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From token loyalty to meaningful relationships: How loyalty programs and Big Data Analytics are facilitating CRM in the retail sector ML00017-011/Published 04/2014

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MarketLine Case Study

From token loyalty to meaningful relationships How loyalty programs and Big Data Analytics are facilitating CRM in the retail sector

Reference Code: ML00017-011

Publication Date: April 2014

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OVERVIEW

Catalyst How is it that a supermarket knows what offers to promote during a certain period, and where to place those offers in-

store? How has Starbucks managed to convince so many of its customers to pay for their drinks by having their

smartphones scanned at the checkout? This case study looks at the importance of customer relationship management to

retailers, and how they have adopted loyalty programs and big data analytics in order to attract and retain customers,

giving themselves a competitive advantage over rivals. Demonstrating just how valuable a loyal customer is to retailers,

and how loyalty programs drive this customer loyalty, this report looks in-depth at how big data analytics can be used to

help a retailer decide where to build a new store, how they can tailor marketing on an individual basis, and how it can be

used to track and analyze non-members of loyalty programs who pay by cash.

Summary

Born out of necessity in Germany, customer loyalty programs evolved from stamps handed out with purchases

which could be exchanged for items in catalogs, to sophisticated marketing tools that harvest customer data.

This is then scrutinized through big data analytics in order to provide loyal customers with the best possible

rewards.

With up to 95% of food retailers' revenues coming from loyalty program members, retailers have recognized the

true value of loyalty schemes and the big data analytics that drives their success. As big data analytics has

evolved and improved, retailers now find themselves in a position where they can predict what will be in a

person's shopping basket, and even tell when a customer is pregnant. Retailers are even using the data to help

them decide what to stock and when, and make other operational decisions such as whether to invest in a

company or not.

The main objective of customer relationship management is the retention of customers and the establishment of

customer loyalty as, with no acquisition costs for existing and loyal customers, costs are reduced and revenues

increase. Customer relationship management differs to other marketing strategies which focus on the product to

achieve a high market share; instead, it has shifted focus to achieving a higher share of customers to sell a wide

range of products to. For it to succeed, three key elements need to work in harmony: people, processes and

technology.

Loyalty programs and big data analytics are facilitating customer relationship management in the retail sector.

Loyalty programs are used to collect customer information such as their buying habits, when and where they

shop, and methods of payment whilst, at the same time, giving customers the feeling they are being rewarded

for their loyalty. Retailers analyze the vast volumes of data their loyalty programs collect using big data

analytics, allowing them to isolate correlations, trends and patterns which can then be acted upon in the form of

targeted marketing and operational decisions.

Big data analytics is used to examine the vast volumes of both structured and unstructured data collected by

loyalty programs, social media, and card and cash transactions. Retailers are then able to discern customer

opinions, behaviors and buying habits from the results. These findings can then be incorporated into their

customer relationship management programs accordingly, whether through targeted marketing, what to stock

in-store, or where to open new stores. As a result, big data analytics has become an essential and powerful tool

in customer relationship management.



TABLE OF CONTENTS

Overview ............................................................................................................................................................................. 2

Catalyst ............................................................................................................................................................................ 2

Summary ......................................................................................................................................................................... 2

Using CRM to retain customers and establish loyalty ......................................................................................................... 5

Retailers utilize CRM to justify their existence ................................................................................................................. 5

Successful CRM combines people, relevant processes and technology ..................................................................... 5

Loyalty programs and big data analytics drive successful CRM in the retail sector ..................................................... 7

From stamps to swipes: the evolution of loyalty programs .................................................................................................. 8

Big data analytics an essential tool in the quest for the loyal cash cow ........................................................................... 8

From incentivized reward tokens to sophisticated marketing tools .............................................................................. 9

Loyalty programs bring big benefits for the retail sector ............................................................................................. 10

Big data analytics used to predict customer pregnancies .......................................................................................... 11

Big data analytics also used to make operational decisions ...................................................................................... 13

Big data analytics, the clairvoyant business tool ............................................................................................................... 14

Unstructured data is being tamed by big data analytics to aid business decisions ........................................................ 14

Spying on the customer through big data analytics .................................................................................................... 15

Using big data to turn your social media profile and your location into quantifiable data ........................................... 16

Conclusions ....................................................................................................................................................................... 17

Retailers to remain one step ahead of the savvy consumer .......................................................................................... 17

Appendix ........................................................................................................................................................................... 18

Sources ......................................................................................................................................................................... 18

Further Reading ............................................................................................................................................................. 19

Ask the analyst .............................................................................................................................................................. 20

About MarketLine .......................................................................................................................................................... 20

Disclaimer ...................................................................................................................................................................... 20



TABLE OF FIGURES

Figure 1: CRM word cloud ................................................................................................................................................... 6

Figure 2: S&H Green Stamps .............................................................................................................................................. 9

Figure 3: The Nectar card and Tesco Clubcard ................................................................................................................ 11

Figure 4: The Target REDcard .......................................................................................................................................... 12



USING CRM TO RETAIN CUSTOMERS AND ESTABLISH LOYALTY Customer relationship management's (CRM) major objective is the retention of customers and the establishment of

customer loyalty, and with no acquisition costs for existing and loyal customers, costs are reduced and revenues

increase. If done correctly, the triumvirate of loyalty programs, big data analytics and CRM can result in reduced costs

and increased profitability through CRM's primary objectives of increasing customer retention and customer loyalty.

CRM differs to past marketing strategies which focused on the product to achieve a high market share; instead it has

shifted focus to the customer. Achieving a higher share of customers to sell a wide range of products and services to

during the span of time they are a regular customer is deemed more profitable than selling a single product to many

customers during the lifespan of the product.

