Big Data

June 2012

A New Retail Paradigm: Solving Big Data to
Enhance Real-Time Retailing
Data from Aberdeen’s October 2011 report, Business Intelligence Analyst Insight
Enhancements in Retail, indicates that for 62% of retailers, escalating big data- Aberdeen’s Insights provide the
related complexities within their enterprises makes day-to-day decision- analyst's perspective on the
making and creating a single view of the product and customer an arduous research as drawn from an
task. The problem is not just data aggregation but also lack of real-time aggregated view of research
access to customer and business information. This impedes customer- surveys, interviews, and
centricity and business process continuity. Another roadblock for retailers data analysis
is also the volume, sources, complexity, and velocity of data. Aberdeen's Big Data in Retail Defined
latest April 2012 survey of 50 retail enterprises shows that 70% of retailers Big data in retail and consumer
are currently grappling with, on average, at least eight disparate sources of markets refers to the overall
business and customer data (both structured and un-structured) within their size or extent of active data an
organization. Such data variability fluctuates quite a bit due to seasonality, organization stores, as well as
number of Stock Keeping Units (SKUs), and types of customers. the size of the data sets it uses
for its business intelligence and
The collection and analysis of customer and business data, from its raw form analysis. Big data is also used to
of analytical data to its polished form of predictive Business Intelligence (BI) describe the common
helps to increase precision and real-time retailing. This includes: product difficulties associated with this
innovation, supply chain, pricing, customer engagement, promotions and active data: size or extent
marketing, and other value chain areas. The benefits associated with real- (storing and accessing the data),
time and precision retailing can be realized at every stage of the cross- speed (how fast the data must
channel retail lifecycle - from product design stage to customer fulfillment, be captured, processed,
and loyalty creation. This Analyst Insight addresses the aforementioned analyzed and delivered),
complexity (the sophistication
complexities and benefits, and identifies a best practices roadmap that
and level of detail in the data
enables companies to apply big data initiatives for real-time customer analysis), and types (the
engagement and agile operations. Four main issues are also addressed: number of different formats the
• Cross-channel impact of big data data takes).

• Consumer pressures and organizational challenges surrounding big
data
• Capabilities and enablers to tame big customer and business data
• Actionable recommendations for overcoming big data complexities

The Cross-Channel Impact of Big Data
For today's consumer, who has multi-faceted channel and shopping
preferences, retailers need to be prepared at all times to provide one view
of the customer and product across all channels. However, this has not
been easy for a majority of retailers. The need for addressing big data is a

This document is the result of primary research performed by Aberdeen Group. Aberdeen Group's methodologies provide for objective fact-based research and
represent the best analysis available at the time of publication. Unless otherwise noted, the entire contents of this publication are copyrighted by Aberdeen Group, Inc.
and may not be reproduced, distributed, archived, or transmitted in any form or by any means without prior written consent by Aberdeen Group, Inc.

