a204e3f4-e300-4e2c-b16d-22f16bad1371 (1)

State of the Industry Research Series :

Big Data in Retail

www.eknresearch.com

EKN is part of the Edgell Family

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2013

Table of Contents

Executive Summary 3

A Pragmatic Approach to Big Data 6

Research Findings 11

Recommendations 21

Appendix A: EKN Big Data Need and Readiness Assessment Toolkit 28

Appendix B: EKN Big Data Vendor Landscape 34

Appendix C: Retail Honor Board 36

Executive Summary

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In a short period of time Big Data has traversed a familiar hype cycle from exuberant optimism to disil-lusionment. In EKNs view, retailers will do well to focus on pertinent issues it brings into focus: Namely, the opportunity to improve customer-centric and operational decision-making by building deeper insight from a massive stream of internal and external data.

However, retailers must approach Big Data as they would any other business strategy, which is to ask 3 im-portant questions; Is this for us, how ready are we and how do we set ourselves up for success?

EKNs 2012 Big Data report was a first of its kind, detailed look at Big Data in retail. This report is based on an additional survey of 70+ retail industry executives, and includes new findings as well as comparison with those from last year. Key findings include:

Big Data is 5 years away from becoming mainstream: For all the game-changer hype surround-ing Big Data, the reality is that it is currently the domain of a few first movers. Largely, retail contin-ues to be an industry with relatively low analytics maturity in all areas of the business. EKN views a natural progression to be one that first focuses on advancing analytics capabilities before being able to leverage Big Data effectively. Retailers investment plans in Big Data support this takeaway.

A billion dollar retailer will spend an average of $75,000 on Big Data in 2013: Retailers plan to spend less than half a percent of their total IT budget on Big Data in 2013. This will increase to 1.2% in 2016. While this means large-scale Big Data initiatives will be few and far between, a major-ity of retailers plan to enter an experimental phase with Big Data, focusing on executing Proof-of-Concepts (POCs) that help them determine value and chart a course for more scalable investments in the future.

Retailers biggest challenge in adopting Big Data is the variety of data, especially unstruc-tured data: In 2012, 1 in 2 retailers said managing the volume of data was their biggest challenge. At that time, we pointed out that retailers have always been used to managing large volumes of data (they have access to decades of historical transactional data) and that we expected this issue to diminish in comparison to others. In this years survey, retailers rate data volume to be the least of their data management concerns. Instead, managing the variety of data emerges as the #1 chal-lenge.

There is no difference in the analytics maturity of large (over $1 billion in annual sales) and smaller (less than $1 billion in annual sales) retailers: The largest retailers with deep pockets and a culture of innovation, such as Target and Wal-Mart, will continue to make oversized invest-ments in Big Data. In some cases, such as Sears, they may even approach it as an opportunity to monetize investments in technology by offering services to smaller retailers or even other indus-tries. Yet, smaller retailers can take advantage of the lack of difference between how strategically they leverage analytics as compared to their larger rivals, by making smart strategic choices and investments of their own.

Executive Summary

State of the Industry Research Series: Big Data in Retail5

It is easy to glorify or dismiss Big Data, but it is much more difficult to develop a measured approach focused on exploring its value for your business. Like any strategy, EKN recommends identifying use cases and build-ing a detailed plan on how you would make a move to Big Data analytics a successful one. EKNs recom-mendations provided in this report are intended to help retailers get started with a pragmatic approach to Big Data, once their need and readiness is established. A toolkit to help assess need and readiness is also provided.

Executive Summary

Research Findings Fast Facts: The percentage of retailers who find data

volume to be the biggest data management challenge is down to 24% from 46% last year.

68% of retailers rate data organization as the biggest challenge in managing unstructured data.

Big Data budgets will grow at 23.8% CAGR till 2016, yet will only account for 1.2% of total IT budget.

Inside the report: EKN Big Data Need and Readiness Assess-

ment Toolkit

EKN Big Data Vendor Landscape

Retail Honor Board

EKN 6

A Pragmatic Approach to Big Data


EKNs 2012 report was one of the first focused studies on the impact of Big Data on the retail industry. We used insights from the study to evolve the areas of investigation this year and focused them specifically on understanding:

How real is Big Data in retail in terms of current levels of adoption and planned investments?

What is the level of retailers preparedness for Big Data analytics in terms of analytics maturity?

How does Big Data impact retailers top analytics goal - customer insights?

What are the biggest impediments to retailers adoption of Big Data?

While findings from EKNs survey follow this section, it is important to establish our point of view on what is increasingly a contentious topic. Consistent with our raison dtre, our outlook towards Big Data is pragmatic rather than dogmatic.

What is Big Data?

Big Data is a trendy buzzword. The hype around it is misleading and revealing at the same time. Misleading, because it positions Big Data as a silver bullet that will resolve all of retails challenges. It is revealing because it shines a light on how some technology marketers and analysts have taken a generally sound concept (that data has grown beyond retailers current means of analysis and it is important to evaluate what they stand to gain from building new capabilities) and turned it into a technology sales pitch.

Yet, Big Data is an important topic. There is more data (volume) being created by enterprises and consumers than ever before, a greater number of sources and types (variety) of data, as well as an increased speed (ve-locity) at which this data flows through a companys information systems. The opportunity to better leverage data to build a better understanding of customers and improve business agility is very real. The importance of doing so has also increased since traditional competitive forces of price, promotions, location and assort-ment are becoming commoditized. The culture, skills and tools required to make sense of this data are very different from the status quo at most retailers.

We must note an important distinction, which is that Big Data is not a new concept. Retailers have had access to large sets of historical transaction data for decades. Some have even been able to perform predictive ana-lytics by integrating other data source such as weather data, before Big Data as a term caught on. We illus-trate this only to point out that advanced analytics and Big Data are different, and can be mutually exclusive.

In our 2012 report we defined Big Data as collectively referring to the strategy, business processes, tools and technologies that pertain to datasets whose size and complexity is beyond the ability of typical database software tools to capture, store, manage and analyze.


