The big data strategy using social media

The Big Data
Strategy using
Social Media

Table of Contents
1.

BIG DATA...................................................................................................................................... 2

1.1

Impact of Big Data ...................................................................................................................... 2

1.2

Why a company should implement Big Data? ............................................................................ 2

2.

OBJECTIVE ................................................................................................................................... 3

3.

BIG DATA ANALYTICS FOR SOCIAL MEDIA ........................................................................ 3

3.1

Barclays and Adidas - Customer sentiment analysis .................................................................. 4

3.2

ING Direct – Using customer feedback for customized product offerings ................................. 6

3.3

DreamWorks’s - Tracking PR events and promotional campaigns ............................................ 7

3.4

TD Bank – Social media for rapid customer Service .................................................................. 8

4.

IMPLEMENTATION FRAMEWORK .......................................................................................... 9

4.1

Information flow ....................................................................................................................... 10

4.2

Operating model........................................................................................................................ 10

4.3

Big Data implementation process ............................................................................................. 11

4.4

Future Scalability ...................................................................................................................... 13

5.

IMPLEMENTATION PROCESS................................................................................................. 13

5.1

Process Map for social media systems ...................................................................................... 14

5.2

Implementing Social Media systems using specialized products ............................................. 14

5.3

Implementing Big Data using Hadoop...................................................................................... 17

5.4

Implementing Big Data analytics systems using packaged products ........................................ 19

6.

RISKS AND MITIGATIONS ...................................................................................................... 20

8.1 Security Risks ................................................................................................................................. 20
8.2 Analytics’ cohesion with business goals ......................................................................................... 21
8.3 Managing complexity and Storage capacity ................................................................................... 21
7.

SUMMARY / CONCLUSION – .................................................................................................. 21

7.1

Focusing on Big Data opportunities.......................................................................................... 21

7.2

Recommending a Strategy for implementing Big Data systems: ............................................. 22

Appendices............................................................................................................................................ 24
References ............................................................................................................................................. 32

1. BIG DATA

1.1 Impact of Big Data
It is not an understatement to say that data has helped many organizations make rational
decisions and take calculated risks in the recent past. Data has helped managers to focus their
efforts on specific portfolio or products or customer group to make organizations profitable.
For a long while, data used for these analyses were usually samples from a larger dataset
analyzed by statistical methods to paint a picture of the entire group.
Now, the concept of Big Data emphasizes the use of the complete data set to analyze process
and predict various phenomena in the business world. It is believed that these decisions are
more accurate if appropriate system and analysis tools are employed.
According to IBM, we create about 2.5 quintillion bytes of data every day and 90% of the
entire amount of data available in the world has been generated in the past two years. The
three V’s namely Volume, Velocity and Variety are believed to be key attributes for a Big
Data system to produce trustworthy results. It essentially means that the Big Data systems
should have capacity to process high volumes of variety of data both unstructured and
structured data at a very high speed to produce desired results.

1.2 Why a company should implement Big Data?
In today’s scenario, companies are threatened by low margins, uncertain economic outlook,
changing trends, and new entrants. It has become very important for companies to utilize the
data they have and study their customer behavior effectively and efficiently in order to retain
their competitive position in the marketplace. Appendix 1 analyzes the factors affecting a
firm.
Today, customers care more about convenience than service provider. Furthermore,
customers are willing to provide more information if firms can then provide better
personalized services to them. The leading firms should offer customers “the right offer at the
right time” by leveraging customer data gathered from stores, websites, social media and
other sources, thus creating one integrated multi-channel experience.

2. OBJECTIVE
Big Data analytics can significantly improve a firm’s ability to improve customer
segmentation, provide personalized services, build brand-awareness trough social media and
provide instant offers facilitated by real-time analytics. To do this, a firm needs to utilize the
data that it already possesses and merge this data with newer data available from external
parties or social media platforms. With deep analysis of data, the firm can develop a
segmentation strategy that would identify each group of customers on relevant attributes and
create effective loyalty programs that would incentivize its customers to stay with the firm
and even recommend it to others.
Various businesses have already successfully implemented these programs leading to higher
profitability and a sustainable long-term advantage. These business cases will be analyzed to
understand how their chosen strategies can be applied to any firm to drive business value.
The document will focus on how a firm should build its data capability to take an advantage
of opportunities provided by Big Data systems.

3. BIG DATA ANALYTICS FOR SOCIAL MEDIA
Social media is the engine that has transformed the web from being a one-way, information
tool to a two-way collaboration mechanism. In the world of social media, customer
preferences for products or services are influenced by ideas, perspectives, insights and
experiences provided by other users. This is achieved through peer reviews, referrals, blogs,
tagging, social networks, online forums and other forms of user-generated content (Oracle,
2009).
Social media Big Data analytics provides a measurable means of gathering, processing,
analyzing and delivering business intelligence from social media channels. The benefits of
having a social media analytics program include micro-target marketing, brand protection,
customer engagement and loyalty, and promotion feedback (Todd Nash, 2013).
Social media has emerged as an influencer of brand awareness and loyalty, as well as a
powerful catalyst for community building, although with new compliance implications. Firms
can leverage social media as an analytics engine. Using social media Big Data to power
analytics applications, firms can better understand customer preferences and align
communications, products, sales strategies, distribution channels, and customer service
strategies to facilitate better individual customer experiences.

