Transcript of a BriefingsDirect podcast on how analyzing chatter on social sites can lead to big gains for companies.
Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.
Dana Gardner: Hello, and welcome to the next edition of the
HP Discover Podcast Series. I’m
Dana Gardner, Principal Analyst at
Interarbor Solutions,
your host and moderator for this ongoing sponsored discussion on how
data is analyzed and used to advance the way we all work and live.
Once
again, we’re showcasing how thought leaders and innovative companies
worldwide are capturing myriad knowledge, gaining ever deeper
analysis, and rapidly and securely making those insights available to more people on their own terms.
Our next big data payoff discussion focuses on the fast developing field of
social customer relationship management or Social CRM. We’ll examine now how the power of
big data
technology can be applied successfully to understanding such complex
concepts as consumer sentiment and intent and to vastly improve user
experience management.
We’ll learn how customer analytics services provider
Attensity has used
natural-language processing (NLP)
technology and
HP Vertica capabilities to effectively listen to the
social web to gain valuable insights and actionable intelligence.
To learn how, please join me now in welcoming our guests,
Howard Lau, Chairman and CEO of
Attensity. Welcome, Howard.
Howard Lau: Good morning. How are you?
Gardner: Good. We’re also here with
Chris Selland, Vice President of Marketing and Business Development at
HP Vertica. Welcome, Chris.
Chris Selland: Thanks, Dana. Great to be here.
JetBlue Case Study
NY-based JetBlue Airways created a new airline market category based on value, service, and style
Goals:
- Provide a unique flying experience that truly satisfies each individual customer and improves services quality
- Better understand and meet
customer needs, as amenities such as its individual TVs and spacious
leather seats are no longer enough to set them apart from the
competition
Solution:
- Attensity Analyze, powered by HP HAVEn with HP Vertica Analytics Engine
Results:
- Instituted Customer Bill of Rights
- More clearly understand what customers need and are able to make improvements and be proactive
- Track complaints by plane’s
tail number, allowing the customer service organization to see which
planes have the most and fewest issues
See more at:
http://www.attensity.com/2014/04/02/jetblue-airways/
Gardner: Howard, let’s start with you.
Sellers and marketers worldwide have always wanted to know what their
customers are anticipating or what they want next. I guess we could go
back hundreds of years with these questions.
But as
someone said recently, it seems that the ability to know what customers
want and how to respond to them rapidly has changed more in the last 5
years than in the past 500. Do you agree with that? And why is that the
case? What’s so new and different?
Lau: Absolutely. What has happened and emerged in the past 10 years or so, especially in the world of
Twitter
-- Twitter has been around since 2006 -- is that consumers are finding a
voice to express their opinions about companies, products and brands.
They can express their voice immediately through social channels.
That’s
one of the new emerging things where, not only are they finding their
voice online, but they’re also realizing that they’re able to amplify
that voice by connecting with their friends and their followers.
Gardner:
Why is that making such a big difference in how we know what customers
want? I understand that the social part is new and innovative, but how
is this changing marketing?
Controlling the conversation
Lau:
The way things have happened before is that companies, as they engage
with consumers, controlled the conversation. Whether you fill in an
online form or you call an 800 number for customer service or purchase,
you’re greeted initially with an automated prompt, and the whole prompt
system navigates your engagement.
What makes Social CRM so unique and empowering for
consumers is that, for the first time, it’s transferring the control and
ownership of the conversation to the consumer, the customer. What that
means is that the customer now controls what they want to talk about,
where they want to talk about it, and what channel they want to use to
communicate their needs or issues.
They don’t want to
do it in a predefined form, where you check off boxes or answer specific
prompts. They want to express their interests more organically and use
the company’s branded channels on
Facebook
and Twitter and non-branded channels on industry forums and
communities. That’s what’s key about Social CRM and that’s what’s so
unique about this new generation of products to analyze the social web.
Gardner:
Let’s go to Chris Selland. Chris, HP Vertica is dealing with a lot of
organizations that are trying to do new and innovative things with
marketing. Do you also agree that marketing and what we can do have
shifted just dramatically in the last five years? Has it really changed
the game?.
Selland: Absolutely. There’s been a
very dramatic shift in the last five years in marketing. That’s driven,
not exclusively, but certainly heavily, by what’s been going on in the
social-media world -- Twitter and other channels, Facebook,
LinkedIn, and so forth.
