Artificial Intelligence Customer Psychological Predictive Method: Appies to Marketing Information Gathering PDF Download

Author: Johnny Ch Lok
Publisher: Independently Published
ISBN: 9781794466005
Category : Business & Economics
Languages : en
Pages : 254

View

Book Description
2.2How can apply (AI) digital channel to predict consumer behaviors?(AI) digital channel can be applied to help businesses to evaluate whether how much the product price is the most attractive to persuade consumers feel it is the most reasonable price to sell. It helps consumers to feel which brands of products which ought change the price to let consumers to choose to buy the brand of product. It can be applied to predict whether how many consumer numbers can be increased or decreased when the brand of product's price is variable. It aims to give opinions to help any brand of product manufacturers or sellers to judge whether which price is the most reasonable to let consumers to accept to choose to buy the brand of product in popular.Thus, (AI) price measurement technology can be preference to be applied online communication ecommerce and mobile phone internet platform aspect. As businesses can enter their past products prices data and past customer number data into computer or mobile. Then, (AI) price measurement technology can gather these data to analyze these product prices and past customer number to compare their prices variable changing range level to find their price variable difference to measure to make conclusion about every product's price variable changing will influence how many customer number increase or decrease changing to choose to sell their different kinds of products more accurate. Then, (AI) price measurement software will help them to analyze all past price variable changing data to compare whether which price range can let customers to feel it is more reasonable and attractive to influence them to choose to buy the product among different brands of product choice.Because any product's price is one important factor to influence consumers to choose to buy the product, instead of quality, durability, shape, appearance, color, brand familiarity etc. factors. Any online businesses with a focus on Asia should considerate (AI) customer care, and virtual shopping experience, whereas is Europe and North America still value face-to-face and/or real human interaction over (AI) or virtual worlds.

Author: Johnny Ch Lok
Publisher: Independently Published
ISBN: 9781793171849
Category :
Languages : en
Pages : 254

View

Book Description
(AI) digital data gather technology predicts food consumer behavior's main barriersWhat are the main barriers to food industry? When the food manufacturer applies (AI) big data gather technology to predict food consumer behavior? The barriers include that the food manufacturer / provider needs to decide whether when the right time is applied to the right (AI) digital big data prediction tool channel to find the right food consumers to be chose to full food consumption satisfactory questionnaires, how to gather multi-class food consumption classifiers on real-world food consumers transactional data from the food sale domain consistently to show the critical numbers of different kinds of food items at which the predictive performance most accurate? So, any food manufacturer / provider's advanced in (AI) digital data gather warehousing and management technologies can provide that opportunities for food business to enhance long term relationship with the food providers' clients. However, food industry's (AI) digital data gather aims to improve food customer product targeting, increase food customer loyalty and food purchase probability to the food supplier. To effective identify, understand and satisfy the needs of their food customers, the food suppliers need to develop the right (AI) digital questionnaire questions and find the right food customers to fill every right questions from every digital questionnaire at the right time through the right channel. Above of all these, they will be the barriers when one food supplier expects its (AI) digital data gather questionnaires which can conclude the most accurate prediction concerns any kinds of consumer food product choices. So, such as (AI) digital data prediction model, it is needed to incorporate into the food market segmentation, food customer targeting, and food challenging decisions with the goal of maximizing the total food customer lifetime. For example, (AI) big data gather transaction data is reasonable and accurate for building predictive models. Transaction data can be electronically collected and readily made available for data mining in lot quantity at minimum extra costs.Suggestion to apply (AI) prototypes of food customer profiles method to predict food customer behavioral changes. Prototypes of food customer profiles mean to be extracted from the discovered bins and multi-class classifies models are built using those prototypes. The learned models can than be used to predict the class of food customer profiles ( e.g. restaurants, school canteens, supermarkets etc. food suppliers) based on their food purchases. The approach is validated on the case study of a food retail and food service company operating in food and beverages market.So, a food customer profile, it is a description (AI) data gather tool will record every of food customer using available information, which help in understanding their background and food consumption behavior. (AI) data gather tool can well develop every food customer profile, every food customer data is essential in food market analysis as they aid food suppliers in saving time and money by highlighting the real potential food consumers whose needs are to be met rather a range of individuals.So, (AI) data gather tool can record every food consumer profile and every can be factual or behavioral food consumption. A factual food customer profile consists of a set of characteristics for (AI) big data gather record, e.g. demographic information, such as food customer name, gender, birth date, when a behavioral food customer profile consists of what the food customer is actually doing and is usually derived from (AI) digital transactional data gather record.