A New Retail Paradigm: Solving Big Data to Enhance Real-Time Retailing
Page 2

cross-channel challenge and a transformation need for retailers. Consider
the following trends:
• The rise in digital retailing. Online (used by two-thirds of
retailers) and mobile commerce (used by one-third of retailers)
have given consumers increased amounts of product information
and ease of access to competitive alternatives. For instance,
smartphone-based UPC scanning capabilities, as well as mobile
search engine accessibility, has allowed both new and existing
customers to closely examine product price and details to make a
more immediate and informed decision within and outside the four
walls of a store. Retailers are challenged to compete with this reality
by offering a more personalized, digital retailing experience or lose
out to a competitor.
• In-store transformation. The proliferation of retail categories in "Impact is more from lack of
non-traditional retail formats (such as Wal-Mart’s in-store banking, analysis / learning from big data
optometry, and hair salon offerings) pressure these organizations to than from data issues
further scrutinize their customer base to match established themselves."
purchase patterns with new purchase patterns. Moreover, multiple
~Vice-President, Logistics,
store formats appeal to product affinity and preferences of multiple
Large Apparel Retailer, North
customer segments. Customer segmentation requires re-thinking of
America
existing store models, precision merchandising, and inventory
localization requirements.
• Voice retailing integration. The increased use of voice retailing
by a third of retailers provides not just another channel sales avenue
but also valuable information about customer experience before,
during, and after a sale. This information yields important clues
about future purchasing patterns across all channels. A stated focus
on electronics, for example, may yield success in the cross-selling of
extension cords, batteries, and other accessories online or in the
store.
• An extended supply chain. Two-thirds of retailers are far from
creating a unified view of product and customer data across all
channels to understand category-level affinity and preferences. A
unified view of product, order management, and customer data also
aids accurate and timely supply chain planning and logistics to deliver
the right product, at the right place, at the right time. Aberdeen's
March 2012 Best-in-Class Strategies to Overcome Disconnected
Customer Experience report indicates that only a third of retailers
overall are sharing customer and product information across all
channels to create one view of the product and customer. Upon
taking a deeper look, retailers find that creating a customer-centric
and localized assortment-mix (71%), shelf-level inventory
optimization (65%), and product innovation (60%) are the most
affected value chain competencies due to big data issues. This means
that while retailers want to be more customer-centric, addressing
big data issues is "front and center" in the way of cross-channel
customer-centric retailing.
© 2012 Aberdeen Group. Telephone: 617 854 5200
www.aberdeen.com Fax: 617 723 7897

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Need for Increased Consumer Insights is Paramount
As detailed in the previous section, as customer shopping options and
channels proliferate, 59% of retailers are compelled to respond to the need
for creating granular consumer insights in areas such as; cross-channel
buying behavior, share of wallet, market basket analysis, and segmentation
strategies (Figure 1).

Figure 1: Lack of Consumer Insights is a Top Market Pain-Point

Need to increase overall consumer
59%
insight
Need to improve speed of access to
45%
relevant business data
Need to move beyond data integration
28%
stage
Need to improve data accessibility for
22%
customer-facing employees
Improve ease-of-use of BI for non-
18%
technical employees

0% 10% 20% 30% 40% 50% 60% 70%
Percent of Respondents
Source: Aberdeen Group, April 2012

More often than not, retailers blame disparate data sources and the Variety of Different Data
enormity of active customer data as the primary reason for lack of adequate Formats- Big Data in Retail (by
and timely consumer insights that inhibits new customer acquisition, % of respondents)
customer retention, and re-activation. Currently, the total amount of active
(non-archive or backup) business data that retailers store is between 1TB √ Pricing data- 68%
and 25 TB for 38% of retailers, and another 21% store significantly higher √ Point-of-sale transaction data
amounts of business data. (in-store, online, call center,
and other channels)- 65%
One of the most fundamental challenges for retailers is revenue growth
despite any economic climate, positive or negative. To accomplish this goal: √ Supplier community
business-to-business data
• 81% of retailers are relying on increased customer insight for new (e.g. EDI)- 65%
customer acquisition
√ Shipping data- 55%
• 75% are increasing efforts to derive additional value from existing
customers - the challenge, however, is how to accomplish this task √ Text resulting from business
effectively activities- 55%

The enormity of customer data coupled with inadequate guidelines for agile √ Merchandising data- 45%
data-driven insights fuels the inability to conduct timely analysis. This √ Other data sources- 43%
inability in turn curtails effective customer-centric merchandising, marketing,
promotions, supply chain planning and pricing strategies, among other √ Social media data- 39%
critical operational competencies. The question that often perplexes √ Human resources data- 30%
retailers is how to accurately analyze customer data and predict customer