For CRM to be a success there are three elements that are key: people, processes and technology. As a business

strategy, CRM has to be supported from the top of an organization to the bottom; there has to be a degree of CRM

evangelism. Furthermore, processes have to serve the needs of the customer while technology has to be capable of

driving the processes. CRM cannot function effectively if either one of these three elements is lacking.

Loyalty programs and big data analytics are facilitating CRM in the retail sector, becoming essential tools in retailers'

quest for customer retention and loyalty. Loyalty programs are used to collect customer information, such as their buying

habits, when and where they shop, and methods of payment whilst, at the same time, giving customers the feeling that

they are being rewarded for their loyalty. Retailers analyze the vast volumes of data their loyalty programs collect using

big data analytics, allowing them to isolate correlations, trends and patterns which can then be acted upon in the form of

targeted marketing and operational decisions. Using these two tools effectively can lead to the successful outcome of

CRM's objectives: increased customer retention and loyalty.

Retailers utilize CRM to justify their existence As noted by Zikmund et al (Customer Relationship Management Integrating Marketing Strategy and Information

Technology) the "satisfaction of consumers' needs and wants is the justification for an organization's existence." When

implemented successfully, a CRM system allows the organization to develop a focus on the customer that has a positive

impact and allows the organization to hear the customer's voice, empathize with the customer and treat their information

with care. If, through the use of CRM systems, an organization is able to learn enough about individual customers, the

customer should be highly satisfied with and trusting of the organization, thus creating the potential for the much sought

after positive 'word-of-mouth' about the organization.

The retention of customers and establishing customer loyalty are major objectives of CRM approaches. As both Toor

(Creating a Competitive Edge Through Improved Customer Relationship Management) and Zikmund et al note, there are

no acquisition costs for existing customers who repeatedly buy the same products and seek other related products, and

higher customer retention rates thus generally increase revenues and reduce costs.

According to Zikmund et al, the development of CRM systems has marked a shift in marketing strategies, from focusing

on the product to achieve a high market share, to achieving a high share of customers whereby the company attempts to

sell an individual customer as many goods and services it can over the length of time that the customer is a patron. This

'share of customer' involves satisfying the customer's needs to the point that they want the organization to sell them

something else. CRM systems attempt to achieve this through the recognition of past purchases and recommending

related products or services. As a result, cross-selling and up-selling are fundamental outcomes of effective CRM

strategies.

Successful CRM combines people, relevant processes and technology

CRM helps businesses use people, processes, and technology to gain insight into the behavior and value of customers,

if one of these elements is neglected, CRM cannot function effectively. Using this insight, CRM's main aims are the

retention of customers and the cementing of their loyalty which leads to reduced costs and increased profitability.



The CRM business strategy amalgamates information from an organization's various data sources which, in the case of

retailers, would include customer information gathered through loyalty programs, and provides a singular holistic view of

each customer. However, it is important to note that CRM has evolved from its initial pigeonhole label as a type of

software (although there are specialized corporate software suites such as Microsoft Dynamics CRM), and is now seen

as a companywide customer-centric philosophy and business strategy.

Using big data analytics enhances the effectiveness of CRM, allowing customer information to be analyzed for trends

and patterns that the company can exploit to better serve the customer while at the same time benefitting the

organization. This allows employees of the organization to make informed decisions on how best to serve the customer.

This can involve staff at every level, whether that is customer facing sales staff cross-selling or upselling relevant

products, marketing departments deciding what offers and marketing to send to specific customers, operations staff

deciding what products to stock, or executive level staff making decisions on where to open new stores.

Figure 1: CRM word cloud

SOURCE: MarketLine M A R K E T L I N E

For CRM to be successful it must look beyond databases and big data analytics, as this myopic view will ultimately result

in its failure. There are three generally recognized key areas that result in successful CRM strategies: people, processes

and technology.

CRM has to be embraced by every level of the organization, from the CEO to the customer facing floor staff; it is a

business philosophy that has to be incorporated into a company's corporate culture and identity. If there is a lack of

support in any level of the hierarchy then CRM will not function to its full potential. Once an organization has embraced

the notion that customer is king, and that relationship management is essential in learning everything they can in order to

satisfy a customer's wants and needs, only then can a company begin to justify its existence.

The next equally important element of CRM, processes, has to be looked at from a customer point of view asking

questions such as "how can this process better serve the customer?"

In respect of loyalty programs, companies could query if the method in which they collect customer data better serves the

customer (for example Starbucks use a mobile app instead of a card for their loyalty program). They could also query if

the data analytics and mining techniques they use are focusing on areas that are benefitting customers. If not, they would

need to reassess and possibly refocus their analytics department. The process variables to be assessed when

implementing a successful CRM strategy are vast, and it is easy to lose sight of the primary objective of serving a

customer's needs and wants.



The third element, again equally as important as people and processes, is technology. An organization has to ensure that

they have the right technology to drive processes whilst providing the highest quality data possible to the relevant

employees. It also has to be simple enough for users not to be dissuaded from becoming a loyal customer (or in the case

of employees, become disillusioned with the CRM strategy).

Most loyalty programs employ passive technology; a customer merely has to remember to swipe a card to collect their

points or rewards. With more active technology, such as Starbucks' mobile app, it is the responsibility of the company to

ensure it is designed well enough to be easy to use for customers not as adept with technology as others, as well as

being reliable and aesthetically pleasing.

At an operational level, if big data analytics, or data collection, is performed in-house the organization has to ensure its

technology is always up to date and capable of processing and storing the vast volumes of data. If it is outsourced, then it

is the organization's responsibility to ensure they are using the right company and the data they want is compatible with

their systems. If one of these three elements (people, processes, technology) is lacking, CRM cannot work effectively.

Loyalty programs and big data analytics drive successful CRM in the retail sector

Retail CRM used to be confined primarily to interactions in the store, such as being greeted as you entered the store, or

having an employee pack your bags. However, with the advent of loyalty programs and the associated technology,

retailers have been able to collect customer information and analyze the data in a plethora of new ways. They no longer

have to rely solely on face-to-face interactions between customers and staff, or ad hoc promotions, to foster customer

loyalty.

