Chapter 4 Secondary Data

Chapter Four

Secondary Data

Chapter Objectives

• Compare the advantages of secondary data and primary data

• Identify the limitations of secondary data in terms of their relevance and accuracy

• Distinguish between (1) original and secondhand sources of secondary data and (2) internal and external sources of secondary data

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Chapter Objectives

(Cont’d)

• Explain why secondary data management is increasingly important

• Define marketing information system and describe its basic components


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What Do These Companies

Have in Common ?

Pure and Persil detergent

Cadbury Chocolates

Huggies Diapers

Birds Eye Fish Sticks

• Different products, different companies, one common database


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Secondary Data

• Data collected for a purpose other than the research situation at hand

• Advantages

– Cost and time

– Availability

– Less expensive

– Less time intensive


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Using Secondary Data: Advantages

• Readily available

– Whirlpool warranty card

– Nielsen/Net Ratings

– U.S. Census Bureau

– Statistics


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Disadvantages of Secondary Data

• Relevance: may not match the data needs of a given project.

– Measurement units

– Differences in category definitions

– Time Period


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Secondary Data:

Small Business Application

• Market Research for a small business: You want to start a pool and spa cleaning and repair service

• How do you find out about market size and competition?


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Secondary Data Relevance:

Measurement Units

• Carpets Unlimited manufactures a variety of carpets

• Sentinel Corporation produces a line of smoke detectors

• U.S. Census of Population and Housing Data can be used to estimate the total residential market potential for their products in different sections of the country


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Secondary Data Relevance:

Measurement Units

(Cont’d)

• Carpets Unlimited requires size data expressed in square feet

• Sentinel Corporation requires size data expressed in number of rooms per household

• U.S. Census of Population and Housing data

– Useful to Sentinel Corporation but not useful for Carpets Unlimited


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Digital BabySitter

Digital BabySitter.com

website


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Digital BabySitter

(Cont’d)

• Specializes in making digital baby monitor devices

• Wants to expand beyond the United States

– Based on birthrates provided by the United

Nations ( www.un.org

), the company decided to target China and India

– Obtained information on computer penetration in urban areas and chose urban populations as its target market


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Digital BabySitter

(Cont’d)

• Secondary Data Analysis is not meaningful in

China and India because children are either with their extended families or at school

– Children are almost never alone

– Secondary data is not always relevant!!!


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Secondary Data Is Not Always Reliable

• GOJO launched Purell as an "instant hand sanitizer"

– Walgreen’s positioned it as a skin care/first aid product

(cleans without water)

– Nielsen and Information Resources Inc. (IRI) categorized it as liquid soap

– Sales varied by location

• Is it a liquid soap or hand sanitizer? What is it?

• Category mismatches make the secondary data not always reliable


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Problems with Census Data

• Category mismatch

• Changes in category definition

• The time period during which secondary data were collected

• Using data that are too old


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The Numbers Game

• THE SHOCKING TRUTH IS THAT

STATISTICS ARE ONLY AS CREDIBLE AS

THE SOURCES THAT PRODUCE THEM!


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Spam Projections Which Numbers to Use?

Message Labs

Brightmail

Postini

Frontbridge

2002 2004

19% 84%

39 65

60

40

78

82

Many accept the above projections without questioning their validity, even when the projections differ by billions of dollars across the competing studies


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Secondary Data Limitations

• Accuracy

– Who collected the data?

– Why was the data collected?

– How was the data collected?


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Types and Sources of Secondary Data

• Internal Sources

– Company held information

• External Sources

– Government

– Syndicated Sources

– Trade Associations

– Miscellaneous Sources


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Exhibit 4.1 Flow Diagram for Conducting a

Data Search


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Secondary Data: Internal vs. External

• Manager of McDonald's wants to know the effect of the company's tie-in with movies like

Shark Tales

• Should the manager purchase this syndicated service from the marketing research firm?


