bigdata retail

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1 BIG DATA FOR RETAIL Abstract A recent survey at the big data Retail forum summit in Chicago suggests 80% of the retailers are aware of big data and 67% are either already in the process of building a big data strategy. With data collection at various touch points ever-growing, and technology enabling data collection and analysis, companies have raced towards adopting BI (Business Intelligence) and big data solutions to earn competitive advantage. The term big data refers to solutions that can handle data that characterizes 4Vs viz., Volume, Velocity, Variety and Veracity. While insights on to merchandising, procurement was nothing new, customer analytics has unleashed the real big data capabilities enabling companies to arrive at insights that were earlier close to impossible. In retail parlance, consumer shopping behavior, preferences etc. are data that was a rarity in itself. Thanks to changing consumer mindset. Consumers are increasingly ready to share data and also allow data capturing by retailers; but a recent IBM study concludes that all such acceptance is with expectations that the retailers would honor their privacy and provide value in return. Big data solution provides have been able to demonstrate ROI

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1BIG DATA FOR RETAIL

Abstract

A recent survey at the big data Retail forum summit in Chicago suggests 80% of the retailers are

aware of big data and 67% are either already in the process of building a big data strategy. With

data collection at various touch points ever-growing, and technology enabling data collection and

analysis, companies have raced towards adopting BI (Business Intelligence) and big data

solutions to earn competitive advantage. The term big data refers to solutions that can handle

data that characterizes 4Vs viz., Volume, Velocity, Variety and Veracity. While insights on to

merchandising, procurement was nothing new, customer analytics has unleashed the real big data

capabilities enabling companies to arrive at insights that were earlier close to impossible. In

retail parlance, consumer shopping behavior, preferences etc. are data that was a rarity in itself.

Thanks to changing consumer mindset. Consumers are increasingly ready to share data and also

allow data capturing by retailers; but a recent IBM study concludes that all such acceptance is

with expectations that the retailers would honor their privacy and provide value in return. Big

data solution provides have been able to demonstrate ROI (Return on Investment) for retail firms

that have invested into these technologies. Changes in consumer buying modes, increased

purchasing power in a hypercompetitive industry has coerced the industry to adapt to such

changing patterns. Omnichannel retailing is the norm of the 21st century. Retailers are

continuously bothered by this multichannel importance that customers showroom but yet

dynamically change the channel where the ultimate sale is completed. The intrinsic change in

patterns around shopping behavior has been a source of research for retailers and their big data

solution vendors. CRM (Customer Relationship Management), loyalty programs and corporate

data responsibility are some of the key focus areas today.

Keywords: Omnichannel retail, consumer shopping behavior, big data, analytics, ROI, CRM

2BIG DATA FOR RETAIL

What is big data?

Big data is the label provided to

extreme forms of BI systems that are

constrained by increased frequency of

data capture combined with ever

increasing touch points in the market.

The new channels of distribution

contributes to the complexity on the

variety dimension.

Retailers handle huge volumes of structured and unstructured data. This is inherent nature of the

industry which is transaction intensive. Data from various touch points are collected and stored

in Hadoop clusters (or any other storage systems) [1]. Through technologies like Hive, special

big data analysts help in generating innumerous insights. The need for such analyst capable of

combining industry expertise with technological acumen are in need. Firms that are looking

forward to gaining competitive advantage through big data are already focusing on some the big

data functionalities through hiring analysts in large numbers. Ability of correlate sensibly and

Figure 1-Hadoop representation of big data [1]

3BIG DATA FOR RETAIL

Figure 2- Big data functional view

arrive at insights is the key capability

expected from such analysts. McKinsey’s

CMAC (Consumers Marketing Analytics

Center) for example has a team that

provides consulting and tools that can

enable firms to generate actionable

insights.

Retailers according to various commentaries and discussion in the big data retail forum summits,

have been focusing more on the areas of CRM, loyalty for value creation to retain customers.

Traditional Business intelligence and big data

Traditional BI has been focusing on operations, consumer expectation, mismatch around value

perceptions and competition as the key areas for optimization [1]. The dimensions that big data

brings in include the predictive analytics, machine learning, and social behavior of consumers

that have become attractive sources of insights. Retailer understand the increased need to

personalize their online and offline store operations for consumers. Informed data collection are

various interactive points and observatory learning systems ([10] application of video analytics)

have been under usage during the last 5 years. The advantage that big data offers is the benefits

of machine learnings to connect individual observations into global insights with great levels of

granularity.

Big dataTraditional BIBig

4BIG DATA FOR RETAIL

Make or Buy decision

The decision process to creating competitive advantage through big data starts with WHAT are

the areas that can be improved using big data. Once there is a conviction to invest into analytics,

the next decision point is whether to develop a solution using the in-house expertise or procure

an off-the-self, customizable solution from a vendor like say CMAC from McKinsey [14]. Our

study suggests that this decision has to be purely contextual for any firm. Some of the factors that

retail firms need to consider before investing into such a solution are summarized below.