Utilizing social networks and link-analysis techniques can also assist in the discovery of
relationships between accounts, customers, households, groups, rings, and institutions, and
lead to more in-depth customer knowledge. By leveraging advanced analytics, firms can also
develop more sophisticated models to understand the stages of customers’ lifecycles,
providing differentiated customer experiences that are relevant to them (Deloitte, 2011). The
Big Data analytics of social media information can help in understanding sentiment drivers,
identifying characteristics for better segmentation, measuring the organization’s share of
voice and brand reputation compared with the competition, determining the effectiveness of
marketing touches and messages in buying behavior, using predictive analytics on social
media to discover patterns and anticipate customers’ problems with products or services
(TDWI, David Stodder, 2012).
In 2013, the social media landscape has evolved far beyond the traditional channels to
include countless data resources, including but not limited to:


Facebook, Twitter, LinkedIn, Google+, etc.



Review sites, like Angie’s List, Yelp, Urbanspoon, TripAdvisor, etc.



Blogs and news sites that include/encourage comments



Video and photo sharing sites, like YouTube, Flickr, etc.



Search engines, such as Google, Bing, Yahoo and others (Todd Nash, 2013).

The following major categories illustrate how various companies use social media Big Data
analytics for specific purposes.

3.1 Barclays and Adidas - Customer sentiment analysis
Sentiment Analysis: Used in conjunction with Hadoop, advanced text analytics tools analyze
the unstructured text of social media and social networking posts, including Tweets and
Facebook posts, to determine user sentiment related to particular companies, brands or
products. Analysis can focus on macro-level sentiment down to individual user sentiment
(Jeff Kelly, 2012).
For instance, Adidas capitalized on social media when it introduced its latest running
innovation, a shoe called Energy Boost. Lia Vakoutis, head of digital strategy at Adidas
America, says the strategy paid off. "We saw a dramatic increase in positive sentiment

around Adidas following the launch of the Adidas Energy Boost running shoe," she told All
Analytics. Vakoutis said the company uses a variety of tools to monitor social media
sentiment, including Salesforce Radian6, Sysomos, and Crimson Hexagon. Combined, she
says, they provide "the most holistic view of Adidas sentiment on the web." (Noreen
Seebacher, 2013).
Similarly, in Feb 2012, Barclays launched a mobile banking application called PingIt. In the
days following the launch, Barclays made significant changes to the application as a result of
real-time social media analysis. A sentiment analysis was carried out for understanding the
sentiments for this newly launched product. Although the application was very well received,
a small proportion of mentions were negative. Barclays was able to drill into this data to see
what was causing the negative mentions and found out quickly that many users were unhappy
that the application didn’t work for under 18’s. It wasn’t only teenagers that were unhappy,
but also parents who couldn’t transfer money to them. This could easily create a PR disaster,
but the data allowed Barclays to act quickly. Within a week, 16 and 17 year-olds were given
access to the application, showing that Barclays were responsive to customer feedback. It
wasn’t only the negative comments that Barclays bank looked into. The positive mentions
also revealed some surprises that they were able to act on. For example, there were a lot of
positive comments about being able to check your bank balance from the app. This was only
intended to be a side feature, but proved to be extremely popular. As a result of this feedback,
Barclays developed new apps specifically for this purpose. (Oursocialtimes, 2012)
The sentiment analysis can be used by a firm in understanding its current customer
satisfaction levels. It can understand what kinds of its products are generating a large
number of negative sentiments or people in what regions are most unhappy with the firm’s
services. Similarly, it can look out for competitor sentiments and upcoming trends in the
industry. The firm can also look at the above example of Barclays bank and utilize the social
media Big Data for understanding the customer sentiments for its newly launched products.
This way, it’ll be able to tweak its product offering to better suit the customer needs.
In terms of data analysis, the firm should gather data from various social networking sites
such as Twitter and Facebook (including their own Facebook and Twitter pages), Blogs and
forums, especially industry specific, consumer complaint websites, log details of consumers
etc. This data can be collectively analyzed with the customer feedback (or customer

complaints) data generated from different sources such as online support, voice support etc.
and consumer research data generated from various surveys.

3.2 ING Direct – Using customer feedback for customized product offerings
ING Direct is a different kind of bank as it doesn’t have bricks and mortar branches. Social
media was an important focus for the bank and it has been in the forefront of social media
usage in the financial services industry. ING Direct’s biggest social media challenge was to
be seen as “more than just a bank”. ING initiated a new product – THRiVE Chequing – an
online no fee daily chequing account that actually pays interest. ING engaged over 22,000 of
their clients in product’s preview, with their feedback directly influencing the final offering.
In addition to the bank’s website, they gathered customer insights through Facebook and
Twitter. They believe in asking direct feedback, which is a great, proactive way to get or keep
the conversation going. This method created many valuable suggestions for the THRiVE
Chequing, including increasing the number of free cheques and increasing number of bill
payees. They continued to ask feedback from its customers to drive its promotions and better
understand client’s needs.
The THRiVE chequing account product was a major success for ING DIRECT. In a very
short time, the campaign attracted over 40,000 active THRiVE-ers. The campaign had over 5
million impressions on social media sites. Blog posts covering the THRiVE chequing public
launch were read 53,000 times and #THRiVETASTIC was mentioned online to an audience
of over 3.6 million users. (Salesforce, 2011)
This method will be beneficial for a firm for developing its new products. While developing a
particular product, the firm can get feedback from its customers on their needs and
requirements related to those products. This way, the firm will be able to create customerfocused products which will have a high chance of success. Additionally, as the customers
are already made aware of this kind of product, it’ll a lot easier for the firm to market its
products to its target customers. The crowdsourcing feature of social networks acts as a
powerful tool to target product development at the financial lifecycle and future needs of its
customer segments. (Kishen Kumar, 2013)
For data analysis in this case, the firm should seek feedback from its customers on
developing similar kind of products and gather data from various customer-touch points such

as personal emails, personal customer visits, personal feedback calls to customers, online
feedback from social networking sites such as Twitter and Facebook (including its own
Facebook and Twitter pages), and blogs and forums (company sponsored or external). This
data can be collectively analyzed with the information on products that the current customers
use and their spending patterns.