Using loyalty programs to collect customer data, correlations, trends and patterns can be found, tracked and isolated by

retailers using big data analytics. In line with the CRM philosophy of focusing on the customer instead of the product, this

allows retailers to identify profitable customers who can then be targeted with special offers, or receive preferential

treatment, to reward their loyalty and increase the value of their lifetime custom.

Using predictive modeling, companies can forecast how customers will respond to certain actions or initiatives the retailer

carries out. For example, this method can be used to predict how well a specific demographic will respond to a

promotion. This information can be used in a multitude of ways by retailers; they can use it to improve customer

experiences through store layouts, the products stocked, and even what type of stores to open and where.



FROM STAMPS TO SWIPES: THE EVOLUTION OF LOYALTY PROGRAMS Early on, retailers recognized the importance of customer loyalty: it may have started with shopkeepers allowing a line of

credit, greeting each individual customer by name, or ordering in products on request. As competition inevitably

increased, retaining customers and nurturing loyalty became increasingly difficult.

Originally born out of necessity in Germany, customer loyalty programs evolved into the S&H Green Stamps which

rewarded loyal customers that collected enough of these stamps with items from a catalog. This basic loyalty program

encouraged customers to shop at participating retailers; however, it was an unsophisticated marketing tool which did little

to address customer relationship issues.

In the early 1980s, American Airlines developed a sophisticated customer loyalty program, an air miles initiative called

AAdvantage, which utilized customer information. Performing simple (by today's standards) data mining using their

reservation system, the company constructed a database of their 150,000 most profitable customers who became

automatic members of their air miles scheme.

By the 1990s, retailers had begun to embrace the idea of loyalty schemes which harvested customer data, and with up to

95% of food retailer's revenues coming from loyalty program members, the true value of integrated loyalty schemes and

big data analytics had become apparent.

As big data analytics has evolved and improved, retailers now find themselves in a position where they can predict a

person's shop, and even tell when a customer is pregnant even if they haven't informed the company, all through

analyzing the data customers provide them with freely through their loyalty cards. Retailers are even using the data to

help them decide what to stock and when, and make other operational decisions such as whether to invest in a company

or not. The integration of big data analytics and loyalty schemes has allowed retailers to develop ever more sophisticated

customer relationship management strategies.

Big data analytics an essential tool in the quest for the loyal cash cow Loyalty programs have evolved from simple tokens or stamps rewarded with a purchase, which could then be exchanged

for products (often from a catalog), to swipe cards and computer programs linked to sophisticated marketing and CRM

tools. As technology has evolved, retailers have learnt that loyalty programs can not only be used to incentivize customer

loyalty and attract new customers, but also to facilitate targeted marketing and CRM.

Research from the Center for Retail Management at Northwestern University in the US shows that 12%-15% of

customers are loyal to a single retailer, and that this small percentage is responsible for 55%-70% of revenues.

Furthermore, for food retailers, loyalty program members are responsible for up to 95% of their sales. Loyal customers

are cash cows, and loyal customers that are also loyalty program members are worth even more money to retailers,

hence the importance of loyalty programs.

Using loyalty programs to collect customer information (everything from purchasing habits and locations shopped at, to

preferred payment methods), retailers are able to analyze the data to create personalized and more effective targeted

marketing. This data can also be used when making operational decisions, such as whether or not to stock certain items

in certain stores (even poor selling items are often stocked if certain types of customers the company wants to attract and

retain regularly purchase them). This data can also be used in larger business decisions, such as whether or not to

expand into certain markets or geographical locations, for example UK supermarket chain Waitrose used loyalty program

data, in conjunction with anonymous Visa card payment transactions, to discover where potential customers were

purchasing their groceries to help decide on new store locations.



With 2.65 billion loyalty program members in the US alone (as of 2012), the scope and volume of data collected is

difficult to imagine. Big data analytics has become an absolutely essential part of loyalty programs, allowing retailers to

spot patterns in people's buying habits that would be otherwise hidden.

This has allowed companies to predict customer purchases and buying habits, allowing them to target consumers with

specific personalized marketing and offers to encourage increased spending. The data is also used in a more passive

way, such as deciding when to stock certain items and when to put certain items on special offer in-store.

A byproduct of this data is the emergence of increasingly effective CRM systems, as retailers have been able to shift

their marketing strategies from away from focusing on achieving a high market share of specific products to achieving a

higher share of customers that the company can sell its products and services to. This has been achieved through big

data analytics, as retailers have become increasingly effective and accurate in predicting what customers want and

serving them appropriately.

From incentivized reward tokens to sophisticated marketing tools

Having originated in Germany as an attempt to circumvent the German government's restrictions on price competition,

loyalty programs have subsequently been used in commerce for many years. One of the early loyalty programs in the

US, S&H Green Stamps, rewarded customers of gas stations, and department and grocery stores, with stamps alongside

their purchases, which could be redeemed for appliances and other merchandise from a catalog.

The program ran from the 1950s to the late 1990s and, during the 1960s, at the height of their popularity, S&H was

printing three times as many Green Stamps than the US Postal Service was producing postage stamps.

Figure 2: S&H Green Stamps

SOURCE: The New York Times Company M A R K E T L I N E

This early "the more you purchase the greater your reward" form of loyalty program was very basic, merely rewarding

customer's loyalty with "prizes" and acting as an incentive for new customers. Since the Green Stamps, the

sophistication of loyalty programs has progressed immeasurably.



In 1981, American Airlines, an industry leader in marketing and computer systems, introduced the first Frequent-Flyer

Program (FFP): AAdvantage. The airline decided an FFP would be a marketing tool that would provide it with a

competitive advantage over its rivals, and would track the number of miles flown by members relative to their revenue

contribution to the company. The scheme would result in free tickets & upgrades being given to passengers with high

mileage flown.

