Chapter Four Secondary Data. Copyright © Houghton Mifflin Company. All rights reserved.4 | 2...

Chapter Four

Secondary Data

Copyright © Houghton Mifflin Company. All rights reserved. 4 | 2

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 second-hand sources of secondary data and (2) internal and external sources of secondary data


Chapter Objectives (Cont’d)

• Explain why secondary data management is increasingly important

• Define marketing information system and describe its basic components


Pure and Persil detergentCadbury Chocolates

Huggies DiapersBirds Eye Fish Sticks

• Different products, different companies, one common database

What Do These Companies Have in Common ?


Secondary Data

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

• Advantages– Cost and time

– Availability

– Less expensive

– Less time intensive


Using Secondary Data: Advantages

• Readily available– Whirlpool warranty card

– Nielsen/Net Ratings

– U.S. Census Bureau

– Statistics


Disadvantages of Secondary Data

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

– Differences in category definitions

– Time Period


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?


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


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


Digital BabySitter.comwebsite

Digital BabySitter


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

http://www.un.org/


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!!!


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


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


The Numbers Game

• THE SHOCKING TRUTH IS THAT STATISTICS ARE ONLY AS CREDIBLE AS THE SOURCES THAT PRODUCE THEM!


2002 2004

Message Labs 19% 84%

Brightmail 39 65

Postini 60 78

Frontbridge 40 82

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

Spam Projections Which Numbers to Use?


Secondary Data Limitations

• Accuracy– Who collected the data?

– Why was the data collected?

– How was the data collected?


Types and Sources of Secondary Data

• Internal Sources– Company held information

• External Sources– Government

– Syndicated Sources

– Trade Associations

– Miscellaneous Sources


Exhibit 4.1 Flow Diagram for Conducting a

Data Search


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?


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


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


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


Syndicated Sources (Cont’d)

• Often allows for customization– Roper reports

• Supermarkets are also a valuable source for secondary data


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


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


Competitive Intelligence: 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


Competitive Intelligence: 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


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


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


Ad Hoc Marketing Research

Stage 1

Marketing Information System

Evolution of MkIS


Full-fledged Marketing Information Systems

• Data warehouse information storage and retrieval system

• Marketing decision support systems – Data Mining

– Data Modeling

• Expert systems


Exhibit 4.2 A Hotel Chain’s Marketing

Information System


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


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


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 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


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


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 cost- effective way to reach the 6-to-18-year-old market


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


Evolution of MkIS


Stage 1Marketing Information

System

Stage 2Decision Support

System


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


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


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


Applications of Data Mining (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



• 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



• 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



• 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


Marketing Decision Support Systems: Models

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


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


Exhibit 4.7 Data Captured in a Single Source Data Base



Stage 1Marketing Information

System

Stage 2Decision Support

System

Stage 3Expert System

Evolution of MkIS


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


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

Chapter Four Secondary Data. Copyright © Houghton Mifflin Company. All rights reserved.4 | 2...

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