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behavior in order to provide timely updates for retail business leaders,
departmental heads, managers and associates.
The second highest business pressure according to 45% of retailers is
related to faster access to business information. More than a fourth (28%)
of all retailers indicated that there is a lag time of at least "a week" between
the time they receive critical actionable operation information and the
actual business events. For instance, delayed reporting of inventory activity
can severely hinder timely on-the-shelf response to customers, suppliers, or
internal stakeholders. This in turn hampers the pace of new retail initiatives,
business transformation, and recovery strategies that turnaround a poor
sales cycle. Moreover, growing hyper-competitiveness on the shelf, has led
to the need for better time-to-information, time-to-decision, and improved
enterprise-wide visibility towards Key Performance Indicators (KPIs).
Another top pressure is related to the inability to move processes beyond
the data integration stage toward departmental and user-level access,
analysis, and reporting. This need for on-demand self-service reporting and
data visualization is not just required at corporate headquarters but also
down to the channel or store-level. Aberdeen's April 2012 retail big data
and analytics survey indicates that 66% of retailers are unable to provide
uniform self-service reporting and data access capabilities that are otherwise
available to the core super user team. For instance, customer-facing
employees need readily accessible real-time sales and service performance
reporting, customer order history, real-time inventory on-hand data access,
product information, cross-selling and up-selling data, among other
resources.
This information enables store or channel-level employees to assist
customers in the best possible way and complete the customer experience
process in an effective way. However, only 25% of retailers indicate that
they have uniformly executed downstream information access among
"Too much unstructured data
customer-facing employees. This has hurt in-store customer engagement
causes delays in compiling
culture the most. Other channel associates (e.g. online or call center agents) actionable information in
who are not necessarily customer-facing, do have access to at least some needed time frames. This
web-based product information that store employees often lack at the relates to CRM, customer
Point-of-Service (POS). data/view; competitive analysis;
social engagement; product line
Organizational Challenges evaluation and sales
promotional programs."
Data from the January 2012 Omni-Channel Retail Experience report shows
that 48% of retailers store customer and business data in two to five ~Vice-President, Marketing,
disparate systems. Another 20% of retailers store data in six to 15 distinct Mid-Market Retailer, North
systems. Relevant customer and business data resides in operational silos America
leading to data duplication, batch processing, and delays associated with
structured and unstructured data integration with other business systems
such as: POS, Customer Relationship Management (CRM), marketing
management, promotions, pricing, inventory management, etc.
As shown in Figure 2, companies find structured and unstructured data
integration with other systems most challenging. These companies are also

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most likely to experience "delayed time-to-information" and "slower time-
to-decision" among customer-facing and non-customer-facing employees.
Structured data sources in retail relate to POS, supply chain, pricing,
shipping data, etc. Unstructured data relates to text resulting from business
activities, data from social channels, and other data sources.

Figure 2: Top Challenges

Lack of structured / unstructured data integration
35%
with business systems

Legacy processes and systems 32%

Little or no expertise related to analyzing large
29%
amounts of data

Too much unstructured data 29%

Lack of data analysis mandate 26%

0% 5% 10% 15% 20% 25% 30% 35% 40%
Percentage of Respondents


Secondly, for 32% of companies, business/customer data management and
related intelligence is fraught with legacy system obstacles. Multi-
generational and legacy processes and systems hinder the advancement of "Systems have improved and
ancross-channel customer experience. Unless channel data is centralized this has led to better customer
information being available. This
and shared in real-time, there is little chance of timely coordination
has helped us sustain a good
between channels. Often, the end result is duplicated efforts, duplicated performance despite the
data, and incremental time and money spent on duplicate customers and economic and other natural
processes. disasters impacting our industry
in the last year."
The line-of-business and IT executives in retail must seek to address unified
big data management in multi-tier, multi-site, and multi-channel user ~ Director, Marketing, Large
organizations. Multi-generational and legacy technology applications do not Consumer Electronics Retailer,
allow organizations to remain agile enough to meet the changing needs and Asia-Pacific Region
desires of their customers. Instead, the users of these legacy technologies
are saddled with out-of-date technology capabilities, and as a result, an out-
of-date and out-of-touch approach to the cross-channel customer
experience. A related challenge facing 29% of companies is scant expertise
within IT teams to handle large amounts of data. As more and more
companies deem IT as a cost center, adequate human resource talent and
associated expenditure is a constant headache for executives.
This is despite the fact that 88% of retailers expect the fastest big data
initiative ROI from agile business forecasting value and agile business

