analytic intelligence

8/3/2019 Analytic Intelligence

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A SAS White Paper

Analytics in Retail

Transform data from existing systems into predictive insightsthat dramatically increase revenues and profitability



Table of Contents

Executive summary ....................................................................................................... 1 Where human intuition meets analytics .................. ................................................... 2 Quality information — the foundation for true distinction........................................ 3 What can retailers gain with analytics? .................. ....................................................5

Analytics in marketing and customer relationship management..................................6 Analytics in merchandising........................................................................................... 7 Analytics in operational optimization............................................................................9 Analytics in performance management........................................................................9

Analytic business intelligence ...................................................................................10 SAS business intelligence solutions for retailers....................................................11

SAS®9, the platform for retail intelligence .................................................................. 11 Summary....................................................................................................................... 12 About SAS .................................................................................................................... 12



Analytics in Retail

Executive summary

Every day, retail organizations ask two related questions: 1) How can we maximize revenues from

our product offerings, retail outlets and customer relationships? 2) How can we maximizeprofitability without eroding the quality of our products and services, the shopping experience or

overall customer satisfaction?

Traditionally, answers to these questions have been forged using simplistic query and reporting

tools coupled with instinct and intuition. Optimized answers to these questions have been all

but impossible.

A great many retailers still rely on operational and transaction data to determine future outcomes

— on the hope that hindsight can generate useful insight and foresight. But knowing how many

transactions took place is not the same thing as understanding why they took place, which factors

influenced the outcome, and how to optimize the result in the future.

“We use SAS because

it is a great analytical

solution. With SAS, we

gain knowledge that really

tells us what drives our

sales and what makes our

forecasts work. SAS will

continue to help Staples

understand how our

business is performing

and where it’s going.”

Alan Gordon

Director of Sales

Forecasting

Staples

Spreadsheets and online analytical processing (OLAP) tools provide a rudimentary understanding

of the business, but they can’t provide the kinds of answers needed to elevate the retail

organization into proactive, differentiated, sustainable success.

That’s where analytics come in. Thankfully, sophisticated, retail-specific statistical methods have

been packaged with prebuilt models and easy-to-use interfaces, so business users — without in-

depth statistical experience — can generate new levels of intelligence from data. Furthermore,

these analytic capabilities have been integrated across organizational areas — from marketing,

merchandising and operations to the extended supply chain and corporate strategy. Decisions

that were once made in isolation can now be based on holistic perspective for the greater good.

Based on a common foundation, intelligence can flow across all related areas of the organization.

Understanding of customer preferences can guide marketing promotions, in-store shelf

assortments, inventory decisions and staffing. Market data can support pricing decisions and feed

into supplier choices and financial strategies for the entire enterprise. Store sales results, clarified

to every level of detail — can guide best practices for inventory replenishment, sales staffing and

locations of new stores.

The potential of this integrated approach should be self-evident. The best way to implement it has

not been, however. Traditionally, data has been trapped in incompatible platforms and

organizational silos that could barely speak to each other. An integrated analytics

platform can help retail organizations extract greater value from all their existing data sources —

transforming operational and transactional data from legacy systems into meaningful, forward-

looking insights that can dramatically increase revenues, profitability and effectiveness.

1



Analytics in Retail

2

Where human intuition meets analytics

Gone are the days when retail operations could be governed by the instinct and intuition of

shopkeepers who knew customers' names, buying behaviors, seasonal trends, productpreferences and likely future purchases. The complexity of today's global, multichannel retail

environment makes it impossible to glean that kind of knowledge only from personal experience

and common sense.

Retailers have turned to a variety of technologies in their quest to improve revenues, customer

service and operational efficiencies. However, customer, transaction and market data collected

from different channels often reside in disparate databases and systems — leaving no practical

and consistent way to analyze the information for personalized customer insights.

As a result, important decisions about merchandise selection, pricing, promotion, positioning,

allocation, inventory replenishment, staffing and other aspects of retail operations are made and

executed based on incomplete or inconsistent information — leading to suboptimal actions andeven costly mistakes.

