Team 7

25 June 2013

Scope

Retail is business activities involved in the sale of goods and services to

consumers for their personal, family, or household use, or the overall business

activity involving the sale of goods and services to consumers to be used individually,

for their family, or household. The retail industry is a driving force in the

American/world economy, so much so that news reports often base at least part of

their perception of the economy on how the retail industry is performing. Aside from

the major economic ebb and flow of the buying seasons and how they affect retail

sales, the retail industry as a whole has a number of other major

problems/opportunities that it must often deal with.

The focus in this report will be on Specialty Retailers. What is Specialty

Retail? Specialty Retail comes in all sizes from the small ma and pa retail to the

large retail chains. There is no specific size or shape. They’re specialty as opposed to

Big-Box that offers everything to the consumer. Specialty retailers are concentrating

on selling one merchandise line of goods for a particular and usually selective

clientele. Which specialize in a specific range of merchandise and related items.

Specialty retailers have a narrow but deep selection in their specialty of goods and

services that provide high levels of service. Usually in the medium to high range

pricing, the products depend on aspects like the type and uniqueness of merchandise

and ownership. It depends on if it is owner operated or a chain operation. A

difference is Specialty retailers do not carry a wide range of merchandise like big box

retailers. Stores such as Gap, Old Navy, Eddie Bauer, Victoria’s Secret, Ann Taylor are

examples of Specialty retailers.

The focus is not on Big Box retail store that occupies an enormous amount of

physical space and offers a variety of products to its customers. These stores achieve

cutting costs by focusing on large sales volumes. Because volume is high, the profit

margin for each product can be lowered, which results in very competitively priced

goods. The term big-box is originated from the store's physical appearance. Located in

large buildings of more than 25,000 to 200,000 square feet, the store is usually

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designed and often resembles a large box. Wal-mart, Target, K-Mart, Best Buy, Sam’s

Club, Costco, Bed Bath and Beyond, and Ikea are examples of big-box retailers.

(Regulation of Large Retail Establishments (Big Box Retail))

The leverage experience with Big Data is the difference between Big Box retail

and Specialty Retail. Big Box usually uses lower prices to get customers into their

stores. Specialty retail wants an emotional attachment between the customer and

their product. Big Box is in every neighborhood in all states in the United States.

Loyalty is important for Specialty Retail. This enables specialty to charge more on

prices and not focus on being the cheapest. Big Box is large scale not an intimate

shopping experience. Specialty retailers must build a relationship with the customer.

They need to know what they usually buy color, size, and style to know what to

suggest ensuring that the right items are in stock. Specialty products will become more

valuable, and visual. Narrower segmentation of customers groups with similar needs

and or desires allows Big Data to tailor products and services. To improve decision

making, unearth valuable insights that would usually remain hidden sophisticated

analytics have improved decision making to minimize risks. The next generation of

development of products and services is in using Big Data. Big Data will unlock

significant value in Specialty Retail.

Business problems in Specialty retail is customers are demanding a more

customized experience, now! The market is constantly changing, and the pressure is

on to act fast or lose revenue. Also Customers are fickle to gain their loyalty takes

work.

Business opportunities for Specialty retailer who react to the demand

have the advantage. They’re predicting trends and preparing for future trends.

Analytics combined with Enterprise data with other information web browsing,

social media, phone, mobile apps will create predictive models for trends. Real time

analytics help Specialty retailers to orchestrate customer demand, competitor activity,

inventory, pricing on demand. Specialty retailers want to focus on customers who

would like to buy their goods and services.

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Key Business Problems/Opportunities Explored

Improve inventory management. Ensure that the right product, color and size

is available when the customers want it – This is important because inventory is the

bread and butter of the retail world. This is where the money comes from. If there is

not a good tracking system, money could be lost.

Improve upselling to create a larger sale. Upselling can help to make a sale

turn into something that is an even bigger sale. Upselling basically means to suggest a

more expensive version of the item or even to suggest that they could get the same

thing for a little bit more money.

Suggestive selling to add additional items to a customer’s sale. This is similar to

upselling but it is suggesting something to the customer that they may not have

thought of otherwise. Many stores place items close to the register that employees can

suggest to the customer before they check out.

Improve the gathering and evaluating of customer feedback to improve

products, stores, service and the customer’s overall experience. Knowing how the

customer is feeling about their experience at the store can really help improve how

the store is laid out and how things are done. Perhaps customers get annoyed with

employees asking them if they need any help several times. Customer feedback is the

ultimate way to get honest opinions of how the company is treating their customers.