It has had two impacts. First, it has amplified the
voice of the customer. I always remember that commercial about I will
tell two friends and she will tell two friends, and so on. Customer
voice has always had an impact, but the impact of customer voice these
days is dramatically amplified by social media.
The
other thing that’s really changed the game entirely is that now
organizations that are seeking to understand their customers can no
longer exclusively rely on internal data, and by internal data I mean
things like
customer relationship management (CRM).
In
the past, when I, as a marketer, or any customer-facing exec running
support or something else, wanted to understand my customer
relationships, as long as we have had computers and applications had
been able to look at something like my CRM system to see when my
customer called the call center or when they bought something. Or I can
view my transaction logs with them.
But what I haven't
been able to look at and analyze is what they are doing when they’re
not interacting with me, when they are interacting with the world, or
when my customer is tweeting or on Facebook. Obviously, there is a very
rich vein of data there. There is also a lot of noise to screen through,
but if you do it right, there is potentially a very rich vein of data
to help enhance relationships.
As I said, companies
can choose to ignore that, but generally that would be strategically
disadvantageous to do. Most companies recognize that there's a
tremendous amount of data out there that doesn’t belong to me and that’s
not necessarily all about me, but I can certainly use it to understand
my present and future customers better.
If you
interview a typical consumer, when are you more truthful, when you are
interacting directly with the company or when you are actually tweeting
or making recommendations to your friends or liking something on
Facebook, a lot of the real information is outside of the walls of
traditional IT. That’s what’s really changed things dramatically as
well.
Quite a challenge
Gardner:
Of course, that’s also provided quite a challenge when the information
is in the form of sentiment or intent that we see through social
interactions. It's more difficult to attain that and assess it.
Let’s
go back to Howard. What are some of the challenges when it comes to
getting information, maybe through NLP in order to extend it into this
analysis capability?
Lau: When people go online
in a social realm, they don’t think about their intent. They just
express themselves. So the challenge is letting people communicate the
way they choose to communicate and then try to figure out and infer what
is their intent and their sentiment.
Trying to
determine that is what we do using NLP in an effort to understand what
the chatter is about and what the sentiment is about that chatter.
When you get down to what people are talking about, you have to understand from which domain they’re talking.
Gardner:
In doing so, have you developed limits in terms of what you can do with
the technology? It seems like this is a fairly a vast amount of
information?
Lau: It's vast, and it's also very
domain specific. There’s different terminology based on the domain. For
example, in the hospitality and travel industry, when you use the word
“service,” service means the service you are getting from the hotel or
from the airline.
But when you use word “service” in
the telecommunications space, that means something totally different. It
means, your service plan, how many minutes you have, do you have text,
and so forth.
So when you get down to what people are
talking about, you have to understand from which domain they’re talking,
infer their meaning and understand their sentiments.
Gardner:
So there is a difficult issue in terms of language issues and then
there are also technology issues around scale and depth, but let’s stick
to the ones about NLP. What is it that Attensity does in order to solve
that problem?
Ingesting data
Lau:
First thing is that we ingest a tremendous amount of data. Most of it
is social, but we also ingest company’s internal emails, customer notes,
employee notes, and online surveys.
Then, we analyze
it and annotate it. Part of the annotation is trying to explain the
meaning of a sentence or a sentence fragment. The way we do annotations
is driven by our proprietary NLP technology.
One of
the first things we do is figure out who is this person and what he’s
talking about. We’re trying to find the right industry domain that they
are talking about and then distill that into the actual meaning -- the
intent, as well as the sentiment.
Gardner:
Howard, tell me a little bit more about how your relationship with HP
has evolved. You have been working with Vertica for a while. Tell us a
little bit about why Vertica was of interest to you as you’re trying to
accomplish your goals with NLP.
Lau: With the
annotations, we generate a lot of intelligence, a lot of metadata. Prior
to our relationship with HP, we basically serviced the online surveys
and certain internal notes and customer notes for corporations. As we
embraced social, we had an explosion of content and annotations.
We’re
trying to find the right industry domain that they are talking about
and then distill that into the actual meaning -- the intent, as well as
the sentiment.
For us, our relationship with HP was indispensable.
HAVEn
is not just a product; it's a platform. And it's a platform that scales
well, not just handling the process of injecting large amounts of data,
but also creating stores, a large store for us, as well as customer
stores for each of our clients.
