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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.

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J I L L A V E R Y

T H O M A S S T E E N B U R G H

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

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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.

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HubSpot and Motion AI: Chatbot-Enabled CRM 518-067

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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 study3 claimed that worldwide,
57% of firms were already using chatbots or planned to begin doing so shortly, and 80% of businesses

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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 study5 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.”

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HubSpot and Motion AI: Chatbot-Enabled CRM 518-067

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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 [email protected] 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 un-
answered. 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

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518-067 HubSpot and Motion AI: Chatbot-Enabled CRM

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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:

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HubSpot and Motion AI: Chatbot-Enabled CRM 518-067

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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

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How Human Is Too …

Case Analysis Template[footnoteRef:1] [1: For general case preparation strategies, see also: http://plato.acadiau.ca/courses/Busi/IntroBus/CASEMETHOD.html.]

Current Situation

Step 1. The Facts

WHO is the decision maker?

WHAT is the task to be done (decision to make, problem to solve, opportunity to seize)?

WHY has the issue arisen now? What is its significance to the organization?

WHEN does the decision maker have to decide, resolve, act or dispose of the issue? What is the urgency to the situation?

Step 2. In Depth Analysis

Analyze the case situation using the core model at right. Consider the following sorts of questions (the exact questions will vary somewhat depending on the case).

1. What business problem are we trying to solve?

2. Why is that problem important to the business?

3. How does the nature of the current IT contribute to or alleviate the problem?

4. How does the current organization (structure, people, culture etc.) contribute to or alleviate the problem?

5. How did we get here? Critically assess the factors that have contributed to our current situation?

Criteria

Use your analysis of the current situation to identify the relevant criteria.

Criteria

Meaning/Measurement

Why Selected?

Analysis of Alternatives

What options are given in the case?

Are there additional options you think need to be considered?

Performance Against Criteria

Options

1

2

3

1

2

3

Decision

Which option do you think is best? Why?

How does this proposed solution address the business problem identified in your analysis of the current situation?

Implementation

How will you go about implementing your decision (who will do what, when, and how)?

Short Term (= ________ days/wks/mths/yrs)

Medium Term (= _____ days/wks/mths/yrs)

Long Term (= ________ days/wks/mths/yrs)

What are the major risks associated with your decision?

What steps will you take to avoid or mitigate those risks?

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