Analyzing data is critical to making content that meets audience intent. How do you choose data that’s actionable?
This is the second in a two-part series. Check out Part 1 here: How to Create Content for Audience Intent
Every branded content project must address what business objectives they would like their content to support. The end goal in most cases is an increase in sales leads. But sales of what, where, and when, exactly?
After answering these basic questions, the next thing to do is to choose which data to pull and analyze. Start by selecting keywords based on common (and sometimes uncommon) perceptions of products and services related to your business.
Don’t worry about what things are called. What does the average person call them? What do they think about them? What problems do your products solve? What do they look like physically and visually?
Most users approach problems by searching for the solution they’re hoping to achieve, rather than the name of a company or product. Sometimes they have a colloquial name for what they’re looking for that is different from the brand name. If Toyota only attempted to attract users looking for Toyota vehicles, they’d miss huge opportunities to appeal to those searching for things like “SUVs”, “sedans” and “cars for sale.”
Dominating searches for your brand is important. But winning a significant share from non-branded search is where the real opportunity lies. In fact, it’s estimated that there are four or five unbranded searches for every branded one.
83% of search query paths begins with an unbranded term. –WebFX
Branded product names Vs. what users search for
If budgets allow, conducting surveys, focus groups, and other user research can help aid a deeper understanding about what people think. Any brand persona work or other original data and insights should inform this exercise. The goal is to understand how your offerings apply to your customer’s life or business in a practical way.
Turning raw data into action
Most SEO analyses stop with keyword research. We take that to the next level by identifying the underlying concepts and marrying them to the functionality that users want—and that provides the most utility for them. This requires a contextual analysis that helps marketers explore and maximize their brand’s potential.
How to create a keyword list to maximize content value
Deciding what keywords are most relevant to your product or service is an important step in defining what your potential customers are looking for. The key is to think about what might come to a user’s mind when they search for a solution to a given problem. Start by creating a list of attributes for every relevant product or service addressing the following:
- Brand Names
- Product Names
- Common Names
- Competitor Product Names
- Model Number
- Product Description
- Problems Solved
- Desired Benefits
- Differentiating Qualities
- Identifiable Features
Keep the list as broad and all-encompassing as possible. You can always weed out marginally relevant words and phrases later. The goal is to reach a starting point universe of related keywords from which to pull estimated search volume for each.
If the list seems short, consider ways to expand it by researching professional association websites, predictive searches, or competitive businesses. An exhaustive list can always be trimmed later for hyper-relevancy.
The categories of search queries
Having an expansive list of relevant and popular keyword search phrases is the foundational ingredient to this analysis. It’s not the end result. Determining the ideal approach to addressing the searcher’s original intent is how we convert these search queries to a roadmap for creating purposeful content.
A famous, peer-reviewed IBM study from 2002 stipulated that all search queries can be classified into three categories:
- Informational: users looking for info
- Navigational: users looking to reach a particular place
- Transactional: users looking to do something
Brands must determine which intent type they want to capture, and then decide what kinds of content are applicable.
While these three classifications are useful, modern digital behaviors and their implications on search behavior have changed since 2002. Assessing searcher intent today means accounting for devices, delivery mechanisms of the results—e.g., Apple Siri, chatbots, and Amazon Echo—and, with the rise of mobile search, the location of the searcher.
Sometimes one search phrase alone is not enough to make a complete inference about user intent. That’s where affinity mapping and topical clustering come into play. An affinity map is a diagram that organizes information into buckets or categories based on shared similarities. Rich clusters with high associated search volume make an optimal starting point to build out detailed content recommendations.
Intent + utility = content gold
Finally, to translate all this underlying data into quality content, the only thing left to do is determine how to provide customer utility relative to the keyword topics. According to Google’s search quality rating guidelines, nothing is more important to search engines than ensuring results provide maximum utility for the user based on the intent of their query.
In other words, if a user searches for “hotels in Philadelphia,” there are a number of content types that could be, in theory, relevant to the search. Results could show an article about the history of hotels in the city, a laundry list of famous hotels, or a piece about hotel-dominated neighborhoods.
However, the optimal search result from a utility perspective would offer options for booking hotels because the user’s true intent is most likely to find a hotel for an upcoming trip to Philadelphia.
Translating user data and insights into “Intent Profiles”
So how exactly do you turn customer data into actionable audience models that offer an alternative to traditional personas? This diagram provides a high level look at a typical workflow.
The first step is to compile all your data. Next is reviewing it to understand what the intent of the user is based on their search or other behavior. The third step is extracting the data that is highly relevant to your product or service, and use it to create clusters of topics. These topics will inform creation of content that solves user needs. The final step is deciding the best way to deliver that content so in the optimum format and channels, with appropriate expertise.
How to provide utility when addressing search queries
A number of indicators can help influence how you can provide utility for a search query. Looking at paid advertisements might provide a clue about what searchers expect to see.
Additionally, Knowledge Graph content—this is the brief, informational content you sometimes see displayed throughout search engine results pages (SERPS), such as featured snippets—may offer great insight into what users find helpful.
Google provides Quick Answers and People Also Search For info at the top of some SERPS pages, giving any potential content creator a virtual blueprint for modeling the structure and format of their content to provide the best answer to a similar query.
Google is constantly tinkering with SERPs and adding new features. What shows up will also vary greatly by device, so make sure you review both mobile and desktop search results when doing your research.
To be successful, intent-driven content recommendations should target brand-relevant, highly searched topics and provide clear solutions that address the identifiable goals of the searcher. Such recommendations form a solid basis for any content marketing program in need of a pre-sold audience and a measurable new customer opportunity. The net result is useful, relevant, and accountable content marketing, which increases business value and decreases risk.
Hero image by Lukas Blazek on Unsplash.