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To clarify what is inherent in this definition - Big Data is a relative concept. Think of it as a generational leap from your organizations current analytics capability (from a data integration and management perspective) to a state wherein it is able to derive strategic value from the analysis of:

A larger volume of data, at a level that represents exponential, not incremental, growth compared to current capabilities

A greater variety of data, necessarily including unstructured and/or semi-structured data

A greater velocity of data, in terms of speed of input and output through enterprise systems

The analysis of larger data sets at greater speed and lower cost have been made possible by technological advances such as in-memory computing, new information management architectures such as MapReduce, Massively Parallel Processing (MPP) and NoSQL databases among others. It is very likely that a move towards Big Data analytics will require an investment in such tools. However, thinking of Big Data only in terms of such technologies is myopic.

Big Data must be thought of as a business strategy focused on upgrading the organizations capability to take action based on insight gleaned from the availability of a much larger data set than it is currently ca-pable of. Having the skills required to execute is an often ignored but critical component of such a strategy. What patchwork of tools enables this strategy is incidental.

Once retailers define Big Data in terms of their own analytics maturity, it becomes easier to ignore the philo-sophical debates that distract them from more pertinent questions - do I need it, am I ready for it, and where do I start?

Do I need it? Am I ready for it?

Part of the Big Data challenge is the perception that it is a natural progression of an organizations analytics capability or maturity. That it is inherently good. Big Data is simply shorthand for the ability to analyze larger sets of more varied data at greater speed than you are currently capable of. The nature of the analysis - from reporting to basic analytics to predictive and investigative analytics - is what determines analytics maturity.

The existence of more data and the availability of tools that allow you to analyze this data should not be the primary driver of interest in exploring Big Data. Unless driven by a clearly focused strategy, Big Data is a solution in search of a problem.



Retailers need to evaluate how seriously they need to pursue Big Data and assess their current readiness based on the factors below. These are available in expanded form as an assessment toolkit in Appendix A. EKN is offering retailers the ability to receive a complimentary assessment by filling out the digital version of the toolkit. More details are available in the Appendix.

Strategic Intent

Innovation Will your Big Data strategy be linked to the companys innovation plans?

Competition Do competitive moves mandate an investment in Big Data?

Customer EngagementHas the company identified customer engagement as a key driver of business strategy and invest-

ments?

Opportunity Qualification

Storage / Data Warehouse Is your current data warehouse and storage capacity insufficient for projected data growth?

BI Tools Are your current BI tools insufficient in terms of speed of analysis?

Social and / or Mobile Do your plans include the need to analyze large sets of social media or mobile data?

Unstructured Data Do your plans include the need to analyze large sets of unstructured data?

Advanced AlgorithmsDo you plan to develop and implement advanced analysis algorithms and apply them to large

data sets?

Organizational Readiness

Acceptance of Failure Is innovation engrained in the organization culture, including acceptance of failure?

Insights-DrivenIs the company culture insights-driven or based on intuition and leadership of strong personali-

ties?

Management Alignment Does Big Data have a C-level executive owner and champion?

Analytics Maturity

Advanced Analytics Capability Does your analytics maturity justify investments in Big Data?

Skills and ResourcesDo you have or are willing to invest in acquiring the skills and resources required to elevate analyt-

ics maturity in the organization?

Data Management

Data Management Strategy How mature is your enterprise data management strategy?

Data Sources Which internal and external data sources are integrated into your analytics initiatives?

Investment Appetite

Technology BudgetWhat percentage of your analytics budget have you earmarked for spending on Big Data initia-

tives?

Technology LeadershipDoes technology leadership come naturally to your organization, or is your strategy to adopt tried

and tested industry standard solutions?

Cloud or Investment Appetite

Is your organization ready to embrace public or hybrid cloud for data storage and analysis? If not,

is it ready for big investments in data management infrastructure such as private cloud or data

centers?


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Where do I start?

Once need and readiness is established, EKN recommends retailers start with a clear definition of their goals, execution approach and success metrics. The recommendations section of this report is focused on providing additional detail on the following steps EKN recommends retailers take to get started.


Identify a business function candi-

date for a POC

Start with a POC focused on no more than 1-2 business functions, such as Customer In-

sights, Marketing, Merchandising, Supply Chain, Stores or Multi-Channel.

Assess analytics maturityPerform a detailed benchmark of your analytics maturity within the identified business

function.

Decisions and use cases definition

Define focused use cases including an identification of business decisions, insights required,

data needed to deliver those insights, type of analysis and expected action that will be taken

based on the analysis.

Data organization and integration

Based on the data requirements identified from the step above, identify the sources of data

that will be tapped for the POC. Build an effort estimate for organizing and integrating the

data.

Analysis modelsBased on the insights required, identify whether existing statistical models are available or if

proprietary models will need to be built.

Systems and toolsIdentify any new systems and tools that will be required as part of the POC. Focus on ones

that have a non-linear ability to process and analyze data.

Training and documentation

Clearly identify the team associated with the project and institute a short-term training plan

to get associates up to speed with Big Data concepts and analysis models. Ensure the POC

exercise is used to capture lessons learnt and as a process template for future projects.

State of the Industry Research Series: Big Data in Retail11The Consumerization of Retail Information TechnologyState of the Industry Research Series:The Consumerization of IT: Personal Devices and Software in the Enterprise

Research Findings

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Survey respondent distribution by segment (Figures are percentage of total respondents)

Survey respondent distribution by annual revenue (Figures are percentage of total respondents)

Survey respondent distribution by designation (Figures are percentage of total respondents)

Research Findings

35%

33%

25%

6% 1%

Specialty

Apparel & Accessories

General Merchandise and Grocery

Online

Other

25%

18%

12%

24%

21%

Less than $100 million

$100 million to $499 million

$500 million to $999 million

$1 billion to $4.9 billion

$5 billion or more

21%

22%

24%

33%

CXO

VP

Director

Manager

State of the Industry Research Series: Big Data in Retail13Research Findings

Big Data is 5 years away from becoming mainstream. 60% of retailers still assess them-selves as having basic analytics capability.

Analytics maturity of retailers (Figures are percentage of total respondents)

The one consistent theme across EKNs 2013 research has been the importance retailers have attached to im-proving analytics maturity across the enterprise and the consistently low levels of current maturity they have reported.

EKN believes retailers will first move from basic to advanced on the analytics maturity curve (reporting, basic analytics, predictive analytics, investigative analytics) before taking on large scale Big Data initiatives.