Author: Johnny Ch LOK
Publisher:
ISBN:
Category :
Languages : en
Pages : 141

View

Book Description
Media economic methods to predict readers' behaviors in publishing industryMedia economics the application of economic theories, concepts and principles to study the macroeconomics and microeconomic aspects of most media consumption and industries, for academic lecturers, policymakers, and industry analysts. Media economics methods include how to apply variety of methodological approaches both qualitative and quantitative methods and statistical analysis, as well as studies using financial, historical and policy driven data.Some economists define land, labor, and capital as the three factors of production and the major contributors to a nation's wealth. Can land, labor and capital be as three main factors of production any books, newspapers, magazines etc. reading products in publishing industry? Some economists believed price was determined by the costs of production, whereas marginal economists equated prices with the level of demand can be any books, magazines, newspapers etc. reading products prices is either determined by the cost of printing production or equated any one kind of these reading products with the level of reader' demand more.The marginal economists contributed the basic analytic tools of demand and supply, consumer utility and the use of mathematics as analytical tools to develop microeconomics. Can apply the basic analytic tools of reader demand and the any one kind of these reading products supply and reading consumer individual reading need, utility and the use of mathematics as analytical tools to predict any kind of reading consumer numbers and reading interesting topic choice in media industry?However, some economists also demonstrated that given a free market economy, such as in free publish industry, the factors of production ( land, labor and capital) were important in understanding the economic system. Can apply the factor of production , e.g. publishing book sale location ( land); publishing book salespeople sale experience ( labor); and attractive book printing quality (capital printing expense) to influence the publishing industry reading consumer reading habit or purchase book activities?However, some economists suggested two important contributions: Analysis of monopoly and price discrimination and the market for labor will influence consumer number. Such as publishing case: Can analysis of which famous royalty publishing book sale firm to the most monopoly and then following its different topic of books sale price to evaluate whether how much every different topic of its similar book topic sale price to be higher to avoid reduce reader numbers, due to the not famous royalty book seller which similar topic book to the famous royalty book seller's prices are too higher than the famous royalty publishers' book prices?

Author: Johnny Ch Lok
Publisher: Independently Published
ISBN: 9781793961334
Category :
Languages : en
Pages : 254

View

Book Description
Chapter Three(AI) tool judges the difference between utility factor and emotionto influence consumer decision making In economic utility or immediate (expected) emotion aspects, whether which is more influential to excite consumption. To analyze whether it is economic utility or immediate ( expected) emotion more influential to excite consumption. It depends on the consumer individual consumption choice is in which situations. For example, if the industry's general consumer individual consumption decision is concentrate on emotion influential aspect, such as cruise entertainment industry, hospital care service industry, theme park entertainment industry, movie watching entertainment industry etc. Above all these industries have same nature, it is service. So, it seems that service industry's main influential factor is immediate ( expected) emotion influence, it is not economic utility influence. Otherwise, product sale industry's main influential factor is utility. 3.1(AI) judges consumer utility factorFor this toy choice situation example, parent choose to buy one toy to give whose child to play. They usually considerate which kind of toy is attractive to their child whom like to play. In many different kinds of toys choice, if the child likes to choose the kind of toy to play. After the child's parents had purchased the kind of toy to let whose child to play one period time, e.g. six month. Then, when the child feel that who has need to buy another new toy to play, due to he/she feels bored to play this toy. So, it seems that the child feels this toy has less utility or it's utility is decreased. So, he/she expects whose parent can buy another new kind of toy to let whom to play. It also implies that it is not emotion factor to influence the child to feel boredom and unfunny to play this kind of old toy after six months. It is the product's utility factor which can not attract the child to play it any more. So, this old toy's utility is decreased when this child spends six months to play it. This toy's value is only six month utility to this child to play. Otherwise, if this kind of toy is bought by another parent. It is possible that the another child like to play this kind of toy one year or more. So, it's utility to another child is one year or more period. So, product's utility period is difference, it depends on how long time of the user's satisfactory time.

