The Research Process - Information System
Information System
Whereas marketing research is mainly concerned with the actual content of the information and how it is to be generated, the information system is concerned with managing the flow of data from many different projects and secondary sources to the managers who will use it. This requires databases to organize and store the information and a decision support system (DSS) to retrieve data, transform it into usable information, and disseminate it to the users.
Databases
Marketing information systems contain three types of information. The first is the recurring market and accounting data that flow into the organization as a result of market analysis research and accounting activities. For example, automobile firms use government sources for monthly data on new-car sales by brand and geographic area. In addition, surveys are conducted yearly to determine the age and type of automobiles currently driven, the lifestyles of the drivers (their activity and interest patterns), their media habits, and intentions to replace their car. The accounting department will submit sales and inventory data for each of its dealers on a continuing basis, to update and supplement the information system.
A second type of information is intelligence relevant to the future strategy of the business. Automobile firms, for example, will collect reports about new sources of fuel to power automobiles. This information could come from scientific meetings, trade organizations, or perhaps from government reports. It also could include information from salespersons or dealers about new-product tests being conducted by competitive firms. Marketing intelligence is difficult to develop because it usually involves diverse and changing sets of topic and information sources and is rarely collected in a systematic manner.
A third input to the information system is the marketing research studies that are not of a recurring nature. The potential usefulness of a marketing research study can be multiplied manyfold if the information is accessible instead of filed and forgotten. However, the potential exists that others may use the study, although perhaps not the way it was originally intended.
Decision Support Systems
Databases have no value if the insights they contain can't be retrieved. A decision support system (DSS) not only allows the manager to interact directly with the database to retrieve what is wanted, but it also provides a modeling function to help make sense of what has been retrieved.
A common example of a DSS in action is that used by many industrial salespeople—especially those selling products that require significant customization. The salesperson will frequently be asked whether or not the price and delivery time of a unique product configuration will meet or exceed a competitor's promises. Without leaving the customer's office the salesperson can plug a lap-top computer into a phone jack and begin communicating with a database stored in the main computer memory. The salesperson types in the product configuration and desired delivery data, and these requirements are compared to the costs, inventory, and assembly time contained in the data bank. In a matter of minutes, the salesperson can propose a price and delivery data—and perhaps close the sale.
Each firm has to develop or adapt models to support its own decision problems. For example, Avon Products Inc., the door-to-door cosmetics firm, has unique problems as the result of a part-time salesforce of almost 400,000 representatives theoretically covering half of the 80 million households in North America. This salesforce carried a large product line which each year had 1600 new or reformulated products added. A variety of computer models were added to their DSS to help cope with these problems:1
• A salesforce turnover model that revealed that the most significant variable influencing the rate was the level of appointment fee, which reps pay for material.
• An order model that explains the components of the average order and isolates the actionable variables such as the size and timing of the catalog and gift incentives.
• A procurement model that helps determine how much of a new product to buy, when to purchase it, and the risks that are involved.
Applying Information Systems to Marketing Research
Often the process of developing and using models and information systems reveals gaps in the data bank that have to be closed. These emergent needs for information become a marketing research problem. For example:
• Performance (sales, market share, contributions, patronage) may be unsatisfactory relative to objectives. Perhaps the condition can be traced to a specific geographic area, but the underlying reasons still must be sought before action can be taken.
• A competitor may launch a new product or employ a new advertising appeal, with unknown consequences for the firm's competitive position.
• An unavoidable increase in costs puts pressures on profitability (or, in the case of a transit system, increases the subsidy requirements to an unacceptable level). Various possible increases in fares or prices must be evaluated.
• An upsurge in interest in health and nutrition may suggest to a snack company a new product line directed toward responding to this interest. Concept testing might be a first step in exploring this opportunity.
Given the sometimes chaotic and usually uncertain nature of most market environments, a large number of problems and some opportunities can emerge. Few will ever be given formal consideration: there may be no further need for clarification, the implications may not appear serious, or the response may appear evident in the judgment of the decision maker. Our interest is in those problems or opportunities that need to be clarified, whose consequences are uncertain, or which involve the development of new programs, products, or services.
The information system serves to emphasize that marketing research should not exist in isolation as a single effort to obtain information. Rather, it should be part of a more systematic and continuous effort by the organization to improve the decision-making process.