Improve effectiveness of advertising campaigns. Advertising is a huge way to get

new customers to notice the store. Sometimes, potential customers have never heard of

the store and are interested in deals that may be coming up. Tailoring advertisements

to the individual customer could also help to real new customers in.

Increase customer base. This goes with increasing effectiveness of advertising,

but increasing a customer base would help to increase sales. There are countless ways

that can help to increase the customer base and in turn increase sales.

Increase current customer sales and loyalty. Big data and new technologies can

help to create an online loyalty program to bring customers back again and again.

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There are several retailers out there that have loyalty programs that have enjoyed

more success with it.

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Industry Context for Big Data Solutions

We narrowed our scope to Specialty Retailers, as opposed to all types of retail.

Many of the Big Box retailers have already invested in some type of Big Data solution.

We felt that we could use some examples from the big retailers to create

recommendations for Specialty Retail. Specialty Retail has a unique opportunity

with Big Data because of their relationship with their customer. They benefit from

“knowing” the customer and providing a more intimate shopping experience. Big

Box retail is large scale and does not create a personalized experience. Big Box

retailers differentiate themselves usually by price and a huge selection of products.

Specialty Retailers create an emotional bond between their product and their

customer. Specialty Retailers are successful when they create a relationship with the

customer. Big Data solutions can help build, strengthen and mature this

relationship so that retailers can be more profitable. Big Data can help a Specialty

Retailer know what the customer usually buys, the size, the color, the style so that

they can first ensure it is in stock and second can recommend other products that

they might like.

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Detailed Use Cases

Computerized Inventory systems

Keeping track of inventory in real time can help to predict the items that will be

needed in the future before the items are actually needed. When a program can help

to forecast what is needed, the store can be ahead of the curve. It is important that all

items are in stock in all sizes because if a customer comes in and they are not finding

their items, they are going to go to another store and make a sale there. Staying up on

inventory is imperative to getting sales.

Having RFID tags on inventory can also help to keep track inventory in a better

way. RFID applications track and update inventory as soon as it is purchased. It also

helps to provide end-to-end supply chain visibility for complete traceability. For

example, a RFID inventory control software program can help to avoid out-of-stock

incidents and increase inventory turnover. RFID can also help to trace the inventory

from production to sale, and can aid in any mishaps.

Providing training or video training for how employees can upsell products

Sometimes it is hard to tell employees to do something like upselling. Having

examples of what employees can say to customers or showing videos to employees as a

part of the training program when they are hired.

Use big data to see what is popular and try to find the best products to suggest with

the popular items

You can gain a deeper understanding of your customer’s interests, shopping

habits and preferences by unifying and analyzing your transactional and loyalty

data with customer demographic and web-based data. These insights can be used to

create personalized customer experiences by offering the right product and offer at the

right time through the right channel.

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When you combine someone's personal information with vast external data sets, you

can infer new facts about that person.

Big data can really help to find out facts about customers that retailers would not

know otherwise. When combining the personal information with data sets, you can

find out more about a person and will be better able to advertise effectively to them.

Positioning most profitable items in the store near the loss-leaders

Research has shown that placing the most profitable items in the store next to the

items that are the least-profitable can help to sell the less profitable items. Since

customers are more likely to want to purchase the best-selling items, they might see

the items nearby and decide to purchase them as well.

Having the right size/color assortment in stock

Specialty Retailers are to satisfy the customer at the present moment. If the

inventory isn’t there when the customer is ready to buy in the right size, color or

quantity the customer isn’t happy and you’ve lost a sale, and perhaps, a customer.

Billions are lost every year simply because inventory isn’t on the shelf. But it doesn’t

have to be that way. With technologies like automated forecasting and replenishment

as part of your inventory program, you can significantly reduce out-of-stocks while

supporting localization of assortment and cost reduction.

Big data for Specialty retail means a chance to see why a sale did or didn’t

occur. Is it product selection? Pricing? Store display? Is it unsuccessful promotional

material? Before, this information was hard to track, but with the initiation of big

data and in-memory computing, two products appropriate for collecting and

analyzing unstructured data like that of retail, are sure to play a significant role in

sales.

Web information shows how consumers navigate through an internet storefront.

The data can be combined with apps and sales data, for generating a clear vision.

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Specialty retailers now have the opportunity to see website traffic for a

particular product and compare it to the sales. Before, if a product was not selling it

would be removed from the line. Now, managers can adjust the prices; ensure there

are enough colors and sizes, and any other aspects that take a look to a sale.

Specialty retail rely heavily on in-store and online purchases, but they are

not successful without making sure their product is delivered on time. Predictive

analysis applications using the first day’s delivery, past delivery data, and real-time

traffic data, provide revised delivery schedules, allowing retail managers to take

immediate corrective action in their inventory.