Author: Johnny Ch Lok
Publisher: Independently Published
ISBN: 9781793110862
Category :
Languages : en
Pages : 254

View

Book Description
ChapterSixIs Artificial Intelligent the most effective andaccurate consumer behavioral tool?Is (AI) the best and the most effective and accurate consumer behavioral prediction tool to compare other kinds of consumer behavioral prediction tools? Nowadays, retailing competitions are serious businessmen often find different kinds of methods to attempt to predict consumer changes. The consumer behavioral predictive methods can include as these below methods, instead of (AI) big data gathering tool.Firstly, statistics is the popular mathematic method, it applies auto-regression, liner regression, structural equation modelling, logistic regression statistic techniques to be used to predict consumer behaviors. Secondly, it is classification method, it sis a support vector machine to assist businessmen to make consumer behavioral prediction, it also includes decision making tress diagram technique. Thirdly, it is rule mining method, it is algorithm, market base analytic etc. business marketing concept analytical tool, it also includes graph mining technique tool. Next, it is psychological prediction model tool, it is psychology prediction model too, it is a kind of psychological method to predict consumer behaviors. Finally, it is the most updated and potential artificial neural network (ANN) machine tool, it gathered big data, then it will carry on analyzing and applies psychological method to conclude the most accurate and reasonable solutions to give recommendation to businesses to predict when and how and why their consumer behaviors will change. So, it is one owned human mind's machine and owned psychological and analytical efforts to replace humans to make any judgement in order to make the most accurate predictive behavioral changes for consumers, instead of the traditional marketing concept and psychological and mathematic methods to predict consumer behavior, (AI) big data gathering tool will be another new tool.What are the advantages of (AI) tool to be used to predict consumer behaviors as well as what are the different between it and other traditional consumer behavioral predictive tools? I shall explain as below: Firstly, as above all case studies are explained to (AI) questionnaire design method benefit, I believe (AI) big data gathering tool can be applied to help human to analyze and design any the suitable valid questions to enquire any kinds of business consumers in order to gather the most meaning and useful opinions to conclude the most accurate consumer behavioral prediction for every questionnaire. So, future (AI)'s analytical effort and decision making effort most be exceed above human's judgement efforts. So, future (AI) can help human to design the most useful and meaning different kinds of valid questionnaire ( survey) questions as well as assist humans to analyze and make accurate decision making and conclusions to give opinions to help businessmen to predict when consumer behaviors will change and how their consumption behaviors will change to influence their businesses in order to help them to make any efficient and effective and accurate solutions to avoid consumer number to be decreased and the most important benefit is that it can give opinions to help businessmen to explain why ( what the factors ) cause their consumer behaviors change suddenly. It will be human's efforts can not achieve to exceed (AI)'s efforts in the future.Secondly, (AI) can make artificial machine judgement and analytical effort, without human misleading or unfair or unreasonable judgement. So, it can make more fair and reasonable and accurate conclusion to give opinions to predict when, how and why consumer behaviors will change suddenly to the kind of business in customer model building process and evaluating the results of customer relationship management -related investment more accurate.

Author: Johnny Ch Lok
Publisher: Independently Published
ISBN: 9781794160682
Category : Business & Economics
Languages : en
Pages : 254

