Nineteen years ago, the company that is now called Alphabet Inc. launched Google Web Search, which would quickly become the most widely used internet search engine in the world.
That search engine, which at first relied primarily on text data and backlinks to determine search priority ranking, has become increasingly sophisticated where its myriad features are concerned. Users can now search images, social, video content, equations, geographic locations and much more, and each of these things impact a brand’s search ranking over all, for better or worse.
But perhaps even more impressive, and particularly relevant to marketers, is the continuous improvement of Google’s understanding of user intent.
Beyond matching keywords alone, Google has parsed its massive stores of data to better understand the phrases, search history and other elements of query to better understand user priorities and states of mind when using the web search platform. As a result, they have optimized their search ranking algorithm and user experience to better align with what data says users really want.
By recognizing how Google and other search engines understand user intent, marketers can poise themselves to put themselves along the route of customer trains of thought. Below, find a breakdown of Google’s intent-recognition methodology–and strategic recommendations for those who want to take advantage of them.
Micro-Moments: How Google understands user priorities
On mobile—which has increasingly become the focus of search marketers after disrupting the way we access information and shop on the go—as well as on desktop, users use a range of terms, exhibit a range of behaviors, and seek out a range of answer-types that, according to Google, develop a profile of the user’s intent. These intent profiles, conveniently articulated by Google as “micro-moments”, are said to be the critical micro-moments at which users are most likely to be swayed by search results.
“I want to know.”
These are the types of searches people launch when they are seeking “information or inspiration”. Users who signify this search intent typically ask questions like “what did the president talk about today”, “what’s the phone number to the chamber of commerce”, and “how much money does a data analyst make?”
“I want to go.”
These are location-based searches that signal an intent to travel to a location whether that be international, regional, local or hyperlocal. Users who signify this search intent increasingly input queries like “restaurants near me”, “directions to the University” and “lodging in North Lake Tahoe.” Searches like these make localization an important question for marketers to be thinking about; how are you reaching people in specific—rather than general—markets?
“I want to do.”
When people want to know how to do something, they turn to action-oriented “I want to do searches”. This search intent is signaled by queries that inquire after methodology like, “How do I get my passport?”, “How do I train my dog?”, and “How to lose weight.”
“I want to buy.”
The consumers have done their research, have looked up coupons, have (one way or another) learned what they want to know about a product or service. Now, they are intent on making a purchase. Prepared to “put their dollars behind their decision”, these users are likely to input more specific search queries like “mixer”, “Las Vegas hotel deal”, and “Amazon Echo”.
Each of these search types articulate—in part through contextualized linguistic triggers like “how”, “why”, “when” and “where”—a different chief priority on the part of the user. Google responds to these priorities by crafting algorithmic outcomes which work to bring hosted content, both organic and paid, before the users based both on terminological relevance and likely user behaviors like—like purchase, or access.
Understanding user experience segmentation
Google’s understanding of user intent also emerges in its segmentation of user experience. Because Google is attempting to serve its users with what it is they want as immediately and seamlessly as possible, the Search Engine Ranking Page (SERP) displays differently from one micro-moment to the next.
Consider the difference in user experience between a search which signals that a person wants to attempt a do-it-yourself project versus a search which signals that a person would like to find a hotel in Central London.
A search for “how to bake a chocolate cake” will likely feature action-oriented instructional content like recipes and will prominently feature organic search results toward the top of the SERP, revealing that Google has detected a primary intent to take an action—”I want to do”—that is not prominently transactional.
On the other hand, a search for a “hotel in Central London” will display a different set of information, prominently featuring a Google map populated with pins that indicate hotels in the requested region. Along with those pins, hotel prices will be displayed—and above the map users will likely notice a number of sponsored search results linking to information about lodging deals they can purchase. This reveals that Google has detected an intent to go someplace—hence the map—with a secondary intent to make a purchase.
Google’s Search Quality Evaluator Guidelines break down and categorise different types of user intent
Small keyword changes still make a big difference
It should also be mentioned that, even as Google becomes increasingly responsive to user intent signals and perceived states of mind, it remains sensitive to the more specific keywords that users input when looking for more specific information.
Consider, once more, that Google bases part of its understanding of intent on how questions are phrased—and that the increasing prominence of voice search is helping Google to understand how people formulate questions in their everyday lives.
For example: while a search for “hotels in Central London” will lead primarily to geographic and transactional data (Google is “thinking” that users want to go and buy in this case), a search for “best hotels in Central London” will more likely point to the geographic information mentioned in the first example, but also more qualitative information in the form of articles (such as top ten lists) that will help users to better understand which Central London hotels are the “best”.
Marketers will likely gather here that the key difference between the two searches is the use of the word “best”–and they’d be correct in that perception. By adding the word “best” to their search for Central London hotels, users have indicated that their chief priorities are to know more about a destination—and then to go there.
Attempting an array of searches across the full spectrum of micro-moments—and exploring small differentiations in keyword usage—will reveal similar differences in user experience on Google and other search platforms.
This suggests that there is significant opportunity for marketers to reach users along very specific trains of thought by understanding the relationship between micro-moments, intent and keywords, and then respond accordingly.
Customer journeys are not always linear, or cut-and-dry
For many marketers, the “customer journey” is the relied-upon model to acquire, convert and retain customers no matter what product or service category they offer; but while the customer journey is of critical importance, it is also important for marketers to carefully consider what they understand the customer journey to be.
Often, when we hear the word “journey”, we tend to think of a linear narrative. We assume that people know where they want to go, and chart a course from one point to the next in a straight line—in large part because mobile has introduced a massive array of data and decision points into our lives, shaping our decisions on a near-constant basis.
The micro-moments model rightly throws this understanding of the journey into question, revealing that people interface with search and content at many different stages and in many different states of mind. Once again, this information can be gathered in part from observations about how search results are displayed.
Consider Google’s sensitivity to primary and secondary orders of intent—like the “hotels in Central London” search example mentioned earlier. That example does not simply reveal that Google is responsive to intent in general; it also specifically suggests that Google understands that users may have more than one intent at any given time, or that their intent could change very quickly—someone who wants to know something could find the information they were looking for and decide that, now that they have come across the key findings they were in search of, they want to buy.
Marketers would be wise to operate from a similar viewpoint, thinking carefully about how their users might articulate searches and develop search-friendly content in response.
Destination marketers might consider pairing pricing, deals and other transactional data with qualitative information about the experiences their region offers; marketing technology vendors might pair service descriptions with instructional material about effective web design, and so on.
Essentially: it is key that brands recognize the many forms customer intent will take, and adopt a practice of flexibility where responding to that intent is concerned.
Customers’ journeys, segmentation and search are not rigid step-by step processes. Google’s vast data as interpreted through its micro-moments model reveals that in plain terms.
While this complicates the work of marketers and search engine specialists—continuously, since search engines are always collecting and responding to data—it also reveals a number of high-impact opportunities for brands to think and act in conversation with their users, in the end building richer and more vibrant experiences at every point of the brand-to-customer relationship.