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Google is on a quest to perfect and grow its Artificial Intelligence applications. From computer programs that can teach itself to play Atari 2600 to driverless cars, the company is focused on creating computer programs that can learn from data and process outputs on its own based on such data. This science is called Machine Learning. Machine Learning-based programs are modeled off of the human brain’s neural networks. Humans learn from past experiences. They identify events and formulate decisions based on context, patterns and senses, and iterate on these decisions as they encounter new experiences. Similarly, computer programs engaged in machine learning, or deep-learning, have an artificial form of neural layers that constantly progress and build themselves based on growing data.

Recently, Google has been looking to apply Machine Learning to search in a couple of ways. Google’s research team wants to develop a software model that grades the site quality and ranking value based on factual content, not links. The program will rank content against Google’s Knowledge Vault, a large storage of information pulled from across the web into one central hub of facts on the world, people, and objects. This may also involve reviewing information from the crowd-sourced Knowledge Graph database, and will help Google identify entities and create predictive models with more confidence as the vault expands. One way to formulate a strategy around your search presence based on these upcoming changes are by making sure your brands have Freebase and Wikidata accounts with accurate, up-to-date information and leveraging entity markup, if applicable.

In one of the more recent Whiteboard Friday segments, Rand Fishkin addressed Google’s intent use Machine Learning on its search algorithm in order to determine rankings. The consensus was that the future of Google’s organic ranking guidelines may end up being even more unpredictable than it already is. Rand did point out one plus side; that there would be “more success for those [brands] that satisfy consistently on a single domain”. What does this mean? This means that the movement towards quality, informative content is not going anywhere. Semantic content and user experience should be even more prevalent than it already is. In short, along with a base layer of past requirements that determine a site’s authority, Google will also favor what works.

What Do You Mean, Google will Favor What Works?

Learning comes from recognizing patterns and storing information to be applied towards future decisions. Machine Learning in search will be based on what users deem as successful results. This might end up being a conglomeration of many variables, from on-site engagement and content that caters to semantic language, to user-fed information that generate viral or opinionated discussions. Sites that appeal to a human’s most intuitive responses will be machine learning’s strongest compass. Because Machine Learning is so closely modeled after the brain’s ability to store knowledge, marketers must now start to integrate neuromarketing tactics into their content strategies more seriously.
This is where I hope everything will come full circle to you in terms of connecting machine learning to optimization. Here’s the most accurate definition of neuromarketing in my opinion by Roger Dooley:

“Neuromarketing is the application of neuroscience to marketing. Neuromarketing includes the direct use of brain imaging, scanning, or other brain activity measurement technology to measure a subject’s response to specific products, packaging, advertising, or other marketing elements. In some cases, the brain responses measured by these techniques may not be consciously perceived by the subject; hence, this data may be more revealing than self-reporting on surveys, in focus groups, etc.”

Humans react instinctively, and that’s likely where Google is headed with machine learning. Neuromarketing taps into these predispositions, pointing out behavioral patterns that are common among consumers and displaying information in ways that have been proven to positively work with their target market. Creating content that generates instinctual reactions and are cognizant of the human brain’s responses to certain tactics is a strong way of staying ahead of the curve. Strategies like landing page optimization and A/B testing will still be relevant when the time comes for this massive shift in algorithmic search outputs. Take into consideration ways of helping your consumers make decisions, such as triggers. These are my top 3 neuromarketing picks to get you started on content strategies for your brands:

1. Foot-in-the-door technique

This concept involves asking people to make a small commitment by making them feel like they identify with the end goal. Content that follows this technique connect consumers with attitudes based on specific circumstances. Mint.com’s landing page does a good job of using this tactic, offering free sign-ups that target common consumer situations.

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2. Owning up to your flaws

You might stop after this point and think, is she crazy? Surprisingly, no, I’m not. In fact, this is one neuromarketing tactic that is widely practiced across a vertical you might not expect – luxury brands. This doesn’t mean ignoring customer feedback or complaints. Rather, standing firm on what your brand represents and weighing the strengths more against minor weaknesses. Consumers are looking for a solution to a primary problem, and they look to your brand for the vision and functionality for that problem. It’s easy for both the seller and the buyer to get carried away with having it all. But Paulo Coehlo once said “Never try to please everyone; if you do, you will be respected by no one.” Focus on perfecting and leveraging your strength against your brand’s mission. Consumers don’t expect anything less than that, and nothing more.

3. Segmenting your audience by promotional and preventive language

It’s important to be cognizant of your customers’ mindset when creating content. They are either people who are optimistic and think in terms of gained opportunities, or pessimistic and focus on the losses if they didn’t commit. Messaging can be subtle, but in order to execute it properly, you must understand how your target market(s) approach your service in the first place. Roger Dooley has a great post on various examples of writing both types of content, but two companies that display both messages are Febreeze and Glade.

Glade’s positioning is one of promotion, focusing on features that enhance a person’s day-to-day life with a variety of fragrances and all of the positive gains of the product:

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Febreze’s primary messaging is more towards prevention, focusing on the losses customers will experience if they didn’t buy the product:

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Even their limited edition messaging is different. Glade positions it in a gain-frame, while Febreeze portrays their limited edition products in a loss-frame.

Glade:

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Febreze:

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Keep these strategies in mind when thinking about your content strategy moving forward. Roger Dooley’s blog has a bunch of other case studies, research and tactics that you can apply towards your marketing tactics.