3.3 DreamWorks’s - Tracking PR events and promotional campaigns
DreamWorks was trying to understand if it could determine how movies would open based
on the social buzz. It found out that the ability to understand public sentiment in real time was
very predictive of how a movie would open and what advertising worked. For example, it
tracked the DreamWorks’ movie Puss in Boots, which had a slower following in the weeks
leading up to its release. After analyzing initial response, it discovered that before the
movie’s release, the Twitter conversation about the film was sparse and surprisingly negative
(Jonathan Taplin, 2012). In response, the studio created a new TV ad campaign that was well
received. When it introduced this new big TV ad, it observed that within two days, Puss in
Boots became the most talked about movie. It analyzed social media posts from Twitter,
Facebook and other social media related to this movie release and immediately observed that
the ad campaign had worked. The movie was a hit, and the Twitter mention volumes and
positive sentiment increased significantly (IBM, 2013).
A firm can effectively utilize social media analytics information to monitor the impact of its
advertising campaigns and can get feedback on its promotion efforts. Additionally, it can
alter its promotional campaigns based on the response received from its customers. This way
the firm will be able to generate better return on its advertising and promotions efforts.
Finally, it can segment its customers at a micro level and target its online marketing efforts
according to specific customer needs.
In terms of data analysis, the firm should collect data about the level of activity observed for
a particular advertisement or a promotional campaign. This information can be collected
from different social networking sites such as Twitter and Facebook (including their own
Facebook and Twitter pages), blogs and forums etc. and the level of activity should be
measured against the keywords related to that particular advertisement or promotional
campaign. Another major source can be the information about number of clicks for the paid

searches and online ads and the related traffic generated for its related products. This all
data can be analyzed together to find out the measure of success for that particular
advertisement or promotional campaign.

3.4 TD Bank – Social media for rapid customer Service
Customers have started putting their complaints on forums, blogs, Facebook, Twitter and
other social media. By listening carefully to these communities, customer concerns can be
easily identified and a better service can be provided. TD Bank understands that customers
place a great deal of trust in their bank and they expect it to be as accessible, helpful and
responsive to their needs as possible. To TD that means being there for customers where they
feel most comfortable, whether it’s in the branch, on the phone or on social media channels.
TD has built social media teams in Canada and the US to provide customer service (or
“Social Service” as they call it). These teams are located in major call centers and are focused
on delivering customer service via Twitter, and engaging customers with help and advice on
blogs and on TD Money Lounge on Facebook. The teams use social monitoring tools to
analyze the Big Data in social media to track mentions, find relevant conversations and
manage the team’s workflow.
Using social media data analytics, the bank has been able to track and identify and provide
help with thousands of customer inquiries on a range of topics, from service issues to banking
hours. For example, during Hurricane Irene, which shut down much of the east coast for
several days, TD was able to update affected customers with information about branch and
ATM availability (Salesforce, 2013). TD Bank has been successfully using social media to
assist their customers, share information and make connections. “People are very candid on
social media, and it gives us the chance to get feedback on our branch hours and services or
help a customer resolve an issue. We find our customers are happy to know that we are
listening and that we are here to help” says Wendy Arnott, VP of Social Media & Digital
Communication for TD Bank. TD Bank does a fantastic job of delivering friendly and
efficient customer service via Twitter and engaging customers with help and advice on blogs
and on their TD Money Lounge on Facebook (Julie Meredith, 2012).
This is an excellent example of how TD, an industry leader in customer satisfaction is using
social media for enhanced customer service. A firm can leverage the social media channels

such as Twitter and blogs to understand customer needs, monitor customer complaints and
provide quick resolution to their queries. The firm can track the results of its social media
customer service in terms of reduction of customer complaint calls it receives. This way, a
firm can reduce customer service costs and improve customer satisfaction.
For data analytics in this case, the firm should get data from 3 different sources. The first
source of information is about online customer feedback or complaints mentioned on various
social media websites and forums. This includes online blogs wherever there is a mention of
this firm, the Twitter feeds or tweets that include any related hash tags, Facebook posts or
comments mentioning about the firm or any of its products and customer complaint forums
where there is any complaint or issue against this firm. The second source of information is
about the customer complaints information from its phone service, online feedback (through
its website) and email feedback. The third source of information is about the details of its
customers related to products they are using, services they are offered and the volume of
their activities. These 3 sources should be collectively analyzed to find out the real issues
concerning its customers and act accordingly to resolve those issues.
Appendix 2 gives the details of data requirements and sources for Big Data analytics business
cases.
Appendix 3 shows the resource architecture for social media from where a firm can collect
the Big Data information on social media.

4. IMPLEMENTATION FRAMEWORK
In the following section we will discuss potential implementation process of the Big Data
systems at a firm with a particular focus on use of social media tools.
According to Deill and Ross (2008), before implementing new systems, a company should
understand what is not working with the current systems and how the new system will help
achieve company’s objectives. It has to be clear to management and data analytics employees
how information flow will happen under the new system, and how conclusions derived from
data will be incorporated in strategic decision making. Authors suggest that a company
should create an integrated IT strategy focused on business processes as opposed to data
management. In this case it means that it has to be clear how new data analytics systems will
contribute to a firm’s business.