There’s absolutely no
way we could have scaled our solution to address the continuing growth
of the social realm without this relationship and partnership we have
with HP and on the HAVEn platform.
Gardner: Just
to be clear, HAVEn, of course, includes quite a few things. Maybe you
could just help us understand which elements of HAVEn you’re using and
which ones are the most beneficial to you?
Lau:
First, it's Vertica. We use Vertica for every customer we have for
analytical tools. Vertica sits behind that. Then, for managing the whole
ingestion and the storage of the documents that we get from the social
space, we use
Hadoop and
HBase from Hadoop. That’s how we embraced the HAVEn platform.
Gardner:
Chris Selland, what is it about the Attensity use case that you think
demonstrates some unique characteristics of Vertica and perhaps even
more elements of HAVEn?
Complementary nature
Selland:
First of all, it demonstrates the complementary nature of Vertica and
Hadoop. The Vertica platform has been built to do very high-performance
analytics on very large volumes of data. That’s really what we’re all
about.
Obviously, Hadoop is also built to scale for
very large volumes of data, and so we have bidirectional integration,
actually huge integration and increasing convergence with Hadoop.
Attensity is doing a great job of showing that.
Then,
as we were talking about, it’s just the massive volumes of data that
they’re managing. When you’re in the realm of the social world, again,
it's not just the volume. I always say that big data is not just big,
but it's the velocity, the variety, the ability to ingest very fast, and
interpret, analyze, and produce results very fast. That’s really what
the Vertica engine is all about, and it’s doing that with very high
performance.
It's a very important market segment for
us, and it's great to have partners. Vertica is a platform. We rely on
our partners to provide solutions to run our platforms. It's social CRM
and social analytics and all the kinds of solutions we’re looking to
highlight. We love it when we have great partners like Attensity
bringing those to market, being successful, and making our joint
customers successful.
The
Vertica platform has been built to do very high-performance analytics
on very large volumes of data. That’s really what we’re all about.
Gardner:
Of course, Howard, your customers are probably not so much concerned
about what’s going on underneath the hood, whether it's Vertica, HAVEn,
or Hadoop. They’re interested in getting results. I’d like to go back to
that Social CRM aspect of our discussion and help people understand why
that can be so beneficial, which then of course makes it clear why the
technology that supports it is so important.
Can you
give us any examples, Howard, of where people have used Social CRM,
where they have leveraged NLP and Attensity and what that’s done for
them in real business terms?
Lau: Absolutely.
Some of the industries we service include industries such as
telecommunications, hospitality, travel, consumer electronics, financial
services, and eCommerce. We provide the services, the tools for our
customers and they implement them for very different use cases based on
their priorities.
One of the leading prepaid mobile
phone providers use Attensity’s deep semantic approach to analyze
sentiment about their service and alert the brand management teams to
their unique
voice of the customer (VoC).
Attensity
effectively measures the overall experience for each brand taking into
account their different products and services to determine the accurate
wants and needs of the customer. Their whole
return-on-investment (ROI) story is how can they use what’s going on in the social realm to manage their install base and minimize customer churn.
Focusing
on that, they were able to achieve a 25 percent reduction in customer
churn. Now, in the mobile telco space, that directly translates into a
25 percent increase in revenue. Keep in mind that this company is
somewhere between half a billion to one billion dollars in revenue.
That’s a very sizable return on investment.
We also
have other cases where we have an insurance company in the financial
services space, and they focus on fraud detection. They use our
technology, not only in social space, but also reviewing claims. They
were able to reduce
workers’ compensation
pretty dramatically, to a tune of over $25 million annually, just using
our technology, and using our NLP to analyze the data and then figure
out which ones they could go after to manage their fraud cost.
Looking toward the future
Gardner:
Where do we go next with this, Howard? We have a capability to deal
with large data and the variety of data. We certainly have a great
treasure trove of information available from the social media and social
web. Combining that with the traditional datasets in CRM, where do you
go next? Are you looking for even more datasets and what do you have
your eye on?
Lau: Getting more datasets is
always helpful. The more you get, the more complete your analysis is,
but the view right now is just analyzing big data. We are finding that,
within that big data, there are tremendous amounts of individual voices.
So the goal is to figure out where these individual voices are and how
to build relationships with ones that are important to you.