To survive and prosper in competitive markets, retailers need more. They need the ability to

readily access and analyze data to gain comprehensive, accurate and forward-looking retail

intelligence — whenever it is needed. That kind of insight isn’t generated by the operational

systems that capture day-to-day transactions; they weren’t designed for that. Nor is it generated

by the spreadsheets and OLAP systems often called “analytic” systems. Those technologies

usually offer rigid and simple views of data. They can tally, track, sort and filter, but they don’t

synthesize data into the best information or provide a window into the future, a window necessary

for proactive decision making. They can’t distinguish meaningful trends from “noise,” clarify why

events occurred, identify the significant factors that would lead to repeatable successes or

accurately predict future outcomes. In short, they don’t deliver strategic analytic insight.

Delivering such advanced insight requires advanced capabilities based on true analytics, the in-

depth mathematical investigation of relationships among many variables. While the definition may

be intimidating, two key circumstances have opened up new opportunities for retailers to exploit

analytics like never before:

• Retail automation systems yield more data than ever. The burgeoning popularity of

loyalty cards and credit cards, the growth of the Internet as an alternative sales channel, the

proliferation of operational automation systems and RFID (radio frequency identification)

systems ... these trends are creating a wealth of data that retailers are beginning to apply to

better understand and optimize their businesses.

• All that data can be transformed into meaningful intelligence more readily than ever.

Advances in data management and computer processing have made it feasible to quickly

distill forward-looking intelligence from huge volumes of disparate operational, transactional

and external data. Advancements in user interfaces and packaged applications enable

business users to quickly conduct in-depth analysis, without relying on IT specialists

or statisticians.



Analytics in Retail

3

These factors — combined with the spiraling cost and competition pressures all too familiar to

retailers — are driving adoption of a new level of information technology based on retail-specific

analytics. Analytics can be applied to optimize many areas of retail business, such as customer

relationships, merchandising, operations and overall performance.

Analytics gives retailers robust ways to understand what is happening and what could

happen with quantified accuracy — within stores, among stores and across chains.

Quality information — the foundation for true distinction

In the old millennium, product and service attributes were prime competitive differentiators.

Excellence in products and service are still essential, of course, but they tend to be differentiators

only for short windows of time before competitors catch up. The only enduring way to stand apart

is to have better information — the critical ingredient that enables you to outmaneuver the

competition through a continuing flow of renewal and innovation.

That means an enterprise’s information management strategy can either be its most compelling

asset or its most limiting deficit. Naturally, organizations vary in the degree to which they

capitalize on information assets. At SAS, we view different levels of maturity through the lens of

the Information Evolution Model, a framework for describing the status of an organization’s

evolution toward becoming an intelligent enterprise.

This model describes five fundamental stages that organizations pass through as they advance in

their use of business intelligence for competitive differentiation:

• Level 1: Operate — At this most basic level are the companies rife with information

mavericks: the people in isolated offices hammering away on desktop spreadsheets. If they

go, the knowledge goes with them. There are no processes, and each request becomes an

ad hoc data rebuild, resulting in multiple versions of the truth.

• Level 2: Consolidate — At this stage, a company has pulled together its data at the

departmental level. Here, a question gets the same answer every time, at least within the

department. However, departmental interests and interdepartmental competition can skew

the integrity of the output and result in multiple versions of the truth.

• Level 3: Integrate — At this point in the evolution, a company bases its decisions on this

more complete enterprise information. This company is beginning to have a true awareness

of additional opportunities for the use of BI to improve processes and profits.

•

Level 4: Optimize — At this stage, the retailer’s knowledge workers are focused onincremental process improvements and refining the value-creation process. Everyone

understands and uses analysis, trending, pattern analysis and predictive results to increase

efficiency and effectiveness. The extended value chain becomes increasingly critical to the

organization, including the customers, suppliers and partners who constitute intercompany

communities.



Analytics in Retail

• Level 5: Innovate — This level represents a major, quantum break with the past. It exploits

the understanding of the value-creation process acquired in the Optimize stage and

replicates that efficiency with new products in new markets. Retail organizations operating at

this level understand what they do well and apply this expertise to new areas of opportunity,

thus multiplying the number of revenue streams flowing into the enterprise. Armed with

information and business process knowledge, organizations approaching this level will

introduce truly innovative products and services that reflect their unique understanding

of the market, their internal strengths and an unfailing flow of ideas from continuously

engaged employees.

We are finding that most large retailers have reached or are approaching the Integrate stage, with

many making great strides toward the Optimize and Innovate levels. There is an enormous

opportunity for the evolution to continue within every retail organization.