Compared to 2012, 9% fewer retailers rate themselves as only having basic analytics capabilities. However, with more than 3 in 5 retailers still assessing themselves as having basic analytics capabilities, clearly the av-erage retailer is data-rich and insights-poor. Tellingly, 83% of retailers consider themselves at par or behind competition in terms of strategic use of analytics.

Even over the next 2 years, 1 in 2 retailers have no plans to implement Big Data initiatives in core retail func-tions such as Stores or Supply Chain.

Given the current landscape of retailers analytics maturity and planned investments in Big Data over the next 2 years (see related finding below), Big Data is still in an experimental stage in retail. A few retailers will make big bets while others will continue to wait and watch or experiment cautiously.

Sources: EKNs Future of Analytics Report & EKNs Big Data in Retail 2012 Report

68% 62%

18% 23%

14% 15%

2012 2013

Reporting and basic analytics Investigative analytics Predictive analytics

Analytics Maturity Strategic Intent Organizational Readiness Opportunity Qualification

While 8 out of 10 retailers are aware of Big Data, only 22% currently use Big Data analytics solutions.

83% rate themselves as lag-ging behind or at par with competitors in their use of customer analytics.

72% of retailers do not have a C-level Big Data champion.

Less than 2 in 10 retailers leverage predictive analytics across business functions currently.

EKN 14Research Findings

The Big in Big Data is not the greatest challenge; organizing and integrating unstruc-tured data is.

Big Data challenges that are the most difficult to manage (Figures are percentage of total respondents)

Top data management challenges in managing unstructured data (Figures are percentage of total respondents)

Timeframe for retailers for integrating data sources into analytics (Figures are percentage of total respondents)

2013

Handling the Variety (Types) of data (48%)

Handling the Velocity of data (28%)

Handling the Volume of data (24%)

2012

Handling the Volume of data (46%)

Handling the Variety (Types) of data (34%)

Handling the Velocity of data (20%)

#1#1

#2#2

#3#3

68%

0

10

20

30

40

50

60

7065%

43%39%

30%28%

17%

Data organization Data integration Data capture Datainfrastructure/tool

Data access Data storage Data privacy

Currently integrated In the next 12 months In the next 12-24 months No plans to integrate

0

20

40

60

80

100

69% 65%

43% 38%30% 25% 22%

14% 24%

35%

19% 35% 42%

14%

6%5%

11%

11%

24% 17%

6%

11% 6% 11%

32%

11% 16%

58%

Store (POStransaction data)

Online Loyalty or CRMdata

Syndicated data(Nielsen, IRI)

Social media Mobile Census data


Importance vs. use of different data sources for Big Data initiatives focused on customer insights (Y-axis: Figures are percentage explaining importance on a scale 0-100, 100 being the most important; X-axis: Figures are percentage of total

respondents)

We know that retail is a data-intensive industry - serving millions of customers, moving thousands of items, stocking thousands of SKUs in hundreds of stores. Retailers are accustomed to large swaths of data.

However, respondents in EKNs 2012 Big Data in Retail survey identified the volume of data as their biggest data management challenge. At that time, we highlighted that we did not expect this to be the case for long and pointed out that retailers with higher analytics maturity found the variety of data to be a greater chal-lenge.

In 2013, as retailers understanding of Big Data has improved and they have gained a clearer understanding of their own capabilities, managing the volume of data is rated the least of their challenges. Data variety has clearly emerged as the #1 challenge.

More than anything else, the variety of data sources and types that enterprises have at their disposal best defines the need for a concept such as Big Data. Data residing in traditional data warehouses and data marts is considered structured data. A greater area of growth and complexity (in terms of analysis) is unstructured (such as documents, audio and video files) and semi-structured (such as email, XML) data. Combining enter-prise data with publicly available data from social networks, websites and public data source such as the Department of Commerce can be a valuable strategy, but one that only adds to retailers data manage-ment woes.

Transactional data

Operational data

Public dataSocial media data

Web logs

Machine sensor logs

Textual data

Weather data

Maps/GIS data

Multimedia data

Consumer mobile geopositioning data

Use of different data sources

Impo

rtan

ce o

f diff

eren

t dat

a so

urce

s

Low High

High


To clarify, the challenge of data variety is driven primarily by the increasing amount of unstructured data (some estimates suggest 80% of business relevant information originates in unstructured form). In terms of managing unstructured data:

Organizing this data and integrating into their analytics initiatives poses the greatest challenge to retailers, with 2 in 3 identifying these as the top data management issues related to unstructured data.

Data storage and access on the other hand are seen as relative non-issues, validating the fact that retailers have moved on from viewing data volume as their top concern.

To put things into perspective, data privacy is rated as the least of retailers worries. This is a topic that justifiably gets a lot of airtime and attention, but retailers do not (yet) see it as holding them back from exploring Big Data. We believe privacy will come to be viewed as more challenging in years ahead once retailers are able to mature beyond the basic issues of organizing, integrating and analyzing the data.

Interestingly, retailers have recognized the importance and complexity of the variety of data even as their current data use for analytics indicates a singular focus on transactional and operational data. The gulf between the percentage of retailers who integrate transactional (POS, eCommerce) data (2 in 3) and social media or mobile data (1 in 4) is huge.

Location-based data is set to grow exponentially over the next few years. Over the next 2 years, retailers plan to drive more GIS location-based promotions (to drive customers into stores) as well as use Wi-Fi or other sensor-based technologies to engage customers at an aisle, shelf or even product level (once inside the store).

EKN expects retailers to continue to face data variety related challenges over the next few years. We see data organization and integration to be perceived as lesser challenges over the next 2 years, much like data vol-ume challenges decreased from 2012 to 2013. We believe these are teething issues that are relatively easily solved with a smart selection of tools and technologies. The real issue will be in developing domain and use case specific analytics models to make sense from unstructured data.


Big Data budgets are byte-sized and focused on Proof-of-Concepts (POCs).

