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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5+1 mistakes B2B marketers should avoid

1. TARGETING THE WRONG LEADS

Many marketing campaigns are simply targeted at the wrong people. Completely understandable, given that the companies we are marketing into may have a hundred thousand employees or more. Being a "data junkie” is the key here – make sure you’re obsessed by the data the campaign will be sent out to. Its relevancy and its quality.

In a B2B company, LinkedIn and sometimes Facebook and Twitter are necessary; these channels are so popular that most people assume a company doesn't exist if they are not on social media.

Also, take the time to build a brand strategy. You may be on all the right channels, but you may not have an identity consistent enough for people to relate to. A brand strategy will help you decide who your target audience is and how to best speak to them.

2. FORGETTING YOUR BRAND

With all the talk about the importance of engagement, many marketers get so caught up in sharing that they forget to speak for their own brand.  Yes, we need to engage, but we also need to have an identity. Think of it as a conversation. You want to contribute to the conversation by listening and talking, not just one or the other. Make sure that you, and every other employee, are clear on what your brand identity is and start promoting it.

3. NOT FOLLOWING UP ON LEADS

Sometimes, the sales team will commit to follow up on your campaigns themselves. As they get busy, or as other leads turn into bids, the salespeople can become distracted and drop the lead follow up. We need to have a plan if this starts happening. Furthermore,develop a strategy for “slow burners”, also known as the people who have expressed an interest but are not ready to move forward yet. How will the campaign keep them warm until they are ready to buy?

4. BUSINESS PEOPLE ALWAYS WANT PERSONALIZED MARKETING

Personalization has always been a very effective marketing strategy to nurture and relocate potential customers. If a potential customer and the brand have a relationship, then personalization is helpful and thoughtful.

However, personalization should be voluntary. Making personalized outreaches to a customer who does not give permission destroys trust and invades privacy. A recent study shows that before a relationship is built up, if the dissemination of information of a brand is too personal, it will do harm to the brand.

5. FAILING TO INTEGRATE WITH OTHER ORGANIZATIONAL ENTITIES 

Partnerships are marketing gold and yet many businesses fail to take advantage of them. Let's partner with a charity, a like-minded company or local businesses and both parties will reap the benefits. With social media, B2B partnering is easier than ever. Simply create a place on your website that talks about your partnership and then both parties will use social media to promote one another. This will lead to greater exposure for everyone and showcase that our company is committed to helping others succeed.

6. MISSING OPPORTUNITIES TO RE-ENGINEER CRM

The convenience and low cost of social media offer the most advantageous opportunities for cost-effective, high-performance customer service and customer relationships since the inventions of the internet and customer relationship management (CRM) software. Companies can offset the cost of social media implementation by re-engineering.

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Behavioral Targeting: The Holy Grail of Online Marketing

Behavioral targeting, under which users are presented with advertisements based on their past browsing and search behavior and other available information (e.g., hobbies registered on a website), has been hailed as the new Holy Grail in online advertising.We will refer to the economic implications when an online publisher engages in behavioral targeting. Revenue for the online publisher in some circumstances can double when using behavioral targeting. On the other hand, increased revenue for the publisher is not guaranteed: in some cases the prices of advertising and hence the publisher's revenue can be lower, depending on the degree of competition and the advertisers' valuations. Although social welfare is increased and small advertisers are better off under behavioral targeting, the dominant advertiser might be worse off and reluctant to switch from traditional advertising.

A simple question

Who benefits (and what are the conditions required) from behavioral targeting as compared to traditional advertising? Would the online publisher benefit from the targeting of advertisements? Because of the increased effectiveness of behaviorally targeted advertisements, conventional wisdom would suggest that the answers to these questions are easily predicted, as summed up in an article in the Economist about behavioral targeting:  [...], Advertisers will be prepared to pay more to place ads, since they are more likely to be clicked on. That in turn means that websites will be able to charge more for their advertising slots.  (Economist, 2008)

However, this expected relationship between charges and clicks does not necessarily emerge when the advertisement slot is auctioned off . Instead, using targeted advertisements turns out to be similar to product differentiation: it causes relaxed competition between the advertisers, and hence it is possible that advertisers need not pay as much as they do under traditional advertising. That is, by focusing on a specific user segment, an advertiser's advertisement may be selected with a relatively low price on this segment, whereas under traditional advertising his advertisement would never have been selected or would have been selected only at a higher price. This competitive effect can depress the online publisher's income by realizing a lower revenue per click-through.

Competitive and Propensity effect

On the other hand, the negative effect of relaxed competition for online publishers might be off set by a positive propensity effect. Through targeting advertisements,the probability of a click-through is increased resulting in a higher volume of click-throughs, which positively contributes to the publisher's revenue. Whether the publisher can benefit from behavioral targeting depends on the trade-off between the competitive effect and the propensity effect. Behavioral targeting outperforms traditional advertising only if the competitive effect is dominated by the propensity effect. In particular,when the advertisers competing for the advertising space are comparable and the number of advertisers is large, behavioral targeting generates more revenue for the publisher. This gain under behavioral targeting is increasing in user heterogeneity and the number of advertisers, and the expected revenue for the publisher can double compared to traditional advertising.

 Online consumer heterogeneity: An advertiser for each face.

Asymmetry

The whole research,conducted by Jianqing Chen and Jan Stallaert,University of Texas and Connecticut respectively, proved that that the effect of behavioral targeting on different advertisers' payoffs is asymmetric. While small advertisers are generally better off under behavioral targeting by winning their favorable users, the dominant advertiser may or may not be better off .The dominant advertiser is worse off under behavioral targeting when it has a significant competitive advantage over its competitors because under traditional advertising, he would otherwise grab a larger group of users and still realize a decent payoff . The real benefit brought by the increased effectiveness of behavioral targeting is realized in social welfare. In the end,the social welfare of both publisher and advertisers can be maximized under behavioral targeting.

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