SlideShare uma empresa Scribd logo
1 de 8
Baixar para ler offline
IBM Software
Big Data
Retail
Capitalizing on the power
of big data for retail
Adopt new approaches to keep customers engaged,
maintain a competitive edge and maximize profitability
2 Capitalizing on the power of big data for retail
The retail industry is changing dramatically as consumers
shop in new ways. With the growing popularity of online
shopping and mobile commerce, consumers are using more
retail channels than ever before to research products,
compare prices, search for promotions, make purchases and
provide feedback. Social media has become one of the key
channels. Consumers are using social media—and the
leading e-commerce platforms that integrate with social
media—to find product recommendations, lavish praise,
voice complaints, capitalize on product offers and engage in
ongoing dialogs with their favorite brands.
The multiplication of retail channels and the increasing use of
social media are empowering consumers. With a wealth of
information readily available online, consumers are now
better able to compare products, services and prices—even as
they shop in physical stores. When consumers interact with
companies publically through social media, they have greater
power to influence other customers or damage a brand.
These and other changes in the retail industry are creating
important opportunities for retailers. But to capitalize on
those opportunities, retailers need ways to collect, manage
and analyze a tremendous volume, variety and velocity of data.
When point-of-sale (POS) systems were first commercialized,
retailers were able to collect large amounts of potentially
valuable information, but most of that information remained
untapped. The emergence of social media and other
consumer-oriented technologies is now introducing even
more data to the retail ecosystem. Retailers must handle not
only the growing volume of information but also an
increasing variety—including both structured and
unstructured data. They must also find ways to accommodate
the changing nature of this data and the velocity at which is
being produced and collected.
If retailers succeed in addressing the challenges of “big data,”
they can use this data to generate valuable insights for
personalizing marketing and improving the effectiveness of
marketing campaigns, optimizing assortment and merchandising
decisions, and removing inefficiencies in distribution and
operations. Adopting solutions designed to capitalize on this
big data allows companies to navigate the shifting retail
landscape and drive a positive transformation for the business.
Imagining the possibilities
How can solutions for big data help retailers? They can
improve the effectiveness of traditional retail processes by
generating new insights while creating new capabilities that
drive better business outcomes. For example:
•	 Personalized shopping experience: To help serve a customer
shopping for a new TV, a retailer could analyze data from
previous transactions, clickstreams, social media, geospatial
services and other sources to understand the customer’s
preferences and push a highly targeted, real-time promotion on
to the customer’s smartphone as he or she shops in a store.
Retailers can also examine broader customer search patterns,
preferences and purchases to generate meaningful and
interesting offers and suggest complementary products to
provide greater value to the customer while boosting revenues.
•	 Optimized merchandising: A retailer could better determine
which products will sell best through each retail channel, at
each store location and at what price. For example, a retailer
could analyze fast-changing social media buzz about an
upcoming superhero movie to gauge demand for particular
action figures across multiple geographic locations. With
insights into which specific product will sell best in each
location, the retailer can ensure that stores in each area are
well stocked with those products when the movie is released.
Real-time competitive price comparisons can help the retailer
set pricing or launch promotions that attract consumers away
from rival retailers.
Retail 3
•	 Operational excellence: Analyzing communications, traffic
patterns, weather data, political news and consumer demand
signals could help a retailer manage retail distribution
networks in real time to ensure timely delivery of products
and achieve high-quality operational performance.
Creating a personalized shopping experience
Effectively analyzing the large volume and variety of
customer data opens new opportunities to gain a deeper,
more complete understanding of each customer and create a
smarter shopping experience.
What if you could:
•	 Increase the precision of customer segmentation by analyzing
customer transactions and shopping behavior patterns across
all retail channels?
•	 Enrich your understanding of customers by integrating
multichannel data—from online transactions to social media
and third-party data—to develop a 360-degree view of each
individual and identify emerging trends?
•	 Optimize customer interactions by knowing where a customer
is and delivering relevant real-time offers based on that location?
•	 Predict consumer shopping behavior and offer relevant,
enticing products to influence customers to expand their
shopping list?
Marketing teams can use solutions for big data to collect and
analyze customer information from a wider range of sources
than before—including POS systems, online transactions, social
media, loyalty programs, call center records and more. That
information deepens their understanding of customer
preferences, helps them more accurately identify shopping
patterns and enables them to generate more precise customer
segmentation. Marketers can then use new insights to deliver
highly targeted, location-based promotions, in real time.
Email
Text analysis
for pattern
identification
Customer
Demographics,
transactions
and shopping
patterns
Drive marketing optimization
Data
• Customer micro-segmentation
and full 360-degree view
• Additional value and insight
from sentiment analysis
• More accurate satisfaction scoring
• Demographics, transactions
and shopping patterns
• Timely delivery of offers
to customers
Call center
Text and audio
call records
Video
Surveillance,
foot traffic
in store
POS
Transaction
logs
Geospatial
Where is the
customer?
Outcomes
• Reduce marketing cost
• Reduce churn
• Increase visits and
conversion
• Increase customer loyalty
