumers, with 88% of them deployed on Facebook Messenger. After Facebook opened its
Messenger platform to chatbots in 2016, 100,000 were created within the first year. 4
Another 2017 study 5 found that among companies using AI, the most common use cases were
customer service (39%), marketing and sales (35%), and managing noncustomer external relations
(28%). (See Exhibit 3 for examples.) It was estimated that in 2017, 60% of customer service support
issues could be resolved by chatbots—and that number was expected to be 90% by 2020. Companies
were finding that chatbots completed customer interactions at twice the speed and a fraction of the cost
of human-provided telephone support. Oracle estimated that the cost of building a chatbot ran from
$30,000 to $250,000 depending upon its sophistication. While chatbots were reportedly saving
businesses $20 million per year in 2017, they were expected to help cut costs by more than $8 billion
per year by 2022.
Chatbots and CRM
HubSpot’s CEO, Brian Halligan, was excited by the potential, saying, “It’s impossible to ignore the
impact of chat and messaging, not just on the way B2B companies operate, but on society as a whole.
We’re in the midst of a massive shift as businesses embrace this new platform and consumers come to
expect more immediate, always-on communication from brands.” Coffey echoed his enthusiasm:
There’s no downplaying what bots could do. For brands and consumers alike, we have
a chance to facilitate a new type of communication and commerce. Research would be
convenient, purchases streamlined, and service personalized. A conversational interface,
powered by bots, can facilitate a response that’s as fast as talking to a human, with the
depth of a full website, and a simple texting-like interface that everyone is already
accustomed to using.
Bots provided instant responses to customers’ needs without the stress of waiting in a call queue or
having to call during business hours. Calling or emailing a company was quickly falling out of favor
with consumers; TechCrunch reported that 9 out of 10 consumers wanted to use messaging to interact
with companies. Because chatbots were deployed within messaging app platforms, such as Facebook
Messenger, WhatsApp, and WeChat, customers could speak with a company and accomplish their task
without having to leave their preferred chat interface and without the hassle of downloading yet
another app to their smartphones or visiting a company’s website. Five billion active users accessed
messaging apps each month, and their usage had surpassed that of social networks. According to
Facebook, “convenience creates closeness . . . messaging makes commerce personal.” 6 Research
showed that 63% of people said chatting with a business made them feel more positive about the
relationship, 55% were more likely to trust the business as a result of their chat conversations, and 53%
were more likely to shop with a business they could contact via a messaging app.
HubSpot’s own research showed that consumers were showing greater interest in using messaging
apps (see Exhibit 4). Explained Public Relations Manager Ellie Botelho, “Consumers want to be able to
engage with a company when and where it’s personally convenient for them, meaning that businesses
that are unable to respond quickly are leaving money on the table.” Added Coffey, “The way folks
communicate externally is shifting towards messaging. Large companies manage these via live chats
with an army of employees responding in real time. Few smaller companies can pull that off.”
4
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HubSpot and Motion AI: Chatbot-Enabled CRM
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Delivering a Human Touch via Artificial Intelligence
A Preference for Humans?
By 2017, consumers could order a Domino’s pizza, hail an Uber, book a flight via Travelocity, and
reorder their favorite lipstick from Sephora via chatbots, all without leaving Facebook Messenger. The
B2C world was rapidly adopting chatbots as an efficient way to execute simple transactions with
customers without devoting human resources to them and without forcing consumers to visit their
websites or mobile apps. Chatbots could be deployed to help with many different types of customer
interactions that were common in B2B customer relationships, such as booking meetings, qualifying
leads, diagnosing problems, and providing customer service to solve them—but it was unclear whether
B2B customers would be open to robotic rather than human support, as B2B customers were often more
demanding than B2C customers. “It’s no secret that today’s consumers expect personalized, relevant,
contextual, and empathetic brand interactions throughout the entire buying process,” proclaimed
digital analyst PJ Jakovljevic. 7 B2B customer relationships were often more complex, more relational,
and less transactional, so they often required the deft touch of a highly trained consultative salesperson.
“Chat is good when powered by humans. Chat is awesome when powered by AI,” claimed
Christopher O’Donnell, HubSpot’s Vice President of Product. Bodnar, however, wasn’t so sure,
responding, “Automation is a funny thing. Too little is the enemy of efficiency. Too much kills
engagement.” He continued:
Think about email. Automated email nurturing campaigns were the answer to
individually following up with every single person who downloaded a piece of content
from your website. In the name of efficiency, marketers queued up a series of emails via
workflows to automatically deliver ever-more-helpful content and insights, gradually
increasing the person’s trust in the company and stoking the flames of their buying intent.
If at any time they had a question, they could respond to the email and get routed to a
person who could help. But as the number of inbound leads skyrocketed, this system
became untenable. The dreaded noreply@company.com address was the solution for
scalability. Over time, this set the expectation with buyers that marketers didn’t want to
have a conversation with them via email. Automation made us more efficient, but at the
cost of relationships—ultimately defeating the purpose.