BusinessValue

4

Figure 1: Business value increases exponentially with intelligence.

But real competitive value — coming from Level 4 or Level 5 — is found beyond the limitations of

operational and transactional software; it requires the ability to transform operational and other

data into meaningful, accurate, enterprise intelligence and predictive insights.

Leading retailers around the globe have begun using analytic business intelligence to make an

array of strategic decisions. Where to place retail outlets, how many of each size or color of an

item to put in each store, how much square footage to allocate to a category, when and howmuch to discount ... the effects of better decisions in these areas can generate millions of

dollars for retailers.

Intelligence

Industry ExpertiseOptimization

Predictive Modeling

What will happen next? Forecasting

What’s the best that can happen? Reporting / OLAP

Data Management

Data Access How Much?

How Many?

What Happened?



Analytics in Retail

What can retailers gain with analytics?

Navigating a retail

enterprise with hindsightreporting is like driving a

car by watching the rear

view mirror. You know very

well where you’ve been,

but the road ahead is still

paved with uncertainty.

Analytic insight is like

having an onboard

navigation system. It

predicts the road ahead

and offers up the best path

to reach your destination,in spite of constantly

changing circumstances.

Many retailers today rely solely upon OLAP capabilities from vendors claiming to be analytic

business intelligence experts. Although OLAP stands for online analytical processing, that’s reallya misnomer because it actually contains little analytic substance. Most OLAP technologies merely

draw on simple descriptive measures and additive capabilities: summaries, weighted summaries,

averages, percentages, minimum and maximum values.

OLAP provides a structured way to view and query data, and it may provide some insight into past

trends and performance, but it is difficult to determine the significance of trends using OLAP tools.

In volatile economies, with so many dependent factors at play, past history (taken at face value)

can be a very poor predictor of future events. You could surf the data for days and not find

anything significant. Even if you did find something interesting, OLAP contains no mechanism to

determine if the pattern, event or anomaly is actually significant.

In contrast, analytics can span not only the past and present to distinguish significance fromhappenstance; it can also predict specific future outcomes.

Analytic processes quantify known attributes, examine complex relationships among many

interdependent variables and detect patterns using techniques from a variety of mathematical

disciplines, such as statistics, econometrics, time-series forecasting, data mining and

operations research.

From huge volumes of raw data comes useful, forward-looking intelligence, presented in

meaningful context. Users can drill into results to view detail, discern useful patterns from mere

statistical “noise,” apply models and scenarios repeatedly to different data, select the

visualizations that best clarify patterns and actions, and change conditions and assumptions

to ask “what if.”

Let’s take a look at how analytic capabilities can enhance success in four key areas of retail

business management:

• Marketing and customer relationship management — Targeting the right customers with

the right messages at the right time to maximize the value of each customer relationship.

• Merchandising — Optimizing the selection, placement and promotion of merchandise

among geographies, store locations and store displays.

• Operations — Optimizing the behind-the-scenes aspects of retail business, such as real

estate decisions, staffing levels and IT portfolio management.

• Performance management — Assessing performance from the individual store to the

whole enterprise and understanding where changes will yield the greatest progress toward

strategic goals.

5



Analytics in Retail

Analytics in marketing and customer relationship management

Your organization spends tremendous amounts of money designing and delivering campaigns to

reach specific target audiences. Do you know how effective those campaigns are and what

factors determine success? Are you delivering the best possible message to the right people,

through the right channel, at the right time? Are campaigns designed for short-term, one-shot

gains or for maximizing long-term customer value? Are campaigns coordinated and integrated

across channels?

Marketers can no longer view their customer audiences with a product-level perspective or as a

snapshot in time. To maximize return from each campaign and customer relationship, retailers are

recognizing that it’s time for a broader approach. It is essential to understand and appeal to

customers as individuals with known preferences and buying habits.

Analytics make this customer-centric vision possible. A host of analytic tools are available that

enable marketers to fully understand their diverse audience segments, assess and maximize the

lifetime value of each customer relationship, model what-if scenarios, predict behaviors and

optimize marketing communications. For example:

The essential foundation

for successful retailing is

a deep understanding of

current and prospective

customers — not only as

market segments, but

as individuals whose

circumstances and

preferences change

over time.

