Target markets and ANN

Once we, marketers, have determined our business products or services, we must accurately identify our target markets. The target market is the actual customer group, or audience, in which our business will attempt to sell its products and services. Target marketing tailors a marketing mix for one or more segments identified by market segmentation and usually contrasts with mass marketing, depending on the industry, the competition, the marketing budget of the business, NGO vs non-NGO, etcetera. Many techniques have been used in order to select the target markets, such as statistical regression (Bult&Wansbeek, 1995), neural computing (Zahavi&Levin, 1997) and fuzzy clustering (Setnes&Kaymak, 2001). Direct marketing is, off course, a well-known type of marketing, used by businesses, in order to target, call to action and increase profits. A well-executed direct advertising campaign can have a satisfying Return On Investment, since many potential customers responded to a clear call-to-action. The growing interest in direct marketing techniques has made it an important application field for marketers and data mining professionals.

CHAID vs ANN

Artificial neural networks (ANN) are "computational models inspired by animals' central nervous systems that are capable of machine learning and pattern recognition". They are presented as systems of interconnected neurons that can compute values from inputs by feeding information through the network. 

Commonly, a class of statistical models may be called "neural" if they

1. consist of sets of adaptive weights, i.e. numerical parameters that are tuned by a learning algorithm, and
2. are capable of approximating non-linear functions of their inputs.

The adaptive weights are conceptually connection strengths between neurons, which are activated during training and prediction.

As aforementioned, marketers should divide the market into segments. A product offer would then be made to those people who are considered to be in the market segment for which the product was meant. Automation segmentation techniques have been used to divide the customers into a number of segments, like CHi-squared Automatic Interaction Detection (CHAID). CHAID is a type of decision tree technique, that is used for prediction as well as classification and detection of interaction between marketing variables. In practice, CHAID is often used in the context of direct marketing to select groups of consumers and predict how their responses to some variables affect other variables.

When a segmentation method such as CHAID is used to determine target segments which are more likely to respond to marketing actions, the data set is split into disjoint groups and the groups that are considered to be most likely to respond are targeted through a marketing action, e.g. mailed preferentially. Neural networks, however, do not segment data into disjoint groups. That means, a different approach is needed to develop target selection models by using neural networks. Since target selection models try to differentiate responders from non-responders, marketers could consider formulating the problem as a classification problem and develop a network that classifies each input pattern as a responder or non-responder. However, the classes are not well separated in target selection problems.  

Furthermore, the miss-classification costs  are asymmetric. Miss-classifying a possible responder as a non-responder and not targeting that individual is more costly to the direct marketing company than miss-classifying a non-responder as a responder. Therefore, another method than pure classification should be used. One solution could be to define a measure for the "likelihood" of an individual to respond (call-to-action) via the marketing action, e.g. mailing. The individuals are then ordered according to this measure, and only the ones that score above a given threshold θj are targeted (target scoring). 

Artificial neural network models for target selection fall into this category. The output of the neural network is then a measure of likelihood of response (Potharst et al., 2001). Now, the goal of determining the neural network would be to determine a correct set of network parameters (weights) such that a good indication of the likelihood of response of the supporters is obtained, given the inputs to the network. Naturally, these inputs must convey information about the characteristics of the supporters, including demographic, geographic, or even psychographic and behavioral variables.

ANN configuration and analysis

In target selection problems we form the model of the likelihood of response at a single point in time, given various supporter characteristics and/or their past behavior. In real life, feed-forward neural networks are sufficient for target selection problems. The nonlinear activation function of the neurons also has an influence on the final neural network model for a given network structure and the number of neurons. The logistic sigmoid function:

is often used since the neural network output is expected to lie in the range [0,1] (likelihood of response). Thus, the neural network model can be analyzed as a generalized nonlinear regression model with the likelihood [0,1] of response to our marketing action as an output .

We may visualize our target selection model through a hit probability chart. 