4.1 Information flow
In order to illustrate the information flow and how it will affect decision-making, we have
created a high-level information flow diagram. To provide reliable, updated and integrated
information, all data should go through one central data analytics group as represented in the
middle of the diagram. The information gathered from previously analyzed data would be
transferred to the relevant departments. For instance, a firm’s data analytics group could help
marketing department to segment customers and suggest customized offers. Additionally,
some data can be distributed to external agencies either to sell data or create shared services
as long as the firm can guarantee sufficient safety of their customer privacy. Although data
from various departments would flow in Central analytics group, the actual meaningful
information would come from this central analytics group as they would have advanced tools
to analyze the various types of data supplied.

Marketing

External
agencies

Management

Central
analytics
group
Commerce

Operations

Adopted from “Hub and spoke” model
for web analytics team deployment
within

the

organization,

(Peterson,

2008)

4.2 Operating model
As part of creating IT strategy, a
firm would have to decide on its
operating model. The operating

model is selected based on how integrated and standardized are the business processes at a
large-sized firm. As can be seen in this table, different systems provide different benefits. For
instance, systems that are integrated, allow gaining efficiencies from sharing information
across different branches whereas standardized systems allow implementing changes quickly.
For Big Data, ability to integrate different systems play a crucial role since data pulled from
various sources allow for more representative and reliable analyses. Standardization of Big
data processes would allow the firm to transfer knowledge gained from data analytics rapidly,
saving time and allowing engaging in more real-time offerings. Thus, we would advise firms
to choose highly integrated and standardized operating model, also called unification model.
This model would provide the firm with nation-wide data access from all its subsidiaries and
foster standardized processes across all of its units.

4.3 Big Data implementation process
According to Microsoft researchers (Fisher et al, 2012) 5 step Big Data pipeline process
outlined in Figure 1, those processes also reflect the main challenges associated with usage of
Big Data.
The first step is acquiring data. Understanding which data is required is a big challenge for
many companies. Data can be generated internally, but also acquired from public databases,
social media or bought from private companies such as Microsoft’s Azure Marketplace and
Infochips. Sometimes linking data from various sources and formats can be technically
difficult although data itself is available. Thus, as highlighted in previous analyses concerning
operating model, it is important to have systems that can be easily integrated.
Figure1. The Big Data pipeline
The second step is selecting the
architecture based on cost and
performance.

Since

the

local

programs cannot perform extensive
Big Data analyses, an appropriate
platform has to be chosen. Several
options are provided through cloud
computing.
platform

It
used

is

likely

that

will

look

substantially different from local programs, thus analysts will have to get acquainted to the
new platform. When selecting the platform, both costs and design should be considered.
Often costs are based on how extensive are the analyses, clients paying more for more
computation and when larger systems are bought. Unfortunately, it is hard to estimate the
costs or duration of computing, as more real time data can be continuously added to systems
and impose non-linear costs in terms of overhead, storage and other aspects. Usually
estimates are made by iteration and re-running the analyses to eventually achieve time-cost
balance point.
The third step involves shaping the data to the architecture. Throughout this process analyst
has to ensure that the data uploaded is compatible with how the computation will be
structured, distributed and portioned. Cloud-computing systems use data storage in a different
way than desktop machines. For instance, there are cloud-based data-systems such as
Amazon’s RDS or Microsoft’s SQL Azure, distributed file systems such as Hadoop and more
novel data structures such as Azure’s queues and blobs. One of the challenges with using
these systems is moving data back and forth from the cloud to the local machines.
Furthermore, large files need to be organized, partitioned and prepared before uploading them
on the cloud. Furthermore, once data is uploaded, it also needs to be cleaned, which is a
difficult process require expertise from multiple people.
The fourth step is focused on writing the code. Basically in this stage analysts decide what
type of analyses will be performed with the data. It could be C£, Microsoft’s SCOPE, but
also such languages as R, Python or PIG over Hadoop. High-level languages have to be able
to support parallelism in order to break down and manipulate different analyses. These high
level languages allow analysts to abstract away from considering where the data is processed,
and focus more on the nature of computation. However, a common challenge is lack of
transparency typical to the Big Data analyses. When analyses are being performed in parallel,
detecting trough system failure is more complicated as true symptoms if failure can be
masked by other problems.
The last step is concerned with debugging and iteration. To test if the system is running
smoothly, analysts will look for potential bugs. However, debugging in a cloud-based
environment can be much more complicated since a single crush might be distributed across
multiple virtual machines with trace files also distributed on a variety of machines.
Furthermore, if a virtual machine fails jobs are moved to different machines hiding errors and

reducing transparency. Another problem associated with Big Data analytics is difficulty to
modify some parameters, decreasing analysts’ ability to adopt iterative approach. It can take
several hours for analysts before trying different parameters due to extensive amount of data
that need to be analyzed beforehand. Finally, ability to visualize and see the context is critical
when working with large data sets. It enables to see correlations between variables and
identify patterns in data.
Working in analytics cloud environment has many challenges. To drive the change, it will
require the firm to carefully assess different systems and how can they be integrated.
Furthermore, firms should analyze security issues related to using cloud-based systems.
Additionally, significant improvements can be expected within the next few years that would
allow to better break down and analyses various types of data allowing partial computations
and more rapid iterations.