This is a great advantage for Specialty retail managers, preparing to better meet

customer expectations and maintain high operational effectiveness. This operational

efficiency is essential when Specialty retailers always want to know what their

customers need before they even know they need it and having sufficient inventory.

For example, using big data retailers now can see, through data from store

cards, cashed-in coupons, and purchase history, when a customer may need a refill

on a product. This data gives retail marketers the upper hand, sending the low

stocked customers promotional material urging them to buy the refill. (Kelly , M. (23,

DEC 2012). Big data for online retailers: Best practices on selling “sized” inventory)

Create a customer loyalty program that will encourage customers to

keep returning with rewards

Customer retention is more than giving the customer what they expect. It’s

about exceeding their expectations so that they become loyal supporters for a

particular Specialty Retail brand. The customer loyalty cards enable retailers, and

many others to provide customer loyalty reward points, discounts and perks. Customer

loyalty card programs also create opportunities to track customer data and use it to

build strong, lasting relationships. A customer with a return is a returning customer.

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Use social media and other keywords to find out what people want, become more

active on social media

A primary goal is to improve the customer experience to increase customer

loyalty. Customers will stay longer and continue buying. Businesses that have higher

levels of customer loyalty experience faster growth compared to businesses that have

lower levels of customer loyalty. Businesses who have Customer Experience

Management programs (CEM) realize that these programs can be data intensive, huge

amounts of data about their customers' attitudes and online behaviors. To enhance

the value from this data, companies need to apply appropriate analytics to provide

insights about how to increase customer loyalty.

The source of data in most CEM programs, not surprisingly, is customer

feedback data. Businesses gain customer insight primarily by collecting and

analyzing customer feedback data from different sources, including customer

feedback surveys, social media sites, branded online communities and emails. Using

customer feedback data, companies identify the customer experiences that are closely

linked to customer loyalty and use that information to allocate resources to improve

those customer experiences, and, consequently, increase customer loyalty. (Topiol, G.

(23, Feb 2012). How to use customer experience management for retail success. )

Create online surveys with coupon rewards to encourage customers to

fill it out and to get their honest opinions of how the business is

doing

Knowing how customers feel about the experience is imperative to improving how the

store is and how the employees act towards customers. Creating a survey with a

potential reward at the end can encourage customers to give their opinions and can

help retailers to improve the customer experience. An unhappy customer is a lost

customer, and if they are willing to share their feelings in a survey, it just might

help the company to never make that mistake again.

Amazon - using Big Data to build a relationship with a customer

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There was a story that went viral of a man who talked to Amazon customer service

about a broken Kindle. Amazon immediately knew his name, his problem, and what

kind of Kindle he had. He didn’t have to spell out his name or address or get sold

anything. Generally, customer service calls are difficult to get through because the

customer service representative doesn’t know anything about the customer and needs

to find out all the information.

Building a relationship with a customer using big data can help to keep the customer

coming back and back again. It is also important to only reference the data that is

needed. There is no need to scare the customer off by telling them all that you know

about them. Listening to the customer but also letting them realize that you have a

lot of tools at hand can really help to keep the relationship with them strong

(Madden, 2012).

Target - able to determine if a customer is pregnant and then tailor related coupons

and offers

A Forbes article shared how Target figured out a girl was pregnant before her own

father did. Target assigns every customer an ID number and ties their purchases and

information with it. A statician ran tests analyzing data from Target, and could tell

when a woman was pregnant by the fact they were buying un-scented lotion and

cotton balls. Target started sending out coupons for these customers that had high

“pregnancy scores”. When Target sent a high school girl those coupons, her dad was

alarmed, but then found out that Target was right (Hill,2012).

Using this data could be helpful, but could also cause damage to the

customer/retailer relationship. If a customer is not comfortable with knowing that the

retailer knows all of this data about them, it could cause them to not want to shop

there anymore.

Sears - improving customer loyalty with Hadoop with personalized coupons and

offers

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Sears has a data store that stores data about every customer that has ever shopped at

Sears. They also keep track of what purchases have been made and tailor coupons to

those individuals. Hadoop can handle huge volumes of data and can be saved long-

term. It is the central hub of all their data management and activity. Sears also has

highly complex software that helps to analyze the data that Hadoop collects.

Sears wanted to personalize marketing campaigns, coupons, and offers down to the

indivudal customer. Improving customer loyalty was important to Sears, and Hadoop

helps to accomplish that. They created a “Shop your Way” rewards program that was

implemented to help the company succeed. Rewards can be sent out weekly through

Hadoop (Henschen).