• Customer profitability analysis projects the initial sales curve and lifetime value of a

customer relationship, enabling more effective use of marketing, sales and service

investments.

• Channel usage and profitability analysis assesses and predicts the most suitable and

efficient channels for each contact activity and each customer.

• Product preference and profitability analysis assesses value and ROI on a product basis

across customer groups and channels.

• Bundling/cross-selling/up-selling analysis identifies products that complement each other

or will sell well together.

• Customer loyalty/churn analysis identifies which customers are loyal, which are likely to

leave, when they are likely to leave and what factors influence their decisions to stay or go.

All this information helps you devise better strategies to keep them.

• Demand forecasting generates reliable estimates of short-, medium- and long-term demand

so that services, products and distribution plans are always in place to meet customer

expectations.

•

Market-basket analysis assesses links and patterns in the mix of choices/responses that acustomer makes with a view to improving cross-sell/up-sell opportunities, improving product

introductions, maximizing browse-to-action conversions on Web sites, and using loyalty

promotions to increase retention.

• Customer segmentation analysis divides the market into groups that share common

characteristics to support manageable, accurate, time-based market response

propensity models.

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Analytics in Retail

• Event-trigger analysis reveals correlations between events, such as demographic changes

or holidays, and the implications of those events.

• Marketing optimization incorporates information about customers, offers and channels;

factors in business objectives and resource/channel constraints; and calculates the optimal

mix of choices for a multichannel, multioffer campaign or set of campaigns.

By understanding customers better, retailers can create better-defined targeted campaigns,

reduce expenses (printing, paper, postage) while increasing response rates, revenues and gross

margins. As retailers gain a better understanding of customers' buying behavior, analysis can

then be used to create more effective merchandising plans for the next season.

"By using analytics, sales and service personnel can identify and exploit cross-sell opportunities.Revenue can grow dramatically once these opportunities are exploited."

“How to Construct a Return on Investment Model for CRM” Gartner, Inc., July 2004

1

Analytics in merchandising

Which items should be stocked, in what sizes and colors, at what quantities, in which stores?

How, where and when should products be displayed, priced, promoted? Traditionally, such

decisions have been based on intuition and historical information from simple planning

applications — both of which are less-than-ideal tools for combating intense competition

and shrinking profit margins.

Because merchandising processes in retail are cyclical, it is vital that the output from one process

be used in another. For example, the customer data used to create merchandise and assortment

plans may also be used in allocation and space plans.

Analytic solutions for merchandising apply a rigorous, objective methodology to this cyclical

process to help manage variability in supply and demand and to support optimal decisions about

assortments, allocation and space planning. For example, by augmenting existing systems with

analytic capabilities such as forecasting, optimization and data mining, retailers can:

When you align supply

and demand chains with

accurate demand

forecasting, you can deliver

the right product at the

place, time and price to

fulfill customer demand.

• Determine how to meet sales, revenue or profitability goals under anticipated conditions that

are based on stores' past, present and future demand; time; and merchandise hierarchy.

• Analyze store-specific needs and quickly respond to emerging business trends in order to

maximize inventory investments, allocation and replenishments while reducing liabilities.

• Build the ideal breadth, depth and visual appeal of product assortments to match customer

needs, ensure a consistent shopping experience, make best use of available space and meet

financial goals for individual stores, clusters of stores and the company as a whole.

• Effectively predict the success of planned promotions and their impact on demand of featured

products, as well as the impact of promotions on other products and categories.

1Gartner Report “How to Construct a Return on Investment Model for CRM” by B. Eisenfeld, D. Hagemeyer. July 16, 2004.

7



Analytics in Retail

When you have in-depth analysis of past performance combined with plans and forecasts of

future customer demand, you can more accurately allocate and restock merchandise across

channels and stores. Truly understanding customer demand patterns — not just what was

purchased, but what those patterns reveal about future potential — enables you to send the

correct assortments, size and case-pack distributions to the correct stores.

Daily price, promotion and markdown optimization ensures that items are priced for optimal

profitability, both preseason and in-season. Space automation and optimization ensure that

departmental sales and profit per square foot are maximized, and that products are given the

correct inventory and space on the shelf. Optimized fulfillment ensures that products are allocated

or replenished according to demand. Accurate analysis also results in a more efficient use of

manpower in picking, packing and shipping the first wave of product while minimizing

additional expenses.

