Saturday, April 12, 2014

Online consumer behavior modeling


How to attract and win over the consumer in the highly competitive Internet marketplace?

What would the best marketer of the world do?

What would Sun Tzu do? 

What would Alexander the Great do? 

Well, the answer is not so simple, since online consumer behavior has become an emerging research area as well as a key factor for many companies. Digital ecosystems are evolving so rapidly and it seems that even the greatest strategists might face difficulties in consumer behavior modeling and optimizing consumer interactivity with the brand.

But how do we, marketers, approach the construct "online consumer behavior" in the first place? As suggested by Douglas et al. (1994), strong theoretical and conceptual frameworks can be developed through an integration of constructs from different disciplines. It is not coincidental that academic literature on online consumer modeling may be found in Journals like the ones of Electronic Commerce, Marketing Management, Decision Support Systems, Economic Psychology, Interactive Marketing, Management Information Systems and many more. Theory of Reasoned Action (TRA) and its family theories including the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB) are dominant theories in process of explaining online consumer actions. Expectation-Confirmation Theory (ECT) and Innovation Diffusion Theory (IDT) have also been repeatedly tested in the study of online consumer behavior.

However, Cheung et al. (2003) tried to approach it by integrating the three key concepts of intention, adoption, and continuance, based on Fishbeins attitudinal theoretical model (Fishbein 1967) and the expectation-confirmation model (Oliver 1980). Their Model of Intention, Adoption, and Continuance (MIAC) seems an interesting framework for developing the framework of their theory.
MIAC
Thus, the modeling framework proposed, as an extension to Hoffman and Novak (1996), it the following:
The five domains of consumer, product, medium, intermediary characteristics as well as enviromental influences were integrated in MIAC (intention, adoption, repurchase) in order to provide a cohesive view on online consumer behavior.

It seems that a wise marketer may segment the market, target the correct target segment with the correct product, both core and augmented, and position it with the correct marketing mix, in order to win competition. However, medium characteristics should not be ignored; Trust and perceived risk have been widely investigated in the study of consumer online purchase intention. Some recent studies (Lee and Turban 2001) focused primarily on the trust formation process in the context of Internet shopping. Furthermore, environmental influences including norms, cultures and cultural contextual sets should also not be underestimated; A consumer behavior pattern, e.g. adoption and success of a fragrance e-shop in Mexico could be totally different in Greece or India.

Electronic commerce is rapidly changing the way people do business all over the globe. In the B2C segment, Internet sales have been increasing dramatically over the last few years. Customers, not only those from well-developed countries but also those from developing countries, are getting used to the new shopping channel. Understanding the factors that affect intention, adoption and repurchase are important both for researchers and practitioners. MIAC is a nice model to begin with.

Classical consumer behavioral theories provide researchers with a good starting point in understanding online consumer behavior. However, we should take the IT component into serious consideration when doing research in online consumer behavior. Instead of blindly borrowing theories and models from other disciplines, researchers and practitioners should test their own behavioral models declaring what is unique and specific to the context of consumer-based electronic commerce. This may be the only way that marketing can survive in the Digital Age.

Tuesday, March 25, 2014

New Brand Assets: Where are we going?


"We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests."

Kosinski et al. 2013 (The Psychometrics Centre, University of Cambridge)


Like. Comment. Engage. Sponsored post again? Uhm.. Is your behavioral targeting ok? No?It's fine. Don't worry. Numbers are fine.

How have brands embraced digital ecosystems like Facebook as a key marketing channel to drive engagement and brand awareness? Let's use FB like waveforms as a metric of brand engagement and let's illustrate some examples of FB like pages:

Multinational retail corporation with 35M likes
Ivy League management magazine:
A worldwide branded commodity

A multinational sports company
Leading Telecommunications company in Greece-edited

Leading retail operator in Greece
It seems that these waveforms are similar to a manipulated oscillograph signal where the peak comes in "Sponsored Campaigns" periods and then to zero, till the next paid campaign. 


There are tons of examples like these. Off course, some of these effects could be explained by deeper analysis of econometric advertising models, mentioned here, but is this the best we can do with these new media? Where is organic traffic? Does paid traffic have a decent ROI in terms of brand engagement?


It depends.


I believe that the virtual digital economies which are developing as we speak will reshape the landscape of marketing as we know it. But do marketers do their best in order to engage with the customer, as brand ambassadors? The images above indicate that apart from the times that we press the button "Boost Post", things do not go very well in terms of brand engagement. Further KPI's, like numbers of comments and shares, retweets and favorites, per brand page, analyzed with statistical tools, should easily verify this hypothesis. I understand that perhaps, the higher the numbers, the higher is the brand perception and other key brand assets, but are the results discrete and measurable? 


