Big Data’s Big Payoff — Social CRM and Customer Experience Insights Drive Newfound Business Advantages
Big Data’s Big Payoff — Social CRM and Customer
Experience Insights Drive Newfound Business Advantages
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. 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
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. We’re here with Howard Lau, the
Chairman and CEO of Attensity. Welcome, Howard.
Howard Lau: Good morning. How are you?
Gardner: Good. We’re also here with Chris Selland, the Vice President of Marketing and
Business Development at HP Vertica. Welcome, Chris.
Chris Selland: Thanks, Dana. Great to be here.
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
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
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
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
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.
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
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?
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
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 distilling 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
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
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
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?
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
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
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
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?
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
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
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.
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
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.
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.
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, the 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, the 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
Listen to the podcast. Find it on iTunes. 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.
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