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execution value. According to Aberdeen's analysis, the disconnect in what
companies want from data insights and their actions, lies in the fact that
nearly half (42%) of big data decisions are still taken by the CIO, the next
closest job-role associated with big data-related decision making is the
CMO (13%).
Somehow, retailers have kept big data and business intelligence-related
process and system improvement decisions non-collaborative, where IT and
line of business do not see eye-to-eye. However, this process of collective
data and BI decision-making needs to be reversed for establishing usage and
access equilibrium.

Realized and Unrealized Benefits of Big Data Strategies
The four leading areas where retailers expect big data initiative ROI include:
business execution information; transparent sales forecasting; product and
customer service innovation; predictive product innovation and customer
service capabilities (see first four rows of Table 1).
However, the realized gains have been in the teens and low double-digits at
best in the aforementioned areas. In fact, the bottom three areas for
expected ROI, namely, performance information, deeper customer
segmentation, and one view of product information have seen better
comparative realization of actual gains from big data initiatives.

Table 1: Expected Benefits vs. Actual Benefits of Big Data
Initiative
Data Summary Expected Actual
Agile business execution value as 90% 23%
information is easily available
Improved product and service 89% 22%
innovation
Agile business forecasting value as 87% 19%
information is transparent
Enhanced predicting capabilities 86% 17%
related to product and customer
problems
Detailed performance information 79% 36%
available for rectifying errors
Possibilities for deeper customer 77% 42%
segmentation
Assistance with development of one 72% 34%
view of product information

The reasons are short-term vs. long-term realized gains. Retailers applied
better organizational focus when it comes to the easiest and fastest route to
big data investment justification. In the last two years, more than a third of

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companies focused on big data initiatives that are geared towards customer
segmentation for tactical business objectives, internal employee and external
trading partner/supplier performance management, and centralized product
information management due to expansive cross-channel needs. Business
execution correction, product/service innovation, and predictive capabilities
were delayed, getting pushed into the category of "long-term aspirational
gains" or "long-term roadmap goals." Retailers show low levels of process
maturity in handling complex and real-time big data models that can be
geared towards accurate forecasts and predictive sales and operations. The
value of business forecasting and predictive sales and operations is
undeniable. For instance, in the area of predictive capabilities, two key
"Detailed knowledge of how
process capabilities have emerged as top strategies retailers are focusing on customers perceive our
in the immediate future: products, our services, our
promotions, and the brands in
• Predict customer purchasing behavior (66% of retailers planning,
all channels give us the most
19% current) important facts to decide how
• Real-time analysis based on segmentation, affinity, and preference to be closely personal with our
(64% of retailers planning, 25% current) customers."
~Director of Marketing, Large
Big Data Capabilities Specialty Retailers, North
America
So how can retailers maximize gains from big data initiatives described in
the previous section? The next two sections address key ways in attaining
benefits from big data initiatives.
To execute a cross-channel big data strategy within retail, enterprises must
develop a solid foundation of business-to-consumer process, organizational,
knowledge, and performance management capabilities.
The top three currently deployed capabilities relate to setting-up guidelines
for data gathering, security, and external sharing of data with business
partners/suppliers (Table 2). Guidelines are required as not all departments
are alike when it comes to the role of solving big data aggregation, analysis,
and access. The capabilities that are critical for laying out common
guidelines include: data access, coding, cubing, querying, security, and job-
role based reporting need to be presented via a common set of data
presentation in varied formats of data delivery tools. The disparate analytics
presentation formats (i.e. dashboards vs. spreadsheets) lead to lack of a
unified view of the brand, customer, and day-to-day operations.
For the above reasons, big data and BI-related processes require adequate
IT expertise, and line of business collaboration to solve big data analyses,
quantitative / statistical analytics or dashboards and drill downs. Only a third
of retailers possess the IT and line of business expertise today to address
big data, however, 55% of retailers plan to adopt these capabilities in the
foreseeable future. If internal resources are inadequate or cost prohibitive,
then companies can turn towards managed and outsourced services for
integrating structured and un-structured data with customer-facing and
back-end systems. This can create a homogenous way of treating the big
data and lack of consumer/business insights in a cost-effective manner. The