4.4 Future Scalability
Future scalability is an important factor to be considered before selecting the technology for
Big Data. With numerous options available in the market, it is important to evaluate the
scalability of these systems for future trends so that the system doesn’t become obsolete
sooner. Adopting tools or software successfully implemented in other businesses is a riskreducing strategy but it is important to understand that those companies based their
implementation decisions on the options that were available at an earlier point in time.
Another important consideration that the organization has to make concerns the capacity of
the disks and the number of disks (or servers) to procure. Capacity is important when data
storage is the predominant use of the system. But, if the requirement is to constantly access
the data, more disks (or servers) are required to reduce the retrieval or processing time. So it
is important to find the balance between the storage capacity and the number of disks (or
servers).

5. IMPLEMENTATION PROCESS
Considering the implementation framework given above, a firm will have to select the most
appropriate tools for the company’s Big Data strategy and its goals. We have outlined a
process map for implementing systems and listed several relevant alternatives for social
media analytics tools in the following paragraphs.

5.1 Process Map for social media systems
To derive benefits of data in social media, a firm should consider the following set of actions:
1) Setting Objectives – Link the data being gathered and/or analyzed directly to the business
goals to be achieved. Typical objectives include understanding customer sentiments,
feedback on marketing or promotions, reducing customer service costs, getting feedback on
products and services and improving public opinion of a particular product or business
division.
2) Defining KPIs - After identifying the business goals, key performance indicators (KPIs)
for objectively evaluating the data should be defined. For example, customer engagement
might be measured by the numbers of followers for a Twitter account and numbers of retweets and mentions of a company's name. It can also be in terms of cost of reduction in
customer service calls due to enhanced customer service on social media.
3) Identifying social media monitoring tools - There are a number of types of software
tools for identifying and analyzing unstructured data found in tweets, blogs, forums and
Facebook posts. (Margaret Rouse, 2012)
4) Test your hypotheses. After gathering the data, filter it and look at it from multiple
perspectives (such as over different time frames) to test your hypotheses.
5) Draw insights. Finally, the data should help the firm arrive at well-informed assumptions
and insights, which can then guide its actions in social media or in other customer-facing
channels. (James A. Martin, 2013)
6) Engage and Act – After analyzing the data and drawing insights from it, engage your
audience. Figure out what they're looking for, so be sure to act upon the data once it has been
analyzed. (Ben Parr, 2009).
The strategy for implementing Social Media Big Data analytics is shown in Appendix 8.

5.2 Implementing Social Media systems using specialized products
The next section gives an overview of the tools available for social media monitoring. A
comparison of various Social Media monitoring tools is shown in Appendix 4, where these

tools are compared on the basis of several factors. A detailed description of top 5 Social
Media monitoring tools is provided below:
1) Radian6 – This Salesforce.com product helps brands to listen more intelligently to their
consumers, competitors and influencers and provides detailed, real-time insights. Beyond its
monitoring dashboard, which tracks mentions on more than 100 million social media sites, an
engagement console is also available that allows a company to coordinate its internal
responses to external activity by immediately updating company’s blog, Twitter and
Facebook accounts all in one spot. Everything is fully automated (J.D. Lasica & Kim Bale,
2011). The Salesforce Radian6 product doesn’t process any information from the firm’s
internal systems such as emails, social media systems, sharepoint and other internal systems.
But its open API has allowed many systems to integrate with Radian6. With features
developed on their platform API and Social Metrics Framework for integrating third-party
data, Radian6 now supports the integration of social customer relationship management
(CRM – only Salesforce), web analytics, and other enterprise systems.
Cost: The dashboard starts at $1000/month (could range higher depending on mentions,
(Zach Ellis, 2013)) and includes the following features within the basic package:
Product

Package

Users

Name
Radian6 –

Features

Additional Price

Clients

features
Basic

Salesforce
Marketing
cloud

1000
users

 Social listening – Web

20,000 mentions
Analytics
 Up to five social
presences
 30 days historical
data
 100 topic profiles
 Training and best
practices
(Salesforce, 2013)

$1,000

TD, Red Cross,

per

Adobe,

month

Cirque du Soleil,
H&R

AAA,

Block,

March of Dimes,
Microsoft,

Pepsi,

Southwest
Airlines

2) Sysomos – its Heartbeat is a real-time monitoring and measurement tool that provides
constantly updated snapshots of social media conversations delivered using a variety of userfriendly graphics. Heartbeat organizes conversations, manages workflow, facilitates
collaboration and provides ways to engage with key influencers. Sysomos also offers a Media
Analysis Platform.

Cost: Entry-level price of $500/month.
Clients: IBM, HSBC, Roche, Ketchum, Sony Ericsson, Philips, ConAgra, Edelman, Shell
Oil, Nokia, Sapient, Citi, Interbrand. (J.D. Lasica & Kim Bale, 2011)

3) Lithium - Lithium monitors the search-specific mentions and sentiments in social media
outlets and outputs them into easy-to-read graphs and numbers resembling the stock market.
Lithium will aggregate information from a variety of platforms including blog posts and
comments, Twitter, Facebook, Flickr and many others, and it’ll assess emotions surrounding
the brand’s pre-, mid- and post campaign so a company can adjust its strategies accordingly.
Cost: Base plan of $249/month for five users and five searches.
Clients: Best Buy BT, Barnes & Noble, FICO, Disney Online, Stubhub, Motorola, Coca
Cola, Focus Features, Netflix. (J.D. Lasica & Kim Bale, 2011)