Potharst et al. 2001

A hit probability chart exposes what percentage of a selected population may respond to our marketing action. The x-axis shows a percentage of the total group selected for targeting and the y-axis shows what percentage of that group is a responder to our call-to-action. The hit probability chart for a successful model starts with high values of hit probability. As the size of the selected group increases, the percentage of responders within the selected group will decrease until the hit percentage is equal to the percentage of responders within the total marketing action eg. mailing considered in the campaign.

In real life, after data preparation and data set selection, marketers may use IBM SPSS Neural Networks as a modern modelling tool.  

With SPSS Neural Networks, marketers may select either the Multilayer Perceptron (MLP) or Radial Basis Function (RBF) procedure. Both of these are map relationship techniques implied by the data. Both use feedforward architectures, meaning that data moves in only one direction, from the input nodes through the hidden layer of nodes to the output nodes. Prior research has indicated that neural network models may perform better than the CHAID model, while they perform on an equal level with the logistic regression models we aforementioned.

Conclusion

In general, ANN for Marketing can be viewed as a modelling tool that helps marketers to take decisions. ANN can recognize patterns, pick up key information that are not easily identifiable and develop relationships among them. Marketers are expected to fully take advantage of the ANN modeling in order to solve their target selection problem faster and more efficiently.

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Capitalistic materialism and happiness; an holistic approach

“The world says: "You have needs -- satisfy them. You have as much right as the rich and the mighty. Don't hesitate to satisfy your needs; indeed, expand your needs and demand more." This is the worldly doctrine of today. And they believe that this is freedom. The result for the rich is isolation and suicide, for the poor, envy and murder.” 

― Fyodor Dostoyevsky, The Brothers Karamazov: A Novel in Four Parts With Epilogue

Materialism, the forgotten child of monist ontology. The nature and definition of matter have occasioned much philosophical debate, even from ancient times.  

In ancient Greece, philosophers like Thales, Epicurus and Democritus prefigure later materialists; their theory mainly suggests that all that exists is matter and void, and all phenomena result from different motions of base material particles. During the Middle Ages, Pierre Gassendi represented the materialist tradition, as opposed to René Descartes' theory, according to which natural sciences may be explained with dualist foundations. In the 19th century, Karl Marx extended the concept of materialism to elaborate an alternative conception of history based on the empirical world of human activity, thus establishing the dialectical materialism. But, how is materialism perceived today, in a global playground? There are three ways of attaining happiness, according to Sartre: by having, doing and being. Over the next decades, sociologists and psychologists began to train their sights on the study of gaining external happiness, Sartre's concepts of having and doing had become the psychosocial ideas of materialism. Materialism may be nowadays described as a way of thinking that gives much importance to material possessions rather than intellectual things. That means, materialism is nowadays considered as a sociological tool in developing an understanding of modern capitalistic culture via referring to the desire of material needs, rather than a philosophical argument or a conception of history. But, would materialism and consumerism be accompanied by greater well-being?

Materialism and wealth are many times perceived as related terms. Actually, wealth may be an elusive as well as an extrinsic goal. Many people chase money; few achieve great wealth. Are rich people happier than poor people? In order to answer these fundamental questions, we will may try to explain materialism as a construct. Materialism, as the devotion to acquisition and possession, may be described as a defining characteristic of our age which has been criticized on religious, philosophical, and social grounds. Materialism as an individual variable has been effectively defined by Richins & Dawson (1992). Their scale measures three components of materialism: acquisition as the central goal in human life, including acquisition as the path to happiness and success in life as defined by possessions. High scorers, compared to low scorers, are in general less satisfied with life, want more money, are less likely to share, and seem to suffer from poor adjustment, much like those who are preoccupied with money. High scorers also value financial security, while low scorers give priority to interpersonal relationships and a sense of personal accomplishment. On the same basis, researchers have conceptualized materialism as the consumption style that results when consumers perceive that value inheres in consumption rather than in experiences and people. 

