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You might not appreciate the scale yet, but there is an escalating battle for legitimacy in the world of affiliate marketing. The discovery of enormous riches in what we do, coupled with a complete disregard for how we get them, has lead to an industry-wide division. There are affiliates, and then there are cowboys. They both chase the same firing of a pixel, but with very different ways in achieving it. Recently, as you've probably read on this site, the FTC has started cracking the whip

Google, eBay, Groupon and other major Internet companies are grabbing a piece of the local online marketing pie. Savvy affiliates that have been searching for new revenue streams can now earn from LOCAL too! Partner with us and capitalize on local shopping with an affiliate model that makes sense!

Being an affiliate and starting a small affiliate marketing business is a great thing. Even in this economy? Yes, absolutely. While there are always going to be conflicting (and frightening) reports about unemployment rates, one thing is certain, affiliate marketing can only grow in popularity.

Like a therapist for the Web, sentiment analysis technology extracts feelings, emotions and attitudes from Web content.  Just as a meaningful counseling session should provide insight for people coping with emotions, sentiment technology should provide insight for companies who want to establish and react to consumer perceptions. Even though sentiment analysis is still an emerging science (or should I say art?) its importance to the continuous online marketing conversation cannot be underestimated.  Large advertisers should learn how to measure sentiment about their brands, as well as their products and/or services in a systematic way.  Google certainly is — and I expect the sentiment signal will continue to grow as Natural Language Processing (NLP) platforms continue to produce healthier results. Here’s some  actionable insights to help prepare for the sentiment search signal. Reviews with Tags: Capture additional data points with review tags (pros, cons) that reinforce sentiment through previous review content or predefined tags.  Sentiment goals can be addressed by incorporating contextual relevant or positive tags.  Much like strategic keyword selection for an SEO campaign, large advertisers can add tags that help differentiate their products or services for consumers and search engines alike. Authentic Conversation: Use corporate voices (across social platforms) to reinforce positive reactions and address negative reactions with pragmatic and authentic talking points. Actions speak louder than words.  Explicitly tell customer what you’re doing to address their concerns and you will initiate a counterbalancing social effect where the negative reactions are balanced by a new wave of relieved (neutral) or ideally positive reactions. Be aware that increasing social spend does not necessarily equate to increases in positive sentiment.  Covario’s recent social media sentiment research demonstrates that advertising spend has no statistical correlation to brand sentiment on Twitter. This research reinforces the notion that social media conversation needs to be shaped by strategic media plans and interdepartmental cooperation.  Conversing for the sake of saying something will not suffice. Encourage members of the executive team or influential voices within the company to actively participate in social forums.  Straightforward demonstrations of concern like asking the viral community how the company is doing can act as a catalyst for positive reactions in the social sphere. If executive tweets sound like an internal political obstacle, lobby for sentiment initiatives by highlighting the 2010 IBM Global CEO Study where the top priority for standout organizations is “reinventing customer relationships” by maintaining a “running dialogue that includes face-to-face and social networking interaction.”  Executives can connect with customers while simultaneously enhancing Web sentiment for potential improvements in search marketing.  This can also extend to other departments, including brand management, customer service, and inventory planning. Content 3.0: Watch for an evolving definition of search engine friendly content.  The definition is currently evolving to meet shifting methodologies of information retrieval including NLP and advanced data classification models.  Search engines are starting to ask questions around the context and meaning of text.  Was the entire article positive or negative? Was the conclusion emotionally charged?  Was the tweet cheerful? The new definition is still being formulated but sentiment and a newfound level of authenticity will likely be part of the equation. Information retrieval models are outside the scope of this article (and my knowledge) but hopefully the following example will better illustrate the type of changes that Content 3.0 could entail.  In the Google research paper Structured Models for Fine-to-Coarse Sentiment Analysis the author discusses the problem of efficiency as it relates to sentiment data-mining.  Looking beyond product reviews, parsing the web for sentiment is a daunting task even for the likes of Google.  In order to streamline the process, search engines might evaluate ending statements where users provide closure or summation: “The relative position of a phrase to a contrastive discourse connective or a cue phrase like “in conclusion” or “to summarize” may lead to improved performance since higher level classifications can learn to weigh information passed from these lower level components more heavily.” In other words, search engines could parse summary statements for sentiment and use inferences to evaluate the rest of the document and assign ranking authority accordingly.  Consider a hypothetical marketing agency report where an analyst favors one agency over another with sentiment-rich closing statements.  Search engines could extract the human judgments made in concluding statement and subsequently update ranking or the authority of that particular agency’s website.  Please note that this research has no quantifiable connection to Google’s current algorithm therefore I am not suggesting sentiment optimization of summary statements (not yet anyway).  Still, it should serve as an example of how the definition of SEO friendly content might evolve in the future. In Summary Sentiment analysis software is fast becoming essential for intelligent and scalable monitoring of consumer conversation around brands and products. Search engines are starting to factor sentiment scores in their algorithmic calculations. This score will only increase as the technology matures. Large advertisers should start monitoring sentiment and developing the internal structure to act in response to sentiment data. Organizations should be aware of an evolving definition of search engine friendly content as a result of enhanced natural language processing techniques including sentiment analysis. Book Mark it-> del.icio.us | Reddit | Slashdot | Digg |