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Internal Data

• Internal data can often be obtained with less time, effort, and expense than external secondary data

• May have relevance to the research being conducted

• Examples include

– A firm’s historical record of sales

– A public service association’s list of donors

– Public opinion polls conducted in the past by a political candidate’s campaign office


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External Data: Government Sources

• Collects extensive data about people, firms, markets, and foreign countries; more than any other secondary data source

• Data collected is readily available on Internet sites

• Documents published are in the form of summary reports based on the raw data collected

• The raw data is often available for a fee

– Public-Use Microdata files


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Syndicated Sources

• Syndicated services offered by marketing research firms

– Nielsen Retail Index

• Fees are required but they are more cost effective than collecting primary data

• Focus directly on the needs of decision makers

• Updated more frequently than government data


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Syndicated Sources

(Cont’d)

• Often allows for customization

– Roper reports

• Supermarkets are also a valuable source for secondary data


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Trade Associations

• Very numerous and diverse

• Many collect data relevant to and about their members

• Also collect competitively sensitive data about members that may not be available to industry outsiders


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Competitive Intelligence:

FIND/SVP Helps Clients

• Industrial products and services company facing a worldwide market decline

• Approached FIND/SVP (a leading knowledge services company) to compare its plant manufacturing strategy and costs with those of competitors

• FIND/SVP

– Undertook a market scan of published information on competitors’ plants

– Obtained Environmental Protection Agency (EPA) documents


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FIND/SVP Helps Clients

(Cont’d)

• Based on FIND/SVP's analysis, the industrial products and services company was able to assess cost structures of its competitors and develop benchmarks for quality, employee performance, and utility costs


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Burger King Corp

• Burger King

– Maintains a brand research library and subscribes to analyst reports that provide a detailed view of competitors' financial and long-term plans

– Gathers syndicated reports that provide sales and cost data and describe the competition's growth plans

– Insights about the restaurant business can be flushed out from interviews with restaurant business leaders, published routinely in these trade journals


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Managing Secondary Data

• Merely keeping abreast of all the available data without being overwhelmed is a challenge

• Effective secondary-data management is necessary in this "information explosion" age


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Ad Hoc Research Projects

• Discrete, situation specific projects that are initiated and completed in response to a particular question, or set of related questions, raised by a decision maker


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Evolution of MkIS

Ad Hoc

Marketing

Research

Stage 1

Marketing

Information System


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Full-fledged Marketing

Information Systems

• Data warehouse information storage and retrieval system

• Marketing decision support systems

– Data Mining

– Data Modeling

• Expert systems


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Exhibit 4.2 A Hotel

Chain’s Marketing

Information System


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Marketing Information Systems (MkIS)

• A continuing and interacting structure of people, equipment, and procedures designed to gather, sort, analyze, evaluate, and distribute pertinent, timely, and accurate information to marketing decision makers


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Data Warehousing

• A centralized database, which consolidates enterprise-wide data from a variety of internal and external sources

• An architecture, which allows individuals to query and generate ad hoc reports in order to perform an in depth analysis


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Exhibit 4.4 A Typical Data

Warehouse Operation


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Exhibit 4.5 Database Model

(Dimensional Model)


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7-Eleven's Information System

Helps in Forecasting

• 7-Eleven Inc. installed an inventory management/sales data system in all of its

5,600 franchisee and company-owned stores nationwide

• The system provides item-by-item sales data allowing managers to determine which of the

2,500 products they carry are selling well


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7-Eleven's Information System

Helps in Forecasting

(Cont’d)

• The system also alerts managers about upcoming events and news that could affect which items will be in demand

• Information system thus helps 7-Eleven in sales forecasting and in collaborative product development with suppliers


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Cover Concepts: Database

• A producer of book jackets with corporate advertising on the cover

• Cover Concepts covered schools' books with free jackets carrying advertisements and interesting messages that appealed to kids, providing national advertisers with a costeffective way to reach the 6-to-18-year-old market


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Cover Concepts: Database

(Cont’d)

• Company's database has grown from 55 Boston-area schools in 1989 to 31,000 schools (out of a total of

85,000) and more than 21 million kids nationwide

• Cover Concepts gathers the database's extensive demographic information, which it updates yearly, from the elementary, junior high, and high schools themselves, as well as from the Census Bureau, private database companies, and other sources


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Evolution of MkIS

Ad Hoc

Marketing

Research

Stage 1

Marketing Information

System

Stage 2

Decision Support

System


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Marketing Decision Support System

(MDSS)

• Definition: An MkIS that permits managers to request special types of data analyses or reports on an as-needed basis

– Interactively generates “What if...” scenarios


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Data Mining

• The process of digging deep into immense amounts of data to extract valuable and statistically valid information

– IBM Intelligent Miner

– Angoss Software’s Knowledge STUDIO


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Applications of Data Mining

• Companies -Telecommunications

• Benefits

– Segmentation of prospective customers to increase new customer accounts at the same time reducing cost per account