View

Book Description
Chapter TwoWhat is (AI) deep learning techniques to forecast environment behavioral consumptionThe (AI) deep-learning technology leads to performance enhancement and generalization of artificial intelligent technology. It influences the global leader in the field of information technology has declared its intention to utilize the deep-learning technology to solve environmental problems, such as climate change. So, it will help agriculture farming businesses can raise any plant food: vegetable, fruit, rice which grow up very easily if farmers can apply (AI) deep-learning technology to solve environment problems to influence their plant food grow. If the whole year seasonal change is very good and it is suitable for any plant food to grow in farming land easily, e.g. rain is enough and soil is enough for any plant food to grow in the farm lands. Then, fruit, rice, vegetable etc. agriculture businesses will have much beneficial attribution to global farmers. The question is how to use deep-learning technologies in the environmental field to predict the status of pro-environmental consumption. We predicted the pro-environmental consumption index based on Google search query data, using a recurrent neural network ( RNN model). To certify the accuracy of the index, we compared the prediction accuracy of the RNN model with that of the ordinary least square and artificial necessary network models. For example, the RNN model predicts the pro-environmental consumption index better than any other model. we expect the RNN model to perform still better in a big data environment because the deep-learning technologies would be increasingly as the volume of data grows. So, deep-learning technologies could be useful in environmental forecasting to prevent damage caused by climate change to influence any rice, vegetable, tomato, potato, fruit etc. different plant food grow in any countries' farming land easily.For South Korea example, over 800 government agencies spent 2.2 trillion Korea won on eco-products in 2014 year. However, green products are rarely purchased outside these agencies. This phenomenon occurs because there is a gap between consumer attitudes and behavior, that is environmental attitude is a major factor in decision making vis-a-vis the consumption of " green" food and services ( Jorea Ministry of Environment, 2015). Therefore, it is necessary to understand those consumer attitude, that will lead to sustainability-conductive behavior and consumption.2.1Environmental consumption predictionRecently, many researchers have studied pro-environmental consumption and household indexes as well as suicide rate predictions using messages posted by internet users on Google trend, Tweets etc. channel. Whether can environmental consumption be predicted by (AI) deep-learning technological internet channel? How can impact the pro-environmental consumption attitudes of green policies? Korea scientists estimated pro-environmental attitudes using search query data provided by Google trend and confirmed through regression analysis, that pro-environmental attitude has a positive correlation with the pro-environmental attitude index. They also explained that environment-friendly attitude of residents plan an important role in policy making. In the past, most household consumption indexed were calculated through surveys, but (AI) deep-learning technological tool " big data" have recently gained research attention ( Lee et al. 2016).It seems that (AI) deep-learning technology can help agricultural export countries' farmers, e.g. US, UK, Canada, New Zealand, Australia, Japan, China, India etc. they can predict environmental behavioral consumption to any rice, tomato, potato, fruit, vegetable etc. plant food consumers. The beneficial advantages to them include as below:

Author: Johnny Ch Lok
Publisher: Independently Published
ISBN: 9781720160410
Category : Business & Economics
Languages : en
Pages : 174

View

Book Description
This book has these two research questions need to be answered? (1) Can apply (AI) learning machine predict consumer behaviors? (2) Can (AI) learning machine replace human marketing research method, e.g. survey or human psychological and micro and macro economic methods to predict consumer behaviors more accurate? Nowadays, many businessmen or marketing research professional hope to apply different methods to predict consumer behaviors in order to know what will be future market activities and market changes to help them to choose to implement what kinds of marketing strategies more accurately. The methods include economic environmental change prediction method, consumer individual psychological change prediction method, micro or macro behavioral economic environmental change prediction method, marketing environmental change prediction method etc. different kinds of methods which can be applied to predict how consumer behavioral changes to influence whose behavioral consumption to the manufacturer products sale within one to two years short term or three to five years middle term, even above five years long term business plans. Hence, if the product manufacturers can apply the most suitable consumer behavioral prediction method to predict how consumers' choice will be changed to influence their products sale easily. It will have more beneficial intangible and tangible advantages to achieve the their product easier sale aim to ensure their businesses' future market share to be increased more easier to their countries' choice target sale markets. Otherwise, if they applied the inaccurate consumer behavioral prediction methods to predict how their consumers' behavioral changes wrongly. Then, it will influence their market shares to be same level, even it will decrease their market shares, when their consumer behavioral prediction inaccurately. In my this book first part, I concentrate on indicate whether any artificial intelligence (AI) tools will be one kind of good consumer behavioral prediction method to be choose to apply to predict consumer behaviors. I shall indicate some examples, cases to give reasonable evidences to analyze whether (AI) tools will be one kind suitable tool to be applied to predict when and how consumer behavioral changes. If (AI) can be one kind tool to attempt to be applied to predict when and how consumer behavioral changes. Will it replace other kinds of methods to predict consumer behaviors? Does it have weaknesses to be applied to predict consumer behaviors, instead of strengths? Can it be applied to predict consumer behaviors depending on any situations of only some situation? Finally, I believe that any readers can find answers to answer above these questions in this book. In my this book second part, I shall explain why and how human can possible apply (AI) tool to predict consumer individual emotion. I shall indicate case studies to explain how consumer individual better or worse emotion how to influence whose consumption behavior in different suitations. Finally, I shall indicate evidences to conclude how and why (AI) tool that can be used to predict consumer individual emotion and it will have direct relationship to influence consumption behavior, as well as how (AI) tool can assist businessmen to judge whether what reasons case the customer does not choose to buy its product, it is possible because the product high price factor, poor product quality or poor staff service performance or attitude etc. different factors to influence the consumer decides to choose to buy the other product consequently, when the (AI) tool can confirm consumer has good or bad emotion to judge what factors are the causes his decision making at the moment.