Social media
Customer sentiment
Events
Weather,
local events
Clickstream
Online
activities
The result? Customers gain a richer, more personalized shopping
experience with promotions and offers that are more likely to
appeal to them. Retailers, meanwhile, are able to retain a
competitive edge and boost revenues by maximizing cross- and
up-sell opportunities, as well as consistently engaging customers
across channels and reinforcing their brands at every turn.
Figure 1. Retailers can draw on a wide variety of data—from transaction and
clickstream data to social media and geospatial information—to enhance the
effectiveness of marketing efforts and deliver real-time promotions.
4 Capitalizing on the power of big data for retail
Optimizing merchandising and supply chains
Implementing a scalable big data platform can also help retailers
build smarter supply chains and optimize merchandising across a
multi-channel retail operation.
What if you could:
•	 Predict optimal pricing and maintain a price leadership
position by analyzing price and demand elasticity?
•	 Select the right merchandise for each channel and fine-tune
local assortment planning by drawing on insights from social
media, market reports, internal sales data and customer
buying patterns?
•	 Optimize inventory across multiple channels by using leading
indicators such as customer sentiment and promotional buzz
to anticipate future demand?
•	 Fine-tune store planograms by analyzing customer buying
patterns and purchasing trends?
•	 Improve logistics by using real-time traffic, weather data and
more to re-route shipments and avoid costly delays?
Today many retailers monitor average prices by competitors on
a weekly basis. With solutions for big data, they can conduct
instant, real-time price comparisons of top competitors, tracking
hourly price changes and synchronizing those changes with
demand trends. Retailers can then use new insights to set their
own pricing, initiate discounts and implement competitive
real-time promotions to avoid losing sales—and gain agility.
Figure 2. With better knowledge of competitive pricing and demand trends,
retailers can initiate sales and promotions that help avoid losing business.
Customer
Demographics,
transactions
and shopping
patterns
Data
• Ability to price by channel,
region, time of day
• Ability to move from store
cluster assortments to
individual store assortments
• Integrated execution knowing
customer’s preferred price point,
profit targets, supply and
timely offer delivery
Product
Availability,
location, margins
POS
Transaction
logs
Geospatial
Where is the
customer?
Outcomes
• Increased revenue and
margins
• Improved marketing ROI
• Fewer stock-out situations
and markdowns
• Optimized inventory
• Increased customer
satisfaction
Social media
Customer sentiment
on pricing and demand
Competitors
Product
availability,
hourly price
changes
Events
Weather,
local events
Execute dynamic pricing and create
localized assortments
Retail 5
Enabling operational excellence
In addition to improving marketing and merchandising efforts,
solutions for big data can help retailers realize a variety of
operational goals, from improving labor utilization to
enhancing financial management.
What if you could:
•	 Optimize staffing levels by predicting changes in customer
demand?
•	 Better match employee skills with retail store needs and create
the right incentives to drive strong sales performance?
•	 Facilitate better-informed financial decision making by
drawing on complete, trustworthy and timely data from a
wide array of sources?
•	 Improve fraud detection by analyzing large volumes of
transactions?
A flexible, comprehensive big data platform can play a key role
in improving labor utilization and performance. Many large
retailers rely on historical data to schedule their thousands of
associates and assign those associates to the thousands or
millions of tasks involved in providing a positive shopping
experience. With solutions for big data, retailers draw on
insights from price optimization, assortment planning and
marketing to improve labor scheduling. They can incorporate
employee performance analysis to optimize work assignments
according to skill sets and manage incentives.
Discovering the value of implementing
big data solutions
Leading retailers are already discovering the tremendous value
of implementing solutions designed to analyze, organize and
apply big data.
Delivering a richer multichannel retail experience
with new customer intelligence
Bass Pro Shops—a leading retailer in fishing, hunting, camping
and other recreational activities—capitalized on solutions for
big data to create a richer multichannel retail experience. The
company needed ways to increase retail shopping consistency
across a full range of channels, including its retail store, boat
dealership, Internet, catalog, wholesale, restaurant and resort
channels. The existing enterprise data warehouse could not
provide detailed analytics on individual customers or purchases
across multiple channels.
The company selected an IBM® Customer Intelligence
Appliance, which provides a single view of each customer plus
the capabilities for business intelligence and analytic reporting
on customer behavior. The solution can generate reports in
less than 10 seconds.
Bass Pro Shops can now increase customer satisfaction and
improve loyalty by providing a consistent experience no matter
how customers choose to shop. New customer insights enable
the organization to tailor offers and fine-tune each of the
customer channels to maximize the appeal of products and
drive more sales.
6 Capitalizing on the power of big data for retail
Enhancing analytics to improve merchandising
decision making
A large discount apparel and home fashion company
capitalized on the potential of big data to optimize
merchandising. The retailer needs timely insights on consumer
demand and changing product prices over the course of a
clothing season to purchase the right inventory for its stores.
Unfortunately, the company’s existing analytics solution
required an entire weekend to generate results, leading to
missed supply chain and merchandising opportunities.
The company implemented an IBM Customer Intelligence
Appliance and deployed analytics capabilities to deliver key
insights rapidly to buyers. Because the solution was a pre-
integrated appliance, it was up and running in just weeks,
without requiring excessive IT services.