Well-being may be easily correlated with materialism. Well-being may assessed in terms of science as subjective well-being or SWB (Diener 1984), which encompasses the cognitive appraisal of one’s life as satisfactory. Given strong motives for acquiring money, with its promises of freedom, power, and even love, the actual impact of income on life satisfaction within a society seems unaccountably meager. Academic researchers such as Ahuvia and Friedman change the focus from objective wealth to subjective appraisals. For instance, they report a strong relationship between perception of income and subjective well-being. SWB increases as income increases from below average to above average within one’s home community. The subjective approach also helps us understand why financial goals seem to have an insatiable quality: as people acquire more, they seem to want even more, with dissatisfaction persisting along with apparent success. However modest the relationship between income and SWB, once the poverty threshold is passed, it is still a positive relationship. Money matter for well-being, but that money’s influence is mediated and limited by personality buffers (self-esteem, control and optimism). Individuals’ happiness level is more or less preset, going up a bit when we experience self-esteem, control, and optimism, and going down a bit when those qualities falter. Increased income has a positive effect all aforementioned buffers, and therefore the happiness level moves toward the upper end of the range. 

Materialism and SWB may be highly correlated. Initial evidence of the relationship between materialism and well-being was provided by Belk (1985), who associated materialism with such undesirable traits as non-generosity, envy, and greed and found that these traits have a significant negative correlation with both happiness and overall life satisfaction. Subsequent studies using Belk’s scale have found that materialism is negatively correlated with satisfaction with personal finances and career accomplishments and positively correlated with social anxiety, dependency or even self-criticism.

Well-being predictors may be categorized as genetically determined, circumstantial, or intentional positive behaviors and cognitions. Genetic factors, such as genes and personality traits, account for a large percentage of the variation in between-subject well-being, but they are very difficult, if not impossible, to alter. Circumstantial factors such as income, marital status, and employment account for only a smaller percentage of the variance in well-being levels due to the phenomenon of hedonic adaptation. Thus, positive behaviors offer the best potential route to longitudinal increases in well-being since people have considerable control over these activities. Hedonic products are those “whose consumption is primarily characterized by an affective and sensory experience of aesthetic or sensual pleasure, fantasy, and fun” (Dhar & Wertenbroch 2000). We may define hedonic consumption as a consumer's regular expenditures on specific hedonic products or services. It reflects how much of the hedonic experience consumers enjoy regularly. Hedonic product usage is positively associated with consumers' well-being, and experiential purchases may even make people happier than material purchases. However, as the human race grows richer, the problems associated with hedonic consumption, may result in negative effects on consumers' well-being. In general, consumers tend to maximize their satisfaction through economic activities that consist of the exchange and consumption of goods. Consumers may enhance their well-being by recognizing their own needs and satisfying them by engaging in consumption activity and attaining consumer products. Consumption, especially of hedonic consumer products, is highly important for happiness among modern consumers, which leads highly developed economies to tend to exhibit an increased emphasis on hedonic consumption. Thus, consumption appears necessary for overall and subjective well-being in modern societies. The possession and consumption of more hedonic products represents the cultural aspiration towards personal happiness and well-being.

We should also refer to materialism and strategic consumption as a means for social affiliation. Humans have an innate need to be a part of social relationships because a social group afforded survival and safety throughout evolutionary history. It is not surprising that people have developed psychological mechanisms that help them ensure that their need to belong is being met. More specifically, exclusion heightens people’s tendency to form new social connections. Excluded people are eager to work and play with others, and they tend to view new sources of social connection in a positive, optimistic light. Consumers use the symbolic ways of consumption as a way to communicate information about themselves to others. Such communication attempts are particularly prevalent when people want to make a good impression on others or facilitate social interaction. Thus, self-presentational motives guide consumption decisions, and people may use consumption as a way to communicate specific information about themselves to others. Excluded people strategically consume in the service of affiliation. Happiness in this case, may be correlated with consumption as a way to be socially accepted and avoid social exclusion.