And so enters a new taxonomy of publisher types, ad units and viewing devices. A recent op-ed piece by Diane Mermigas of OnMedia was really great, describing the growth of new types of mobile devices as making ad spend impossible to predict, and the lack of forecasts around mobile spending somewhat deceiving as to the actual spend growth. Building on her point, we believe digital ad spend at the macro level has always been difficult to predict, and in fact the current taxonomy that is the standard contributes to the confusion and needs an overhaul. Forrester Research has the best ad spend prediction model out there, and most advertisers, agencies, investment analysts and tech companies use Forrester estimates as the gospel.   However, as the digital marketing industry has evolved, the lingo and taxonomy has created a new opportunity for re-definition. For example: -  What really is mobile marketing? Is that a small banner ad on top of a mobile app you see on your phone?  Why isn’t that a “display” spend? Is it spending on a paid search ad that will appear higher on a mobile device than on a PC device?  Why then isn’t that “search” spend? And what is a mobile device?  Smartphones obviously are.  Tablets are said to be.  But then isn’t a laptop a mobile device? - Similarly on search spend- how much of that is paid search spend versus SEO spend, which are often considered two very different media types. -  On the social spend estimates- most “social spend” until recently was either banner ads or text ads on mySpace and Facebook, fan page spend, or possibly applet/game spend.   For the banner and text ads again- should that be classified as social spend or display spend- or mobile spend by the way- if it is a banner ad unit showing up on a social publisher on a mobile device. -  And a great new confusing element will be video: Is the whole video itself an ad? Are we talking about pre-roll, post-roll, interstitial video ad units? What about popover display units? What about the display and search ads around the videos? How about in-video product placements? What about social video?  On mobile devices?  That are primarily found through Google searches? I think you get my point.  What we really need now is a new taxonomy of digital marketing, one that will help advertisers, agencies, publishers, analysts and others sort out where money is being spent and where to invest next. An initial proposal would be to cube the media spend model into three dimensions:  publisher/publisher type, ad unit type, and viewing device. -  Publisher types might be broken out into categories such as:  content publishers and networks (Yahoo, WashingtonPost, Wall Street Journal), search/directory publishers (Google, Bing), social publishers (Facebook, Twitter, LinkedIn), and other yet to be determined publisher types -  Ad unit types would be things like the “impression”-based IAB standard display ad units, the Google/Bing text ad units, as yet to be defined and standardized video ad units, and any new ad units as they become popularlized.  Earned media listings such as SEO would need to be broken out. There might also need to be an entire category of “expression”-based units such as UGC, Ratings and Reviews, Tweets and Posts that advertisers, agencies and adtech software providers spend serious money on creating, but not through buying impressions.  This spend certainly deserves a line item. -  Viewing devices would probably be broken down into: desktop, laptop, tablet, smartphone, dumb phone, smart TV (IP/internet-enabled TV), cable/satellite TV (which is now getting digital ad unit insertion capabilities). Across this matrix, we already know that certain publisher types favor certain ad units on certain viewing devices.   Facebook is more display ad units on a smartphone and tablet device.  Google tends to be more text ad units on PC, laptop and smartphone devices.   YouTube and Hulu, as primarily video content publishers, tend to have more display and video ad units on laptops, smartphones, tablets and soon internet-enabled TV. So a spend and effort matrix broken down by publisher type, ad unit type and viewing device type is Covario’s initial contribution to the discussion on how to create a new taxonomy for predicting and deploying marketing spend and effort. We’d love to hear your thoughts! Book Mark it-> del.icio.us | Reddit | Slashdot | Digg |