The solution’s performance enables the company to run queries
20 times faster than before, producing results to some queries in
just seconds. Now 500 employees across the company use the
analytics capabilities to quickly identify new opportunities and
make key merchandising and supply chain decisions.
Expanding customer analytics to optimize marketing,
merchandising and operations
For a global electronics retailer, solutions for big data helped
expand its customer analytics efforts. The company needed to
replace its 13-year-old CRM system, which offered only a
store-centric view of customer patterns, required more than six
weeks to build new models and generated reports too slowly to
keep up with business demands. The retailer needed a solution
that could analyze customer information across a widening
array of customer data, including social media posts and
clickstreams. The goal was to improve customer satisfaction
and loyalty, allow marketers to create personalized offers,
enable merchandisers to optimize assortment and pricing, and
help managers to optimize the placement of in-store displays.
The retailer replaced its existing CRM system with a new
solution that combines an IBM Customer Intelligence
Appliance with SAP software for analytics and reporting. The
company now has a single view of each customer across
channels, plus analytics capabilities to build segmentation
models, score customers and run campaigns in hours.
Figure 3. The IBM Big Data Platform offers an array of integrated capabilities to
address the tremendous volume, variety and velocity of big data.
IBM Big Data Platform
Analytic applications
Applications and
development
Visualization
and discovery
Systems
management
Accelerators
Stream computingApache Hadoop
system
Data warehouse Data exploration
Information integration and governance
Cloud | Appliances | Mobile | Security
BI/Reporting Exploration/
Visualization
Functional
Application
Industry
Application
Predictive
Analytics
Content
Analytics
Retail 7
Marketing and merchandising teams can draw on that single
view of the customer to deliver more personalized offers and
loyalty rewards, fine-tune merchandising for customer
preferences and optimize the store layout. Predictive analytics
capabilities enable the retailer to anticipate the next customer
actions and improve interactions across channels and at each
step of the customer lifecycle.
Creating a data-driven retail enterprise
Offering a broad portfolio of solutions and capabilities, the
IBM Big Data Platform is helping retailers capitalize on the
vast potential for big data in retail. The platform-based
approach allows organizations to leverage their investments in
technologies and skills by allowing them to start with
capabilities for executing one particular use case and easily add
others using the same platform. Pre-integrated capabilities
help accelerate the time to value.
Leading retailers can adopt IBM InfoSphere® BigInsights™ to
collect, process, analyze and manage a large volume and variety
of customer data from multiple sources. They could analyze
everything from transactional data to unstructured social media
data, learning more about customer preferences and future
behaviors. Using IBM InfoSphere Data Explorer would enable
these retailers to rapidly search massive volumes of historical or
unstructured data.
By implementing IBM InfoSphere Streams, retailers can
continuously capture, analyze and cleanse data in motion to
facilitate real-time decision making. A marketing team could
gauge the success of a campaign by analyzing trending topics in
social media. Merchandisers could analyze customer calls,
e-mails and social media posts to assess rapidly changing
demand for particular products by location.
Using the IBM Customer Intelligence Appliance, retailers can
integrate information from multiple retail channels and
customer touch points to build a complete view of each
customer. The more complete data set also enables retailers to
produce more accurate models. Employing predictive analytics
could help better anticipate future behaviors and optimize
customer interactions.
Keeping retail focused on the customer
The multiplication of retail channels is empowering consumers,
providing them with access to more information and new ways
to research, compare, purchase and provide feedback on
products. For retailers, the customer data produced through
these multichannel interactions presents valuable opportunities
to optimize marketing, merchandising and operations.
The IBM Big Data Platform offers a comprehensive array of
capabilities for addressing the growing volume, variety and
velocity of available customer data. Whether they are enabling
one, two or multiple retail processes by analyzing big data,
retailers can implement IBM solutions that help protect existing
investments and allow retailers to scale as needed. With IBM
solutions for big data in place, retailers can build a foundation
that supports a customer-centered, data-driven enterprise that
helps them sustain a competitive edge.
For more information
To learn more about how IBM solutions help you capitalize on
big data, visit:
• ibm.com/bigdata
• ibm.com/smarterplanet/us/en/consumer_advocacy/ideas
© Copyright IBM Corporation 2013
IBM Corporation
Software Group
Route 100
Somers, NY 10589
Produced in the United States of America
January 2013
IBM, the IBM logo, ibm.com, BigInsights and InfoSphere are trademarks
of International Business Machines Corp., registered in many jurisdictions
worldwide. Other product and service names might be trademarks of IBM
or other companies. A current list of IBM trademarks is available on the
web at “Copyright and trademark information” at ibm.com/legal/
copytrade.shtml
This document is current as of the initial date of publication and may be
changed by IBM at any time. Not all offerings are available in every country
in which IBM operates.
The performance data and client examples cited are presented for
illustrative purposes only. Actual performance results may vary depending
on specific configurations and operating conditions.
THE INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS”
WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED,
INCLUDING WITHOUT ANY WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE
AND ANY WARRANTY OR CONDITION OF NON-
INFRINGEMENT. IBM products are warranted according to the terms
and conditions of the agreements under which they are provided.
Please Recycle
IMW14679-USEN-00