Figure 2: Data mining, optimization and forecasting create true merchandise intelligence.

8



Analytics in Retail

9

Analytics in operational optimization

In-store and customer-facing activities rely on a multitude of support functions behind the

scenes — all of which must also be optimized. For example, now that analytics have given you an

accurate forecast of demand — by hour, by day, by location, by promotion and by price change —

this knowledge must guide decisions for inventory replenishment, as well as for staffing on

all store floors, catalog call centers and fleet crews delivering orders from distribution center

to stores.

That’s just one example. In each operational area, retailers need to answer complex questions.

How do I align resources with corporate strategy? Which locations will provide the most profitable

return on real-estate investment? How can I leverage IT investments for maximum value?”

Operations intelligence solutions based on analytics enable you to answer those questions more

effectively and profitably. For example, by using analytic capabilities to delve into operational

data, retailers can:

• Deliver predictive insight into supply chain costing, financial planning and

activity-based costing.

• Plan more effective staffing strategies for all areas of the organization.

• Enable the organization to realize the full potential of each IT resource through

proactive planning.

• Establish the most effective supplier strategies, based on a multitude of

interdependent factors.

Without analytics a typical operations report might tell you how many units of a given product

were sold through each outlet or inventory levels for a specific product at various locations over a

given time period. Such information provides a useful rear view into operational performance, butnot a road map on which you can confidently guide the business forward.

By bringing analytics into the picture, the same foundation data could reveal why the products

sold better at Region I locations than in Region II, what pricing modifications would produce the

best combination of customer loyalty and business profitability, the anticipated impact of a

specified promotion or merchandising strategy, and what would happen if you adjusted any

factors, from number of drivers and vehicles in the delivery fleet to product placement on

store shelves.

Analytics in performance management

With retail outlets each responsible for their share of organizational success, there’s always the

danger that strategies serving the local good could undermine higher-level goals. Or that a

promotion that boosts sales of one product could cannibalize sales of another. Or that strategies

designed to increase short-term profits could undermine long-term profitability. With performance

management analytics, you can align day-to-day decisions with goals and initiatives across the

entire value chain and for the entire organization.



Analytics in Retail

Performance management analysis uses balanced scorecard methodologies to align diverse

business processes toward shared goals, communicate those goals across the enterprise and

measure progress toward achieving targets. This type of analysis quickly identifies areas where

one marketing activity might be eroding others, or where product-level successes do not

contribute to overall company success.

A consistent performance management process enables the organization to fully understand how

business processes are performing and where trouble is brewing. Retailers can then better align

investments — people, infrastructure and capital — with overall business strategy in ways that

deliver expected results and meet overall objectives.

Analytic business intelligence

Retail intelligence success

stems from an integrated

suite of applications and

technologies working

together from a common

data foundation to create a

unified perspective —

generating consistent

advantage within a climate

of constant change.

The portfolio of available analytic processes targeted for retail organizations is extensive, but the

real intelligence story is much more than a shopping list of discrete point solutions. True business

insight is about more than making smart investments in individual technologies. It's about whathappens when those individual technology areas come together into a synergistic system. Retail

intelligence success stems from an integrated suite of applications and technologies working

together from a common data foundation to create a unified perspective — generating consistent

advantage within a climate of constant change.

The retailers getting the most significant returns on their investments are those that take a

purposeful, pragmatic approach — establishing an intelligence platform on which they base all

other enterprise business intelligence solutions. A single, reliable demand forecast, for instance,

can also be used in merchandising, marketing, logistics, store operations or call center staffing for

operational benefit.

Business intelligence that remains segmented by functional area can provide some value, but

retailers gain much more value from the same IT investment when those functional areas operate

from a shared, cohesive foundation. The requisite foundation is a bedrock of solid data

management capabilities designed to ensure that analysis starts with the best quality data.

In the ideal IT framework, a unified, integrated data repository stores and manages all relevant

data for the interdependent arena of retail activities — including data from disparate databases

(such as merchandising, inventory management and marketing), proprietary tools and external

sources (such as purchased demographic or market data).

Sophisticated data management processes transform operational data into cleansed,

consistent, structured data in a form suitable for detailed analysis. This data management process

is more than simply integrating data from disparate sources; it applies embedded rules that

ensure data quality, so users can have faith in the accuracy of plans, reports and analyses based

on that data. Common metadata (the information about how data values are derived and used)

enables the system to readily use data from across functional areas and adapt easily to business

changes — historical and ongoing.