4) Collective Intellect - Using a combination of self-serve client dashboards and human
analysis, Collective Intellect offers a robust monitoring and measurement tool suited to midsize to large companies with its Social CRM Insights platform. It applies spam management
techniques and text analysis to clean data sets, delivering customers rich intelligence.
Collective Intellect blends heavy-hitting technology and algorithms to search, collect, filter,
cleanse, analyze and produce robust reports. Collective Intellect uses impressive real-time
social analytics for powerful monitoring and sentiment accuracy (Toptenreviews, 2013).
Cost: Pricing starts at $300/month and scales based on specific client needs, according to
published reports.
Clients: General Mills, NBC Universal, Pepsi, Walmart, Unilever, Advertising Age, CBS,
Dole, MTV Networks, MillerCoors, Paramount, Verizon Wireless, Viacom, Hasbro,
Siemens. (J.D. Lasica & Kim Bale, 2011)

5) Alterian SM2: This tool tracks mentions on blogs, forums, social networks like Facebook,
microblogs like Twitter, wikis, video and photo sharing sites, Craigslist and ePinions. SM2
monitors the daily volume, demographics, location, tone and emotion of conversations
surrounding a brand and aggregates results into positive and negative categories for quick
review by anyone on staff. Cost: Pricing is based on volume of results and ranges from
$500/month to $15,000/month. “Freemium” trial plan allows for five keyword or phrase
searches and a total of 1,000 results. Alterian also provides additional custom solutions.
Clients: Rosetta, MDAnderson, Pursuit, YouCast.

Other specialized vendor services for social media monitoring includes Brandwatch,
Beevolve, UberVU, Viralheat, Trendrr, Attensity360, Simplify360 etc. (J.D. Lasica & Kim
Bale, 2011)
Appendix 4 shows the comparison of the tools in a tabular format.

Recommendations: Depending on the goals established by a firm, the company can choose
between various options. As can be seen, TD already uses Salesforce tool Radian6 that
allows receiving insights about costumers, competition and influencers as well as
coordinating internal responses to external activity via all accounts. In contrast, HSBC along
with IBM and Shell Oil use Sysomos that provide real-time monitoring and measuring tool
with user-friendly graphics. These two tools have also been ranked as the top two in a
comparison of social media tools for 2013, as can be seen in appendix 4 due to their vast
amount of features. We would advise the firm to use Appendix 4 to evaluate features that
would be required for achieving its goals. However, we would advise either Radian6 or
Sysomos as they have all the necessary features if required.

5.3 Implementing Big Data using Hadoop
Many of the previous cases have shown that applying such systems as Hadoop can bring
great benefits to the company. Apache Hadoop is a high scale, open-source distributed
computing platform that includes the Hadoop Distributed File system and an implementation
of MapReduce(Lamont,2012). For instance, using Hadoop allowed Sears (Henschen, 2012)
to reduce campaigns for its loyalty club from six weeks to weekly analyses. This was
achieved because Sears moved from its mainframe Teradata and SAS servers to Hadoop’ s
cloud environment. Furthermore, it allowed Sears to perform more granular targeting, which
in some cases included even individual customers. Previous models used 10 % of available
data whereas analyses performed by Hadoop used 100% of data provided by Sears.
Hadoop’s strengths come from its ability to divide workloads across many servers and
perform analyses simultaneously. According to Shelley, the CTO of Sears, Hadoop also
enables the company to create significant cost savings, since mainframe computers would
cost between $3000 to $7000 whereas Hadoop’ s costs are small fraction of that. Another
upside of Hadoop is its ability to store data in a raw format. If a company wants to perform

analyses with a different model five years from now, it has all the data available in the right
format.
However, the downside of Hadoop is that this platform is relatively immature and there is a
lack of Hadoop talent. For instance, Sears had to learn everything about this platform by trial
and error with a limited help from external consultants. Furthermore, for Sears it takes 90
minutes to extract the data from mainframe servers to Hadoop and bring results back to
servers. This is a cost Sears has to pay for using legacy systems while simultaneously
operation o Hadoop.
Recommendations: Hadoop has proven to give relevant insights for many companies
including Sears by allowing it to reduce time required to perform analyses, enabling more
real-time offers and personalized services for many different segments. However, a firm
should also consider downsides of Hadoop such as scarcity of talent and expertise as well as
the time required to transfer information forward and backward from Hadoop systems.
Furthermore, the firm should also perform risk analyses to assess safety issues related to
transferring data.
Additionally, Hadoop can be combined with many different applications, and serve as bases
for more advanced tools. One of those tools created by IBM will be discussed in the next
paragraph.
Infosphere BigInsights by IBM
Infosphere BigInsights by IBM is based on Apache Hadoop, and by combining power of
Apache Hadoop with its own innovations, IBM provides companies with insights from new
and emerging type of data that previously were not possible to analyze (IBM, 2013).
BigInsights provides tools on advanced analytics, performance optimization, enterprise
integration, visualization and others. Furthermore, application connectors make BigInsights
data accessible to any Java Database Connectivity compatible data store, including Cognos
Business Intelligence. Additionally, IBM’s own unique innovations include “sophisticated
text analytics module, IBM Big Sheets for data exploration and a variety of performance,
reliability, security and administrative features" (IBM, 2013). With Infosphere BigInsights
IBM has integrated individual Hadoop components and their own added features into one
single product to simplify development, implementation and management for enterprises.
This allows companies to both optimize their day-to-day operations and gain micro-level
understanding of “customer attitudes, trends and relationships” “sophisticated text analytics

module, IBM Big Sheets for data exploration and a variety of performance, reliability,
security and administrative features" (IBM, 2013).
Recommendations: Using IBM’s Infosphere BigInsights would allow a firm to leverage
knowledge possessed by IBM and avoid drawbacks of not having sufficient expertise in
Hadoop systems. Furthermore, Infospheres BigInsights have added security and reliability
features to their tools, ensuring more safety for data gathered by the firm. This tool is based
on open source platform, but is user focused and simplifies and accelerates implementation
process of Big Data processes in the company. Considering all these aspects, it would be a
valuable alternative for using and understanding Apache Hadoop in its raw form.
Furthermore, Infosphere BigInsights is particularly suitable for analyzing customer segments
based on Big Data. However, for smaller scale analyses such tools as SPSS Advanced
Statistics or Intelligent Miner data mining suite can also be used.