Mais conteúdo relacionado

Mais de IBM Software India

Achieving Scalability and Speed with Softlayer
Achieving Scalability and Speed with SoftlayerAchieving Scalability and Speed with Softlayer
Achieving Scalability and Speed with SoftlayerIBM Software India
 
Build your own Cloud & Infrastructure
Build your own Cloud & InfrastructureBuild your own Cloud & Infrastructure
Build your own Cloud & InfrastructureIBM Software India
 
Web version-ab cs-book-bangalore
Web version-ab cs-book-bangaloreWeb version-ab cs-book-bangalore
Web version-ab cs-book-bangaloreIBM Software India
 
Maa s360 10command_ebook-bangalore[1]
Maa s360 10command_ebook-bangalore[1]Maa s360 10command_ebook-bangalore[1]
Maa s360 10command_ebook-bangalore[1]IBM Software India
 
Maa s360 10command_ebook-bangalore
Maa s360 10command_ebook-bangaloreMaa s360 10command_ebook-bangalore
Maa s360 10command_ebook-bangaloreIBM Software India
 
Web version-ab cs-book-bangalore
Web version-ab cs-book-bangaloreWeb version-ab cs-book-bangalore
Web version-ab cs-book-bangaloreIBM Software India
 
White paper native, web or hybrid mobile app development
White paper  native, web or hybrid mobile app developmentWhite paper  native, web or hybrid mobile app development
White paper native, web or hybrid mobile app developmentIBM Software India
 
Buyer’s checklist for mobile application platforms
Buyer’s checklist for mobile application platformsBuyer’s checklist for mobile application platforms
Buyer’s checklist for mobile application platformsIBM Software India
 
Social business for innovation
Social business for innovationSocial business for innovation
Social business for innovationIBM Software India
 
The Forrester Wave - Big Data Hadoop
The Forrester Wave - Big Data HadoopThe Forrester Wave - Big Data Hadoop
The Forrester Wave - Big Data HadoopIBM Software India
 
Forrester Wave - Big data streaming analytics platforms
Forrester Wave - Big data streaming analytics platformsForrester Wave - Big data streaming analytics platforms
Forrester Wave - Big data streaming analytics platformsIBM Software India
 
Analytics - The speed advantage
Analytics - The speed advantageAnalytics - The speed advantage
Analytics - The speed advantageIBM Software India
 
The next generation data center
The next generation data centerThe next generation data center
The next generation data centerIBM Software India
 

Mais de IBM Software India (20)

Achieving Scalability and Speed with Softlayer
Achieving Scalability and Speed with SoftlayerAchieving Scalability and Speed with Softlayer
Achieving Scalability and Speed with Softlayer
 
Build your own Cloud & Infrastructure
Build your own Cloud & InfrastructureBuild your own Cloud & Infrastructure
Build your own Cloud & Infrastructure
 
Web version-ab cs-book-bangalore
Web version-ab cs-book-bangaloreWeb version-ab cs-book-bangalore
Web version-ab cs-book-bangalore
 
Maa s360 10command_ebook-bangalore[1]
Maa s360 10command_ebook-bangalore[1]Maa s360 10command_ebook-bangalore[1]
Maa s360 10command_ebook-bangalore[1]
 
Maa s360 10command_ebook-bangalore
Maa s360 10command_ebook-bangaloreMaa s360 10command_ebook-bangalore
Maa s360 10command_ebook-bangalore
 