Percentage of analytics budget allocated towards Big Data initiatives, 2013 vs. 2016 (Figures are average percentage of total analytics budget allocated towards Big Data initiatives for the corresponding year)

Timeframe for the deployment of technologies enabling Big Data initiatives (Figures are percentage of total respondents)

3.6%

6.8%

2013 2016

Technologies Next 12 months Next 12-24 months

Specialized Big Data BI tools 19% 24%

Big Data Visualization 16% 32%

Simulation 15% 16%

Software framework for data-intensive distributed applications (e.g. Hadoop) 12% 18%

BI Appliances 12% 19%

NoSQL databases 10% 19%

In-memory platforms (e.g. SAP HANA) 10% 22%

Unified Information Access Platforms 9% 11%

Traditional BI tools (eg: SAS, IBM) 8% 9%

Distributed Database Management System (e.g. Cassandra) 7% 12%

Semantic analytics tool 6% 13%

Column databases 6% 15%

Programming model for parallel-processing large datasets (e.g. MapReduce) 6% 16%

Traditional Database Management System 6% 6%

Statistical Computing (e.g. R programming language) 4% 19%

Traditional Data Storage (SAN, NAS) hardware 2% 6%


In our 2012 Big Data report, we strongly recommended retailers start with targeted POCs by identifying high impact use cases where it makes sense for them to experiment with Big Data analytics. We suggested pricing optimization, customer segmentation and marketing effectiveness as potential candidates.

The 2013 survey follows up with more details on retailers Big Data budgets, and it is a stark reality. The spend on Big Data as a percentage of total IT budget is miniscule (0.49%), and is only expected to grow to 1.3% by 2016. In effect, a billion dollar retailer spent $75,000 on average on Big Data initiatives in 2013.

The reality is that other than a few large retailers such as Target, Wal-Mart and Nordstrom who have made and are making big investments in building Big Data capabilities, retailers have adopted a wait and watch approach. Their efforts range from do nothing to the targeted POCs we recommended in 2012.

Retailers plans over the next 2 years also do not indicate aggressive plans to upgrade their Big Data technol-ogy infrastructure:

Less than 1 in 5 retailers plan to invest in any Big Data technology in the next 12 months. Their plans are more aggressive in the 12-24 month timeframe, which begs the question as to whether retailers are certain of those investments or are simply deferring intent to adopt to a time in the future? The Big Data budget (above) suggests investments over the next few years will continue to be circumspect.

Specialized Big Data BI tools and Big Data visualization will outpace other solutions in terms of adoption over the next 2 years.

Retailers stated intent to invest in Big Data (in terms of budgets and solution adoption) does not match up against the hype. Retailers will continue to invest in Proof-of-Concepts to establish ROI. Large-scale invest-ments in building technology capabilities to store, manage or analyze data will be rare. They will predomi-nantly be the domain of large retailers with deep pockets, or smaller retailers who will decide to make a strategic bet on leapfrogging competition in terms of insights driven retailing.

The tools and solutions retailers will focus on in the short-term are ones that are quickly scalable, provide them retail format or use case specific data and analysis models, and have a low cost of failure (both mon-etarily and in terms of impact on the business).


An opportunity for smaller retailers to improve competitiveness.

Comparison of large ($1 billion+) vs. smaller (less than $1 billion) retailers (Figures are percentage of total respondents)

Analytics maturity is a great leveler in retail. Unlike other areas such as supply chain, merchandising, multi-channel operations and marketing where larger retailers ($1 billion+ in annual revenue) display a greater maturity of technology adoption and maturity compared to smaller retailers (less than $1billion in annual revenue), there is little to no difference between them in terms of their ability to:

Conduct advanced analytics

Use insights to differentiate against competition

Augment transactional data with other valuable sources of data

This is where larger retailers are better positioned. Data from EKNs research reveals a significant difference between these two sets of retailers in terms of investment priorities and outlook towards Big Data:

Larger retailers topmost Big Data investment priority is training and team augmentation. More Tier One retailers (by a degree of magnitude) plan to train their executives and existing analysts on Big Data over the next 12 months.

Smaller retailers on the other hand identify Big Data tools or software as their topmost investment priority.

In fact, whereas 83% of larger retailers consider data integration as their top data management challenge, smaller retailers are relatively less concerned, perhaps because the scale and com-plexity of data in their enterprise systems is not as daunting.

Simply put, Big Data is not a technology challenge. It is a business strategy that the organization must commit to, and building pro-prietary analysis models based on use case and retail format are central to an effective strategy.

While there is not much difference in smaller retailers adoption of Big Data or their analytics maturity as compared to larger rivals, they must look at Big Data more strategically, rather than as a technology silver bullet.

60% 83% 53% 50%

57% 17% 13%

Smaller Retailers

Investment priorities

Large Retailers

Smaller Retailers

Large Retailers

Outsourced analyticsNew tools or software New hardware

Have basicanalytics maturity

Data integration as adata management challenge

Plan to train analystsover the next 12 months

Plan to train the executiveteam over the next 12 months

67% 51% 30% 32%

77% 30% 27%


Improving their analytics maturity may provide smaller retailers a rare opportunity to build a competitive advantage against retailers - regardless of size - that do not.

To do so however, they need to evolve their approach towards Big Data. Look at it more strategically rather than as a technology silver bullet. Invest aggressively in upgrading enterprise-wide analytics maturity regard-less of any accompanying investments in Big Data tools. Define use cases focused on customer engagement and operational improvement and seek management buy-in to invest in 1-2 POCs.

Experimenting with Big Data, especially if the organization is willing to expose certain data sets to trusted public cloud providers, does not need to be expensive.

The costs of storing, managing and analyzing data are decreasing.

Availability of cloud-based Software-as-a-Service solutions provides access to Big Data infrastruc-ture.

A wide range of tools, from statistical computing to visualization, are available at an extremely low cost (some are free).

Data from public sources such as social media networks, weather data, GIS data and data from the US Census is also available at relatively low cost.

The biggest issues are defining where there is value, building strong use cases and developing analytical models that allow you to extract value from the data being integrated.

State of the Industry Research Series: Big Data in Retail21Recommendations

Given how important offering a unique in-store experience is to retailers ability to differentiate against on-line retailers, EKN asked respondents to rate the in-store experience they provide customers in their stores against other brick and mortar competitors.