Then came live chat. Buyers were empowered to get answers to their questions in real
time from a real person. Better yet, this interaction took place directly on the company’s
website—where they were already doing their research. We started using website chat at
HubSpot in 2013. Over the past four years, live chat has facilitated countless conversations
between curious prospects and our business. But, just like what happened with email
nurturing, at a certain point the system started to strain. According to our usage data, one
in every 30 website visits results in a chat. For companies that receive thousands of
website visits a day, trying to keep up is daunting. And, customers are again the ones
suffering when companies can’t manage the demands of live chat.
Recent research found that 21% of live chat support requests go completely unanswered. Even if the buyer gets a response, they can expect to wait an average of two
minutes and 40 seconds for it. I wouldn’t call this “live”—would you? Responding slowly
(or failing to respond at all) on a channel advertised as “live” is a contradiction in terms.
Forcing customers to wait after we’ve set the expectation of immediacy is unacceptable.
We can do better. Today, we’re at the same inflection point we came to with email. What
5
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HubSpot and Motion AI: Chatbot-Enabled CRM
should companies do to accommodate the tidal wave of live chat conversations? Hiring
an increasing number of chat coordinators clearly isn’t a scalable answer. If marketers are
going to advertise “live” channels—we need to step up and deliver.
Consumer research offered conflicting opinions. While 40% of people claimed they didn’t care if
they were serviced by a person or an AI tool as long as they were helped quickly and easily, 42% of
people wanted a human agent to help answer complex questions and requests. Moreover, 75% of
people didn’t think chatbots would be sufficient for complicated troubleshooting, and 90% felt they
should always have the option to transfer to a live agent. Direct experience with existing IVR phone
systems and online chat demonstrated that many consumers still preferred speaking with a live
customer service representative in an instantaneously synchronous manner, pressing “0” for an
operator in IVR phone systems, and bailing out of online chat conversations to dial in to a call center
for help.
Botched Bots
Although bots were chatting with customers at astonishingly high rates in 2017, their record of
success was less high-flying. Facebook reported that chatbots failed to serve customer needs 70% of the
time. As another example, only 12% of bot interactions in the health care sector were completed without
the need to pass off the customer to a human operator. Lamented Coffey:
Bots provide a scalable way to interact one-on-one with buyers. Yet, they fail when
they don’t deliver an experience as efficient and delightful as the complex, multi-layered
conversations people are accustomed to having with other humans. Too often, bots today
don’t understand conversational context, or forget what you’ve said two bubbles later . .
. . Consider why someone would turn to a bot in the first place. Of the 71% of people
willing to use messaging apps to get customer assistance, many do it because they want
their problem solved, quickly and correctly. And, if you’ve ever struggled to have Siri or
Alexa understand what you’re asking, you know there’s a much lower tolerance for
machines to make mistakes.
Despite rapid advances in artificial intelligence, most chatbots were still quite reactive and “dumb.”
Programmed to only recognize a very limited set of commands, they had difficulty with back-and-forth
conversation with humans. According to Tim Tuttle of MindMeld, “The opportunity is clear, but today
most companies still have huge challenges building chat applications that actually work. The industry
is in a state of shock at how hard this is.” 8 Explained Sarah Guo of Greylock Partners, “Language is
hard to model (and program) because it is so ambiguous. Similar sentences can have very different
meanings; seemingly different sentences can have the same meaning. Humans are strange, unruly,
unconscious, and inconsistent in their communication, but make up for that by being so flexible in their
ability to understand imperfect, ambiguous communications from others—based on context.” 9 While
humans effortlessly dealt with this complexity of language, bots stumbled.
While advancements in machine learning were helping, AI required “big data” to be effective, said
Robert C. Johnson, CEO of TeamSupport: “Accurate machine learning requires a huge number of data
points and experiences to pull on. Without that volume, you really can’t do machine learning. In B2B
interactions, you’re dealing with a lower volume of interactions but higher complexity, which can lead
to higher error rates. Chatbots are good for B2C interactions where there’s a high volume and the value
of each customer is not very high.” 10 Bots also struggled to handle complex problem solving. Explained
Daniel Polani of the University of Hertfordshire:
6
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There is an art to handling the exception, and good customer service is often about the
unusual or unanticipated cases involving potentially angry customers. While chatbots can
convincingly source answers to basic questions, AI isn’t yet smart enough to deal with the
rare and exceptional examples . . . . Automated systems might be able to handle regular
cases. But they can’t yet adapt themselves to exceptional circumstances or even recognize
that the flexibility of human intervention is needed. And . . . some situations require not
just human understanding and problem solving, but a level of compassion and empathy.
A chatbot can be programmed to adopt a certain style of interaction, but that will still
sound oddly out-of-place in unexpected or difficult contexts. 11
However, much of the challenge of creating an effective chatbot derived not from the limitations of
the technology, but rather from the difficulties associated with designing a conversational UI—one that
anticipated the conversational flow that a bot would need to have with diverse customers. “The
difficulty in building a chatbot is less a technical one and more an issue of user experience,” said Matt
Harman, Director of Seed Investments at Betaworks. 12 Proclaimed Bodnar, “We need conversational
strategy and the automation of bots. This is what will make us more efficient, but more importantly,
more effective for our customers. This is automation that creates relationships instead of frustration.”