Web version-ab cs-book-bangalore
Web version-ab cs-book-bangaloreWeb version-ab cs-book-bangalore
Web version-ab cs-book-bangalore
 
White paper native, web or hybrid mobile app development
White paper  native, web or hybrid mobile app developmentWhite paper  native, web or hybrid mobile app development
White paper native, web or hybrid mobile app development
 
Buyer’s checklist for mobile application platforms
Buyer’s checklist for mobile application platformsBuyer’s checklist for mobile application platforms
Buyer’s checklist for mobile application platforms
 
SoftLayer Overview
SoftLayer OverviewSoftLayer Overview
SoftLayer Overview
 
Standing apart in the cloud
Standing apart in the cloudStanding apart in the cloud
Standing apart in the cloud
 
Social business for innovation
Social business for innovationSocial business for innovation
Social business for innovation
 
Liking to leading
Liking to leadingLiking to leading
Liking to leading
 
Focus on work. Not on inbox
Focus on work. Not on inboxFocus on work. Not on inbox
Focus on work. Not on inbox
 
The Forrester Wave - Big Data Hadoop
The Forrester Wave - Big Data HadoopThe Forrester Wave - Big Data Hadoop
The Forrester Wave - Big Data Hadoop
 
Forrester Wave - Big data streaming analytics platforms
Forrester Wave - Big data streaming analytics platformsForrester Wave - Big data streaming analytics platforms
Forrester Wave - Big data streaming analytics platforms
 
Analytics - The speed advantage
Analytics - The speed advantageAnalytics - The speed advantage
Analytics - The speed advantage
 
The Future Data Center
The Future Data CenterThe Future Data Center
The Future Data Center
 
The next generation data center
The next generation data centerThe next generation data center
The next generation data center
 
The road to hybrid computing
The road to hybrid computingThe road to hybrid computing
The road to hybrid computing
 
The Individual Enterprise
The Individual EnterpriseThe Individual Enterprise
The Individual Enterprise
 

Último

TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxDwiAyuSitiHartinah
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptaigil2
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best PracticesDataArchiva
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?sonikadigital1
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerPavel Šabatka
 
YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.JasonViviers2
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxVenkatasubramani13
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...PrithaVashisht1
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)Data & Analytics Magazin
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionajayrajaganeshkayala
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introductionsanjaymuralee1
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Vladislav Solodkiy
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024Becky Burwell
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructuresonikadigital1
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityAggregage
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationGiorgio Carbone
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Guido X Jansen
 

Último (17)

TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptxTINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
TINJUAN PEMROSESAN TRANSAKSI DAN ERP.pptx
 
MEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .pptMEASURES OF DISPERSION I BSc Botany .ppt
MEASURES OF DISPERSION I BSc Botany .ppt
 
5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices5 Ds to Define Data Archiving Best Practices
5 Ds to Define Data Archiving Best Practices
 
How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?How is Real-Time Analytics Different from Traditional OLAP?
How is Real-Time Analytics Different from Traditional OLAP?
 
The Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayerThe Universal GTM - how we design GTM and dataLayer
The Universal GTM - how we design GTM and dataLayer
 
YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.YourView Panel Book.pptx YourView Panel Book.
YourView Panel Book.pptx YourView Panel Book.
 
Mapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptxMapping the pubmed data under different suptopics using NLP.pptx
Mapping the pubmed data under different suptopics using NLP.pptx
 
Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...Elements of language learning - an analysis of how different elements of lang...
Elements of language learning - an analysis of how different elements of lang...
 
AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)AI for Sustainable Development Goals (SDGs)
AI for Sustainable Development Goals (SDGs)
 
CI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual interventionCI, CD -Tools to integrate without manual intervention
CI, CD -Tools to integrate without manual intervention
 
Virtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product IntroductionVirtuosoft SmartSync Product Introduction
Virtuosoft SmartSync Product Introduction
 
Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023Cash Is Still King: ATM market research '2023
Cash Is Still King: ATM market research '2023
 
SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024SFBA Splunk Usergroup meeting March 13, 2024
SFBA Splunk Usergroup meeting March 13, 2024
 
ChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics InfrastructureChistaDATA Real-Time DATA Analytics Infrastructure
ChistaDATA Real-Time DATA Analytics Infrastructure
 
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for ClarityStrategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
Strategic CX: A Deep Dive into Voice of the Customer Insights for Clarity
 
Master's Thesis - Data Science - Presentation
Master's Thesis - Data Science - PresentationMaster's Thesis - Data Science - Presentation
Master's Thesis - Data Science - Presentation
 
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
Persuasive E-commerce, Our Biased Brain @ Bikkeldag 2024
 