1 in 5 assess themselves as being industry leaders

1 in 2 consider the in-store experience they provide to be better than competitors

None of the respondents in EKNs survey rated the in-store experience they provide as lagging competition

The respondents view of the customer experience in-store is more favorable than EKNs view. Since the response categories do not speak to the quality of experience, perhaps the response indicates one or many of the following:

A bias introduced due to a competitive comparative question

A subtle acknowledgement of the low bar set by the industry as a whole in terms of in-store experi-ence

A variable understanding of in-store experience by the respondent set, given EKN did not describe it in the context of Omni-channel retailing

Investments in store technologies account for nearly one-third of the total IT budget.Percentage of the total IT budget allocated towards store technologies

(Figures are average percentage of total IT budget allocated towards store technologies for the corresponding year)

Recommendations

EKNs 2012 report contained a set of detailed recommendations over a short, medium and long-term horizon. Most of them still apply as retailers analytics maturity and Big Data adoption hasnt changed dra-matically in the last year. We recommend readers of this report consult the recommendations section of the 2012 report as well.

For this reason, we have focused our energies in the 2013 report on detailing out more specific short-term recommendations, providing retailers an actionable roadmap for 2014.

EKN 22Recommendations

Establish if and why you need Big Data, and determine if you are ready for it.

Adopting a Big Data strategy is a long-term, resource-intensive strategic initiative that will likely change many things about business-as-usual. As established previously, a majority of retailers are a few generations be-hind achieving the type of analytics maturity that would lend itself to an effective Big Data strategy. The industry, in general, is not racing to adopt Big Data for Big Datas sake, and neither should you.

As introduced earlier in the report, it is critical to first establish a need for pursuing Big Data and subse-quently assessing the organizations level of preparedness. We view these as foundational to a meaningful approach to Big Data.

EKNs Big Data Need and Readiness Assessment Toolkit in Appendix A provides an easy reference toolkit that retailers can use to conduct an assessment of whether they need Big Data. This is an excerpt and retailers can download the toolkit from the EKN portal or reach out to EKN for an assessment exercise.

Start with a POC focused on 1-2 high priority Big Data use cases. Detail out the use cas-es, including decisions, data inputs, sources, delivery and consuming audience and re-source requirements.

Based on results from EKNs Big Data Need and Readiness Toolkit, or similar assessment templates, retail-ers that fall in the Gently test the waters category should evaluate a focused POC or two to build strategic capability and test ROI on a low-cost, low-risk basis.

The Big Data Opportunity-Impact Matrix is a decision framework that outlines Big Data opportunities for retailers, taking into account time taken to implement and potential impact of outcome. This is an illustrative one for a Billion dollar brick and mortar retailer. Things will looks different based on the size of the retailer since impact and action can differ.

Highlighted opportunities correspond to the functional area that the EKN Big Data Use Case Template covers

Impa

ct

HighImprove Customer

Insights

Improve Sourcing &Procurement

Dynamic PricingReal-time Segmentation

Optimize PromotionsImprove Demand

Forecast

Improve ReplenishmentCycle

Improve InfrastructureAvailability

Improve TransportationRoutes

Plan & Execute New Store Opening

Improve Store Assortments

Manage Recalls

HighExecution TimeLow


Due to the subjective nature of what could be considered Big Data, we find an increasingly blurred line be-tween advanced analytics (such as predictive and investigative analytics), data visualization and Big Data. For this reason, a lot of reported Big Data activity is actually retailers improving their analytics maturity in terms of being able to perform predictive or investigative analytics.

EKN recommends retailers detail out use cases, such as the one illustrated below, in order to develop a clear understanding of all the parameters that qualify it as truly being a Big Data initiative vs. a business-as-usual analytics project. We recommend focusing on one or more of the following areas:

Customer segmentation

Pricing optimization

Promotion optimization

Infrastructure availability optimization

EKN Big Data Use Case Template:

Functional area Marketing

Problem statement How can Big Data help us improve our campaign or promotion effectiveness?

Data Sources

POS data#

eCommerce data#

CRM or loyalty data#

Promotions calendar and execution data#

Public data (Census)#

Syndicated data (Nielsen/IRI)*

Social data (Facebook, Twitter (Firehose))#

GIS Data#

Consumer mobile data*

#Must have; *Good to have

Toolset

An end-to-end Big Data platform or individual components

Analytics#

Infrastructure#

Data Management#

Visualization*

Data-asa-service providers like FICO*

#Must have; *Good to have

Approach

Methodology

Phase 1: Problem definition

Boundary up the POC by deciding which product categories/SKUs and promotions to analyze.

Phase 2: Data review and standardization

Understand the data sources that are required for the analysis. Clean and standardize all data and

create templates for data inputs.

Phase 3: Analysis and model development

Analyze the data and build algorithms to model campaign performance. Build multiple models and

conduct scenario and sensitivity analysis.

Phase 4: Model optimization

Run tests to optimize the model on historical data and also on some live promotions. Finalize a set

of models for roll-out.

Phase 5: Roll-out


Approach

Base case scenario:

Do a complete analysis of your marketing campaigns, by product and by location using your entire

transactional and customer data instead of a subset. Integrate all transactional data from the POS

(sales, discounts), operational data like CRM or loyalty, marketing promotions calendar, public data,

syndicated data along with social data (Facebook, Twitter etc.) to segment customers and promotion

types.

Conduct statistical analysis to understand campaign performance by customer segments and the

influence of factors such as product type, time, device and past purchase. Create predictive models

to assess the campaign effectiveness and apply them on historical data to improve it. Setup new

campaigns based on customer segments, choose and adjust statistical models and conduct experi-

ments to test them. Use a library of models to drive more campaigns and use them to reduce off-price

promotions and thereby increase gross margin.

Next Case:

Integrate GIS data and overlay the analysis on a map to look at response rates of campaigns. Enable

interactivity on the map and allow users to run simulations. Integrate with marketing mix analysis to

help optimize spend.

Advanced Case:

Enable dynamic real-time promotions online and on the mobile device. Combine navigation path,

wish lists and customer location information to perform real-time customer segmentation and per-

sonalization. You can also combine social activity (that you have access to) to create a broader set of

interests that you might not have deciphered otherwise. Sensing the intensity and frequency of social

activity - recent tweets, Facebook likes - can help you position more relevant promotions.

A retailer can deliver real-time campaigns to customers while they are in the store targeting them as

a segment of one - Jill gets a 10% coupon, but Jennifer gets 15%.

What makes this Big Data?