Track customer traffic pattern within the store

Example: A store design/layout team could analyze data about how customers travel

through the store and which displays catch their attention and which ones they

walk right by

Having cameras in the store to watch customer traffic could help to generate a better

layout that can help to increase sales. Many brick and mortal stores have been forced

to rely on customer surveys. Mall operators are monitoring shopper’s behavior with

mobile-phone signals. Security cameras are also being used to see what affects a

purchase. Is there a bright red shirt on the manikin or a white one? Software is out

there that can help to analyze videos and correlate it with sales data. There is also

software that can note how many customers turn left when entering the store or how

many people picked up an item off the shelf. There are countless ways that

monitoring the activity of customers can help to re-create a store layout that can be

the most beneficial for sales (Lutz and Townsend).

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Implementing Big Data

Sources of Data

Data to support a Big Data effort can come from many different sources. The

best place for a retail company to start looking for data is internally. Some examples

of internal data sources include sales data, inventory data, financials and customer

data. These are all likely structured data sources (Burst, Andrew).

Some of the most creative Big Data solutions use external data from Social

Networking sites. This includes sites like Facebook, Twitter and Pinterest. Another

external source of data is the “Internet of Things”. This source provides more passive

data about customers and their habits and can be a good source for retailers. Cloud

applications also provide an external source of data. An example is a cloud

application such as salesforce.com that provides sales information. Public data is

another source for Big Data. Some examples include SEC data, census data and

market data (Burst, Andrew).

Big Data Platforms and Tools

We have researched several tools and platforms, such as Pentaho Business

Analytics, SAS Big Data Analytics, IBM InfoSphere and Big Insights, EMC Greenplum,

SAP, Kognito and Microsoft. The Gartner Magic Quadrant for Business Intelligence

and Analytics Platforms was released February, 2013. We found that most of the

major players are in the leader box including IBM, Microsoft, Oracle, SAS and SAP

(Schlegel, Kurt).

There are several different types of solutions available including Boxed,

Hosted/Cloud, and In-House. Each are described below:

Boxed Solution involves buying a ‘boxed solution’ from a major vendor. The

company would then use the appropriate components and configurations as needed.

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IBM, SAS, Oracle are major Big Data Vendors that provide Boxed solutions. Their

business solution is tailored for your needs.Whether its analyzing the datasets you

have today OR wanting to collect new information, these vendors are good.Selecting

the modules and features you need will help in budgeting initial and configuration

costs.

With Cloud computing pricing dropping and becoming more affordable to

small and mid-range businesses, this is an attractive alternative to scaling enterprise

hardware. Companies can host the data in Cloud and not take the large upfront

investment costs. Mitigating some cost risks by leveraging cloud technologies.

Cloud or Hosted solutions are becoming more popular due to the cost, the

scalability and the quick ramp up times. This is no different in a Big Data

solutions. Cloud computing involves virtualized servers, which is a computing

resource that presents itself as a regular server, rentable per consumption. The

company can then configure their own tools such as Hadoop cluster or NoSQL

database. (http://strata.oreilly.com/2012/02/big-data-in-the-cloud-microsoft-

amazon-google.html)

An In House solution requires the most resources and startup costs. In House

solutions require adequate in-house staffing and expertise. The benefit of an in-

house solution is the flexibility and customization to make the solution whatever

you company wants and needs.

The solution options from the vendors include everything from a completely in

house solution, to a hosted solution to a cloud solution. We recommend the following

approach:

1. Pick a vendor that you have an existing relationship and do a pilot

2. Focus on a current business issue (i.e., increasing sales with existing

customers) that the outcome can be tracked and measured

3. Start primarily with data that you currently have; do try to incorporate

some new data (sales, customer demographics)

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4. Data Scientist - contract with a vendor for this resource first (try before you

buy)

Infrastructure

Big Data has many of the traditional technology infrastructure requirements

including processors, storage, memory and network. A new type of Infrastructure with

Big Data is the new type of database and platform that supports the storage of large

unstructured data. Some exmaples of these platforms include Apache Hadoop,

LexisNexus HPPS and MapReduce.

Expertise

Big Data introduces the need for new types of skills and roles in the IT

organization. One of the most exciting roles is called the Data Scientist. The Data

Scientist is a technical resource skilled in diverse technologies, including data,

databases, software tools, statistics and math. The Data Scientist also has a deep

knowledge of the business. They combine these two with innovation and curiosity to

develop solutions using Big Data to address business needs. Sometimes this role is

called a blending of art and science. The Data Scientist works closely with the

Business Intelligence teams and Data Warehouse teams, as well as the business teams.