Coffey believed that chatting with a bot should be like talking to a human that knew everything.
But, Altimeter suggested, emotional intelligence was as important as IQ: “Detecting emotion, expressed
in word choice or tone, [is] also critical to ensure that conversational experiences are satisfying for
users.” 13 A strong conversational UI could capture users’ attention through an engaging and evolving
narrative that combined automation with intimacy. However, this required significant relational
intelligence and the ability to perceive differential relational styles and trajectories. Clara de Soto of
Reply.ai agreed, saying, “You’re never just ‘building a bot’ so much as launching a ‘conversational
strategy’—one that’s constantly evolving and being optimized based on how users are actually
interacting with it.” 14 And this was difficult, explained David Shingy of AOL: “The challenge [with
chatbots] will be thinking about creative from a whole different view: Can we have creative that scales?
That customizes itself? We find ourselves hurtling toward another handoff from man to machine—
what larger system of creative or complex storytelling structure can I design such that a machine can
use it appropriately and effectively?” 15 According to Advertising Age’s Annie Fanning:
Fully owning your conversational relationship with your customers requires building
a brand-specific chatbot personality . . . you’ll need word nerds on both the front and back
end to feed and teach your new baby chatbot. Not only does someone need to craft chatbot
responses with personality (brand-guided voice and tone) but a writer/strategist/UX
expert will need to think through the customer journey and provide sample customer
input. To build an effective bot, every use case needs to be considered and a chatbot
response written for every type of interaction you can think of . . . . This means knowing
what your customers are asking, and how they [will] phrase their questions, is just as
important as knowing how the bot will respond. 16
Consumers were getting frustrated with many of the bots with which they interacted. Said one after
interacting with travel-related bots, “Every experience I’ve had has been a total waste of time. I would
love to hear at least one positive anecdote about using artificial intelligence.” 17 Fanning cautioned
marketers about the downside of bots, remarking, “When a chatbot guesses wrong and serves up
content we didn’t ask for, it is at best hilarious, but at worst offensive and embarrassing.” 18 Echoed
USA Today, “These early days of . . . bots . . . are a cautionary tale. Technology may be good and getting
better but nothing replaces a person. That’s unlikely to change for a while, and maybe ever.” 19
7
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How Human Is Too Human?
As HubSpot looked ahead to a world of chatbots, one thing it needed to address was to what extent
bots should behave like humans. Some were suggesting that companies should not disclose that
customers were interacting with AI, but rather, allow them to assume that they were chatting with a
live human in order to reap the benefits of human-built relationships. Said Beerud Sheth of Gupshup,
a bot creation platform, “Chatbots are everywhere. Inside a messaging app, everything is just a thread.
If you’re chatting with an entity, it could be a human or just as easily be a program. Businesses can now
develop a whole range of services that to the user seem like just another user you’re messaging.” 20
“People don’t even always know they’re interacting with bots. The whole thing only works when it’s
just so easy that you don’t even think about the fact that it’s a bot,” said Matthew Hartman of
Betaworks. 21 Left to their own devices, humans had a tendency to interpret computer-generated
conversation as coming from a person anyway, so customers often anthropomorphized chatbots,
observed Arte Merritt, CEO of bot analytics platform Dashbot: “People think about bots for customer
service, but they’re so much more . . . . Users treat the bots as people.” 22 In a humorous example, the
company x.ai humanized its meeting scheduling bot so well that customers were asking “Amy” out on
dates, not realizing that “she” was an AI-driven personal assistant. 23
This often led to an uncomfortable situation labelled “the uncanny valley.” While people generally
preferred to engage with computer programs that were more rather than less human-like, their
response to an anthropomorphized robot would abruptly shift from empathy to revulsion if the robot
suddenly failed to act human enough. Explained Justine Cassell of Carnegie Mellon, “When a bot is
clearly a bot, the person interacting with it generally knows how limited its functions are . . . The bot’s
narrowly defined purpose guides the human that’s interacting with it. By contrast, a smooth-talking
virtual assistant that tries to mimic human speech . . . can create different assumptions. The more
human-like a system acts, the broader the expectations that people may have for it.” 24
However, hiding the fact that a customer was interacting with a bot might make it awkward to
manage the handoff from bot to human when things went wrong. The chatbots of 2017 were not ready
to handle most customer interactions from start to finish; in fact, marketers reported that chatbots were
able to conduct less than 20% of a consumer interaction before they had to pass the conversation off to
a live customer service representative. Advised Bodnar, “Businesses need to help bots and human
service reps to ‘tag team.’ When a complex question arises, the right technology can loop in a human
chat coordinator, and provide a unified record of everything that’s happened in this interaction as well
as the customer’s entire history. This way, the context never gets left behind in the handoff between
bot and human, or the switch from one communication channel to another.”