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April 2012 retail big data and analytics survey indicates that 36% plan to use
IT / systems integrator consulting services within two years. In fact, within
the next 24 months, some of the leading retail data and infrastructure -
related planned technology improvements for companies that aspire to
become Best-in-Class include delivery models such as: managed/outsourced
services (33%), and cloud services (36%).

Table 2: Current and Planned Process and Organization
Capabilities
Data Summary Currently Use Plan to Use
Established data gathering and assembly 54% 43%
guidelines
Guidelines for external data sharing (e.g. 52% 30%
EDI) with suppliers and trading partners
Guidelines for data security, privacy, 48% 48%
and consumer / client rights protection
Alignment of new product releases with 21% 59%
customer preference and affinity
Job-role based access to customer 36% 49%
behavior and purchase trends
IT expertise to solve Big Data analyses, 31% 55%
quantitative / statistical analytics or
dashboards and drill downs
The ability to provide performance data 15% 55%
at the associate level

In studying the varied cases of big data initiatives in retail organizations,
Aberdeen's analysis indicates that retailers need an enterprise-wide big data
strategy. These companies must apply an enterprise-wide strategy if they
want to see customer and business dynamics through the same prism in
order to scale, differentiate, and grow in these challenging times.
Finally, as seen in Table 3, as companies embark upon an enterprise-wide big
data complexity solving mission, it is important to take into consideration
the extent of real-time data capture (from varied sources) capabilities that
companies currently possess or plan to use in the future. These capabilities
most likely impact "time-to-information" and "time-to-decision" goals as
companies also need to ensure rapid data processing and intelligence so that
all departments and teams have an equal measure of real-time customer
needs, response times, collaborative, and performance improvement
requirements.
For instance, retailers not only need to capture POS data in real-time across
channels but also drive real-time promotions to customers by analyzing POS
and loyalty data so that channels can benefit from real-time offers and
customer mapping. The real-time nature or velocity of data capture,


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processing, analysis, and reporting depends on several factors such as
database processing, data mining grids, in-memory computing processes, etc.
We will explore some of these technology enablers in the next section.

Table 3: Knowledge Capabilities
Data Summary Currently Use Plan to Use
Real-time customer data capture at the 55% 29%
point of service (POS)
call center
website
headquarters
Real-time customer data capture within 27% 54%
online communities

Technology Enablers
There are four broad categories of big data complexity-solving enablers sub-
divided in four broad groups: size or extent (storing and accessing the data);
speed (how fast the data must be captured, processed, analyzed and
delivered); complexity (the sophistication and level of detail in the data
analysis), and types (the number of different formats the data takes).
For addressing data size or extent needs, on average a third of
retailers indicate usage of distributed databases, data integration tools,
enterprise data warehouses, distributed file systems, cloud computing data
center tools, among other solutions that support data aggregation and
assembly.
From a data speed and complexity standpoint, retailers currently
indicate affinity towards real-time enterprise-level data processing and
intelligence tools such as in-memory computing processes/analytics, cloud
computing data delivery models, and Massively Parallel Processing (MPP)
databases. At least a third of retailers plan to invest in these tools in the
near future.
As shown in Table 4, retail databases initiatives for real-time customer
engagement and agile operations can be supported through the use of in-
memory computing processes. These tools help support real-time data
processing and delivery of intelligence as in-memory computing removes the
latency factor of storing and accessing from multiple disks, on multiple
computers that are installed across multiple retail store, channel or
headquarter locations. In-memory processes help move data and intelligence
faster than other processes as in-memory processes move data from
different computers to the central memory location.