Utilizing early computerized data storage systems and database software, American Airlines compiled a database, using

its Sabre reservation system, of 150,000 of its best customers.

Simple, or at least simple by today's standards, data mining techniques were used via a computer search of Sabre, with

frequent flyers identified through recurring phone numbers used for bookings being correlated with customer names.

These 150,000 customers became the first members of AAdvantage, and the first ever members of an FFP in general.

American Airlines' FFP became one of the first instances of a loyalty program being used to gather consumer data and

use it as a marketing tool, targeting specific types of customers with specific rewards through analysis of their travel

habits.

Other airlines followed American Airlines soon after with their own FFPs, with hotels and car hire firms also offering their

own equivalents. However, not every airline, hotel and car hire firm was convinced by the merits of the loyalty programs,

calling into question their effectiveness as marketing tools and their respective running costs.

Those that resisted FFPs, and the hotel and car hire equivalents, soon realized that they had underestimated their

effectiveness as marketing tools as they suffered significant market share losses (primarily amongst their most lucrative

customers, high-yield frequent travelers) and were forced to play catch-up with already well-established competitors.

One of those forced to play catch-up was Southwest Airlines, with former CEO Herb Kelleher stating we didnt want an

FFP. But it came to my attention that FFPs were siphoning business travel away from us. We did it defensively, and I

think if we had not done that we would have been terribly disadvantaged. The Hilton hotel group was also forced adopt a

loyalty program in 1987 after it lost a significant market share to Holiday Inn and Marriott who reaped the benefits of their

own schemes early on.

Loyalty programs bring big benefits for the retail sector

In the US alone, according to the 2013 COLLOQUY Loyalty Census, there were 2.65 billion loyalty program

memberships in 2012, a 26.7% increase over 2010's 2.09 billion memberships. Despite the growth in total membership,

COLLOQUY's report noted that the percentage of active members (members that had used their loyalty card/program

membership at least once in the previous 12 months) had fallen to 44% of total memberships (1.16 billion) from 2010's

46% (961.4 million).

The US market has a plethora of loyalty programs for every type of conceivable business, with some of the most popular

including the Target REDcard, My Starbucks Rewards, Best Buy's My Best Buy reward program (which has around 40

million members), and Sears' Shop Your Way Rewards (which has over 80 million members). The average US

household is typically signed up for 18 programs, actively participating in eight of those programs, with benefits

amounting to around $48bn a year.

In retail, the importance of loyalty programs, usually in the guise of loyalty cards, is well documented. Research

conducted by the Center for Retail Management at Northwestern University in the US states that only 12%-15% of

customers are loyal to a single retailer; however, this small percentage of shoppers are responsible for 55%-70% of

company sales.

The Food Marketing Institute reveals that, for food retailers offering a loyalty program, members are responsible for up to

95% of their sales. With around 53% of food retailers offering loyalty programs, and an average of 75% of loyalty

program members using their loyalty cards weekly (with 88% using them at least once a month), loyalty programs are

responsible for a significant proportion of sales.



A prime example of the kind of competitive advantage a loyalty program can bring lies with the UK supermarket chain

Tesco. In 1995, the retailer launched its Tesco Clubcard, a loyalty program whereby users collect points on their

purchases which are then periodically exchanged for coupons and money off their grocery bills.

Rival food retailer Sainsbury's had previously led Tesco in terms of UK market share. However, just a month after

launching the Clubcard, Tesco surpassed Sainsbury's market share and has remained in front ever since. The

economics of Tesco's Clubcard resulted in a significant return on investment.

Costing 10m (approximately $15.8m) to launch, the supermarket calculated that it required a 1.6% increase in sales to

cover the cost of issuing Clubcards and reward vouchers. The initial increase in sales was more than double this amount

at 4%, before settling to over 2%, with some stores experiencing double-digit increases in like-for-like sales.

The overwhelming success of the Tesco Clubcard led Sainsbury's to launch its own loyalty card in 1996: the Sainsbury's

Reward Card. Sainsbury's signed an exclusive deal with Air Miles, enabling Reward Card members to earn Air Miles. Air

Miles moved across to Tesco in March 2002, forcing Sainsbury's to launch another temporary loyalty scheme which ran

until September of the same year, after which Sainsbury's became one of the founding partners in a loyalty program

coalition, the Nectar card.

Figure 3: The Nectar card and Tesco Clubcard

SOURCE: J Sainsbury plc & Tesco PLC M A R K E T L I N E

The UK's largest loyalty program, Nectar has over 19 million cardholders with over 4,000 locations to collect points. Since

its 2002 launch, over 1.5bn of rewards have been spent by collectors. Furthermore with over 500 online retailers as part

of the loyalty program, consumers can collect points on everything from their gas and electric bills to groceries, vacations

and DIY supplies.

Big data analytics used to predict customer pregnancies

Loyalty cards and programs are a useful marketing tool for companies, serving the dual purpose of retaining and

attracting existing and new customers through rewards, and acting as a data source for targeted marketing strategies

that can be used to encourage customers to spend more money.

This is achieved through big data analytics and the data mining of customer information collected through loyalty cards

and programs. Retailers and other businesses that utilize loyalty programs build up a demographic profile of their

customers by collecting data on how loyal a customer is, how much they spend and what they spend their money on.

For the larger companies, the amount of data involved can be monumental. As previously mentioned, the Nectar loyalty

card has around 19 million members, Best Buy's loyalty program has in the region of 40 million members, while Sears

has over 80 million. Big data analytics is therefore being increasingly used to analyze the data in order to effectively

target customers with relevant rewards and offers.



This can be achieved by changing what a customer sees when logging in to a company's online store, making it easier

for them to find products that the data suggests they will buy. The data is also used to send customers coupons they will

find useful, encouraging them to buy more of a product, or to start buying a new product. It can also be used to target

customers with specific marketing that is of relevance.