Make decision – Factors to consider

If you see it as a Long term strategic asset and core competency

Cost of operation increases due to need in house expertise.

Lead time available to develop

Flexibility and selectivity

Security concerns around the data collected

Fixed cost model

Buy decision – Factors to consider

Initial period of ROI Lull

Payment may be for features not required.

Off-the-shelf in most cases; can be setup and be running quickly

Can be less flexible due to constraints with customization

Data security and privacy can be an issue

Figure 3 Traditional BI and Big data

Big

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Variable cost model

Omnichannel retailing

Retail industry has two sectors primarily viz., unorganized and organized. These are called

General trade and modern trade in retail parlance. In developing countries, the proportion of

unorganized retail is usually higher vis-à-vis developed countries. The adoption of big data is

predominant as expected in the developed countries, where accessibility is not a major concern.

The ability to retain and satisfice consumers is the key challenge that has forced retailers in the

developed countries to focus on CRM, loyalty programs etc. The data collected at various

interaction points with the consumer (cognizant and observatory) are collated and analyzed

usually to personalize their experience during shopping at their outlets (online portals as well as

retail stores). The resultant insights are fed to other operations like merchandising (merchandise

assortment and planning (MAP), procurement, inventory forecasting, warehousing and vendor

engagement etc. Changes in lifestyle has created new avenues of convenience and value for

consumers. Brick-and-mortar companies have been forced to now consider online competition

from online retailers. Price parity is a key challenge. Creating the right value to compensate

inherent disparity in cost structures through differentiation is inevitable. There is a challenge for

brick-and-mortar companies with venturing into the online space. They further need investments

into a whole new vertical and traverse through the learning curve to compensate the

disadvantage. Instead, brick-and-mortar companies could embrace other in-store data capture

technologies and develop teams that span the competition and its offerings constantly.

IBM Research Results:

“Although a large amount of consumers are willing to share personal data, the key finding is

that they expect a personal experience in return. It is imperative that retailers go about using

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data in such a way that it results in customers trust and loyalty” - IBM Research [5, 8].

Figure 4 – Shopper categories based on purchase decision process

Key takeaways from global retailers

Key takeaways from the commentaries of global retailers include the following:

Time sensitive decisions, Monitor emerging trends and taking corrective actions are

critical for generating value out of big data investments

Insights Decisions Execution

Spend most of your time on WHAT and not HOW [1]

Spend 70% on WHAT to analyze and only 30% on HOW to analyze

Ability of gather relationships across variables in structured data is immense

Build a solution to drive data gathering at various touch points into a common repository.

Consumer Insights

Business Insights

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Product Insights &

Social Insights

When you create a Big Data team:

Form teams comprised of right people

Partnership with your IT department is critical

Concrete goals (else leads to ‘analysis paralysis’)

Success with Big Data is primarily through

Choosing simple ways to compute insights

Knowing how much to complicate

Honest data management practices that does not tamper with the privacy promises

made to the consumer

How do you begin with Big Data?

Augment to enhance insights

Do not treat it as a revenue handling system

Improve quality of existing decisions

Improve time to market

Big Data - Retail Use cases

During course of this term paper we conducted a literature review [3] and existing business

solutions from customer’s case studies to see various places where big data technologies are used

in retail Industry. Apparently, we seem to notice that a lot of multinational retail brands already

kick started their efforts to leverage value out of big data. Through this study, we were able to

bin most of these uses into following categories

8BIG DATA FOR RETAIL

CRM & Customer Experience

A customer interacts with company during number of times to buy, use and avail

product/services. Whenever a customer interacts with an organization during various touch

points, it is vital that amount of information available on customer informs and guides process

that will help maximize their experience. For this to happen, retail companies need a 360 degree

view of customers. A 360 degree view of customer includes aspects related to past, present and

future. The following are sources from which retailers can capture such information.

− Empowered with large volumes of information relating to customers, retailers can

improvise by personalizing their services. To help customer buy a new smart phone,

retailer can analyze data from his previous transactions, their click stream activity, social

media activity, geospatial & demographic information and push highly targeted real time

promotion through search engine and social media advertisements.

Figure 5 - A general overview of various sources from which we can capture customer data.

9BIG DATA FOR RETAIL

− Ecommerce websites can come up with better recommendation systems to suggest

relevant products that customers might be willing to buy.

Merchandizing, Pricing

Merchandizing deals with retail activity which primarily focuses on promoting sales of goods

especially by presentation in retail outlets. Big data can aid retailers to manage merchandizing by

effectively making use of data aggregated from a variety of sources.