Whereas marketing research is mainly concerned with the actual content of the information and how it is to be generated, the information system is concerned with managing the flow of data from many different projects and secondary sources to the managers who will use it. This requires databases to organize and store the information and a decision support system (DSS) to retrieve data, transform it into usable information, and disseminate it to the users.
Databases
Marketing information systems contain three types of information. The first is the recurring market and accounting data that flow into the organization as a result of market analysis research and accounting activities. For example, automobile firms use government sources for monthly data on new-car sales by brand and geographic area. In addition, surveys are conducted yearly to determine the age and type of automobiles currently driven, the lifestyles of the drivers (their activity and interest patterns), their media habits, and intentions to replace their car. The accounting department will submit sales and inventory data for each of its dealers on a continuing basis, to update and supplement the information system.
A second type of information is intelligence relevant to the future strategy of the business. Automobile firms, for example, will collect reports about new sources of fuel to power automobiles. This information could come from scientific meetings, trade organizations, or perhaps from government reports. It also could include information from salespersons or dealers about new-product tests being conducted by competitive firms. Marketing intelligence is difficult to develop because it usually involves diverse and changing sets of topic and information sources and is rarely collected in a systematic manner.
A third input to the information system is the marketing research studies that are not of a recurring nature. The potential usefulness of a marketing research study can be multiplied manyfold if the information is accessible instead of filed and forgotten. However, the potential exists that others may use the study, although perhaps not the way it was originally intended.
Decision Support Systems
Databases have no value if the insights they contain can't be retrieved. A decision support system (DSS) not only allows the manager to interact directly with the database to retrieve what is wanted, but it also provides a modeling function to help make sense of what has been retrieved.
A common example of a DSS in action is that used by many industrial salespeople—especially those selling products that require significant customization. The salesperson will frequently be asked whether or not the price and delivery time of a unique product configuration will meet or exceed a competitor's promises. Without leaving the customer's office the salesperson can plug a lap-top computer into a phone jack and begin communicating with a database stored in the main computer memory. The salesperson types in the product configuration and desired delivery data, and these requirements are compared to the costs, inventory, and assembly time contained in the data bank. In a matter of minutes, the salesperson can propose a price and delivery data—and perhaps close the sale.
Each firm has to develop or adapt models to support its own decision problems. For example, Avon Products Inc., the door-to-door cosmetics firm, has unique problems as the result of a part-time salesforce of almost 400,000 representatives theoretically covering half of the 80 million households in North America. This salesforce carried a large product line which each year had 1600 new or reformulated products added. A variety of computer models were added to their DSS to help cope with these problems:1
• A salesforce turnover model that revealed that the most significant variable influencing the rate was the level of appointment fee, which reps pay for material.
• An order model that explains the components of the average order and isolates the actionable variables such as the size and timing of the catalog and gift incentives.
• A procurement model that helps determine how much of a new product to buy, when to purchase it, and the risks that are involved.
Applying Information Systems to Marketing Research
Often the process of developing and using models and information systems reveals gaps in the data bank that have to be closed. These emergent needs for information become a marketing research problem. For example:
• Performance (sales, market share, contributions, patronage) may be unsatisfactory relative to objectives. Perhaps the condition can be traced to a specific geographic area, but the underlying reasons still must be sought before action can be taken.
• A competitor may launch a new product or employ a new advertising appeal, with unknown consequences for the firm's competitive position.
• An unavoidable increase in costs puts pressures on profitability (or, in the case of a transit system, increases the subsidy requirements to an unacceptable level). Various possible increases in fares or prices must be evaluated.
• An upsurge in interest in health and nutrition may suggest to a snack company a new product line directed toward responding to this interest. Concept testing might be a first step in exploring this opportunity.
Given the sometimes chaotic and usually uncertain nature of most market environments, a large number of problems and some opportunities can emerge. Few will ever be given formal consideration: there may be no further need for clarification, the implications may not appear serious, or the response may appear evident in the judgment of the decision maker. Our interest is in those problems or opportunities that need to be clarified, whose consequences are uncertain, or which involve the development of new programs, products, or services.
The information system serves to emphasize that marketing research should not exist in isolation as a single effort to obtain information. Rather, it should be part of a more systematic and continuous effort by the organization to improve the decision-making process.
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