In the long term, does the world have enough strategists to make optimum use of the new technologies and channels of communication?


For the time being we have:

  • Google, Facebook, Twitter charging and gaining billions for "awareness" and "engagement".
  • Overvalued IPOs, overnight millionaires, questionable business models, startups offering frivolous services, fake engagement tools and similar services.
  • Marketers trying to deliver actions with ROI but frequently unable to achieve optimum brand engagement, let alone incremental sales or other corporate goals.
  • 3B customers who are now online but have the same purchasing power that they would have if digital marketing wouldn't exist in a parallel universe.
  • Academics trying to follow up and model consumer behavior versus both real-life and digital marketing stimuli as technology advances.
A nice bubble? I think, yes.

How would Aristotle structure his philosophical pillars if he was writing his "Athenian Constitution" in his free time while working as a Chief Marketing Officer for Facebook or Google?


21st century. Who will pay for the news?

Wednesday, March 12, 2014

5+1 Latest Viral Commercials



The latest February commercials that went viral!

5. M&M'S Superbowl Ad


4. Jägermeister UK


3. Jerry Ricecake Superbowl Ad


2. U2 "Invisible" & Bank of America


1. Wonderful Pistachios Superbowl Ad


Lucky Star: SOS Children Villages


Friday, March 07, 2014

Win with Adwords Strategic Decisions


I make myself tiring when i say to my friends and colleagues again and again that what is missing nowadays is not skill, but critical thinking. Strategic insight and then tactical implementation.

Marketers are not an exception.

Now, if we think Adwords from a marketer’s holistic point of view, caution is necessary; otherwise our campaign is doomed to fail. What else could we put emphasis on, apart from grouping the keywords correctly, using optimized settings and then sitting back to drink scotch (or soda if you prefer), thus leaving our account to run without intervention?
Ignoring our Unique Selling Proposition
USP stands for Unique Selling Proposition. The USP states that such campaigns made unique propositions to the customer that convinced them to prefer or even switch brands. In Google Adwords, no part of the marketing campaign needs a USP more than our ads. If we write the headline, benefits and URLs without strategic insight on how to sell the offer to a specific customer, our ads will fail. The stimuli provided by the ad will not cause attention and perceptual selection, causing CTR and ad position in the search results to go down.
Instead, before writing any Adwords ads, we should try to understand and be ready to circulate the benefits of our advertised products or services. Understand our target customer, the needs and solutions they would value. Reflect all of these points in our ads and landing pages. Bid on the brand, not the money. Google may reward us with high quality score (QS) and customers will reward us with a good Click-Through-Rate (CTR) and, why not, feasible lead generation. This is the quintessence of marketing, after all. Satisfying customer needs.

“Highest bid for the highest position” strategy

“Turnover is vanity and profit is sanity”. I will bypass the theory behind the auction computational problem that is called Google Adwords, even though I strongly recommend deeper digging in publications of researchers like Aranyak Mehta or Nikhil Devanur as food for thought for the computational mechanism behind Adwords. The truth is that we must be stupid if we think that we must bid the most to get higher search result positions. This is a common Google Adwords budgeting mistake that will waste money and leave us with nothing more to spend very soon. It may get us to the top temporarily, but it’s definitely not a sustainable strategy for our resources. Especially if our PPC campaign is the part of our strategic marketing plan and we have limited budget.

Instead, we may opt for the long-term, budget-safe solution. Improve the relevancy and consistency of our ads, ad groups, keywords and landing pages, then Google will increase our Quality Score (QS). A good QS may increase the frequency of our ads at better positions and our CPC may go down. By improving overall quality in PPC campaigns, we end up to spend less budget for a higher position in the long-term.

Forgetting conversion profit margins & costs p.u.

Not setting up conversions for sales or enquiries so that ROI of this marketing action can be measured? No conversion tracking? Not understanding the profit margins and marketing costs per unit for our products or services so that we can set a price per click? FAIL.

When we spend €10,000 on Google Adwords, this is a cost. Marketing costs need to have a ROI. Wise planning of cost per conversion/impression/click/unit/day etcetera is highly recommended. Off course, there is always the opportunity cost, the cost of the missed opportunity. Ok, it’s time for some critical thinking, I guess. But come on, had we managed the account better and spent our budget in a smarter way, we may have had more satisfying financial results. Which is why we are paid for, at the end of the day.




Sunday, February 23, 2014

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.