A prime example of big data analytics being used in this way involves Target's "pregnancy-prediction" model. Target

realized new parents offered the company the perfect opportunity to exploit customer loyalty. Research shows that

previously ingrained shopping habits fall apart when customers become parents due to a sense of overwhelming,

exhaustion and various other behavioral factors, leading to them becoming more willing to purchase everything in one

place rather than from separate outlets, i.e. groceries from a grocery store, toys from a toy store, and clothes from a

clothes store, etc.

New parents are worth a lot of money to retailers if they become loyal customers; the first few years of a new child's life

involves the purchase of a vast range of products in prolific volumes in addition to their regular purchases. Target was

aware that targeted marketing would increase their chances of attracting these potential cash cows.

However, birth records are public in the US, resulting in new parents often being inundated with advertisements, offers

and incentives from retailers after the birth of their child. This meant that Target faced fierce competition from rivals for

potential new customers, with new parents often overlooking or disposing of much of the marketing without even looking

at it. Target needed to find a way to get to these potentially loyal customers before their rivals; they needed to find a way

of figuring out when a potential customer was pregnant, even if that customer had never told them.

Using its baby-shower registry and REDcard customer information, the company was able to analyze the data and

discover specific buying habits emerged for various stages of pregnancy. As a result, Target was able to assign each

shopper a pregnancy prediction score and even estimate a due date for customers based on the purchasing habits of

around 25 specific products.

Figure 4: The Target REDcard

SOURCE: Target Corporation M A R K E T L I N E

With this data, Target is able to send coupons to customers it believes are pregnant, timed to specific stages of their

pregnancy. This type of predictive marketing can be unsettling for some customers, as Target's test marketing

discovered. There was a recorded incident of a disgruntled father demanding to see a Target store manager and asking

why the company was sending his high school daughter coupons and marketing for baby products. The father also

questioned if the company was encouraging his daughter to get pregnant. It turned out the daughter was pregnant

unbeknownst to the father. The test marketing also discovered that pregnant women reacted badly to unsolicited

marketing for pregnancy and baby products, as they felt they were being spied upon; however, they reacted more

favorably when the marketing and coupons were mixed in with products unrelated to their pregnancy.



For example, when a customer that had primarily purchased CDs or DVDs from Target, once Target had established

they were pregnant through analysis of their other purchasing habits, they would send marketing that not only trigger

habits to buy more CDs and DVDs, but also start including offers for an array of products that someone at their stage of

pregnancy may need.

This single example provides a stark example of the power of big data analytics, and also highlights the moral issues

associated with it. Big data analytics can be combined with CRM to improve the relationship between customers and the

retailer; however, as the example shows, it is a fine balance between fostering the customer relationship and alienating

customers.

Big data analytics also used to make operational decisions

Big data analytics is also used in a more passive way, with retailers using it to make decisions on how they stock their

store. For example, through analysis of customers' Nectar card data, in addition to the purchasing habits of non-Nectar

card users, UK supermarket chain Sainsbury's discovered Grape-Nuts cereal was worth stocking, despite poor sales.

This was due to the fact that those that did purchase it were found to be extremely loyal and were often big spenders.

Sacrificing shelf space in order to retain and attract big spending customers is a small price to pay for a retailer, and is a

connection that they would be unlikely to discover without big data analytics.

Furthermore, big data analytics of customer data collected through loyalty cards and programs can also aid companies in

making operational decisions. After further analysis of its Nectar data, in 2013 Sainsbury's decided to purchase the

remaining 50% of Sainsbury's Bank, a joint venture with Bank of Scotland. Results showed that after taking out a bank

product from Sainsbury's Bank (the bank's products are linked to the Nectar reward scheme), shoppers became more

loyal and spent more in store. This made taking complete control of the bank an attractive prospect for the retailer.

When customer loyalty programs were first introduced, it is unlikely that retailers would have imagined they would be

able to predict when a customer is pregnant to the extent that they know when they will give birth. It is also unlikely that

they would have stocked poor selling products; as they did not have the ability to connect them with loyal customers that

spent a lot of money.

With the advent of big data analytics, customer loyalty programs are now facilitating the implementation of CRM in ways

only restricted by an analyst's imagination. As big data analytics becomes more sophisticated, the ways retailers will

apply it to their CRM initiatives will become increasingly diverse, and with increasing retail channels such as mobile apps

and online shopping, it is in the best interests of retailers to remain at the forefront of big data analytics and CRM

innovations.



BIG DATA ANALYTICS, THE CLAIRVOYANT BUSINESS TOOL The 21

st century will be defined as the digital era; everything we do can now be recorded and stored digitally. In fact,

home movies, family pictures, conversations with friends online, even that run you do every Tuesday after work using the

run tracker app on your smartphone can all be turned into quantifiable digital data.

Big data analytics is used to examine this data and discover correlations, patterns, trends and behaviors. Data is the new

oil. Retailers realized this early on when they first started collecting customer information through loyalty programs, and

big data analytics soon came to the fore to give them the edge over rivals by incorporating it within their CRM programs.

In addition to data collected through loyalty schemes, big data analytics is also used to analyze card and cash payments,

with social media and sensor data also subjected to analysis in an effort to better understand customers. Customers no

longer have to be shopping in-store or even on a retailer's website for the company to track their opinions, behaviors and

habits. The power of big data is far from reaching its full potential.

Unstructured data is being tamed by big data analytics to aid business decisions CRM is a process that brings together information about customers, sales, marketing effectiveness and responsiveness,

and market trends. As has been discussed, to achieve this effectively retailers are increasingly relying on big data

analytics. The purpose of big data analytics is to aid companies in business decisions by enabling data scientists to

analyze vast tracts of data that may be untapped by conventional business intelligence programs.