10



Analytics in Retail

11

The right solution must be able to integrate with any other system or platform and take full

advantage of existing IT infrastructure investments. For example, if you want to use customer

behavior data to make better merchandising or marketing decisions, the retail intelligence solution

must interface with sales transaction systems, loyalty systems, in-house credit systems, coupon

redemption systems, catalog and Internet customer data systems, regardless of operating

system or hardware.

This integration must be a two-way street. There should be a closed-loop, continuously

improving process between the operational systems that transact day-to-day business and the

business intelligence systems that help guide that day-to-day business to maximum efficiency

and profitability.

SAS business intelligence solutions for retailers

Tailored to meet the unique needs of the retail industry, SAS solutions for retail intelligence

deliver valuable insights about customer behavior, store performance, supply chain costs andpromotional campaigns. Retailers have successfully used this intelligence to formulate consistent

business strategies and performance metrics for individual stores, products and campaigns —

while maximizing both customer satisfaction and profitability for the organization as a whole.

These solutions combine award-winning SAS analytics and data management capabilities with

retail industry expertise and prebuilt models for faster implementation. In addition, a business

scorecard with retail-specific key performance indicators (KPIs) delivers a strategic, enterprise

perspective that drives profitability for any type of retail business — in-store, online or direct mail.

No doubt your organization already has many elements of an intelligence infrastructure in place:

data captured from businesses processes, data storage and manipulation capabilities, and

various analysis and reporting tools, perhaps from multiple vendors. With SAS, you can extend

the value of these existing systems while setting the stage for new levels of retail intelligence and

resource optimization not previously possible.

Each component adds value to the overall solution. Synergies among modules make the total

solution truly greater than the sum of the parts, as each module offers functionality that enhances

other components in the integrated, end-to-end intelligence architecture.

SAS ® 9, the platform for retail intelligence

SAS solutions for retail are offered on the SAS®9 platform, the only analytic business intelligence

software platform that delivers these advantages:

• A single technology platform that includes data integration, reporting and analytics and is

built on your existing technology investments to deliver high-quality information to every

desktop or departmental server.



Analytics in Retail

12

• Expanded access. SAS®9 delivers industry-leading SAS analytics to users throughout the

organization, so they can understand the past, monitor the present and reliably predict

the future.

• Targeted user interfaces that are designed and tailored to the varying skill levels and usage

patterns of information consumers, domain experts, executives and technologists.

• Query and reporting tools that give users the highest quality information in the appropriate

format, where and when needed, via multiple platforms and channels, including secure Web

portals. Call centers, field sales representatives, distribution and service agents always know

how they’re doing and how they are contributing to overall goals.

• An ever-expanding range of SAS analytic solutions that help retailers exploit data about

customers, merchandise, operations and strategic performance to optimize all aspects

of the business.

Summary

The future retail landscape will be defined by the retailers that know how to maximize customer

satisfaction and profitability with the right combination of quality products, friendly and efficient

service, unique value, differentiated shopping experience and a business model that truly serves

local and global communities.

How will this be accomplished? It starts with understanding the customer and then linking

that insight into every decision thereafter, from merchandising and marketing to distribution,

store operations and finance, so retailers can predict how best to serve their customers' ever-

changing needs.

SAS solutions support that very scenario, delivering an intelligence platform and retail-specific

applications for customer intelligence, merchandise intelligence, operations intelligence and

performance management. Together, this suite of solutions equips retailers to ascend to Level 4

or Level 5 in the Information Evolution Model — to succeed through continual renewal and

innovation.

About SAS

SAS is the leader in providing a new generation of business intelligence software and services

that create true enterprise intelligence. SAS solutions are used at more than 40,000 sites —including more than 80 percent of FORTUNE Global 500® general merchandisers and specialty

retailers. These successful enterprises use SAS business intelligence to develop more profitable

relationships with customers and suppliers; to enable better, more accurate and informed

decisions; and to drive organizations forward. SAS is the only vendor that completely integrates

leading data warehousing, advanced predictive analytics, and traditional BI applications to create

intelligence from massive amounts of data. For nearly three decades, SAS has been giving

customers around the world The Power to Know®.



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