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Data from Aberdeen's April 2012 retail big data and analytics survey
indicated that companies that have adopted in-memory computing
processes are two-times more likely to experience real-time operational
information availability, and as a result, faster decision making compared to
retailers that do not use in-memory computing. Even in the area of retail
data processing and intelligence-related complexity, our data shows that in-
memory computing processes/analytics and MPP support close to actual
business activity availability of information.
The real-time multi-location data processing capability of in-memory
computing can be of immense value as at least 50% of retailers are still
executing overnight or delayed polling of POS data for various types of
customer and business analyses. In fact, in-memory computing can enable
faster and more real-time access to customer and business information in
the following areas:
1. One view of the customer through segmented customer purchase
behavior, affinity, and preferences-related insights for optimized
assortments, real-time pricing management and promotions
management
2. Easier mining and granular shelf-level insights provide deeper
merchandising insights for category optimization, in-stock, and
store/channel product sell-through strategies
3. Creating one view of product, inventory, and order management
data-from design stage to customer fulfillment/delivery
4. Solve retail supply chain big data with improved product visibility,
data exchange, and supplier collaboration

Table 4: Enablers
Data Summary Currently Use Plan to Use "Our greatest big data
complexity is difficulty in
In-memory computing 35% 36% matching strategy to actions
processes/analytics and outcomes. It is very difficult
Data cleansing tools 24% 55% to set the right KPIs and even
more difficult to measure
Customer segmentation application 32% 52% them."
Source: Aberdeen Group, April 2012 ~ Senior Executive, SMB
Retailer, Asia-Pacific Region
Finally, in terms of types or formats (the number of different
formats the data processing and intelligence takes), departmental
and store-level data access, viewing, and analysis capabilities are also
important, and this is where the concepts of dashboards and scorecards
come into play. Data from the April 2012 retail big data and analytics survey
indicates that at least half of the companies plan to use dashboards for
multiple departments and functions. Real-time data processing via in-
memory computing can help support faster data uploads to the enterprise
dashboards and scorecards.


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Conclusion
The enormity of data coupled with lack of adequate guidelines for agile data- Big Data Demographics
driven insights fuels the inability to conduct timely analysis. This inability in Of the responding retail
turn curtails effective retail planning and execution within: customer-centric organizations, demographics
merchandising, marketing, promotions, supply chain planning and pricing include the following:
strategies, among other critical customer value chain areas.
√ Job title: Senior Management
Few retailers would argue that a difficult economic recovery requires new (23%); EVP / SVP / VP (11%);
and creative ways of reaching customers to offer products and services. Director (11%); Manager
Most of these creative ways depend on a closer, more intimate (26%); Consultant (20%);
understanding of consumer activity at all touch points to personalize the Other (9%)
shopping interaction. This is for the benefit of the retailer in the form of √ Department / function: Sales
increased cross-sells, up-sells and consumer loyalty. It is also for the benefit and Marketing (30%); IT
of the customer in the form of a more direct, informed, and relevant (7%); Business Management
experience to decrease the time needed for product searches and overall (19%); Operations (6%);
interaction steps. Logistics (15%);
Procurement (11%); Other
In order to realize these benefits, however, retailers must rely on solving big (12%)
data issues to help guide this personalized selling experience goal into
√ Segment: Consumer markets
fruition. This can start with data collection processes at, for example, the
(25%); Retail/Apparel (15%);
POS, continue into a predictive analytical model, and end with increased Software (17%); Automotive
business intelligence for a dynamic, macro and micro view of customer and (6%); Food and Beverage
business operations at all levels in the retail enterprise. In a challenging (6%); Other (31%)
economy, such insight can be a competitive differentiator for a more
satisfied and profitable existing and new customer base. √ Geography: North America
(67%); APAC region (14%)
The end use of big data is not defined as mere reporting or analytics-related and EMEA (19%)
capabilities but what companies actually do with big data initiatives, i.e.
√ Company size: Large
finding solutions for filling business gaps and addressing customer process
enterprises (annual revenues
complexities. This involves the ability to access information affecting the above US $1 billion)- 40%;
entire business as the data is created from multiple sources. This can involve midsize enterprises (annual
one or multiple sets of data sources, and can affect one or many sets of revenues between $50
decisions, actions, departments and people. Retail organizations that take a million and $1 billion)- 17%;
strategic approach to enterprise big data complexities and the access to and small businesses (annual
relevant data - when, how, and where people need it - will be better revenues of $50 million or
positioned to achieve organizational success. One of the ways to alleviate less)- 43%
data and intelligence latency is via in-memory computing that helps remove
the latency factor of storing and accessing from multiple disks, on multiple
computers, across multiple locations, which is very common in retail. In-
memory processes help move data and intelligence faster from multiple
locations than other processes as in-memory processes move data from
different computers to the central memory location.