Another group that is impacted by Big Data is the User Experience Team. This

team is focused on user-centric design and decision flow of the user experience of a

system. This is important with Big Data because the accurate and efficient collection

of data from users is critical. Good examples of the User Experience Team can be

found in companies like Yahoo, eBay and Apple.

Big Data also introduces new roles at the IT Senior Management level.

Examples include the Chief Data Officer and the Chief Analytics Officer. Both data

and analytics become more valuable as a company invests in Big Data and the

management oversight of these resources in IT becomes important (Schmarzo, Bill).

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Introducing Big Data

How would a company get started on a Big Data initiative? According to IBM in

the report “Analytics: The real world use of big data”, there are four adoption stages

of a big data adoption initiative. (IBM, p.13)

1. Educate - to focus on knowledge gathering and market observations. 2. Explore

– developing strategy and roadmap based on business needs and challenges. 3.

Engage – Piloting big data initiates to validate value and requirements. 4.

Execute – deploy big data initiatives and apply analytics.

Companies need to adopt a strategy to embark on an initiative that could be the next

innovative customer product or become a money pit and bad investment.

To begin, it is imperative to have support from senior and C-level management.

They need to make informed decisions on use and benefits from big data. Gathering

information, they can develop a business case – What’s a business issue we might be

able to solve? Next, develop a question – why use it? What would be a problem we

could solve? Creating “What If?” scenarios could identify new competitive advantage

and market shares. What would be a new opportunity that could turn out to be the

‘Next Big Thing?

Begin reviewing what would be the value in targeting consumers with similar

product offerings or having them be directed to your online shopping experience. The

business may have existing data sets they could analyze Before purchasing data

analytic services, check into services from companies and governments to use their

consumer data. Most would be a free or minimal charge.

Most importantly, when starting a big data initiative, have a measurable

outcome. How will the business know if it was worth time and money spent.

Potential measured outcomes could be:

- improved customer shopping experience

- improved new customer counts

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- change product offering or augment with accessories to keep shopper

engaged with your business

- Revenues generated by new product offerings, use of coupons, or

special pricing.

Cost-Benefit

Potential cost and benefit analysis would be beneficial. Premise could be the

impact of increasing advertising budget to see effects in a new customer retention

program. Example:

(i.e., if a company invests $500,000 in a solution to strengthen current customer

relationships so that customers that normally spend $100 per year with us now spend

$500 per year and we have 5,000 customers in this situation: $2,000,000 increase

in revenue and we spent $500,000, ROI is $1,500,000 in one year!)

Starbucks and Amazon have been proponents of big data and analysis of new

technologies. Using Near Field technology and RFID tags, they have tracked everything

from inventory control to customer preferences. (Higgambotham, 2011)

Using computerized inventory control can be costly but it is much more effective

than traditional methods. Having tags on all the merchandise and computers

recording what has been purchased and what is in inventory is largely beneficial for

maintaining lost/stolen inventory and knowing just how much stock you have.

Advertising in different ways can also benefit a company immensely. Knowing

what a person is talking about on the web and taking key words from their emails

and social media statuses can help to tailor specific advertisements to that

individual. Investing some money in advertising using Big Data will only help your

company to grow and thrive in the future.

Investing in a program to keep your customers around will keep your best

customers and encourage them to spend more money. When companies offer a reward

for spending x amount of dollars, many customers are drawn to the reward and will

spend the required purchase to receive the reward. Having a program that uses data

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from the customers as well as provides rewards that customers will enjoy and keep

coming back for is a great way to keep customers around and encourage new

customers to stay loyal.

Risks

Risks of Pursing Big Data

Some of our key business problems many of these retailers already do without

Big Data. Pursuing big data in this case may make some of the retailers and

employees uncomfortable as they have been doing it “their way” for a long time. This

is the same for any big change.

Another risk of pursuing big data is how the company can use it. There a lot

of benefits to be had from using big data, and if the retailers are not using it in a

way that will be beneficial, it is almost pointless in the long run as they would have

to hire resources to really delve deep into learning about Big Data.

A third risk could be that Big Data just does not benefit the company. Perhaps

they do not have the resources to advertise effectively or they are not consistent in

using Big Data to help with the business problems.

Predictions are becoming more difficult to make: Predicting consumers’ actions

is becoming increasing more difficult. The adoption of technology is a two-edge

sword. On the one hand retailers have more access to consumers (e.g., online coupons,

mobile devices). On the other hand, technology enables consumers to make a snap

purchase decisions and switch brand and/or company loyalty. Technology can blur

consumers’ intentions. For example, the addition of a mobile device may change a

consumer group into a frequent purchaser of a different retailer due to convenience.