Thus, HubSpot had to decide whether to advise its customers to build their apps with a human-like
conversational UI or a more utilitarian, non-human, “get things done efficiently” functional UI. People
in the digital age had already been trained to use functional UIs such as search and menu-driven
systems, which were efficient and straightforward and offered a streamlined path to an answer. Bots
should be solution-focused, warned Coffey, particularly for busy B2B customers:
The challenge of building a bot often isn’t a technical one. It’s a conversational
challenge. Your job is to understand the interactions your audience is already having with
your brand. Then, harness the chat interface in a way that surfaces the information your
audience needs effectively. Yes, witty banter is a plus. But, the ultimate mission of a bot
is to provide a service people actually want to use. The best bots identify the core use cases
consumers are looking to solve on a daily basis and provide a conversational approach to
accomplishing that task. Whether it’s adjusting a reservation, updating shipping info for
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an order, or giving medical advice, bots provide a less time-consuming solution than
talking to a human, and a simpler one than digging through a whole website of content.
An alternative thesis was that a more conversational UI would encourage customers to engage more
deeply. Advocated Altimeter, “Consumers have been conditioned to interact with businesses in ways
that are often unnatural and inconvenient: typing in boxes within rigid interfaces that may or may not
accomplish their objective. What experience wouldn’t be better if it were more natural and more
attuned to the way people really communicate—by writing, talking—even gesturing?” 25
AI and Chatbots at HubSpot
The HubSpot team envisioned a number of roles that chatbots could play in HubSpot’s own
business, ranging from taking inquiries from customers at early stages of the buying process, to
assisting salespeople in gathering information about customers and competitors during the sales
process, to providing convenient customer service when questions or problems arose after the sale.
Including chatbots somewhere in the marketing process was the next frontier. Marketing
communications had always evolved along with technology. After the birth of the telephone allowed
people to talk to one another over long distances, marketers immediately used the device to prospect
for new customers. After email was invented and allowed immediate written communication,
marketers started promotional email campaigns. It was only a matter of time before the same would
happen with chatbot communication, so there were a number of opportunities and risks that the team
had to consider.
Coffey explained, “This time, unwelcome marketing has the potential to hit even closer to home.
When you spam someone’s email, there is technology to filter out the noise. With bots functioning
inside messaging apps, you’re invited into a historically personal space. If you use that invitation to
push unwanted and interruptive spam, it can really hurt your brand.” Would consumers readily accept
having chatbots become part of their messaging habits or would there be a revolt? This might be a
particularly interesting challenge for HubSpot, given that the company had grown its brand with
strong anti-spam messages in homegrown social media ads such as 2008’s “Dude, Cold Calling Is for
Losers,” which criticized traditional marketers for intrusive outbound marketing techniques.
Dharmesh Shah, Co-Founder and Chief Technology Officer of HubSpot, was intrigued by the
potential of this new technology, seeing it as the next horizon of marketing. In April 2016, he launched
GrowthBot, which could help him and others within HubSpot answer many of the questions that he
wanted to know—both about his customers and his competitors. GrowthBot was an intuitive,
conversational interface that allowed users to answer questions by querying internal and external
databases (see Exhibit 5). Unlike a website, in which users would point-and-click their way toward
answers, GrowthBot allowed them to find information intuitively. For example, a user could ask,
“What is my open rate on MailChimp?” and the bot would respond with the appropriate answer.
Through his experience with GrowthBot, Dharmesh learned several lessons about how bots needed
to be constructed if they were going to switch people from the familiarity of search to the conversational
interface of chatbots. First, the onboarding process had to be simple and example-based. When a user
first started interacting with it, GrowthBot would immediately prompt the user with “Hi, My name is
GrowthBot. For ideas, just ask: what can you do?” (see Exhibit 6). If the user typed this question,
GrowthBot would instantly provide multiple ideas about how to use the software. A longer list of
questions and commands that could be answered by GrowthBot can be found in Exhibit 7.
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Second, the bot had to provide compelling reasons for the user to return. Part of this was simply
ensuring that the bot could answer relevant questions for the user—that it was truly useful. Bots
provided an interesting opportunity in this sense because users were always telling the bot what they
wanted by asking questions, making them an important market research channel to understand what
was on customers’ minds. Paying attention to the chat log created by these interactions provided
product designers with the consumer feedback they needed to develop product design roadmaps that
better anticipated consumers’ needs. Additionally, bots provided another opportunity for CRM
because they could re-engage users that had gone dormant by suggesting new ways to interact.
Third, UI widgets, the suggestion boxes that bots presented, could simplify the user’s experience if
designed judiciously. Widgets were effective in suggesting new uses and reducing the amount of
typing users needed to do to derive their desired information. However, relying on an interaction that
was too scripted limited how broadly users were likely to interact with the chatbot and did not provide
the designer with the same insights into users’ thought processes as open-ended question designs did.
The HubSpot team was sure that other design principles would arise as they worked more with the
technology. Could they design a bot that would help people abandon their search habit and develop a
new chat habit to obtain information about the businesses with which they were interested in
interacting? Did the rules governing behind-the-scenes, internal bots like GrowthBot differ from
external customer-facing bots that interacted with customers along their purchase journeys?