With the profusion of social networking sites and user activity, a lot of social media buzz is

generated online which could be valuable for companies. Using big data technologies, companies

can figure out latest trends, fashion styles promoted by public icons & movies and push these

merchandize to storefront.

Using Big Data technologies, retailers can use data captured from news related to local events

and launch promotional buzz. Retail managers can also get information from disparate data

sources and fine tune their pricing strategies accordingly resulting in competitive edge in their

respective industries.

Supply chain & Operations

In addition to improving CRM and merchandizing, big data technologies can also be leveraged

to improve supply chain and regular day to day operations.

Armed with huge volumes of real time data, retailers can use real time tracking options to track

order shipments. In case of any major political, natural or social events, companies can react

spontaneously and by efficiently rerouting their shipments, they can improve their logistics.

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On similar lines, big data technology can be harnessed to improvise day to day operations. For

instance, looking at data trends from local news and events and data demand and supply patterns,

companies can work on dynamically staffing people which could result in cost savings.

Marketing

Big data delivers competitive advantage for retailers in marketing activities too. Empowered

with Big data, retailers can indulge more effectively in targeted marketing, micro segmentation,

location based selling and cross selling. In addition to targeting, retailers can also pull out

valuable insights of their product performance or product launches if any from social media.

A lot of context specific insights related to customer activity is captured day to day by many

sources. This data can be used for marketing and delivering ad’s to users based on digital

footprint and context in which user performs digital activities.

Social

With the rise of social networking activity online, social networking sites capture phenomenal

amount of social activity from public. This information could provide valuable insights on

customer perceptions related to various products and services they consume. Companies can

conduct sentiment analysis on social networking sites and get various insights on how customers

feel and perceive about their products.

Visual Analytics

This is another trending domain under big data umbrella. Although in its nascent stages, mining

real time in store closed circuits cameras and other offline video content could provide useful

insights onto how can retailers improve their customer service. For instance, firms like IBM are

coming up with augmented virtual reality shopping application that will automatically deliver

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personalized coupons, offers, customer reviews and hidden product details. This would close the

gap between wealth of product information on internet and traditional retail stores [10].

On similar lines, a lot of face matching analytics technology is being developed which can

identify customers walking into store and pass their profiles to sales representatives so that

retailers can deliver personalized customer service.

Some Key Issues in Big Data

Communication & co-ordination of disparate data silos:

One of the most critical issues with big data is related to managing sheer quantity of data

produced by various sources. Owing to large volumes and variety of data, organizations build

huge data silos to capture and mine data. Most of the time, coordinating and consolidating these

massive silos could issue potential problems pushing existing infrastructure to its limits.

Technology and research could eventually deal with this problem but however, a lot of

streamlining work needs to be done in this area so that systems can co-ordinate and efficiently

process this information.

Regional, Cultural & linguistic factors

Weather it is Social Network Analysis or Analysis of data from news and newsgroups, there are

always regional, linguistic and cultural factors that kick in and need to be accounted for. Since

Big Data technology deals with a diverse wealth of information, systems that handle and process

this data should be flexible enough to account for all these factors.

Privacy & Regulation issues

Historically, most of the data collected does not attribute to specific individuals. This is more

often de identified information and big data is traditionally used for tracking interests of

12BIG DATA FOR RETAIL

customer groups in de-identified form. Information in this form is not regulated across many

nations and companies are free to conduct their business using this. However, with technology

evolution, we have enough analytical tools developed which could track and attribute data to

specific individuals. This resulted in number of governments regularly coming up with

legislative acts like Privacy Act, The Spam Act etc. (Australia).

All these issues complicate environment in which companies operate business with big data

technologies. These factors have to be strictly honored and technology needs to flexibly

encompass all the same.

Corporate responsibility on Customer data

Another issue that gets frequently highlighted whenever people talk about big data is related to

the issue of corporate responsibility on customer data. There is a growing concern on to what

extent corporate are responsible in making ethical use of data at their expense [9].

With profusion of data, corporate look for many ways with which they can monetize this. As the

case study “The dark side of customer analytics” discusses, when a company gets hands on data,

marketers can be tempted to use information in ways that seem attractive but ultimately end up

damaging relationships with customers.

Also, with the amount of data that companies collect, they would be able to profile customers

more accurately without possibly having any direct interaction with them. All these actions call

for an ethical business conduct which is critical when retailers do their business.

Some Radical trends that we could foresee going forward.

Significance of Context Awareness

13BIG DATA FOR RETAIL

Going forward, since data will be all around us, the context in which it exists and its relevant

starts to matter more. Empowered with contextual information, companies can produce better

analytical insights into data which is more pertinent to customer. With the increase in number of

devices like the mobiles, tablets and more trending wearable the capability to collect context

specific information becomes easier and companies would use this to obtain better insights from

data [12].