The loss of the consumer is not due to anything the original retailer did, it simply is

because the original retailer is not available via a mobile device. Without looking at

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longitudinal data and a large market share, predicting the number of consumers

your company will lose or gain will be difficult.

Consumers are unforgiving: There is a saying “happy consumers will tell one

person; unhappy consumers will tell 10 persons.” Social media is changing this

statement. Facebook, twitter and YouTube reaches hundreds if not thousands of our

“closest” friends. A consumer’s description of a negative encounter with a company

can go viral overnight. If the description is humorous, it may even end up on a late

night talk show. United Airlines learned this lesson all too well when the company

broke a passenger’s guitar. The YouTube video generated 12,788,590 views. While

viewers of the video are laughing, United probably doesn’t find anything funny

about the video.

Trends are changing faster: Trends are changing at an increasingly faster pace.

This can be attributed to changing technology (e.g., updated cell phones), improved

methods of communication (e.g., social media) and BIG Data. SKUs, loyalty cards,

security cameras, email addresses, purchases and returns provide companies with

valuable information regarding changing trends.

Advertisers are wasting money if consumers do not purchase the product:

Companies spend millions of dollars annually to promote its products. Advertisements

and promotions may make the consumers laugh, cry or sigh – tugging on the

sentimental heart strings. While all of this may be nice, advertisements and

promotions fail if the consumer does not purchase the product. The analysis of data

are required to understand (a) why consumers purchase a particular, (b) how

consumers purchase the product, (c) the demographics and psychographics of the

purchaser of the product and (d) the ultimate user of the product.

Streamlining data is critical: Companies are becoming increasingly dependent

on internal and external data to make wise financial decisions, refine logistics and

operations and improve sales and marketing initiatives.

This growing reliance on data-driven decision-making creates challenges for

some of the market research providers. For expediency purposes, readily consumable

insights are required. Some market research firms use data that are already sorted,

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tabulated and analyzed. This static reporting method may provide fast results but the

findings may not be insightful or critical to the retailer.

Our team’s recommendation would be to pursue a pilot big data initiative. In

summary, technology and BIG data are changing the retail world. Together they help

bring insightful and analytics to the boardroom, buying office and consumer’s home.

If your company is not currently maximizing the power of its data, find a service

provider that will:

Streamline data and data gathered for you by market researchers

Help you manage, tabulate, analyze, visualize and deliver data in interactive,

web-based and mobile formats

Provide insight into your target market

Offer solutions to pressing business problems.

The effective use of technology and BIG Data can give you the competitive

advantage you need in today’s competitive and rapidly shifting environment.

RISKS:

Changing too much of social media analytic collections without purpose

Not evaluating current customer feedback loops in place today with consumers

(savings cards, etc.)

Deciding to use big data and not being able to scale your systems up fast

enough to process information

Do not chase Big Data solutions if company cannot define potential uses.

Risks of Not Pursuing Big Data

Companies can only benefit from Big Data in the long run. Big Data is a tool

to help retailers find solutions to the key business problems. Not pursuing Big Data

could mean that competitors will pull ahead and leave these retailers behind since

they are not taking advantage of Big Data and its usefulness. It also might mean that

competitors can advertise more efficiently to gain more customers and also retain

their current customers. One of the business problems is customer retention, and Big

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Data can help aid with having online programs to encourage consumers to keep

coming back with rewards systems.

Other

Something else for retail companies to consider is the equipment that could be

used to gather data in the physical “brick and mortar” stores. Video equipment and

other infrastructure would be required to gather customer counts throughout the data

and also to track customers’ physical behavior and their traffic pattern within the

store. For example, a store design/layout team could analyze data about how

customers travel through the store and which displays catch their attention and

which ones they walk right by.

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Concepts and Terminology

Concept/Term Description

Volume It’s the increase in data volume. Transaction data stored over the

years, text data always streaming in from social media, amassed

amounts of sensor data being collected. Before, excessive amounts

of data volume, it generated a storage issue.

Variety Data presently comes in all types of formats. It has a large variety

from long established databases to hierarchical data, to text

documents, email, video, audio, and financial transactions.

Analysis and assessments must be made.

Velocity It’s how rapid data is being produced and how rapid the data

must be processed to meet demand.

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Annotated Bibliography (for Presentation Audience)

Burst, Andrew (2012). Top 10 categories for Big Data sources and mining

technologies.http://www.zdnet.com/top-10-categories-for-big-data-sources-and-

mining-technologies-7000000926/

This article offers a nice list of sources of data and tools for Big Data.

