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Understanding Intent: Stop selling. Start helping.

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Acronym CMO Mike Grehan recaps his recent presentation at MediaPost’s Search Insider Summit.

Fifteen years ago, the former Chief Scientist at a search engine called Alta Vista, authored a scholarly paper with the title: A taxonomy of web search. His name is Andrei Broder, he’s currently Distinguished Scientist at Google, and his seminal work has been cited in the information retrieval research community thousands of times.

The nature of the research was based around trying to determine the “intent” behind an end user query. For instance, if an end user back then typed the words “digital camera” into the search box, the search engine could find any amount of content about digital cameras. But specifically, what do you mean? Do you want to buy one, or sell one, or repair one? If the search engine algorithm can determine the “intent” behind the query, then the result set will be so much more relevant, and have a greater ability to satisfy the information need.

Broder’s original work placed query intent into three categories: Informational, Navigational, Transactional. More recently, a similar framework of discerning intent has been developed by Google Digital Marketing Evangelist, Avinash Kaushik. Kaushik buckets it this way: See, Think, Do. This is a framework we have successfully adopted at Acronym and it has proven to be hugely beneficial to clients in several ways.

By starting with a content gap analysis, we’re able to discover exactly where clients are underrepresented with good quality, relevant, and (more importantly) useful content. Following that, content mapping allows for extra thought to the actual “content experience.” With federated search results (or “universal search” as it’s commonly known in the industry), sometimes a simple image can be more immediate and more useful than a web page. Or a thirty second video clip may be better than a text based result.

More often than not, it’s no surprise to discover that a client may have access to huge amounts of content designed for the “do” (transactional or checkout) stage, but very little early stage content on the path to purchase. And yet, “see” and “think” stages are ideal for creating much earlier brand affinity.

Dr. Robert Heath has a worldwide reputation as an expert in the role of emotion and attention in the field of brand communication. In 2001, he developed the Low Attention Processing Model of advertising. His book “Seducing the Subconscious – The psychology of Emotional Influence in Advertising received widespread international acclaim when it was published in 2012. I’ve personally believed, for a long time, that high impact in-your-face advertising messages may work well later on the path to purchase, where it’s likely that a brand connection already exists.  But there is no doubt in my mind that Heath’s slow burn, low level, passive attention model is the perfect scientific approach for early stage marketing communications.

Google has an excellent tool that anyone can use to get an indication of just how much more content is being consumed by the end user at the early stages compared to the “checkout” transactional stage. Use it to create a cool infographic showing what percentage of content across channels – organic, generic paid search, branded paid search, display, social, and email –  is being consumed on the path to purchase

For so long in the search marketing community, we’ve chained ourselves to specific keywords or phrases. In fact, the search industry economy has been built around the relative value of the keyword. And yet, so frequently, the result set following a query at any given search engine, will consist of content experiences that don’t even include the keywords used in the query. A simple example of this would be a search for the term “fish tank” that results in content featuring tropical fish aquariums. A truly relevant and useful result indeed, but no mention of “fish tank” anywhere.

With machine learning techniques and AI filtering into marketing, particularly in search, we’re more focused on concepts and topics, as opposed to specific keywords.  Machine learning allows search engines such as Google to understand so much more about end user behavior, and the intent behind that behavior. But there’s also a lot of hype around advances in artificial intelligence. To get a better feel of exactly where a company such as Google stands, with its many world leading experts in AI, you can watch a short video they created which captures the technology moment perfectly.

Of course, you can’t talk about artificial intelligence in search without the subject of digital assistants and speech based search coming up. Concierge search is a term I use when discussing the role of digital assistants. The future of how we interface with the burgeoning number of electronic devices in our daily lives may be leaning towards all talk. But that doesn’t mean it’s all search when we’re communicating wants and needs to Siri, Cortana, Alexa, Google Now and other human sounding entities embedded in everything from smartphones to salad choppers. In fact, most of these interactions tend to be more about getting service than search results.

The analogy of the concierge stems from the amount of travelling I do. For instance, if I stay in a hotel on Union Square in San Francisco that I’ve stayed in many times before and know the area well, I may ask the concierge to book a table for four at my favorite steakhouse across the street. That’s service. But if I’m staying in an area I don’t know so well, in a hotel I haven’t stayed in before, I may tell the concierge that I have four guests for dinner, and ask if he could recommend good steakhouses in the area. That’s search. My friend Lisa Lacy at The Drum wrote a good column about this here

I do believe that there is general shift in the industry towards the “content experience analyst” a role that is more focused on identifying and fulfilling content gaps going forward. The mission is to provide a more useful experience and more extensive visibility and touch points on the end user path to purchase.

Of course, going back to the headline of this recap, I don’t suggest that there’s something wrong with selling online. It’s more a case of knowing when to do it, and exactly how to do it. A little help goes a long way to moving the potential customer more smoothly towards the checkout than the barking, interruptive advertising that we’re so used to tuning out, not in.

 

 

 

Comments 2

  1. David Roberts

    Enjoyed the article Mike. You don’t reference Kaushik’s post-transactional or ‘Care’ stage as he referred to it. How do you see this fitting with intent.

  2. Mike Grehan

    I did mention at the event that Avinash had added care to the framework. I mentioned that STD was not actually a good acronym for the model 🙂 So that’s another thing taken “care” of!

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