Key Takeaways
The following are some recommendations that can be applied by end-users
to help alleviate big data and BI-related complexities:
• Develop a robust relationship between line of business needs for
customer analytics and IT to increase operational visibility. To


Page 12

maximize the ROI from big data solutions, retailers should be able
to trace the need for increased customer insights to a retailer's
number one reason for existence: selling a product and increasing
revenue. From a customer-centric retailing standpoint, companies
need to invest in providing access to real-time customer purchase
affinity, preferences, and segmentation data across; procurement,
finance, marketing, merchandising, pricing, promotions, supply chain,
and other departments. Enterprise-wide consumer insights have the
potential to transform the assortment-mix towards a level of
precision that can increase customer recency and frequency in
increasingly competitive retail environment.
• Create a roadmap for addressing complex unstructured and
structured data integration with business systems so that
enterprise-wide data processing and intelligence can be streamlined.
Take into account all unstructured data streams including new
customer interaction channels (such as social networking data).
• Provide deeper business insights to employees for improving
customer, inventory, and merchandise assortment-related decision
making. Real-time customer/business data intelligence reporting and
delivery enables retailers to develop a knowledge-driven culture,
one that encourages rapid decision-making during a typical retail
sales day, week, quarter, and fiscal year.
• Predicting customer purchasing behavior speaks to the very essence
of increased cross-selling and up-selling for retailers, no matter the
channel. If a retailer can understand what type of purchase a
consumer is likely to make, they can not only tailor marketing
efforts to ensure a timely purchase is made, but they can also offer
similar companion products to increase order size at the same time.
• When considering on-premise or hosted end-to-end big data
initiatives, it is vital that retailers create a framework that ties the
top enterprise-wide productivity needs to specific data processing
and intelligence processes such as data gathering, aggregation,
cubing, reporting, and delivery. If on-premise deployment is deemed
difficult to implement, consider managed services/outsourced
services and/or private cloud computing models that address real-
time data processing, intelligence, and delivery options in a
resource-constrained IT environment.
• Consider in-memory computing processes that help support real-
time data processing and delivery of intelligence as in-memory
computing removes the latency factor of storing and accessing from
multiple disks, on multiple computers, across multiple locations,
which is very common in retail.
For more information on this or other research topics, please visit
www.aberdeen.com.


Page 13

Related Research
Best-in-Class Strategies to Overcome State of Multi-Channel Retail Marketing:
Disconnected Customer Experience; A Paradigm Shift for Reaching New
March 2012 Customers; June 2011
Mobile and Tablet Shopping Demystified: State of Customer-Centric Retailing: A
Adoption and the ROI Business Case; Best Practices Guide for Higher Sales,
September 2011 Customer Retention, and Satisfaction;
Early Consumer Insight Delivers Revenue May 2010
Growth Opportunities for Retailers; July
2011
Author: Sahir Anand, VP/Research Group Director,
(sahir.anand@aberdeen.com)
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