Capitalising on the power of big data for retail

  • 1. IBM Software Big Data Retail Capitalizing on the power of big data for retail Adopt new approaches to keep customers engaged, maintain a competitive edge and maximize profitability
  • 2. 2 Capitalizing on the power of big data for retail The retail industry is changing dramatically as consumers shop in new ways. With the growing popularity of online shopping and mobile commerce, consumers are using more retail channels than ever before to research products, compare prices, search for promotions, make purchases and provide feedback. Social media has become one of the key channels. Consumers are using social media—and the leading e-commerce platforms that integrate with social media—to find product recommendations, lavish praise, voice complaints, capitalize on product offers and engage in ongoing dialogs with their favorite brands. The multiplication of retail channels and the increasing use of social media are empowering consumers. With a wealth of information readily available online, consumers are now better able to compare products, services and prices—even as they shop in physical stores. When consumers interact with companies publically through social media, they have greater power to influence other customers or damage a brand. These and other changes in the retail industry are creating important opportunities for retailers. But to capitalize on those opportunities, retailers need ways to collect, manage and analyze a tremendous volume, variety and velocity of data. When point-of-sale (POS) systems were first commercialized, retailers were able to collect large amounts of potentially valuable information, but most of that information remained untapped. The emergence of social media and other consumer-oriented technologies is now introducing even more data to the retail ecosystem. Retailers must handle not only the growing volume of information but also an increasing variety—including both structured and unstructured data. They must also find ways to accommodate the changing nature of this data and the velocity at which is being produced and collected. If retailers succeed in addressing the challenges of “big data,” they can use this data to generate valuable insights for personalizing marketing and improving the effectiveness of marketing campaigns, optimizing assortment and merchandising decisions, and removing inefficiencies in distribution and operations. Adopting solutions designed to capitalize on this big data allows companies to navigate the shifting retail landscape and drive a positive transformation for the business. Imagining the possibilities How can solutions for big data help retailers? They can improve the effectiveness of traditional retail processes by generating new insights while creating new capabilities that drive better business outcomes. For example: • Personalized shopping experience: To help serve a customer shopping for a new TV, a retailer could analyze data from previous transactions, clickstreams, social media, geospatial services and other sources to understand the customer’s preferences and push a highly targeted, real-time promotion on to the customer’s smartphone as he or she shops in a store. Retailers can also examine broader customer search patterns, preferences and purchases to generate meaningful and interesting offers and suggest complementary products to provide greater value to the customer while boosting revenues. • Optimized merchandising: A retailer could better determine which products will sell best through each retail channel, at each store location and at what price. For example, a retailer could analyze fast-changing social media buzz about an upcoming superhero movie to gauge demand for particular action figures across multiple geographic locations. With insights into which specific product will sell best in each location, the retailer can ensure that stores in each area are well stocked with those products when the movie is released. Real-time competitive price comparisons can help the retailer set pricing or launch promotions that attract consumers away from rival retailers.
  • 3. Retail 3 • Operational excellence: Analyzing communications, traffic patterns, weather data, political news and consumer demand signals could help a retailer manage retail distribution networks in real time to ensure timely delivery of products and achieve high-quality operational performance. Creating a personalized shopping experience Effectively analyzing the large volume and variety of customer data opens new opportunities to gain a deeper, more complete understanding of each customer and create a smarter shopping experience. What if you could: • Increase the precision of customer segmentation by analyzing customer transactions and shopping behavior patterns across all retail channels? • Enrich your understanding of customers by integrating multichannel data—from online transactions to social media and third-party data—to develop a 360-degree view of each individual and identify emerging trends? • Optimize customer interactions by knowing where a customer is and delivering relevant real-time offers based on that location? • Predict consumer shopping behavior and offer relevant, enticing products to influence customers to expand their shopping list? Marketing teams can use solutions for big data to collect and analyze customer information from a wider range of sources than before—including POS systems, online transactions, social media, loyalty programs, call center records and more. That information deepens their understanding of customer preferences, helps them more accurately identify shopping patterns and enables them to generate more precise customer segmentation. Marketers can then use new insights to deliver highly targeted, location-based promotions, in real time. Email Text analysis for pattern identification Customer Demographics, transactions and shopping patterns Drive marketing optimization Data • Customer micro-segmentation and full 360-degree view • Additional value and insight from sentiment analysis • More accurate satisfaction scoring • Demographics, transactions and shopping patterns • Timely delivery of offers to customers Call center Text and audio call records Video Surveillance, foot traffic in store POS Transaction logs Geospatial Where is the customer? Outcomes • Reduce marketing cost • Reduce churn • Increase visits and conversion • Increase customer loyalty Social media Customer sentiment Events Weather, local events Clickstream Online activities The result? Customers gain a richer, more personalized shopping experience with promotions and offers that are more likely to appeal to them. Retailers, meanwhile, are able to retain a competitive edge and boost revenues by maximizing cross- and up-sell opportunities, as well as consistently engaging customers across channels and reinforcing their brands at every turn. Figure 1. Retailers can draw on a wide variety of data—from transaction and clickstream data to social media and geospatial information—to enhance the effectiveness of marketing efforts and deliver real-time promotions.