High Volume (large data sets)

High Variety

Low Velocity*

*Base case is a point in time analysis. The advanced use case has high data velocity

Business Benefits

Increased conversion rates

Faster analysis

Improved accuracy

How much should you

spend on a POC?Less than USD 75,000


Prioritize resourcing, training and team structuring.

Already, the #1 challenge facing retailers from an analytics perspective isnt the lack of available insights or a lack of analytics tools; it is the inability to deliver the relevant insight to the right person at the right time. Big Data wont address this issue; it will only serve to amplify it.

Further, EKN expects this issue to evolve from one focused on delivery of insights to one about inability to take action. Retailers analytics investments over the next two years include solutions such as digital dash-boards and mobile BI tools that should help bridge the delivery of insights gap. Improved visualization will also help bridge the gap between statistical knowledge and business acumen.

But, two core issues remain that cannot be addressed by technology intervention alone. Retail employees in general - merchants, buyers, store associates and managers, business executives, marketers, technology professionals - are not statistical wizards. Also, insights-driven decision making may not come naturally to them unless it has been part of their personal DNA. On the other hand, there are only as many analytical re-sources retailers can hire, and they are unlikely to have the business acumen and retail expertise that other retail employees do.

Retailers must take the following steps to ensure they are better prepared in terms of the ability to act on insights as they prepare to make investments in Big Data analytics:

Training executives and analysts: For a large part, the creators and consumers of insights will have differing skills and capabilities. Retailers must institute cross-competency training for execu-tives to better understand analytical concepts and analytics resources to understand the most im-portant insights their analysis enables.

Resourcing: In more than one EKN Peer Forum (a closed door meeting of EKNs retailer member-ship), smaller (

EKN 26

Data organization and integration will be a huge challenge. Strengthen your enterprise data management strategy.

Respondents in EKNs survey identify data organization and integration as their two biggest data manage-ment challenges. Countless anecdotal conversations come to mind from retailers and solution providers alike that highlight how rampant this issue is. Stories of projects with a clear analytics goal and well-defined use case getting mired in issues relating to data availability, quality and completeness must resonate with readers of this report.

The importance of retailers developing or strengthening their enterprise data management strategy cannot be overstated.

With the democratization of data - i.e. more freely sharing your data, using someone elses data and making data available across the enterprise - stricter governance and standards are required. This also holds true as retailers manage more data, of more types, from more sources, flowing through information systems at greater speed.

The industry has spoken of a common view of the customer for decades. With cross-company shar-ing of data along with retailers needing to tap into external customer data, the need for a common customer data model (MDM) is amplified.

The data architecture needs to support the need for real-time visibility and quicker decision mak-ing, which means the ability to consume and provision data on the fly.

Data privacy and security will gain importance as a move to the cloud becomes inevita-ble.

Even though privacy and security is an important topic of discussion in the retail industry, respondents in EKNs survey rated it the least of their data management challenges. That being so, we believe privacy and security will be important issues that will only increase in importance and complexity.

The use of cloud-based storage and Software-as-a-Service providers is inevitable. The cost structure of IT and the expected ROI from Big Data initiatives does not support a move to in-network storage expansion for all retailers. Even if retailers move to a private or hybrid cloud model for larger scale Big Data investments, smaller POCs will require exposing their data to other providers or hosting it in an environment not directly controlled by their own security policies.

Retailers are increasingly interested in rich customer data from partners and 3rd party sources. These include telecom companies such as AT&T, consumer mobile applications such as ShopSav-vy, data-as-a-service providers and industry data pools. As retailers explore quid-pro-quo data ex-change arrangements or as such providers provide commercial services on sharing customer data, both parties will need stronger privacy and security controls.

Privacy related action within the ecosystem would also have an impact on the data that is available to retailers. Googles move to discontinue sharing keyword referrer data from being monitored in analytics tools and Apples decision to stop supporting meta-referrer in the mobile version of Sa-fari are two recent examples. Not only will retailers need to strengthen how they protect privacy of their customer data, they will need to learn to operate in an environment where less may be avail-able to them from consumerized web services.

Recommendations


EKN believes that in time an over-arching opt-in legislation and framework will allow consumers to have deeper control over their personal information, providing them visibility and control over with whom it is shared and for what purpose. In the meantime, retailers will benefit from developing a similarly transparent process identifying sensitive customer information flowing through their systems, use cases that it is used in, alternatives in case of legislation or other changes restricting or prohibiting it. More progressive retailers may consider exposing a complete digital footprint of a customers information and how they use it to enrich engagement with them via their loyalty programs.

Learn from the leaders and avoid common pitfalls as you build your Big Data strategy.

Along with retailers assessing their own analytics maturity, EKN recommends they benchmark themselves against competitors within their retail format, similar sized retailers (in terms of revenue or store footprint) and importantly against industry leaders such as Target and Wal-Mart.

In addition, retailers should develop a quick reference guide to avoid common pitfalls that plague Big Data analytics strategies and initiatives. An illustrative list is below:

Recommendations

No use case Embarking on a Big Data project without a clear use case and ROI definition.

Big Data = Technology Thinking of Big Data as a technology rather than as a strategy.

Data for datas sake

Retailers have access to more data than they know what to do with. Tracking Facebook likes or

integrating the Twitter Firehose API is easy. Doing so without a clear definition of why will only in-

crease the data overload and could create more data silos.

Lack of data readinessProceeding with a Big Data project without investing time and effort in auditing the impacted data

sources for data availability, quality and completeness.

Scalability

POCs will ultimately need to scale across the enterprise.

POCs should not be jigged to succeed, rather used to test readiness, uncover issues and help path

a roadmap to scalability.

Lack of management buy-inEven a skunkworks project requires C-level alignment to ensure the ensuing results and lessons are

accepted, internalized and scaled quickly.

Lack of analytics resources

Related to the point about thinking of Big Data as a technology intervention. Without the right an-

alytical resources and analytics training for existing associates, projects can fail due to a lack of

proper interpretation and utilization of insights.


Appendix A: EKN Big Data Need and Readiness Assessment Toolkit The assessment toolkit that follows is an excerpt from EKNs detailed Big Data Need and Readiness Assessment Toolkit. The scope of the toolkit is too large to provide in this report. However, EKN invites retailers to customize the provided toolkit for their own use. EKN has also made available a digital version of this assessment toolkit at the following URL, and invites retailers to receive a complimentary Quick Assessment Score by submitting their responses on the online questionnaire.