The Marketing and Sales Processes at HubSpot
Coffey, Bodnar, and Nelson were excited to think about how bots could be incorporated into
HubSpot’s own marketing and sales processes. They were certain that this new technology could
significantly change how marketers and salespeople interacted with their customers. But where in the
marketing and sales process could bots provide the most value? And what were the risks of inserting
chatbots into these processes? Would different relational trajectories evolve if customers interacted
with a machine rather than a person? How might this change the types and strength of relationships a
customer formed with the company? Could HubSpot’s customer relationships be handled by
machines, and what might be the consequences to brand loyalty if they were?
Coffey encouraged the team to evaluate using chatbots at different points throughout a customer’s
purchase journey. As he thought about HubSpot’s marketing and sales funnel, he thought about how
to insert bots into the top of funnel, when the company’s content had just attracted the attention of
prospective customers; into the middle of the funnel, when prospects needed to be educated and
nurtured as they shaped their needs and evaluated HubSpot’s products versus competitive offerings;
and through to the bottom of funnel, where consultative salespeople provided guided demonstrations
of the product and helped prospective customers understand how to integrate the product into their
existing systems. Bots could even possibly provide customer support following the purchase. He said:
At HubSpot, we study human behavior and then build products to match the way
modern buyers shop, learn, and communicate . . . . While the trend towards messaging
was obvious , the key question remained, how could we make it work for our customers?
To get there we started with a simpler question: How could we make it work for
ourselves? We asked our marketing team to see if they could leverage this transition to
find a new way to reach our prospects, have better conversations, and ultimately grow
our business.
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Using Chatbots in the Marketing and Sales Funnel
HubSpot’s prospective customers moved through a process that changed them from a stranger
attracted by inbound marketing content to the company’s website; to a visitor that could be engaged,
educated, and tracked; to a lead that could be identified, nurtured, qualified, and then passed along to
sales for further development, product demo, and the closing of the sale. HubSpot knew that there
were many prospective people in the stranger stage, and designed its inbound marketing content to
serve as magnets to draw them into its ecosystem. Its content was quite successful, and by 2017 this
content was driving millions of website visits per month—amply filling the top of HubSpot’s funnel,
but creating a situation where the company’s sales team couldn’t possibly conduct one-to-one outreach.
Top of the Funnel (ToFu) Once visitors arrived, HubSpot tracked their interactions with its
content through clickstream analysis and began constructing a customer profile for them in its CRM
system. The company worked hard to encourage visitors to self-identify through landing pages and
forms designed to capture prospects’ information (such as contact information and qualifying
information such as size and type of business) so that the company could decide whether to invest in
a one-to-one relationship with a particular customer. However, form completion rates were low; only
about 4% of people who visited hubspot.com filled out a form or interacted in live chat (when
available). Each of these leads ended up costing HubSpot $50 to generate. Bodnar wondered if using a
bot with a more conversational tone in place of a landing page form could help the company garner
more information: “A bot could make you feel like you were just talking to somebody at the company.
It could say things like ‘Oh, what’s your email address so that I can send this to you?’ rather than
providing a utilitarian website form. We could even add in a joke or something in the request to make
it representative of who we are in the kind of tone that our brand would take, so that it wasn’t boring
or stuffy.” The additional upside of a bot was that it would be available 24/7 and wouldn’t be
constrained by the availability and bandwidth of the live chat reps the company was using.
Nelson saw great potential in scripting bots to speak with the voice of the brand, believing that a
unique voice contributed to the formation of a stronger connection. He was also intrigued by the
possibilities of dynamically adjusting a bot’s tone to mimic the desired relational style of an individual
consumer and of using artificial intelligence to perceive signals in consumers’ speech patterns that
would indicate whether they were eager to buy or more cagey and pensive, so that the persuasive
technique that the bot used could be adjusted in real time. He remarked, “We can certainly envision
doing more with real-time sentiment analysis to get to the point where we could refine the tone of the
conversation based on input that we are receiving from the customer. Then, companies would have to
decide whether they wanted to stay in their brand’s voice or mirror the conversational style of the
individual customer the bot is interacting with.”
At the top of the funnel, customers often needed consultative assistance to define the business
problems they were trying to solve and to specify the needs they had for HubSpot’s products. Top of
the funnel tasks included making customers aware of HubSpot’s offerings and educating them about
how these products might meet their needs. Could a chatbot serve this need, or would HubSpot forgo
more profitable customer relationships if a salesperson didn’t engage with customers early to upsell,
cross-sell, and engage them in strategic discussions about the future of their businesses?
Additionally, industry experts noted that broad changes in buying patterns were coming as a result
of the rise of digital technologies. Many prospective customers were delaying or eliminating physical
contact with salespeople, choosing to progress along their purchase journeys without the perceived
pressure of persuasive selling techniques. Digital access was empowering buyers to bypass direct
interaction with a company altogether. The Corporate Executive Board estimated that, in general, 57%
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of the marketing and sales process was often complete before a prospect made physical contact with a
seller. This number was expected to rise as buyers gained greater access to information through the
internet and other digital sources, so that they didn’t need to rely on live interaction with salespeople
to get the information they needed to make their decisions. The HubSpot team wondered if chatbots
could be inserted early into the marketing and selling processes to ensure that the company had an
opportunity to engage with prospects in a more interactive way during the crucial early stages of the
buying process, thereby giving HubSpot a voice and a chance to form a relationship before customers
were willing to speak with a live salesperson.