Let's take for granted that, for the time being, in Western societies, capitalistic materialism cannot be avoided. Brands need to sell and consumers need to consume, at least for the time being. Academic researchers, brands and marketers, having understood how crucial the situational correlation between materialism and well-being may be, need to develop a general framework of mechanisms and ethics in order to make materialistic habits better influence consumers' lifes. For instance, they could provide external stimuli and motivation for emphasizing that materialism may improve social relationships. Actually, financial aspirations are often egocentrically motivated, to get ahead in life. But they can also be sociocentrically motivated. Materialism could be actually motivated to satisfy the need for relatedness. Possessions can be important stores of social memories, tools of social protection, connection, or production. People may cherish particular possessions for such sociocentric motivations, but they may also cherish possessions in general for these motivations, and this could directly improve their social relationships. Both academic research and brand positioning on this possibility would provide new insights about the virtuous, positive sides of materialism, and it would contribute to a different, more holistic outlook on people’s material and social relationships, for a better future.

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5+1 January 2014 Top Commercials

The first crazy commercials of 2014 that are going viral as we speak! Enjoy!

5. King of Shaves "Just Add Water"

4. Booking.com "Booking Epic"

3. New Zealand "Don't Drive Fast"

2. Bud Light "Arnold Zipper"

1. P&G, "Thank You, Mom"

Lucky Star: Bud Light "Don"

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Econometric models of advertising

Advertising is a powerful form of marketing communication used to "encourage, persuade, or even manipulate an audience to take or continue to take some action". The final desired result is to drive consumer behavior with respect to a commercial offering, although political and ideological advertising is also common. The marketing mix has been the key concept to advertising. The marketing mix refers to four variables (the 4 P's) that a marketing manager can control in order to influence a brand’s sales or market share. The basic question that managers face nowadays is, what level or combination of these variables maximizes sales, market share, or profit? Or, otherwise, "How do sales or market share respond to past levels of or expenditures on these variables"?

Researchers have developed a variety of econometric models of market response to the marketing mix, most of these which have focused on market response to advertising or pricing (Sethuraman, 1991). The reason may be that expenditures on these variables seem the most discretionary, so marketing managers are most concerned about how they manage these variables. The basic philosophy underlying the approach of response modeling is that past data on consumer and market response to the marketing mix contain valuable information that can enlighten our understanding of response. 

Seven response models to advertising are have been already patternalized: Current, shape, competitive, carryover, dynamic, content and media effects of advertising. The first four are common across price and the other marketing variables. The last three are unique to advertising. 

Current and Carryover Effect

The current effect of advertising is the change in sales caused by an exposure of advertising occurring at the same time period as the exposure. The x-axis here is time, while sales are on the y-axis and the baseline sales are the dashed line. The current effect of advertising is the spike in sales from the baseline given an exposure of advertising. Decades of research indicate that this effect of advertising is small relative to that of other marketing variables and quite fragile, as it can be easily drowned out by the noise in the data. It can be captured by the model:

Yt = a + b1 At + b2Pt + b3Rt + b4Qt +  et 

where Y represents the dependent variable (e.g. sales), while the other capital letters represent variables of the marketing mix, such as advertising (A), price (P), sales promotion (R), or quality (Q). The parameters a and bk are coefficients that the researcher wants to estimate. bk represents the effect of the independent variables on the dependent variable, where the subscript k is an index for the independent variables. et are errors that can be approached as "white noise".

The carryover effect of advertising is that portion of its effect that occurs in time periods following the pulse of advertising. The carryover effect may occur for several reasons, such as delayed exposure to the ad, delayed consumer response, delayed purchase due to consumers’ backup inventory, delayed purchase due to shortage of retail inventory, and purchases from consumers who have heard from those who first saw the ad (word of mouth). The carryover effect may be as large as or larger than the cur-rent effect. Typically, the carryover effect is of short duration, rather than of long duration. It can be captured by the multiplicative model:

Yt = Exp(a) At b1 Pt b2 Rt b3 Qt b4 et

with sales (Y), advertising (A), price (P), sales promotion (R), quality (Q) inter correlated. Here, the independent variables have a synergistic effect on the dependent variable. In many advertising situations, the variables could indeed interact to have such an impact. For example, higher advertising combined with a price decrease may enhance sales more than the sum of higher advertising or the price drop occurring alone.