I’m going to go back to a book that
Malcolm Gladwell wrote way back called
The Tipping Point.
He talks about mavens and the influence of mavens. In the social
chatter, there are all these people that have outside influence on other
people. The next step in applying our NLP technology in the social
realm is uncovering these mavens, so that companies can build
relationships with these outside influencers. So that’s one of the next
things that we’re really excited about.
Gardner:
Tell us also where you are going in terms of services for business.
Obviously we have talked about marketing, but are their other aspects --
maybe product development? How deeply does this extend into how it can
influence a business, not just on the selling and marketing, but perhaps
even knowing where their business should be going, a strategy level?
Having an analytical store where you can do what-if scenarios after the fact is incredibly useful for them.
Lau:
When people hear about social, the first thing they do is listen, but
there is a whole model for how people adopt business solutions in the
social realm. We have a model we call
LARA, and it stands for Listen, Analyze, Relate, and Act.
The
first thing that a lot of companies do is become aware that they need
to pay attention to what’s being discussed socially. So they put out
these listening posts and they use us to ingest all this information and
analyze it for them. The benefit of that is sentiment analysis on
companies, on brands, and products. They want this type of sentiment in
real time, and we’re able to deliver it in real time.
The
next thing companies want to do is analyze the data they have
accumulated, and it's for variety of different use cases. I mentioned
fraud detection and customer churn. They also want to surface emerging
trends. Having an analytical store where you can do what-if scenarios
after the fact is incredibly useful for them.
Once
they have the store of customer data and they’ve analyzed and segmented
their customers, they want to define how they want to relate to the
customers, in aggregate or in smaller segments.
The
last and final thing they want to do as part of the whole consumer
experience is figure out how to engage with the ones that are important
to them.
As an example, if someone tweets that they
like this phone, that’s great sentiment. But if somebody else tweets
that they don’t like the service they’re getting from this mobile phone
provider, if that mobile phone provider is an Attensity customer, we
actually take that tweet, route it into their customer-care
organization, route it to the proper person, and respond to someone in
the social realm.
This ability to kind of close that
loop, from a person just tweeting generally to his friends about an
experience, and then actually getting the customer to hear them and
respond to them is incredibly powerful for organizations.
Following the path
Gardner:
For companies that see the value here pretty readily, what steps should
they take in order to be in the position to follow that path, that LARA
path? Do they need to gather this data themselves? Should they try to
ramp up how social media interactions are focused on their products or
services? Are there any steps that companies should take in order to
better leverage something like Attensity, that’s built on something like
Vertica, to get these really powerful insights? Howard?
Lau:
That’s part of the value that we bring. All the customer needs to do is
recognize that social is important for them. We’re not just talking
about corporations that are in the
B2C space, but also in the
B2B. Once they have that recognition, we’ll handle it for them afterwards.
Part
of our products and services offering is that we ingest all this data
for them, whether from the social sphere or in the companies emails or
customer service notes. We ingest all that information, and they're all
defined by one common trait, which is that they are unstructured data.
We apply our NLP technology to provide an understanding of the big
stream of data and then we create the analytical store for them.
All
companies need to do is recognize the importance of wanting to hear
their customers, listen to the customers, and ultimately, engage with
them socially. They just have to have that motivation, and we will work
with them as a partner to realize that solution for them.
Part
of our products and services offering is that we ingest all this data
for them, whether from the social sphere or in the companies emails or
customer service notes.
Gardner: Chris
Selland, I’m thinking that organizations that are sophisticated about
this will go to a company like Attensity and get some great value, but
eventually they’re going to want to try to get that holistic view of
analysis. That means that, not only would they leverage what services
and insights that Attensity could provide to them, but they’re going to
want to share and correlate and integrate that with what they have going
on internally and across many other systems.
Is there
something about HAVEn that we should bring out for them in terms of
open standards and integration capabilities that allows, over time, for
more and more of these different data activities to relate to one
another, so that we do get a whole greater than the sum of the parts?
Selland:
HAVEn certainly provides a very broad platform of which, as we
mentioned, Vertica is obviously a key part, the V in the middle. Yes is
the short answer. The solutions ultimately need to be part of a much
broader data architecture and strategy around how to leverage all sorts
of different types of data, that’s not even necessarily customer data.