Globally, 2.5 exabytes (2.7 billion gigabytes) of data is produced per day, 80% of which is unstructured, with the majority

of this unstructured data having untapped potential. Big data analytics is the process of examining the huge volumes of

this untapped data to reveal patterns, undiscovered correlations and trends, and any other useful information which

would otherwise be hidden.

The information that can be gleaned from big data analytics can provide a competitive advantage over rival

organizations, resulting in more effective marketing and increased revenues.

Although big data analytics can be done with commonly used software tools in disciplines such as predictive analytics

and data mining, standard data warehouses such as relational database management systems (RDBMS) are not

generally capable of handling the processing demands of big data analytics. Unlike structured data sources, which can

be stored in traditional data warehouses (i.e. RDBMS such as Microsoft Access and Oracle) the unstructured data used

for big data analytics requires a different type of technology entirely (although it should be noted not all big data is

classed as unstructured as many consulting firms also consider structured data and other transactions as big data).

Instead, a new class of technology has emerged to deal with the unique challenges big data analytics brings. A series of

open source software frameworks, such as NoSQL databases, Hadoop and MapReduce, support the processing of large

scale data sets across clustered systems.

The emergence of these new technologies designed to process the relatively new phenomenon that is big data has led to

the development of specialized big data analytics firms such as Kaggle and Gnip (the new), and industry stalwarts IBM

and General Electric (the old).

Other firms such as Aimia (which owns the Nectar loyalty program) have embraced big data analytics to improve their

services. Retailers that run their loyalty programs in-house have followed suit, with Sears' Shop Your Way Rewards

loyalty program using the open-source Hadoop analytics platform to great success.

Reaching tens of millions of members, the company's Shop Your Way loyalty scheme exceeded its 36 months

membership target in just 17 months. Furthermore, during the second quarter of 2013 65% of Sears' sales came from the

program's members, a 10% increase over the previous year. The number of members shopping regularly (more than four

times in the past 12 months) also saw a rise.



Big data analytics is not restricted to traditional forms of data; increasingly, it is being used to analyze web server logs

and Internet clickstream data, social media activity, mobile-phone call records and information captured by sensors.

Social media in particular is proving to be of particular interest to retailers as it is being scrutinized to provide further

insight about customers and their shopping behavior.

Spying on the customer through big data analytics

Using big data analytics, retailers are now in a position to interpret relationships between data points. For example,

retailers can identify which items drive higher purchase amounts, those that increase the chances of other purchases,

and patterns of repeat purchases. These insights are essential in CRM and are being used to better serve customers and

encourage loyalty, leading them to spend more money.

However, UK supermarket chain Tesco has taken its CRM in another direction. In May 2013, the company announced

that it was going to use its loyalty card data to help customers find healthier alternatives in their grocery shopping in an

attempt to tackle obesity and diabetes. Tesco chief executive Philip Clarke stated "our customers have told us they'd like

help choosing healthy options, so on an individual level, we want to see whether customers would welcome tailored

suggestions for how they could shop more healthily." Customers would have to opt in for this service; however, it

demonstrates the power of big data analytics and loyalty programs when used in conjunction with one another.

In the case of Tesco's attempts to promote healthier eating, utilizing loyalty programs and big data analytics in this was

serves a dual purpose; providing the company with positive publicity and increasing revenues by encouraging the

purchase of fruit and vegetables which have become increasingly expensive in the UK (prices rose 10% in 2013).

Although data analytics and loyalty programs work in tandem, retailers are increasingly gathering customer data through

alternative means.

If a customer pays via credit or debit card, retailers can track what has been purchased by an anonymized card number.

Although they don't have personal data such as sex, marital status, name and address, retailers are able to track what a

specific card number purchases each week. This data can be used to measure the effectiveness of promotions and

events, and analyze the loyalty of customers who are not members of loyalty programs.

Monitoring shopping patterns by analyzing aggregated card payment data is not the only application of big data

employed by retailers. For supermarkets and other retailers that offer "coupon-at-till" incentives (whereby certain

products that are scanned trigger coupons which are printed at the checkout in real time), big data analytics is used to

determine if the customer's buying preferences match a particular product. This results in the possibility of real-time

marketing strategies. For example, if a customer purchases a jar of coffee and cereal, does not have a loyalty card and

pays by cash, they may receive a coupon after paying offering a discount on milk, or one that offers bonus points if they

purchase milk in an attempt to encourage them to sign up for the company's loyalty scheme.

These product triggers and buying preference matches have all been developed using big data analytics, finding patterns

and correlations in data collected by card payments and loyalty schemes. This can still work for retailers without loyalty

programs, such as Wal-Mart in the US or Morrisons in the UK, as they can purchase detailed and segmented

demographic data and then use it to analyze a shopping basket of goods, extrapolating purchasing trends and targeting

consumers with appropriate offers. However, a customer loyalty program can provide a retailer with deeper insights and

richer information specific to particular stores and the company as a whole. Retailers without loyalty program are

restricted in the degree of targeted marketing they can perform, for example only being able to provide "coupon-at-till"

incentives.

UK supermarket chain Waitrose, provides another example of how big data can be used. The grocery retailer hired data

analytics firm Beyond Analysis to use anonymous Visa card transactions and Waitrose's own data to calculate the

proportion of potential customers purchasing groceries from rival supermarkets, and their general locations. Waitrose

then used this data to help decide on new store locations.



Using big data to turn your social media profile and your location into quantifiable data

As can be seen from the examples throughout this case study, the uses for big data are only limited by technology; the

demands and imagination of retailers; and the abilities of data scientists and analysts. It has become an increasingly

powerful tool, and the proliferation of social media has meant that retailers have turned their attention to how best exploit

this freely available information to serve their needs in terms of CRM.