Author: Johnny Ch Lok
Publisher: Independently Published
ISBN: 9781793111111
Category :
Languages : en
Pages : 254

View

Book Description
Part ThreeEconomy And Marketing Predictive MethodChapter OnePsychological method predictsconsumer behavior1.1Can apply economic models solve marketing changing challenges?Economists indicate economic modeling can provide a logical, data to help organize the analyst's thoughts. The model helps the economist logically isolate and sort out complicated chains of cause and effect and influence between the numerous interacting elements in an economy. There are four types of models used in economic analysis: Visual models, mathematical models, empirical models and simulation models.Visual models are simply pictures of an abstract economy: graphs will lines and curves that tell an economic story. It is one kind of micro or macro-economic method to predict consumer behavioral change. Some visual models are diagrammatic such as which flow the income thought the economy from one sector to another ( micro economic environment). It is mathematical model, when it is presented the mathematics are explained what the data analysis is or not. The model does not normally require a knowledge of mathematics, but still allow the presentation of complex relationship between economic variable.For example, the common supply-and demand model is meant to show the effect of inflationary expectations upon price and output. In this application, an increase in inflationary expectations causes demand to shift, raising prices and outputs (macro-economic environment). For another example, a very simple micro-economic model would include a supply function (explaining the behavior of products or those who supply commodities to the market), a demand curve ( explaining the behavior of purchasers) and an equilibrium equation, specifying the simple conditions that must be met if the model's equilibrium is to be satisfied. So, the variables in a model like this represent a type of economic activity (such as demand) or data ( information ) that either determines or is determined by that activity ( such as a price or interest rate variable change activity).Dynamic models, in contrast, directly incorporate time into their structure. This is usually done in economic modeling by this mathematical systems of difference of differential equations. For example, it can use a difference equation from a business cycle model, investment now depends upon changes in income in the past. Time is incorporated into the model. Dynamic models, when they can be used, sometimes better represent the business cycles, because certainly behavioral response and timing strongly shape the character of a cycle. For another example, if there is a delay between the time income is received and when it is spent. A model that can capture the delay is likely to those higher consumption desire to the consumer. It is a micro-personal behavioral consumption predict method. So, the user can experiment with an endless variety of values and assumptions to see whether results obtained are realistic or insightful. Since computers are now powerful and cheaper, the importance of dynamic simulation models should follow the future prediction time, when the consumer income receive and when it is spent to predict how much degree of the consumer's consumption desire in micro-economic view point.Another model to be applied to predict consumption behavior. It is expectations and enhanced model, it includes one or more variables based upon economic expectations about future values. For example, if consumers for whatever reason, expect the inflation rate to be much higher next year, then this year, they are said to have formed inflationary expectations. If numerical values are being used in a model and the current inflation rate is 9%, if they expect inflation to be higher next year, the variable for inflationary expectations might be given be a value if 12% or more.

Author: Johnny Ch Lok
Publisher: Independently Published
ISBN: 9781099445835
Category :
Languages : en
Pages : 574