Go to the digital version: https://www.surveymonkey.com/s/EKNBigDataToolkit

= Analytics Maturity

= Data Management

= Strategic Intent

= Opportunity Qualification

= Investment Appetite

= Organizational Readiness

AM

DM

OQ

OR

IA

SI

State of the Industry Research Series: Big Data in Retail29Appendix A: EKN Big Data Need and Readiness Assessment ToolkitA

Area Question Options Score

How would you rate your organizations

analytics capabilities?

We do reporting -1

We do basic analytics 0

We do investigative analytics 1

We do predictive analytics 2

Compared to your competitors, how

would you describe your organizations

use of customer analytics?

Better than our competitors 2

At par with competitors 1

Lagging our competitors 0

Please rank the top 2 challenges that

prevent you from leveraging analytics

more strategically.

We dont have a clearly articulated analytics strategy linked with

specific business outcomes0

Our software and tools are outdated 1

We dont have adequate resources who can interpret the output of

analytics tools1

Our data quality is a big stumbling block before we can analyze it

meaningfully0

We have the insights but can do better in terms of delivering it to

the right resource at the right time2

Our management style prevents us from making data-driven deci-

sions0

A previously failed analytics investment 3

Difficulty in measuring analytics ROI 2

How much data do you currently store

across the enterprise?

< 10 GB 0

10 GB 100 GB 1

101 GB 1 TB 2

1 TB 5 TB 3

5 TB 100 TB 4

100 TB+ 5

What is the size of largest dataset that

you currently analyze or plan to analyze

in the next 12-24 months?

< 10 GB 0

10 GB 100 GB 1

101 GB 1 TB 2

1 TB 5 TB 3

5 TB 100 TB 4

100 TB+ 5

Our current Business Intelligence (BI) in-

frastructure is capable of handling your

analytics needs for the next 3 years:

Yes 1

No 0

AM

AM

AM

DM

DM

DM

EKN 30


Which of the following data sources do

you currently integrate to do customer

analytics?

Store (POS transactional data) 0

Loyalty or CRM data 1

Online 1

Mobile 4

Social media 2

Census data 3

Syndicated data (Nielsen, IRI) 3

Which of the following data sources do

you plan to integrate to do customer

analytics in the next 12-24 months?

Store (POS transactional data) 0

Loyalty or CRM data 1

Online 1

Mobile 4

Social media 2

Census data 3

Syndicated data (Nielsen, IRI) 3

When will you run out of your existing

capacity to store and process data?

I dont know 0

We are already out of capacity -1

Within the next 2 years 2

In the next 2-4 years 3

Beyond 4 Years 2

Never 2

What is the rate of growth (per year) of

your enterprise data?

< 19% 0

20% - 39% 1

40% - 59% 2

60%+ 3

What is your biggest data management

challenge?

Handling data Volume 1

Handling data Variety 3

Handling data Velocity 2

In your organization what is the highest

level at which Big Data strategy is being

championed?

I am not aware 0

CXO Level 3

VP Level 2

Director Level 1

Manager Level 0

Its not being championed by anyone -1

What is your closest competition set

doing about Big Data?

They are actively pursuing Big Data 2

They are investigating Big Data 1

They are not doing anything 0

We dont know 0

DM

DM

DM

DM

DM

SI

SI

Appendix A: EKN Big Data Need and Readiness Assessment Toolkit



What impact will Big Data have on the

Retail industry?

Completey transform it 4

Have a very high impact 3

High impact 2

Medium impact 1

No or low impact 0

Do your plans over the next 12-24

months include the need to analyze

large sets of social media, mobile or

machine log data?

Yes 1

No 0

I dont know 0

Do your plans over the next 12-24

months include the need to analyze

large sets of unstructured data?

Yes 1

No 0

I dont know 0

Do you plan in the next 12-24 months

to develop and implement advanced

analytics algorithms and apply them to

large data sets?

Yes 1

No 0

I dont know 0

Is your current BI toolset sufficient to

handle your analytics needs over the

next 12-24 months?

Yes 1

No 0

I dont know 0

From a business perspective our com-

pany prefers to be a:

An innovator/leader 3

A close follower 2

A distant follower 1

A reactor to industry conditions and competitors moves 0

From a technical perspective our com-

pany prefers to be a:

An innovator/leader 3

A close follower 2

A distant follower 1

A reactor to industry conditions and competitors moves 0

What are your plans to specifically

budget for Big Data projects?

I dont know 0

Already have a Big Data budget 3

Within the next 2 years 2

In the next 2-4 years 1

Beyond 4 Years 0

Never -1

Approximately what percentage of your

organizations total technology budget is

allocated towards in BI & analytics solu-

tions this year (2013)?

< 5% -3

5% - 10% -2

10% - 15% -1

15% - 20% 0

20% - 25% 1

25% - 30% 2

30% - 35% 3

35%+ 4

OQ

OQ

OQ

OQ

IA

IA

SI

SI

SI


EKN 32


Approximately what percentage of your

organizations total technology budget

will be allocated towards BI & analytics

solutions in 2016?

< 5% -2

5% - 10% -1

10% - 15% 0

15% - 20% 1

20% - 25% 2

25% - 30% 3

30% - 35% 4

35%+ 5

How much are you willing to invest on a

a Big Data Proof-of-Concept in the next

12 months? (Please select)

Nothing

*

< 50k

50k - 100k

100k - 250k

250 k - 500k

500k - 1M

1M+

Is your organization ready to embrace

public or hybrid cloud for data storage

and analysis?

Yes 1

No 0

Dont know 0

What best describes the decision mak-

ing in your organization?

Complete data-driven 2

Mostly data-driven 1

Mostly intuition-driven 0

Completely intuition-driven -1

How many dedicated analytics re-

sources do you currently have within

the organization? (Please select)

I dont know

*

None

1 - 5

5 - 10

10 - 15

10 - 20

20 - 25

More than 25

Do you have plans to invest in acquiring

the analytics skills and resources over

the next 12-24 months?

Yes, we expect to make heavy investments 2

Yes, we expect to make some investments 1

No, we dont plan to make any investments -1

I dont know 0

How is the analytics team in your or-

ganization currently structured?