Middle of the Funnel (MoFu) In the middle of the funnel, customers were learning about
what HubSpot could do for them, comparing HubSpot’s products to competitive offerings, and
developing a sense of the important attributes and evaluative criteria they should use in making their
final choice. A combination of tools, both self service (such as search-driven frequently asked questions
[FAQs] and webinars) and low-cost, light-touch service (such as live chat and email), was used to help
prospective buyers move through the middle of the funnel without engaging too much of the
company’s valuable human resources. HubSpot’s live chat representatives could handle up to three
conversations at one time via email or chat, but as Bodnar noted, if they were handling three at a time,
there was a noticeable delay in their ability to respond to each customer. The cost of a chat rep was
approximately $60,000 per year. Could chatbots replace these reps and still achieve the CRM functions
they were providing? Or would there be a decrease in customer satisfaction with a bot versus a human?
HubSpot used lead qualification to determine which leads were more or less promising. Based on
their responses to self-identifying questions and their interaction with HubSpot’s content, prospects
were ranked from 1 (low) to 10 (high) in terms of probability of converting to a customer. In 2017,
HubSpot was attracting a pool of leads where 40% were graded with scores ranging from 7 to 10,
making them worthy of a salesperson’s attention. This scoring was often done in close to real time,
giving chat reps valuable insights into which leads to pass on to sales.
Another challenge was that the nature of the customer interactions significantly changed throughout the marketing funnel. In the early stages of the buying process, prospects were often naïve,
knowing very little about the HubSpot product, and therefore asked fairly simple questions. As
prospects became more knowledgeable, however, they asked more complicated questions and would
expect more sophisticated answers customized to their needs. Would allowing chatbots to be the first
line of communication to handle simple questions change the nature of the resulting customer
relationships that developed? A strength of chatbots was that they could provide a lot of basic
information quickly, but it was likely that the company would bring humans into the process once
questions got difficult or nuanced. The team wondered if it could seamlessly pass a prospect from a
bot to a human or if the prospect would become frustrated and bail out of the relationship.
Bottom of the Funnel (BoFu) The transition from marketing to sales at the bottom of the funnel
was a tricky part of the process, and how bots might be used to make this transition easier was an open
question. The company was not willing to unleash its higher-priced salespeople (the cost of a highly
trained salesperson was approximately $120,000 per year, not including benefits) to interact with
prospects in the bottom of the funnel until after it had converted visitors into leads and qualified the
leads to determine whether expending sales resources on them made sense. Many visitors who
engaged with HubSpot’s content at the top of the funnel were not likely to become customers, so it was
important to weed out those people before assigning salespeople to interact with them. Chat reps often
played a role in engaging with qualified leads, and were able to set up meetings with the more
expensive salespeople for 12.5% of these leads. As this program grew, there was approximately one
salesperson for every three chat reps.
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At the final stage of the sales process, a salesperson would guide the customer through a live demo
of the software to help show how it could be used in the customer’s business setting. Salespeople were
available to answer technical questions about the product, such as how the software would integrate
with the company’s other systems, and provided advice on the buying decision, such as the right
product package and price point for the buyer. This often included a lengthy, consultative selling
process that could last up to 45 days, but which yielded a conversion rate of 20%. Each salesperson
closed five customers per month.
The remainder of the leads were left untouched. Lead qualification was as much art as science,
which meant that some leads that were not passed on to the salesforce likely had some residual value
to HubSpot, particularly if those leads could be further nurtured and developed. However, the cost of
doing so, given their lower probability of conversion, made this a difficult business case to support.
Thus, many leads that HubSpot had spent money acquiring were ignored. Could chatbots pick up
some customers in this lower scoring lead pool and work to further nurture them? Or would this
require investing more money in chasing prospects who would likely never convert to customers?
Additionally, how would HubSpot handle bot failure for these prospects? HubSpot could face
significant staffing constraints if large numbers of lower quality leads suddenly began requesting
human support as the bots failed to serve them well.
An early test of chatbots generated a conversion rate of 10% into sales meetings for qualified leads,
an exciting result that HubSpot expected to be able to iterate and improve upon.
Chatbots in the Post-Sale Customer Service Process
Once HubSpot signed a customer, it engaged in an onboarding process where a customer support
team worked with customers to install the software, integrate it with existing systems, and train users
on its capabilities. Customers paid a fee of $500 for four hours of this high-touch support. Once the
onboarding process was completed, HubSpot’s relationship with its customers became much less hightouch. If issues arose, customers could log on to a website that offered additional training and
resources, tune in to webinars, and/or talk to other users in web forums. The team wondered if there
was a role for chatbots in this post-sale period. Because HubSpot was a SaaS company, customer
retention was a critical part of its business model. Could chatbots help strengthen its customer
relationships? Was there a way to continue to nurture customer relationships beyond the sale to
encourage continuous usage of the software, to answer customers’ questions and help them solve
problems, and to reduce churn from dissatisfied customers who abandoned the product in frustration?