– Understanding individual customer preferences and needs to deliver relevant long distance products and services


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(Cont’d)

• Companies - Insurance

• Benefits

– Improving profitability through timely valuation of insurance products

– Effective financial data management by balancing market, regulatory, and insurance pressures to provide superior customer/patient care


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(Cont’d)

• Company - High Tech Design

• Benefits

– Profitability analysis and product life cycle planning leading to increased focus on non traditional customer segments thereby expanding the market


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(Cont’d)

• Companies - Retail

• Benefits

– Demographic analysis, financial planning, and forecasting, leading to precise buying, merchandising and marketing

– Improving profitability through optimal shelf space allocation

– Tighter end-to-end integration of internal as well as vendor systems, leading to better inventory and merchandise management

– Reducing returns and thereby improving margins


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(Cont’d)

• Companies - Banking

• Benefits

– Consumer intelligence helps create new products and manage collections while containing delinquency rates

– Profitability analysis by customer segments

– Market penetration through personalized promotion strategies


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Marketing Decision Support Systems:

Models

• A marketing response function is a mathematical model that represents the relationship between marketing input and output variables


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MDSSs: Retail Databases

• Scanner-based databases allow retailers and packaged goods manufacturers to monitor and analyze sales trends:

– Changes in brand shares

– Shifts in consumer preferences

• Information Resources, Inc.’s BehaviorScan and Nielsen’s Scantrack capture scanner data from many retailers


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Exhibit 4.7 Data Captured in a

Single Source Data Base


4 | 53

Evolution of MkIS

Ad Hoc

Marketing

Research

Stage 1

Marketing Information

System

Stage 2

Decision Support

System


Stage 3

Expert System

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Expert System (ES)

• An MDSS that proactively makes managers aware of market situations warranting their attention

• An MDSS can recommend appropriate courses of action

– Artificial intelligence is utilized


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Expert System (ES)

(Cont’d)

• 7 - Eleven Maximizes Space and Selection

– Alerts store managers and suggests how to reallocate shelf space to maximize profits from nutritional snack bar sales

– Uses its expert system to determine the best allocation of shelf space among the various products it sells

– Analyzing sales, cost, and promotional data, the system translates the results into “Plan-a-Grams,” printouts that show store managers, shelf by shelf, exactly where to place their stock to maximize profit


4 | 56

Chapter 4 Secondary Data

Chapter Four

Secondary Data

Chapter Objectives

Chapter Objectives

What Do These Companies

Have in Common ?

Secondary Data

Using Secondary Data: Advantages

Disadvantages of Secondary Data

Secondary Data:

Small Business Application

Secondary Data Relevance:

Measurement Units

Secondary Data Relevance:

Measurement Units

Digital BabySitter

Digital BabySitter

Digital BabySitter

Secondary Data Is Not Always Reliable

Problems with Census Data

The Numbers Game

Spam Projections Which Numbers to Use?

Secondary Data Limitations

Types and Sources of Secondary Data

Exhibit 4.1 Flow Diagram for Conducting a

Data Search

Secondary Data: Internal vs. External

Internal Data

External Data: Government Sources

Syndicated Sources

Syndicated Sources

Trade Associations

Competitive Intelligence:

FIND/SVP Helps Clients

Competitive Intelligence:

FIND/SVP Helps Clients

Competitive Intelligence:

Burger King Corp

Managing Secondary Data

Ad Hoc Research Projects

Evolution of MkIS

Full-fledged Marketing

Information Systems

Exhibit 4.2 A Hotel

Chain’s Marketing

Information System

Marketing Information Systems (MkIS)

Data Warehousing

Exhibit 4.4 A Typical Data

Warehouse Operation

Exhibit 4.5 Database Model

(Dimensional Model)

7-Eleven's Information System

Helps in Forecasting

7-Eleven's Information System

Helps in Forecasting

Cover Concepts: Database

Cover Concepts: Database

Evolution of MkIS

Marketing Decision Support System

(MDSS)

Data Mining

Applications of Data Mining

Applications of Data Mining

Applications of Data Mining

Applications of Data Mining

Applications of Data Mining

Marketing Decision Support Systems:

Models

MDSSs: Retail Databases

Exhibit 4.7 Data Captured in a

Single Source Data Base

Evolution of MkIS

Expert System (ES)

Expert System (ES)

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