5.4 Implementing Big Data analytics systems using packaged products
1) IBM SPSS modeler – is a data mining and modeling tool that helps the clients see the
trends and patterns in their data. Clients can easily build predictive models quickly without
any programming. SPSS modeler helps you make effective decisions by analyzing structured
data, utilize advanced linguistics technologies and process large unstructured text data. It also
includes social network analysis depicting social behavior of individual or groups, and
identifying social leaders influencing behavior of others. IBM SPSS modeler performs
automated modeling by estimating and comparing number of different modeling methods in
order of their effectiveness generating results in very interactive and visual format. Appendix
6 shows the sample architecture for real-time implementation.
2) IBM Analytical Decision Management – helps the clients make best business decisions by
optimizing and automating high-volume decisions and solid data analysis. It helps in
applying predictions within real-world constraints to reach optimal decisions using analysis
of structured and unstructured data. The client can adapt recommendations through feedback
mechanism. For example, customer service agent can access marketing offers tailored to
specific customers in real time thereby improving customer attainment, growth and retention
(IBM, 2012)

3) Cloudera Enterprise – gives 360 degree customer view by combining information stored
on different systems such as CRM, financial, point of sale, marketing, customer support etc.
It is a combination of Cloudera’s open source Hadoop stack (CDH), powerful management
platform (Cloudera manager), and Cloudera’s expert technical support. Financial institutions
can create central data hubs that combine large diverse data and after in depth- analysis can
provide personalized recommendations to its customers by uniquely targeted offers, crossselling and up-selling products (Cloudera, 2012). Appendix 7 explains the sample for
integration of transaction data and interaction data.
Recommendations: In this case a firm might consider using any or all three tools as each of
them has different advantages. Cloudera provides a comprehensive analytical view of the
data that any big firm might need but it is important to note that it integrates different
technologies appended to existing systems which may increase complexity and integration
issues in future. But its use of open source systems and ease of integration with them may
bring down the cost considerably. Implementing the IBM tools such as SPSS modeler and
Analytical decision management tool would make seamless integration among them and also
provide the features other systems do. Also support will be available from the vendor as
compared to possible scarcity of resources for Hadoop and related systems.

6. RISKS AND MITIGATIONS
Though Big Data has many advantages, implementing Big Data system in an organization
has some inherent risks associated with it and sincere mitigation efforts are required to reap
the benefits of the system. Below are some of the risks and corresponding mitigation
techniques that are to be employed to maximize the effectiveness of the Big Data systems.

8.1 Security Risks
Organizations that deal with sensitive customer data like financial institutions may face a
huge security risk by implementing Big Data, if proper control measures are not employed.
Security breached and loss of information also makes organizations face law suits apart from
losing customer satisfaction.
To avoid security breaches, IT has to use additional security products built to specifically
apart from using usual security procedures like restricted access, encryption of confidential
data.

8.2 Analytics’ cohesion with business goals
It is important to set the goals and specific objectives for the Big Data system so that it meets
the business goals. Many organizations lose direction as what they want to accomplish
through the Big Data system and cannot obtain the expected benefits. Also they should be
completely aware of data sources they are going to use and the integration between them.
To reap maximum benefit it is important to develop an implementation road map and clear
process to classify and utilize data.

8.3 Managing complexity and Storage capacity
An inherent issue that Big Data has is the complexity that builds up with more and more data
the system handles and the effective maintenance of the stored data. With time the
complexity will nothing but increase and it will require storage capacity and investment to
expand the capacity.
To make effective usage of the Big Data system, the firm has to allocate budget for
maintenance, and periodic upgradation of technology, security features and hardware.

7. SUMMARY / CONCLUSION –

7.1 Focusing on Big Data opportunities
After looking into various cases above of how Big Data is utilized by various firms to derive
benefits for different purposes, it becomes apparent that a firm can considerably benefit from
the Big Data analytics. Following is a summary of the different cases that a firm can consider
to implement Big Data in Social Media space:
BIG DATA ANALYTICS FOR SOCIAL MEDIA


Customer sentiment analyses



Using customer feedback for customized product offerings



Tracking PR events and promotional campaigns



Social media for rapid customer Service

7.2 Recommending a Strategy for implementing Big Data systems:
For implementing Big Data analytics, there are several alternatives that a firm can consider.
According to a research done by a non-profit organization AIIM (Association for Information
and Image Management), many firms that are planning to implement Big Data systems are
tempted to press ahead with in-house developments using open-source components (Such as
Hadoop) as it might give them early-mover competitive advantage. A lot of vendors are
moving quickly to provide packaged product sets, and this is driving a need for standardized
connectors to provide unified data access to as many different databases as possible.
However, usability outside of the technical department is important, and for Big Data,
assurance of robust security is essential. (AIIM, 2012). As observed from the below survey, a
majority of firms prefer a combination of options for implementing Big Data systems.