The HubSpot team was excited to explore how the role of machines and people would change
throughout the entire marketing, sales, and customer service processes for their own business as well
as that of their customers. Prior to the acquisition, HubSpot deployed Motion AI bots to take its
customers through a journey specifically built for them, offering a more personalized, always-on
buying experience (see Exhibit 8). HubSpot married chatbot technology with its repository of userrelated data, which allowed the chatbot to customize the conversation to deliver more helpful
information and meet individual customer needs. The chatbot test had generated six times as many
qualified leads as the company had achieved using email to nurture customer relationships.
Proclaimed Coffey, “As we were going through that, it just validated some of the assumptions we had
in our belief that there was a massive opportunity here for marketers.”
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Nelson was optimistic, but at the same time cautious: “People appreciate the vast responsiveness of
bots. But I think it is equally important that we are not trying to say that bots are the be all, end all of
CRM. Being able to facilitate a personal relationship as only a person can is really important for the
long-term health of a customer. Even with phenomenal technology, we still want to ensure that there
is a personal relationship between the company and its customers.”
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HubSpot and Motion AI: Chatbot-Enabled CRM
Exhibit 1
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HubSpot’s Financial Performance (in $ thousands)
2012
2013
2014
2015
2016
Revenue
$51,604
$77,634
$115,876
$181,943
$270,967
Cost of Revenue
15,693
27,504
35,080
47,923
61,865
Total Gross Profit
35,911
50,130
80,796
134,020
209,102
Operating Expenses
R&D
Sales & Marketing
General & Administrative
Total Operating Expenses
10,585
34,949
9,117
54,651
15,018
53,158
16,204
84,380
25,638
78,809
24,958
129,405
32,457
112,629
35,408
180,494
45,997
162,647
45,120
253,764
Loss from Operations
(18,740)
(34,250)
(48,609)
(46,474)
(44,662)
Net Loss
(18,778)
(32,274)
(48,229)
(46,053)
(45,562)
Source:
Company documents.
Note:
HubSpot went public via an initial public offering (IPO) on October 8, 2014.
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Exhibit 2
How Firms Are Using Chatbots
Company
Chatbot
1-800-Flowers
Sephora
Purpose
Enables customers to order flowers directly from
Facebook Messenger. Makes gift suggestions and
delivers updates on shipping.
Color Match
H&M
Enables customers to find a color cosmetic that
matches a color shade from a photograph.
Makes style recommendations based on a consumer’s
answers to a quiz. Styles a complete outfit based on a
consumer’s preference for one item.
Disney
Officer Judy Hopps
Provides the ability to solve crimes by chatting with a
character from the Zootopia movie.
Casper
Insomnobot-3000
Talks to insomniac consumers about a range of fun
topics from 11:00 p.m. – 5:00 a.m.
Hipmunk
Starbucks
Recommends travel destinations and books travel.
Barista
Duolingo
Orders coffee drinks for pickup.
Provides a foreign language speaking partner for those
learning a new language.
Shopify
Kit
Provides a virtual employee that executes marketing
tactics such as creating targeted Facebook ads, running
reports, and emailing customers.
Microsoft
Xiaoice
Provides a virtual friend for conversations.
National Geographic
Genius
Allows customers to “converse” with Albert Einstein.
Whole Foods
Source:
Enables customers to search for recipes, products, and
food inspiration by texting a food emoji.
Casewriters.
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HubSpot and Motion AI: Chatbot-Enabled CRM
Exhibit 3
Source:
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What Can Chatbots Do?
Etlinger, Susan, “The Conversational Business: How chatbots will reshape digital experiences,” Altimeter @ Prophet,
http://www2.prophet.com/conversational-business-how-chatbots-will-reshape-digital-experiences,
accessed
November 2017.
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Exhibit 4
Source:
Consumers’ Willingness to Use Messenger Apps for Customer Assistance
Company documents.
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HubSpot and Motion AI: Chatbot-Enabled CRM
Exhibit 5
Source:
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Interacting with HubSpot’s GrowthBot
Company documents.
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HubSpot and Motion AI: Chatbot-Enabled CRM
Exhibit 6
Source:
HubSpot’s GrowthBot Prompts Users to Ask, “What Can You Do?”
Company documents.
20
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Company documents.
Source:
Exhibit 7
Examples of GrowthBot’s Ability to Respond to Different Queries
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HubSpot and Motion AI: Chatbot-Enabled CRM
Exhibit 8
Source:
HubSpot’s Customer Journey Assisted by Chatbot Technology
Company documents.
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Endnotes
1 Amrani, David (2017) “Attack of the Chatbots,” Digiday Research, August 7, 2017, https://digiday.com/media/digiday-
research-attack-chatbots/, accessed 11/13/2017.