Focus on real problems rather than product selling; Semantic shift

Right now, most of business vendors are in mode of selling big data technology applications and

services. Empowered by these, retailers usually focus on selling their products to individuals.

With the evolution of new research areas like semantic technologies, deep data mining etc, and

retailers will be riding on tools with which they will be able to go beyond selling to solving more

real world problems like solving customers’ needs, proactively reacting to their demands etc.

Visualization driven insights

Visual communication aids in simplifying and understanding large number of insights processed

from data. Companies like Trulia, a real estate website, can use a single visualization to display

many data sets like crime maps, school districts and neighborhood prices etc. Likewise, GE

extensively uses visualization in its strategic information systems to reach its goals because it

makes data comprehension easier for senior executives. Thus, visualization driven insights

would be a commonplace.

Improvements in raising status quo bar of existing technologies

Academia research, Company collaborations and events and annual summits like Big Data &

Analytics for Retail Summit etc would constantly improvise on existing big data and related

14BIG DATA FOR RETAIL

technologies thereby raising status quo bar. For instance, the key themes of 2014 summit will be

relating to areas like Customer Engagement, E-Commerce, Consumer Insight, Data Analytics,

Web Analytics, and CRM etc.

Conclusion

“Retail firms could increase operating margins by up to 60% for those pioneers that maximize

their use of Big Data.” — McKinsey

The retail industry is different from other industries in that it has intrinsic structure to capture

huge volumes of data. It is important for companies to understand the importance of this data and

apply analytics in a sensible way to achieve positive improvement. Some of the key learning

have created retailers to optimize their cost of operation immensely. While the data analytics has

helped the operations in a positive way, an oversupply of analytical surge has also caused

detrimental results for firms. Most of these failure scenarios are attributed to cognizant or

nonsensical application of correlative relationships to arrive at insights. More problems arise

through reporting of such insights. Key issues dangling around today, discussed above have been

confirmed by multiple retailers, confirming the veracity of these issues.

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Our observations indicate that data analytics on the operational front has immense

potential to improve and bring about a structure to the operations. Most retailers in developing

countries that collect/have started collecting information about shopper behavior and personal

information have to consider these insights and escape this curse of analytics.

Big data enables firms to gather finer details about data that they already have. This

includes customer data as well. Usage of customer data has to be in line with the privacy policies

promised to the customer. Any violation can be detrimental to the business [9]. Questions that

retailers face today on customer privacy have to be dealt without prejudice keeping customers at

the center of the business. This must be despite the fact that legal restriction are in place or not.

References

1. Big data – Calculating ROI

http://www.bigdataretailforum.com/FormDownloadThankYou.aspx?target=http://

www.bigdataretailforum.com/media/7832/14512.pdf&eventid=7832&m=14512#

2. Connecting consumer experience across all retail touch points

http ://www.retailbiz.com.au/2013/03/11/article/Opportunities-along-the-digital-breadcrumb-

trail/GJNDBBKEAL

3. Big data uses for retail

− http://www.ngdata.com/solutions/solutions-and-use-cases-for-retail-companies /

− http :// wikibon.org/wiki/v/Big_Data_in_the_Retail_Industry

4. Retail efficiency using big data

http:// cdn2.hubspot.net/hub/173001/docs/boosting_retail_revenue_wpp.pdf?t=0

5. Personalizing in the world of Omnichannel retailing http ://in.teradata.com/retail/?

LangType=16393&LangSelect=true

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6. http :// www.slideshare.net/srinivasbobby/big-data-trends-in-retail-industry-31724077

7. http ://www.forbes.com/sites/barbarathau/2014/01/24/why-the-smart-use-of-big-data-will-

transform-the-retail-industry /

8. Result of IBM Retailers survey, originally Published January 13, 2014 on Retail-

Merchandiser.com

9. The dark side of customer analytics : A HBR Case Study

10. Video Analytics for Retail

http://www.securitymagazine.com/ext/resources/2012/September-2012/Retail-Video-

Analytics---feat.doc

11. Volumes of information on big data for retail

− www.bigdataretailforum.com

− http://theinnovationenterprise.com/summits/big-data-analytics-retail-2014-

chicago#sthash.odh83E3y.dpuf

12. Ideas economy: Finding value in big data

http://www.oracle.com/us/technologies/big-data/finding-value-in-big-data-1991047.pdf?

ssSourceSiteId=ocomin

13. Gartner research reports

14. CMAC at McKinsey

http://www.mckinsey.com/client_service/retail/expertise/~/media/mckinsey/dotcom/

client_service/retail/articles/cmac_creating_competitive_advantage_from_big_data.ashx