View

Book Description
Is (AI) the best and the most effective and accurate consumer behavioral prediction tool to compare other kinds of consumer behavioral prediction tools? Nowadays, retailing competitions are serious businessmen often find different kinds of methods to attempt to predict consumer changes. The consumer behavioral predictive methods can include as these below methods, instead of (AI) big data gathering tool.Firstly, statistics is the popular mathematic method, it applies auto-regression, liner regression, structural equation modelling, logistic regression statistic techniques to be used to predict consumer behaviors. Secondly, it is classification method, it sis a support vector machine to assist businessmen to make consumer behavioral prediction, it also includes decision making tress diagram technique. Thirdly, it is rule mining method, it is algorithm, market base analytic etc. business marketing concept analytical tool, it also includes graph mining technique tool. Next, it is psychological prediction model tool, it is psychology prediction model too, it is a kind of psychological method to predict consumer behaviors. Finally, it is the most updated and potential artificial neural network (ANN) machine tool, it gathered big data, then it will carry on analyzing and applies psychological method to conclude the most accurate and reasonable solutions to give recommendation to businesses to predict when and how and why their consumer behaviors will change. So, it is one owned human mind's machine and owned psychological and analytical efforts to replace humans to make any judgement in order to make the most accurate predictive behavioral changes for consumers, instead of the traditional marketing concept and psychological and mathematic methods to predict consumer behavior, (AI) big data gathering tool will be another new tool.What are the advantages of (AI) tool to be used to predict consumer behaviors as well as what are the different between it and other traditional consumer behavioral predictive tools? I shall explain as below: Firstly, as above all case studies are explained to (AI) questionnaire design method benefit, I believe (AI) big data gathering tool can be applied to help human to analyze and design any the suitable valid questions to enquire any kinds of business consumers in order to gather the most meaning and useful opinions to conclude the most accurate consumer behavioral prediction for every questionnaire. So, future (AI)'s analytical effort and decision making effort most be exceed above human's judgement efforts. So, future (AI) can help human to design the most useful and meaning different kinds of valid questionnaire ( survey) questions as well as assist humans to analyze and make accurate decision making and conclusions to give opinions to help businessmen to predict when consumer behaviors will change and how their consumption behaviors will change to influence their businesses in order to help them to make any efficient and effective and accurate solutions to avoid consumer number to be decreased and the most important benefit is that it can give opinions to help businessmen to explain why ( what the factors ) cause their consumer behaviors change suddenly. It will be human's efforts can not achieve to exceed (AI)'s efforts in the future.Secondly, (AI) can make artificial machine judgement and analytical effort, without human misleading or unfair or unreasonable judgement. So, it can make more fair and reasonable and accurate conclusion to give opinions to predict when, how and why consumer behaviors will change suddenly to the kind of business in customer model building process and evaluating the results of customer relationship management -related investment more accurate.

Author: Johnny Ch LOK
Publisher:
ISBN: 9781790590667
Category :
Languages : en
Pages : 553

View

Book Description
Can scientific research method predict Disney visitors behavior by (AI) marketing research survey method?Using scientific research methods to predict Disney consumption behavior phenomena in this field, some experts had attempted to do research in predictive validity to evaluate whether a measure of scientific achievement to consumer behavior. There are three groups thought to have varying knowledge of and ability to predict consumer behavior are academics, marketing practitioners and consumers in general. Academic groups use their scientific knowledge of consumer behavior as a basic for such activities as teaching, consulting for corporations, and testifying in legal and regulatory proceedings. In contrast, marketing practitioners are likely to be as familiar with this scientific literature. However, practitioners gain expertise through their experience. This expertise might help them to make accurate predictions of consumer behavior. Finally, when few studies on consumer behavior reach the general public, consumer's personal experiences should help them to predict certain aspects of customer behavior. So, it seems that whether Disney ought choose to do consumer behavioral psychological method to predict consumer behavior, such as personal experiences ( psychological feeling) method is more accurate than to scientific method, such as marketing research method. In this discussion, I shall imply two hypotheses about why Disney experts ought measure consumer behavior by behavioral psychological method predictions more than scientist method, such as marketing research method . The first hypothesis, experts can make more accurate predictions than novices as well as the second hypothesis, academics can make more accurate predictions them practitioners. Thus, these hypotheses bring the questions and asked the subjects to predict whether each hypothesis tended to be true or false. For example, whether the more frequently an adolescent interacts with peers about consumption matters is the greater the tendency to use peer preferences in evaluating products? ( Moschis & Moore, 1979). Will a person be more satisfied with their recently purchased car if the car met or exceeded whose expectations? ( Westbrook 1980). Hence, using behavioral psychological prediction method , it will ask these these questions to gather the data to attempt to analyze what factors can influence whose satisfied feeling during who play any Disney entertainment facilties. These survey questions can include: Will a disney visitor feel more satisfied to play any Disney entertainment facilities with who recently visited Disney if the Disney entertainment facilities met or exceed whose expectation? Whether the more frequently Disney entertainment facilities a player with whose friends who have more satisfied feeling to play any Disney entertainment facilities to compare the less frequently Disney entertainment facilities another player alone or no any friend?A long term survey research indicated that a consumer research result for the practitioner group, who worked with marketing problems, but who were unlikely to be familiar with scientific research on consumer behavior. For example, systematic sampling was used to select 100 practitioners from the 1984 year American Marketing Association Membership Directory ( academic addresses were excluded), a self addresses envelope was enclosed in the original mailings, and two postcard reminders were sent. Replies were received from 20 academics and 13 practitioners.