Multi-Criteria Decision Analysis (MCDA):
We carried out a Multi-Criteria Decision Analysis for evaluating the above mentioned
alternatives. In total 4 alternatives were evaluated for implementation of Big Data Practice
within a low-risk oriented firm – Option 1 is in-house development using open source tools
such as Hadoop, Option 2 is going with Specialized products (Such as Radian6 mentioned
above), Option 3 consists of “Packaged product sets” (e.g. Packaged products from Oracle or
IBM) and Option 4 is about having a mix of Specialized products and Packaged products.
The model consists of two components. “Evaluating Strategy Value” and “Ability To
Implement”. The “Evaluating Strategy Value” component consists of rating the options on

several Strategy factors - Quick Impact, Data Security, Low Risk, Integration and Features.
While the “Ability to Implement” was evaluated on factors such as Funding, Skills, Ease of
use, Support and Flexibility. Appendix 9 gives the details of analysis.

Strategy Value Matrix
245

Opt4
230

Opt2
Opt3
Opt1

215

S
t
r
a
t
e
g
y

V
a
l
u
e
Options

200
0

100
200
Ability to Implement

300

Recommendation:
From the above Strategy Value Matrix, it can be observed that Option 4 (or Mix of multiple
models) looks as the best suitable option for implementing Big Data systems for a firm. This
option is followed by Option 2 which is about implementing “Specialized Products” and
Option 3 which is about going with “Packaged Product Sets”.

Appendices
Appendix 1: Porter 5 forces for analyzing factors affecting a firm

Threat of new
entrants – Few
barriers to entry
and high profit
margins may attract
new entrants

Threat of
substitutes – New
trends or products
that might
substitute existing
products

Power of customers
– More choices and
consumer driven
markets

Firm

Competitive rivalry
– Price war and
similar products

Power of suppliers
– change in
supplier prices and
availability

Appendix 2: Data requirements and sources for Big Data analytics business cases

Data Sources

Data requirements
for Big Data
analytics

Internal

External

Availabl
e Firm
Products

Online forums,
blogs,
consumer
complaint
websites

×

×

×

×

×

×

Customer
transactio
ns

Online
social
networking
sites
(Twitter,
Facebook)

×

Cases

Customer
demograp
hics

Set of
services
provided
to a
customer

×

×

×

Customer
support voice,
online

Paid
searches,
online ads

Barclays and
Adidas
Social
Media

ING Direct

×

×

×

DreamWorks
TD Bank

×

×

×

×

×

Appendix 3: Social Media Resource architecture Sample

Retrieved from http://www.apogeesocialmediagroup.com/wpcontent/uploads/2012/04/ebook_CraftaSuccessfulMarketingPlan_SalesforceRadian6.pdf

Appendix 4: Comparison of Social Media Tools

Appendix 5: CRM Software comparison

CRM software System
Strengths

Weaknesses

Overall

Sales force

On Contact
Sage ACT
-More tools than other CRM Best for viewing social
Has more detailed functions
softwares apart from networking sites & company
than any other CRM function
Salesforce
websites
Only available as an online Weaker marketing and sales
package
functions

No unique features

Avidian Prophet
A lot of assistance

Not intuitive

AIM CRM
Track routes and redirect
customers to right
departments
Only webhosted

The best overall alternative
Good for social networking,
Great in sales and marketing
Easy to use, many detailed
Provides a lot of assistance,
for available CRM software
but not as good for other
assistance, but not as good
options
lacks some features
packages
functions
for other functions

CRM software reviews. Retrieved from: http://crm-software-review.toptenreviews.com/aimcrm-review.html

Appendix 6: Real-time data architecture

Appendix 7: Big Data is confluence of transaction data, interaction data and Big Data
processing

Retrieved from: http://www.informatica.com/

Appendix 8: Social Media Strategy – As social media strategy evolves, valued customer
relationships grow stronger

Social Media Strategy: Retrieved from http://ccbb.casselsbrock.com/files/file/docs/PWCCBB%20PowerPoint%20Slides%20-%20Final%20-%20September%2027,%202011.pdf

Appendix 9: Multi-Criteria Decision Analysis Model (MCDA)

EVALUATING STRATEGY VALUE

CRITERIA

Weight

Option 1 –
In-house
Developme
nt

Quick Impact
Data Security
Low Risk
Integration
Features

7
7
7
4
5

5
9
9
6
6

Option 2 Specialized
products
(SaaS)

Option2 Weighted
Rating

Option 3 Packaged
product
sets

35
63
63
24
30

30

Option1 Weighted
Rating

9
7
7
6
9

63
49
49
24
45

7
8
7
8
7

215

230

Option3
Weighte
d
Rating
49
56
49
32
35

Option 4
- Mix of
options 2
&3

Option4
Weighte
d Rating

9
8
7
7
8

63
56
49
28
40

221

236

EVALUATING ABILITY TO IMPLEMENT

CRITE
RIA

Optio
n 1 Ratin
g

Option
1
Weighti
ng
criteria

Option
1
Weight
ed
rating

Optio
n 2 Ratin
g

Option
2
Weighti
ng
criteria

Option
2
Weight
ed
rating

Optio
n 3 Ratin
g

Option
3
Weighti
ng
criteria

Option
3
Weight
ed
rating

Opti
on 4
Rati
ng

Option 4
Weightin
g criteria

Option
4
Weight
ed
rating

Funding

6

8

48

8

7

56

6

8

48

8

7

56

Skills
Ease of
use

6

8

48

7

4

28

7

4

28

8

4

32

6

4

24

8

6

48

6

5

30

8

6

48

Support
Flexibili
ty

7

5

35

9

6

54

8

7

56

9

6

54

7

5

35

8

7

56

7

6

42

9

7

63

30

190

30

242

30

204

30

253

Results
Ability to implement
Strategy value

Opt1
190
215

Opt2
242
230

Opt3
204
221

Opt4
253
236

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