  • 4. 4 Capitalizing on the power of big data for retail Optimizing merchandising and supply chains Implementing a scalable big data platform can also help retailers build smarter supply chains and optimize merchandising across a multi-channel retail operation. What if you could: • Predict optimal pricing and maintain a price leadership position by analyzing price and demand elasticity? • Select the right merchandise for each channel and fine-tune local assortment planning by drawing on insights from social media, market reports, internal sales data and customer buying patterns? • Optimize inventory across multiple channels by using leading indicators such as customer sentiment and promotional buzz to anticipate future demand? • Fine-tune store planograms by analyzing customer buying patterns and purchasing trends? • Improve logistics by using real-time traffic, weather data and more to re-route shipments and avoid costly delays? Today many retailers monitor average prices by competitors on a weekly basis. With solutions for big data, they can conduct instant, real-time price comparisons of top competitors, tracking hourly price changes and synchronizing those changes with demand trends. Retailers can then use new insights to set their own pricing, initiate discounts and implement competitive real-time promotions to avoid losing sales—and gain agility. Figure 2. With better knowledge of competitive pricing and demand trends, retailers can initiate sales and promotions that help avoid losing business. Customer Demographics, transactions and shopping patterns Data • Ability to price by channel, region, time of day • Ability to move from store cluster assortments to individual store assortments • Integrated execution knowing customer’s preferred price point, profit targets, supply and timely offer delivery Product Availability, location, margins POS Transaction logs Geospatial Where is the customer? Outcomes • Increased revenue and margins • Improved marketing ROI • Fewer stock-out situations and markdowns • Optimized inventory • Increased customer satisfaction Social media Customer sentiment on pricing and demand Competitors Product availability, hourly price changes Events Weather, local events Execute dynamic pricing and create localized assortments
  • 5. Retail 5 Enabling operational excellence In addition to improving marketing and merchandising efforts, solutions for big data can help retailers realize a variety of operational goals, from improving labor utilization to enhancing financial management. What if you could: • Optimize staffing levels by predicting changes in customer demand? • Better match employee skills with retail store needs and create the right incentives to drive strong sales performance? • Facilitate better-informed financial decision making by drawing on complete, trustworthy and timely data from a wide array of sources? • Improve fraud detection by analyzing large volumes of transactions? A flexible, comprehensive big data platform can play a key role in improving labor utilization and performance. Many large retailers rely on historical data to schedule their thousands of associates and assign those associates to the thousands or millions of tasks involved in providing a positive shopping experience. With solutions for big data, retailers draw on insights from price optimization, assortment planning and marketing to improve labor scheduling. They can incorporate employee performance analysis to optimize work assignments according to skill sets and manage incentives. Discovering the value of implementing big data solutions Leading retailers are already discovering the tremendous value of implementing solutions designed to analyze, organize and apply big data. Delivering a richer multichannel retail experience with new customer intelligence Bass Pro Shops—a leading retailer in fishing, hunting, camping and other recreational activities—capitalized on solutions for big data to create a richer multichannel retail experience. The company needed ways to increase retail shopping consistency across a full range of channels, including its retail store, boat dealership, Internet, catalog, wholesale, restaurant and resort channels. The existing enterprise data warehouse could not provide detailed analytics on individual customers or purchases across multiple channels. The company selected an IBM® Customer Intelligence Appliance, which provides a single view of each customer plus the capabilities for business intelligence and analytic reporting on customer behavior. The solution can generate reports in less than 10 seconds. Bass Pro Shops can now increase customer satisfaction and improve loyalty by providing a consistent experience no matter how customers choose to shop. New customer insights enable the organization to tailor offers and fine-tune each of the customer channels to maximize the appeal of products and drive more sales.