With the proliferation of online channels, a retailer needs to understand a customer's behavior and opinions regardless of

where they shop. One of the newer areas that retailers are now able to apply big data analytics to, and learn more about

their customers from, is social media. They can determine what somebody likes and dislikes via online forums and

Facebook, what they tweeted, and any other information deemed useful. Firms such as Gnip specialize in social media

analytics. With Gnip having access to every major social media channel such as Facebook, foursquare, Twitter,

Instagram, Vimeo, WordPress and YouTube, there is very little that retailers can't discover about existing and potential

customers.

Furthermore, retailers are able to track shoppers' movements with the latest geo-fencing location technologies. Geo-

fencing uses GPS or radio frequency identification (RFID) in software programs to define a geographical boundary. For

example, it allows retailers to set up a trigger so that, when a device enters or exits a defined area, they can track a

shoppers movements and shopping behavior in-store or areas near the store.

Marketers can also use this data by creating a geo-fence around a retail store, sending a coupon to a customer who has

downloaded a particular mobile app when the customer and their smartphone cross the boundary. This is another

example of targeted real-time marketing, and one which has been taken up by Target.

In June 2013 Target launched its Cartwheel app; a mobile application customers can use in-store to find promotions and

deals whilst shopping. Target has sought to encourage customer usage by providing free Wi-Fi Internet access in its

stores, allowing shoppers to download and use coupons in-store whilst doing their shop.

Again, this geo-fencing data would be subject to big data analytics, providing further insights into what makes customers

tick and allowing retailers to pre-empt and predict what a customer will purchase, or wants to purchase.

In big data terms, every aspect of a customer and their behavior can be quantified into data of some description, whether

it be through purchasing habits, voicing opinions on social media, or through video or sensor data (for example through

geo-fencing). With the sheer volume of this data available to retailers, whether it has been collected through loyalty

programs, card or cash payment purchases, social media or even the physical location of customers, big data analytics

has become an essential tool for retailers and their CRM programs to stay ahead or even keep up with competitors.

Retailers' quest for a customer's loyalty has become ever more sophisticated, reducing a person and their personality to

nothing more than a digital algorithm designed by a data scientist who has never met that person, yet seems to know

more about them than they themselves do. The question that begs to be asked is will the future see deliveries of

groceries and goods arrive on our doorstep before we even realized we wanted, or indeed needed them?



CONCLUSIONS

Retailers to remain one step ahead of the savvy consumer Loyal customers are indispensable if retailers are to succeed. According to the Chartered Institute of Marketing, it can

cost between 4 and 10 times as much to acquire a new customer (primarily through marketing costs) than it does to

retain an existing one. Recognizing the benefit of loyal customers (loyal customers are responsible for 55%-70% of

revenues), retailers developed loyalty programs very early on.

From early beginnings in Germany to get around a ban on price competition, to the popular S&H Green Stamps in the

1950s, and the advent of computerized data mining with American Airlines' AAdvantage air miles program in the 1980s,

loyalty programs have increased in their importance over the past few decades.

Retailers began to embrace loyalty programs in the 1990s, primarily using loyalty cards which allowed customers to

collect points, rewards, or enjoy discounts whilst in return their purchasing habits were recorded by the retailers. With up

to 95% of food retailer's revenues coming from loyalty program members, it is in the best interest of companies to

develop a successful loyalty program.

Loyalty programs work in tandem with CRM, and have become one of the essential tools companies employ in their

efforts to understand the customer to better serve them and engender loyalty. The main objective of CRM is the retention

of customers and the establishment of loyalty as, with no acquisition costs for existing and loyal customers, costs are

reduced and revenues increase.

CRM focuses on the customer rather than the product. A higher share of customers increases upselling and cross-selling

opportunities for other products, resulting in a higher return on investment than focusing on a single product's market

share. Loyalty programs are a perfect tool to assist in achieving this as they have evolved to reward customers on an

individual basis through targeted marketing, rather than rewarding all customers in the same manner.

Loyalty programs have become sophisticated marketing tools through the application of big data analytics, which

analyzes the collected customer information for correlations, patterns and trends in customer purchasing behavior. Using

the results of this analysis, retailers are now able to accurately predict the contents of a customer's shopping basket and

can apply targeted marketing appropriately. Big data analytics is also used by retailers to make operational decisions

such as where to open a new store, what products to stock and what discounts to offer in-store, all for the good of the

customer and the revenues of the retailer.

Big data analytics is facilitating customer loyalty programs, which is, in turn, facilitating highly effective CRM strategies.

As such, this triumvirate has become one of the retailers' most important tools in winning the heart, mind and wallet of the

customer.

As retailers have become more sophisticated in the methods they employ to part a customer with their money, customers

have also become savvy in their shopping. With the proliferation of online retail channels, there are a multitude of tools

available to consumers to ensure they're getting the best possible deal, for example price comparison websites, online

coupon sites such as Groupon, and consumer advice websites and forums.

Furthermore, according to the 2013 COLLOQUY Loyalty Census, active loyalty scheme members as a percentage of

total members actually declined 2% between 2010 and 2012 in the US. Customers are beginning to understand that

loyalty programs gather and utilize customer data to make marketing decisions, and many see this as an invasion of their

privacy.

Whether or not this trend in declining active memberships continues will be a minor concern to retailers. Using big data

techniques, retailers are now also capable of analyzing card and cash payment transactions of non-members, as well as

all forms of social media, and even physical locations through technologies such as geo-fencing. As technology evolves,

the means and methods of gathering and analyzing data will evolve in tandem. Due to the importance placed on the

value of the loyal customer, retailers will inevitably endeavor to remain one step ahead of the consumer in the battle for

their wallets.