Just
to give you an example and to make that tangible, there was an airline
that I was engaged with not too long ago, probably about a
year-and-a-half ago at this point. I can’t name them, but it's a
well-known airline, and it was one that didn’t have a particularly good
reputation for customer service.
They were working on their
social-media
strategy and trying to figure out how to make customers who were
tweeting unhappily that they hated the airline say nicer things -- so
how to analyze and respond more quickly.
What they
quickly discovered was the reason so many of these customers were angry
and saying they hated the airline was that their flight wasn’t on time.
What they also realized was they had an awful lot of data on their
maintenance operation, and sensor data from the planes, and so on from
their fleet.
Predictive maintenance
They
saw that by maybe doing a better job of predictive maintenance, keeping
their flights on time, and keeping their fleets better maintained, they
would actually have much more impact on customer satisfaction than
responding to the tweet from the customer who was stranded, which kind
of makes sense, if you think about it.
I just bring
that example out because that’s an example of data that has nothing to
do with the customer. It might be a sensor on an engine, or it might be a
performance data of some sort, but it's related obviously to customer
satisfaction.
So ultimately, yes, there needs to be a
data infrastructure and a data strategy that spans the different
solutions. It's not to say you don’t absolutely still need Social CRM
solutions and all sorts of different solutions, predictive maintenance
solutions and operational, financial analytic solutions, but ultimately
the data infrastructure needs to be unified.
That’s
really where this is going next. In many leading organizations that’s
where it's going already, which is, these solutions absolutely play a
key role, but they can’t be 24/7. So there needs to be an infrastructure
and a strategy behind them that is very, very holistic.
What
he’s driving towards is a world where it's really the Internet of
Things, where everything is wired to the Internet and they broadcast
messages or communicate messages related to their purpose and their
focus.
We're talking about the competitive bar
moving here, and that’s the direction that the competitive bar is going
to continue to move in.
Gardner: Howard, do you
have any reaction to what Chris has said in terms of seeing a value of a
holistic data architecture, not only from what Attensity can do, but
extending it across many aspects of business?
Lau:
I totally agree with what Chris just said. What he’s driving towards is
a world where it's really the Internet of Things, where everything is
wired to the Internet and they broadcast messages or communicate
messages related to their purpose and their focus.
Where we provide our value is that before we get to the world of
Internet of Things,
there is the Internet of People. People need to express themselves the
way they normally do. Where we add value is trying to understand,
distill the customers in a person’s voice, and have that complement the
future of the Internet of Things.
I totally agree that
having an integrated architecture, integrated approach to data
management, big data management is crucial going forward.
Gardner:
Very good. I’m afraid we’ll have to leave it there. You’ve been
listening to a thoughtful discussion on the power of big-data technology
and how it's being applied successfully to understanding such complex
concepts as consumer sentiment and Social CRM.
And we
have seen how analytics services provider Attensity has used NLP
technology and HP Vertica and HAVEn capabilities to effectively listen
to the social web to gain these valuable insights and then also develop
actionable intelligence.
This discussion marks the
latest episode in the ongoing HP Big Data Podcast Series, where
leading-edge adopters of data-driven business strategies share their
success stories and where the transformation nature of big data takes
center stage.
So please join me now in thanking our
guests. We’ve been here with Howard Lau, Chairman and CEO of
Attensity. Thank you, Howard.
Lau: Dana, thank you very much for having me today.
Gardner:
And we’ve also been here with Chris Selland, Vice President of
Marketing and Business Development at HP Vertica. Thanks so much, Chris.
Selland: Thanks so much, Dana, and thank you, Howard, as well.
Gardner:
To learn more about how businesses anywhere can best capture knowledge,
deep analysis, and rapidly and securely make those insights available
to more people on their own terms, please visit the HP HAVEn Resource
Center at
hp.com/haven.
I’m Dana Gardner, Principal
Analyst at Interarbor Solutions, your moderator for this ongoing
sponsored journey into how data is analyzed and used to advance the way
we work and live. Thanks so much for listening, and come back next time
for the next episode in the HP Big Data Podcast Series.
Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.
Transcript
of a BriefingsDirect podcast on how analyzing chatter on social sites
can lead to big gains for companies. Copyright Interarbor Solutions,
LLC, 2005-2014. All rights reserved.
You may also be interested in:
Gardner: So that’s a big challenge in terms of coming up with a test environment for each of those iterations. 