Pramanick, Sushil (2012). Think BIG, Act SMALL: Big Data use case series – Part 2

of 5 (Using Big Data in Retail, Manufacturing and Automotive).

http://pramanicks.wordpress.com/2012/08/02/think-big-act-small-use-case-series-

part-2-of-5-using-big-data-in-retail-manufacturing-and-automotive/

This article offers a list of Big Data use cases for the Retail Industry.

Kalakota, Ravi (2012). Multi-channel to Omni-channel Retail Analytics: A Big

Data Use Case. http://practicalanalytics.wordpress.com/2012/01/19/omni-channel-

retail-analytics-a-big-data-use-case/

The article describes the Big Data use case for retail companies and multi-

channel sales.

Banks, Byron (2012). Big Data for Retail is Flying Off the Shelves.

http://www.forbes.com/sites/sap/2012/05/11/big-data-for-retail-is-flying-off-the-

shelves/

This is another Big Data use-case article for retail companies.

AIIM Association for Information and Image Management. What is Information

Management? http://www.aiim.org/what-is-information-management#

Team 7 23

This is a good summary about Information Management.

Kurt Schlegel, Rita L. Sallam, Daniel Yuen, Joao Tapadinhas (2013). Magic

Quadrant for Business Intelligence and Analytics

Platforms.Gartner.http://www.gartner.com/technology/reprints.do?id=1-

1DZLPEP&ct=130207&st=sb

Includes the graphic image of the Gartner Magic Quadrant. The article

compares the solutions from all the major providers of Big Data tools.

Schmarzo, Bill (2012). New Roles in the Big Data World. EMC2.

http://infocus.emc.com/william_schmarzo/new-roles-in-the-big-data-world/

This article describes the new types of roles and talent that will evolve with

Big Data, including the Data Scientist, User Experience Team and Senior

Management roles.

Zhang, Ling (2012). The Nature of Big Data and the Skills of Data Scientists.Smart

Data Collective.http://smartdatacollective.com/ling-zhang/89161/nature-big-data-

and-skills-data-scientists

This article describes the skills and characteristics of a Data Scientist.

Williams, Alex (2012). Amazon Is Not A Commerce Company.

http://techcrunch.com/2012/12/30/amazon-is-not-a-commerce-company/

This article describes the Amazon example of how they use Big Data.

Madden, Sean (2012). How Companies Like Amazon Use Big Data To Make You Love

Them. http://www.fastcodesign.com/1669551/how-companies-like-amazon-use-

big-data-to-make-you-love-them

Team 7 24

This is another good article about Amazon and Big Data.

Hill, Kashmir (2012). How Target Figured Out A Teen Girl Was Pregnant Before Her

Father Did. http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-

figured-out-a-teen-girl-was-pregnant-before-her-father-did/

This article describes the classic Target example.

Henschen, Doug (2012). Why Sears Is Going All-In On Hadoop. Information

Week.http://www.informationweek.com/global-cio/interviews/why-sears-is-going-

all-in-on-hadoop/240009717

This article describes the Sears implementation and use of the Big Data

platform, Hadoop.

Big Data Analytics.http://www.sas.com/reg/gen/corp/1532330-big-data-

analytics?tid=2252256&gclid=CM21sZ-dnrUCFcc-MgodmHcAZg

This page describes the SAS offering for Big Data.

Pentaho.http://www.pentaho.com/big-data/

This page describes the Pentaho offering for Big Data.

Big Data Analytics.http://www-01.ibm.com/software/data/infosphere/bigdata-

analytics.html

This page describes the IBM offering for Big Data.

Team 7 25

Works Cited

Burst, Andrew (2012). Top 10 categories for Big Data sources and mining

technologies.http://www.zdnet.com/top-10-categories-for-big-data-sources-

and-mining-technologies-7000000926/

Schlegel, Kurt, Rita L. Sallam, Daniel Yuen, Joao Tapadinhas (2013). Magic

Quadrant for Business Intelligence and Analytics

Platforms.Gartner.http://www.gartner.com/technology/reprints.do?id=1-

1DZLPEP&ct=130207&st=sb

http://strata.oreilly.com/2012/02/big-data-in-the-cloud-microsoft-amazon-

google.html)

Schmarzo, Bill (2012). New Roles in the Big Data World. EMC2.

http://infocus.emc.com/william_schmarzo/new-roles-in-the-big-data-world/

Madden, S. (2013). How companies like Amazon use big data to make you love them.

Retrieved from

http://www.fastcodesign.com/1669551/how-companies-like-amazon-use-big-

data-to-make-you-love-them

Hill,K. (2012). How Target figured out a teen girl was pregnant before her dad did.