2 Bughin, Jacques et al. “Artificial Intelligence: The New Digital Frontier?” McKinsey Global Institute, June 2017,
https://www.mckinsey.com/~/media/McKinsey/Industries/Advanced%20Electronics/Our%20Insights/How%20artificial
%20intelligence%20can%20deliver%20real%20value%20to%20companies/MGI-Artificial-Intelligence-Discussion-paper.ashx,
accessed 11/17/2017.
3 Wang, Xiaofeng (2017) “Chatbots Are Transforming Marketing,” Forrester, May 29, 2017, https://go.forrester.com/blogs/1705-29-chatbots_are_transforming_marketing/, accessed 11/17/2017.
4 Johnson, Khari (2017) “Facebook Messenger Hits 100,000 Bots,” Venture Beat, April 18, 2017,
https://venturebeat.com/2017/04/18/facebook-messenger-hits-100000-bots/, accessed 11/17/2017.
5 Wilson, H. James; Paul Daugherty; and Nicola Morini Bianzino (2017) “When AI becomes the new face of your brand,”
Harvard Business Review, June 27, 2017, https://hbr.org/2017/06/when-ai-becomes-the-new-face-of-your-brand, accessed
11/17/2017.
6 (2016) “More Than a Message: Messaging Means Business,” Facebook IQ, December 1, 2016,
https://www.facebook.com/iq/articles/more-than-a-message-messaging-means-business, accessed 11/02/2017.
7 Jakovljevic, PJ (2017) “INBOUND 2017: HubSpot announces that it’s bolstering its AI chops” (blog post), Technology
Evaluation Center, October 13, 2017, https://www3.technologyevaluation.com/research/article/inbound-2017-hubspotannounces-that-its-bolstering-its-ai-chops.html, accessed 10/30/2017.
8 Etlinger, Susan (2017) “The Conversational Business: How Chatbots Will Reshape Digital Experiences,” Altimeter @ Prophet,
http://www2.prophet.com/conversational-business-how-chatbots-will-reshape-digital-experiences, accessed 11/02/2017.
9 Guo, Sarah (2016) “The Conversational Economy: 5 Reasons Mobile Apps May Still Rule,” Greylock Partners, June 26, 2016,
https://news.greylock.com/5-reasons-mobile-apps-may-still-rule-3a0f0469cb3f, accessed 11/13/2017.
10 Marvin, Rob (2017) “How chatbots can transform your business,” PC Magazine, August 18, 2017,
https://www.pcmag.com/article/355518/how-chatbots-can-transform-your-business, accessed 10/31/2017.
11 Polani, Daniel (2017) “Emotionless chatbots are taking over customer service—and it’s bad news for consumers,” The
Conversation, September 4, 2017, http://theconversation.com/emotionless-chatbots-are-taking-over-customer-service-and-itsbad-news-for-consumers-82962, accessed 10/31/2017.
12 Schlicht, Matt (2016) “The complete beginner’s guide to chatbots,” Chatbots Magazine, April 20, 2016,
https://chatbotsmagazine.com/the-complete-beginner-s-guide-to-chatbots-8280b7b906ca, accessed 11/02/2017.
13 Etlinger, “The Conversational Business: How Chatbots Will Reshape Digital Experiences,” p. 13.
14 Brandon, John (2017) “Why building an AI company should not remind you of a blind date,“ Venture Beat, July 12, 2017,
https://venturebeat.com/2017/07/12/why-building-an-ai-company-should-not-remind-you-of-a-blind-date/, accessed
11/02/2017.
15 Shing, David (2016) “What Chatbots Are Teaching Us About the Future of Marketing,” Adweek, October 30, 2016,
http://www.adweek.com/digital/what-chatbots-are-teaching-us-about-future-marketing-174309/
16 Fanning, Annie (2017) “Someone has to feed the chatbots,” Advertising Age, September 12, 2017,
http://adage.com/article/digitalnext/feed-chatbots/310427/, accessed 10/31/2017.
17 Elliot, Christopher (2017) “For travelers, chatbots and AI can’t quite take you there,” USA Today, August 27, 2017,
https://www.usatoday.com/story/travel/advice/2017/08/27/travel-chatbots-ai/602262001/, accessed 11/02/2017.
18 Fanning, “Someone has to feed the chatbots.”
19 Elliot, “For travelers, chatbots and AI can’t quite take you there.”
20 Marvin, “How chatbots can transform your business.”
21 Coldewey, Devin (2016) “What Are Chatbots? And Why Does Big Tech Love Them So Much?” NBC News, May 11, 2016,
https://www.nbcnews.com/tech/innovation/what-are-chatbots-why-does-big-tech-love-them-so-n572201, accessed
11/17/2017.
23
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22 Marvin, “How chatbots can transform your business.”
23 Ehrenkranz, Melanie (2016) “Amy the virtual assistant is so human-like, people keep asking it out on dates,” Mic, April 1,
2016, https://mic.com/articles/139512/xai-amy-virtual-assistant-is-so-human-like-people-keep-asking-it-out-ondates#.hFCLHsvpp, accessed 11/17/2017.
24 Waddell, Kaveh (2017) “Chatbots have entered the uncanny valley,” The Atlantic, April 2, 2017, https://www.the
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24
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