  • 6. 6 Capitalizing on the power of big data for retail Enhancing analytics to improve merchandising decision making A large discount apparel and home fashion company capitalized on the potential of big data to optimize merchandising. The retailer needs timely insights on consumer demand and changing product prices over the course of a clothing season to purchase the right inventory for its stores. Unfortunately, the company’s existing analytics solution required an entire weekend to generate results, leading to missed supply chain and merchandising opportunities. The company implemented an IBM Customer Intelligence Appliance and deployed analytics capabilities to deliver key insights rapidly to buyers. Because the solution was a pre- integrated appliance, it was up and running in just weeks, without requiring excessive IT services. The solution’s performance enables the company to run queries 20 times faster than before, producing results to some queries in just seconds. Now 500 employees across the company use the analytics capabilities to quickly identify new opportunities and make key merchandising and supply chain decisions. Expanding customer analytics to optimize marketing, merchandising and operations For a global electronics retailer, solutions for big data helped expand its customer analytics efforts. The company needed to replace its 13-year-old CRM system, which offered only a store-centric view of customer patterns, required more than six weeks to build new models and generated reports too slowly to keep up with business demands. The retailer needed a solution that could analyze customer information across a widening array of customer data, including social media posts and clickstreams. The goal was to improve customer satisfaction and loyalty, allow marketers to create personalized offers, enable merchandisers to optimize assortment and pricing, and help managers to optimize the placement of in-store displays. The retailer replaced its existing CRM system with a new solution that combines an IBM Customer Intelligence Appliance with SAP software for analytics and reporting. The company now has a single view of each customer across channels, plus analytics capabilities to build segmentation models, score customers and run campaigns in hours. Figure 3. The IBM Big Data Platform offers an array of integrated capabilities to address the tremendous volume, variety and velocity of big data. IBM Big Data Platform Analytic applications Applications and development Visualization and discovery Systems management Accelerators Stream computingApache Hadoop system Data warehouse Data exploration Information integration and governance Cloud | Appliances | Mobile | Security BI/Reporting Exploration/ Visualization Functional Application Industry Application Predictive Analytics Content Analytics
  • 7. Retail 7 Marketing and merchandising teams can draw on that single view of the customer to deliver more personalized offers and loyalty rewards, fine-tune merchandising for customer preferences and optimize the store layout. Predictive analytics capabilities enable the retailer to anticipate the next customer actions and improve interactions across channels and at each step of the customer lifecycle. Creating a data-driven retail enterprise Offering a broad portfolio of solutions and capabilities, the IBM Big Data Platform is helping retailers capitalize on the vast potential for big data in retail. The platform-based approach allows organizations to leverage their investments in technologies and skills by allowing them to start with capabilities for executing one particular use case and easily add others using the same platform. Pre-integrated capabilities help accelerate the time to value. Leading retailers can adopt IBM InfoSphere® BigInsights™ to collect, process, analyze and manage a large volume and variety of customer data from multiple sources. They could analyze everything from transactional data to unstructured social media data, learning more about customer preferences and future behaviors. Using IBM InfoSphere Data Explorer would enable these retailers to rapidly search massive volumes of historical or unstructured data. By implementing IBM InfoSphere Streams, retailers can continuously capture, analyze and cleanse data in motion to facilitate real-time decision making. A marketing team could gauge the success of a campaign by analyzing trending topics in social media. Merchandisers could analyze customer calls, e-mails and social media posts to assess rapidly changing demand for particular products by location. Using the IBM Customer Intelligence Appliance, retailers can integrate information from multiple retail channels and customer touch points to build a complete view of each customer. The more complete data set also enables retailers to produce more accurate models. Employing predictive analytics could help better anticipate future behaviors and optimize customer interactions. Keeping retail focused on the customer The multiplication of retail channels is empowering consumers, providing them with access to more information and new ways to research, compare, purchase and provide feedback on products. For retailers, the customer data produced through these multichannel interactions presents valuable opportunities to optimize marketing, merchandising and operations. The IBM Big Data Platform offers a comprehensive array of capabilities for addressing the growing volume, variety and velocity of available customer data. Whether they are enabling one, two or multiple retail processes by analyzing big data, retailers can implement IBM solutions that help protect existing investments and allow retailers to scale as needed. With IBM solutions for big data in place, retailers can build a foundation that supports a customer-centered, data-driven enterprise that helps them sustain a competitive edge. For more information To learn more about how IBM solutions help you capitalize on big data, visit: • ibm.com/bigdata • ibm.com/smarterplanet/us/en/consumer_advocacy/ideas
  • 8. © Copyright IBM Corporation 2013 IBM Corporation Software Group Route 100 Somers, NY 10589 Produced in the United States of America January 2013 IBM, the IBM logo, ibm.com, BigInsights and InfoSphere are trademarks of International Business Machines Corp., registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM trademarks is available on the web at “Copyright and trademark information” at ibm.com/legal/ copytrade.shtml This document is current as of the initial date of publication and may be changed by IBM at any time. Not all offerings are available in every country in which IBM operates. The performance data and client examples cited are presented for illustrative purposes only. Actual performance results may vary depending on specific configurations and operating conditions. THE INFORMATION IN THIS DOCUMENT IS PROVIDED “AS IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND ANY WARRANTY OR CONDITION OF NON- INFRINGEMENT. IBM products are warranted according to the terms and conditions of the agreements under which they are provided. Please Recycle IMW14679-USEN-00