A dedicated analytics team across all business functions 1

Each department is primarily responsible for their own analytics resources 2

IT is primarily responsible for analytics 1

Use contract resources 1

We dont use analytics resources -1

Other model 1

Not aware 0

OR

OR

OR

OR

IA

IA

IA




What was your organizations annual

revenue in the last fiscal year?

< $100 million 0

$100 million - $499 million 1

$500 million - $999 million 1

$1 billion - $4.999 billon 2

$ 5 billion + 3

Which primary retail format does your

organization belong to? (Please select)

Apparel & Accessories

*

Electronics & Appliance

General Merchandise Stores

Grocery & Food

Convenience

Health & Personal Care

Home & Furniture

Sporting Goods, Hobby, Book, and Music Stores

Online

Other

*There is no score provided for these questions as the score will be based on the size of the organization and some of the other re-

sponses from the assessment kit.

OR

OR


EKN 34RecommendationsRecommendations

State of the Industry Research Series:The Consumerization of IT: Personal Devices and Software in the Enterprise

Appendix B: EKN Big Data Vendor Landscape

State of the Industry Research Series: Big Data in Retail35Appendix B: EKN Big Data Vendor Landscape

Big Data Platform

Big Data Visualization

Big Data Analytics

Big Data Specialized Analytics

Semantic Retail

Nic

he V

endo

rsEn

terp

rise

Vend

ors

# Also offers Big Data Appliance* Offers with partners

#

#

#* *

#*

SolutionsVendors

EKN 36

Appendix C: Retail Honor Board

State of the Industry Research Series: Big Data in Retail37Appendix C: Retail Honor Board

Retailer Action

Amazon.com uses real-time analytics to create custom cross-sell offers based on a customers profile. Using Big Data analytics the online retailer offers customized content and dynamic pricing to custom-ers. The retailer allows its competitors to post advertisements for the same product that the customer is looking at on Amazon.com. Data analytics allow it to monitor clicks those promotions produce, and determine where, when and from what customer segments they are poaching sales. It is able to ac-cordingly modify its own pricing, marketing and product mix. A dedicated Client Experience Analytics Team runs customer simulations to measure website latency across the globe, identify trends or issues, simulate website activity, and more. The simulations are done on a massive scale to mimic the 98 mil-lion active customer accounts across more than 10 web properties.

Gilt Groupe uses a comprehensive solution consisting of advanced data analysis, visualization and re-porting on both structured as well as unstructured data. The solution helps Gilt adjust inventory by analyzing customer clicks, match that information with the characteristics of the days merchandise and predict which products consumers might be interested in. It then sends members customized alerts about product offers based on software-driven recommendations. The retailer updates its predictions daily and shares detailed demographic portraits with its brand suppliers of whos buying what.

Macys uses advanced analytics to analyze millions of terabytes of data each day, including data from social media, enterprise systems and store POS systems. The use of best-of-breed technologies helps Macys to generate deep insight into the data, align customer interactions to customers preferences and automate processes. Using Big Data analytics it adjusts prices for the entire product range sev-eral times a day. Time taken to analyze two terabytes of data reduced from 30 hours to less than 120 minutes; churn rate reduced by approximately 20 percent and Macys saved more than $500,000 in productivity annually in terms of FTE time saved.

Nordstrom, an American upscale fashion retailer, provides customers a more personal shopping expe-rience by using Big Data analytics to identify what products to promote to which customers when and via what channel. Nordstrom Data Lab creates products that rely on a wide spectrum of data resources from within the company and social media to reinforce a consistent brand experience to customers. Apart from its website and POS, the retailer generates huge amount of data from its 2 million likes on Facebook, 4.5 million followers on Pinterest, 300,000 followers on Twitter and the Fashion Rewards Program.

Sears Holdings revamped its technology infrastructure by integrating a new software framework into its data architecture to lower costs, deliver data faster, enhance the speed of processing cycles and personalize marketing campaigns. The new integrated model allows the retailer to analyze marketing campaigns for loyalty club members weekly, analyze certain online and mobile commerce scenarios daily, while using 100% of the available data. It also uses Big Data to set prices - nearly in real-time - and move inventory by giving loyalty shoppers customized coupons.

Target uses its predictive analytics capability to deduce whether an individual shopper possesses char-acteristics that make them particularly good targets for a specific marketing effort. It assigns each shop-per a unique Guest ID that ties demographics with shopping behavior and preferences. The Guest Mar-keting Analytics department helps Target to gain competitive advantage from knowing its customers better than its competitors. An Active Data Warehouse effectively manages complex user queries on large data volumes in a mixed workload environment across the enterprise.

Wal-Mart launched Wal-Mart Labs in 2011 to gain actionable insights from its digital data and cus-tomer transactions data. It launched three special Big Data projects: Social Genome allows the retailer to reach customers or their friends who have mentioned a product online to inform them that it has the exact product at a discount. ShoppyCat recommends products to Facebook users based on the hobbies and interests of their friends. Get on the Shelf, a crowd-sourcing solution that provides its vast customer base the chance to pitch their product idea to a large online audience.

Premier Sponsor

About SASIn retail, where companies can go quickly from rags to riches - or riches to rags - the customers perception of, and experience with, your brand is critical not only to staying in business, but growing your business as well. SAS enables retailers across all segments - apparel, grocery, specialty, general merchandise, online and hardlines - to uncover in-depth customer insights that you can use to delight customers by engaging them in profitable ways. With high-performance analytics at the core and backed by more than three decades of retail experience, SAS solutions for retail enable you to find new and better ways of doing business.

www.sas.com/retail

About EKNOur research agenda is developed using inputs from the end user community and the end user community exten-sively reviews the research before it is published. This ensures that we inject a healthy dose of pragmatism into the research and recommendations. This includes input of what research topics to pursue, incorporating heavy practi-tioner input via interviews etc., and ensuring that the bend of research takeaways are oriented towards a real-world, practical application of insights with community sign-off.

For more information, visit www.eknresearch.com

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Disclaimer:

EKN does not make any warranties, express or implied, including, without limitation, those of merchantability and fit-ness for a particular purpose. The information and opinions in research reports constitute judgments as at the date indicated and are subject to change without notice. The information provided is not intended as financial or invest-ment advice and should not be relied upon as such. The information is not a substitute for independent professional advice before making any investment decisions.

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