APPENDIX

Sources Aimia Coalition Loyalty UK Ltd., About Us, 2014; http://www.nectar.com/about-nectar/corporate/about-us.points

The Chartered Institute of Marketing, Cost of customer acquisition vs customer retention, 2010;

http://www.camfoundation.com/PDF/Cost-of-customer-acquisition-vs-customer-retention.pdf

Crain Communications Inc., Is Sears Holdings' Loyalty Program Helping or Hurting It?, 2013;

http://adage.com/article/cmo-strategy/sears-holdings-loyalty-program-helping-hurting/243796/

CRMTrends, Loyalty Programs, 2014; http://www.crmtrends.com/loyalty.html

Food Marketing Institute, Loyalty-Marketing Programs In the Retail Food Industry, 2014; http://www.fmi.org/docs/media-

backgrounder/loyaltymarketing

Forbes.com LLC, Sears Competes On Big Data and Loyalty Programs, 2012;

http://www.forbes.com/sites/tomgroenfeldt/2012/05/02/sears-competes-on-big-data-and-loyalty-programs/

Forbes.com LLC, Talking Big Data And Analytics With IBM, 2014;

http://www.forbes.com/sites/chrisversace/2014/04/01/talking-big-data-and-analytics-with-ibm/

Guardian News and Media Limited, How supermarkets get your data and what they do with it, 2013;

http://www.theguardian.com/money/2013/jun/08/supermarkets-get-your-data

Haymarket Media, Inc., Three Upgrades Retailers Must Make in Their CRM Ops, 2013; http://www.dmnews.com/three-

upgrades-retailers-must-make-in-their-crm-ops/article/287944/

IBM Corp., Rethinking Loyalty Programs through Big Data, 2013; http://www.ibmbigdatahub.com/blog/rethinking-loyalty-

programs-through-big-data

Information Today, Inc. (ITI), What Is CRM?, 2010; http://www.destinationcrm.com/Articles/CRM-News/Daily-News/What-

Is-CRM-46033.aspx

Internet Brands, Inc., History of Loyalty Programs, 2014; http://www.frequentflier.com/programs/history-of-loyalty-

programs/

Minnesota Public Radio, Retailers' loyalty programs popular with consumers, 2013;

http://www.mprnews.org/story/2013/01/02/business/retail-rewards-programs

National Retail Federation, Loyalty's Cost Benefits: What are retailers getting out of reward programs?, 2012;

http://www.stores.org/stores%20magazine%20october%202012/loyalty%E2%80%99s-cost-benefits

New Jersey On-Line LLC, Made in Jersey: S&H Green Stamps - in the sixties, Americans were stuck on them, 2013;

http://www.nj.com/business/index.ssf/2013/11/made_in_jersey_sh_green_stamps.html

The New York Times Company, How Companies Learn Your Secrets, 2012;

http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?pagewanted=all&_r=0

Target Corporation, Cartwheel, 2013; http://cartwheel.target.com/

TechTarget, Inc., Big data analytics, 2012; http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics

TechTarget, Inc., Geo-fencing, 2013; http://whatis.techtarget.com/definition/geofencing

Telegraph Media Group Limited, Nearly one in five adults across the UK are eating less fruit and veg as prices soar,

2013; http://www.telegraph.co.uk/foodanddrink/healthyeating/10423536/Nearly-one-in-five-adults-across-the-UK-are-

eating-less-fruit-and-veg-as-prices-soar.html



TIBCO Software Inc., 5 Reasons to Integrate Big Data Analytics into Your CRM, 2012;

http://spotfire.tibco.com/blog/?p=12660

Time Inc., A Disloyalty Movement? Supermarkets and Customers Drop Loyalty Card Programs, 2013;

http://business.time.com/2013/07/11/a-disloyalty-movement-supermarkets-and-customers-drop-loyalty-card-programs/

Toor, T.P.S., 2009. Creating a competitive edge through improved customer relationship management. Business

Strategy Series, 10(1), 55-60.

Watershed Publishing, Total US Loyalty Program Memberships Numbered More Than 2.5 Billion Last Year, 2013;

http://www.marketingcharts.com/wp/uncategorized/total-us-loyalty-program-memberships-numbered-more-than-2-5-

billion-last-year-30663/

William Reed Business Media Ltd, Tesco: Clubcard data could be used to help shoppers make healthier eating choices,

2013; http://www.thegrocer.co.uk/companies/supermarkets/tesco/tesco-reveals-clubcard-healthier-eating-

plans/343589.article?utm_source=RSS_Feed&utm_medium=RSS&utm_campaign=rss

Wise Research Limited, An overview of supermarket & grocery loyalty programmes around the world, 2006;

http://www.thewisemarketer.com/features/read.asp?id=97

Yahoo! Inc., Target's CEO Discusses Q2 2013 Results - Earnings Call Transcript, 2013;

http://finance.yahoo.com/news/targets-ceo-discusses-q2-2013-165801660.html

Zikmund, W.G., McLeod, R., and Gilbert, F.W., 2003. Customer Relationship Management Integrating Marketing

Strategy and Information Technology. Hoboken: John Wiley & Sons, Inc.

Further Reading MarketLine (2014) The World's Most Ethical Companies

MarketLine (2013) Target Corporation: Holiday strategy 2013

MarketLine (2013) Wal-Mart Stores, Inc.: Growth of the world's largest retailer

MarketLine (2014) Tesco PLC's US experience: How to lose 1bn, the Fresh & Easy way

MarketLine (2013) Supermarket fashion: a growing phenomenon

MarketLine (2014) Tesco PLC

MarketLine (2014) J Sainsbury plc

MarketLine (2014) John Lewis Partnership plc

MarketLine (2014) Wm Morrison Supermarkets PLC

MarketLine (2014) Wal-Mart Stores, Inc.

MarketLine (2014) Target Corporation

MarketLine (2014) Sears Holdings Corporation

MarketLine (2014) Starbucks Corporation

MarketLine (2014) Best Buy Co., Inc.

MarketLine (2014) Southwest Airlines Co.

MarketLine (2014) American Airlines Group, Inc.



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