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 customer-

focused 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.

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

Central analytics

group

Marketing

Management

Operations Commerce

External agencies

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. It is likely that

platform used 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 risk-

reducing 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 re-

tweets 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

Name

Package Users Features Additional

features

Price Clients

Radian6 –

Salesforce

Marketing

cloud

Basic 1000

users

Social listening –

20,000 mentions

Up to five social

presences

30 days historical

data

100 topic profiles

Training and best

practices

(Salesforce, 2013)

Web

Analytics

$1,000

per

month

TD, Red Cross,

Adobe, AAA,

Cirque du Soleil,

H&R 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 user-

friendly 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 mid-

size 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, cross-

selling 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.

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”.

200

215

230

245

0 100 200 300

Strategy Value Matrix

Options

Opt4

Opt2

Opt3

Opt1

Ability to Implement

S

t

r

a

t

e

g

y

V

a

l

u

e

Appendices

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

Firm

Threat of new entrants – Few

barriers to entry and high profit

margins may attract new entrants

Power of customers – More choices and

consumer driven markets

Power of suppliers – change in

supplier prices and availability

Competitive rivalry – Price war and similar products

Threat of substitutes – New trends or products

that might substitute existing

products

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

Data requirements

for Big Data

analytics

Data Sources

Internal External

Cases

Customer

demograp

hics

Customer

transactio

ns

Set of

services

provided

to a

customer

Availabl

e Firm

Products

Customer

support -

voice,

online

Online

social

networking

sites

(Twitter,

Facebook)

Online forums,

blogs,

consumer

complaint

websites

Paid

searches,

online ads

Social

Media

Barclays and

Adidas

× × ×

ING Direct × × × × × ×

DreamWorks × × ×

TD Bank × × × × × ×

Appendix 3: Social Media Resource architecture Sample

Retrieved from http://www.apogeesocialmediagroup.com/wp-

content/uploads/2012/04/ebook_CraftaSuccessfulMarketingPlan_SalesforceRadian6.pdf

Appendix 4: Comparison of Social Media Tools

Appendix 5: CRM Software comparison

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

Appendix 6: Real-time data architecture

CRM software System Sales force On Contact Sage ACT Avidian Prophet AIM CRM

Overall

The best overall alternative

for available CRM software

packages

Easy to use, many detailed

options

Good for social networking,

but not as good for other

functions

Provides a lot of assistance,

lacks some features

Great in sales and marketing

assistance, but not as good

for other functions 

-More tools than other CRM

softwares apart from

Salesforce

Only webhosted

Strengths

Best for viewing social

networking sites & company

websites

A lot of assistance

Track routes and redirect

customers to right

departments

WeaknessesOnly available as an online

package

Weaker marketing and sales

functionsNo unique features Not intuitive

Has more detailed functions

than any other CRM function

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/PWC-

CBB%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

Option1 -

Weighted

Rating

Option 2 -

Specialized

products

(SaaS)

Option2 -

Weighted

Rating

Option 3 -

Packaged

product

sets

Option3

-

Weighte

d

Rating

Option 4

- Mix of

options 2

& 3

Option4

-

Weighte

d Rating

Quick Impact 7 5 35 9 63 7 49 9 63

Data Security 7 9 63 7 49 8 56 8 56

Low Risk 7 9 63 7 49 7 49 7 49

Integration 4 6 24 6 24 8 32 7 28

Features 5 6 30 9 45 7 35 8 40

30 215 230 221 236

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 6 8 48 7 4 28 7 4 28 8 4 32

Ease of

use 6 4 24 8 6 48 6 5 30 8 6 48

Support 7 5 35 9 6 54 8 7 56 9 6 54

Flexibili

ty 7 5 35 8 7 56 7 6 42 9 7 63

30 190 30 242 30 204 30 253

Results Opt1 Opt2 Opt3 Opt4

Ability to implement 190 242 204 253

Strategy value 215 230 221 236

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