Shape Effect

The shape of the effect refers to the change in sales in response to increasing intensity of advertising in the same time period. The intensity of advertising could be in the form of exposures per unit time and is also called frequency or weight. The x-axis now is the intensity of advertising in a period, while the y-axis is the response of sales.  It can be captured by the exponential attraction model:

Mi = Exp (Vi )/Σ Exp Vj 

where Mi is the market share of the ith brand (measured from 0 to 1), Vj is the marketing effort of the jth brand in the market, Σ stands for summation over the j brands in the market, Exp stands for exponent, and Vi is the marketing effort of the ith brand. We know that Vi = a + b1 Ai + b2Pi + b3Ri + b4Qi + ei, as aforementioned, thus:

Mi  = Exp (Vi )/Σ Exp Vj = Exp (Σbk  Xk  + e)/ Σj Exp(Σkbk Xik + ej),

where Xk (0 to m) are the m independent variables or elements of the marketing mix, a = b0 and Xi0 = 1. The use of the ratio of exponents in the above equations ensures that market share is an S-shaped function of share of a brand’s marketing effort.

The diagram above shows three typical shapes: linear, concave and S-shape. Of these three shapes, the S-shape seems the most plausible because it suggests that at some very low level, advertising might not be effective at all because it gets drowned out in the noise. At some high level, it might not increase sales (advertising elasticity-->0) because the market is saturated or consumers are insensitive from the repetitive advertising.

Competitive Effect

Advertising normally takes place in free markets. Whenever one brand advertises a successful innovation or successfully uses a new advertising form, other brands quickly imitate it. Competitive advertising tends to increase the noise in the market and thus reduce the effectiveness of any one brand’s advertising. The competitive effect of a target brand’s advertising is its effectiveness relative to that of the other brands in the market.  The simplest method of capturing advertising response in competition is to measure and model sales and advertising of the target brand relative to all other brands in the market with more complex models, mainly based on the aforementioned econometric model of the "Shape Effect". These first four models may also apply to "Price" instead of "Sales" dependent variable.

Dynamic, Contents and Media Effect

Dynamic effects are those effects of advertising that change with time. Included under this term are carryover effects discussed earlier and wearin, wearout effects. Wearin is the increase in the response of sales to advertising, from one week to the next of a campaign, even though advertising occurs at the same level each week. Wearout is the decline in sales response of sales to advertising from week to week of a campaign, even though advertising occurs at the same level each week. Wearout typically occurs at the end of a campaign because of consumer indifference. 

Content effects are the variation in response to advertising due to variation in the content or creative cues of the ad. This is the most important source of variation in advertising responsiveness and the focus of the creative talent in every agency. Thus, the challenge for marketers is to include measures of the content of advertising when modeling advertising response in real markets.

Media effects are the differences in advertising response due to various media, such as TV, or newspaper, and the programs within them, such as channel for TV or section or story for newspaper.

The last three effects (dynamic, content, media) may be captured by models similar to the S-shaped response with modeling interactions integrated due to the competitive environment or even competitive distributed lag Models, however in reality their mathematics can be quite complex (Tellis, 2006).   

Conclusion

Prudent marketing managers need to take into account how markets have responded to the marketing mix in the past. Past may predict future with relative certainty but it also contains valuable lessons that might enlighten the future. The aforementioned econometrics of response modeling describe how a researcher should model response to the marketing mix and especially to advertising in order to capture and control the most important effects validly. Which may be the "raison d'être" for marketing managers, in the first place.

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