Retrieved from

http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-

out-a-teen-girl-was-pregnant-before-her-father-did/

Duhigg, C. (2012). How companies learn your secrets. Retrieved from

http://www.nytimes.com/2012/02/19/magazine/shopping-

habits.html?pagewanted=1&_r=1&hp

Henshen, D. (2012). Sears Hadoop plans: check out data warehousing’s future.

Retrieved from

Team 7 26

http://www.informationweek.com/software/information-management/sears-

hadoop-plans-check-out-data-wareho/240134931

Lutz, A and M. Townsend. (2011). Big brother is watching you shop. Retrieved from

http://www.businessweek.com/magazine/big-brother-is-watching-you-shop-

12152011.html

Schroek, M., Shockley, R., Smart, J., Romero-Morales, D., Turano, T, (2012)

Analytics: The real-world use of big data. Retrieved from http://www-

935.ibm.com/services/us/gbs/thoughtleadership/ibv-big-data-at-work.html

Higginbotham, S. Building a Better Starbucks With Big Data. (2011) Retrieved from

http://gigaom.com/2011/03/23/netezza-jim-baum/

Team 7 27

Contributions of Kathy Eke

Administrative responsibilities

o Shared with team 7

o Team Lead of slides for Power Point Presentation

o Contributions to and updating Trello

Project management LM participant

Areas you researched

o Scope

o Specialty Retail

o Big Box Retail

o Leverage Experience of Big Box with Big Data

o Structured Data

o Unstructured Data

o What is Big Data?

o Characteristics of Big Data

o Big Data and e-commerce

Content you contributed to your final presentation

o Team Lead of slides for Power Point Presentation

Scope

Specialty Retail

Big Box Retail

Leverage Experience of Big Box with Big Data

Structured Data

Unstructured Data

What is Big Data?

Characteristics of Big Data

Content you contributed to the interim and final report, etc.

o Interim Report

Scope

Specialty Retail

Big Box Retail

Key Business Problems

Evidence

Exploring the Potential of Big Data

Helped answer questions to Opportunities

Exploring Opportunities

Leveraging Big Data

o Final Report

Team 7 28

Scope

Specialty Retail

Big Box Retail

Leverage Experience of Big Box with Big Data

Other

o Present in team discussions

Contributed positive energy when team was frustrated or

exhausted

Completed assignments on time

Team 7 29

Contributions of Karon Fluharty

Our team shared leadership of the meetings. I volunteered to be the first team

liaison. Recognizing a personal conflict, I needed to share role with others on

team. (They picked up without hesitation. (thank you))

Created Webex sessions for weekly team chats

Created and contributed potential retailers using big data for possible solution.

(Sears, Target, Starbucks, etc.)

Researched TRELLO. Created team site. Contributed to task assignments and

moved tasks through ‘Done’ process.

Created first outline (aka Strawman) from interim paper for Powerpoint

presentation

Created first slide deck of template slides for team to begin builing presentation

Researched:

o How Big Data initiates are strategized

o What it takes to get started with Big Data

o Several ‘Out of Box’ vendor solutions such as Oracle, SAP, SaaS, etc.

o Investigated risks and pitfalls associated with Big Data. Reported on

White Paper findings

o Researched minimal cost-benefit analysis

o Interviewed IT personnel at workplace for insights to what it takes to

kick off a big data initiative

o Additional research as needed

I contributed content on the final presentation for the above areas.

I contributed information on the areas I researched for the interim report.

I contributed information on the areas I researched.

Team Cheerleader!! Tried to keep spirits up when life was getting hectic for all

of us.

.

Team 7 30

Contributions of Emily Gedert

Shared leadership roles with other members of the team

Researched information management, retailers initiatives for 2013 and

beyond, and found out how we derived value from data for the enterprise

Set up slides for the business problems and researched on that

I contributed information for all of the research above as well as other areas of

the final presentation

Helped introduce Google Documents as a new technology

Set up some meetings with agendas

Turned in files as needed during the process

Team 7 31

Contributions of Becca Zeman

Our team shared leadership of the meetings. I contributed and lead the

discussion as needed.

I created the first version of our “outline” to determine where we might have

holes in our research (this was before the Interim Report format was released).

I researched:

o Roles and Responsibilities in IT

o Sources of Data

o Tools, Platforms, Software

o Use Cases

o Examples from Larger Retailers

o Information Management

I contributed content on the final presentation for the above areas. I assisted

with combining data in the first draft of the presentation (before Kathy started

the visual improvements!)

I contributed information on the areas I researched for the interim report.

I created the first draft of our final report and offered to be the “keeper” of the

report. I cleaned up the final version of the report to ensure consistency,

formatting, that all content was included, etc.

I contributed information on the areas I researched.