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When it comes to search, there are two dominant models: Google and Amazon. Google is built to answer broad questions, casting a wide net to bring back pages of potential results. Amazon, on the other hand, is designed to find a specific item in a massive catalog, quickly and accurately. An analysis of the most common searches on amazon shows that users arrive with a clear goal; they need to find a specific product and its corresponding information without delay. For technical documentation, where precision and speed are critical, the Amazon model is the clear winner. A user looking for an API key isn't browsing; they're on a mission.
Does Your Search Bar Read Your Mind?thought about.
Modern search tools are sophisticated and useful. They also tend to follow one of two examples.
Google and Amazon.
When it comes to your content management, Google might be known for its auto-fill search bar and cute google-doodle, but we believe that Amazon is the better model.
We know, it’s a strong claim to make between two titans of tech. Stay with us because a lot of what we discuss here has to do with how you interact with search, both voluntarily and involuntarily.
What Amazon's Most Common Searches Teach Us About User Expectations
When users approach a search bar, they carry a set of expectations shaped by their daily digital habits. Amazon, in particular, has conditioned millions of people to anticipate fast, relevant, and predictive search results. This learned behavior doesn't just apply to shopping; it extends to every search box they encounter, including your technical documentation portal. By examining what people search for on Amazon, we can get a clear picture of the baseline expectations for any modern search experience. Users want a system that understands their needs, whether they’re looking for a new gadget or a solution to a complex technical problem. Their patience for clunky, ineffective search is lower than ever.
Top Searched Products and Categories
The sheer variety of popular searches on Amazon shows that users trust the platform to handle a wide range of queries, from the general to the highly specific. They expect the search algorithm to interpret their language and deliver exactly what they’re looking for on the first try. This behavior highlights the need for content to be meticulously organized and tagged so a search tool can easily find and rank it. When your documentation is well-structured, your search can perform at the level users have come to expect, effortlessly guiding them to the right answer among a sea of information.
Electronics and Entertainment
It’s no surprise that electronics consistently top the search charts, with terms like 'Apple Watch,' 'AirPods,' and 'Kindle' leading the pack. These aren't just simple product queries; they represent the starting point of a customer's entire journey with a complex device. That journey inevitably involves seeking support content for setup, feature discovery, and troubleshooting down the line. The person searching for 'AirPods' today is the same one who will be searching your knowledge base for "how to reset AirPods" tomorrow. Their expectation for a seamless, intuitive experience continues from the point of purchase to the moment they need technical support, making findable, clear documentation critical for brand loyalty.
Home, Kitchen, and Personal Care
Alongside high-tech gadgets, users also search for everyday essentials like 'paper towels,' 'shower curtains,' and 'vitamin d.' While these items seem simple, their constant presence in top search results demonstrates a crucial user behavior: people turn to search for everything. They expect the same level of accuracy whether the query is for a low-stakes household item or a complex piece of hardware. For technical content teams, this means your search must be robust enough to handle both basic keyword queries and complex, multi-word problem descriptions with equal precision, delivering the right information every single time.
Decoding Search Behavior and Intent
Amazon’s data reveals that simple, direct keywords like 'blanket' or 'shower curtain' have incredibly high click-through rates, indicating strong user intent. When someone searches for these terms, they know exactly what they want and expect to find it immediately. This same principle applies directly to technical documentation. A user searching for "install guide" or "API key location" has a specific, urgent need. They aren't browsing for fun; they are on a mission to solve a problem. Fulfilling this high-intent query requires content that is not only accurate but also easily discoverable. By creating structured content, you ensure that each piece of information is optimized to meet these precise, mission-critical user needs.
Rapidly Trending Products and Market Growth
The data is clear: electronics are a dominant and growing category, making up nearly a quarter of the most searched products and over 40% of best-sellers. This trend highlights the increasing number of complex products entering the market, each requiring its own library of support documentation. As new products launch and existing ones are updated, content teams face the immense challenge of keeping documentation current and consistent across all versions. This fast-paced environment makes a scalable content management system essential. A Component Content Management System (CCMS) allows teams to efficiently manage and publish content through reuse, ensuring that users always have access to the most up-to-date information for their specific product.
What Do Users Really Expect from Search?
As search algorithms have evolved and become unnervingly advanced, we’ve become spoiled. Finding something becomes easier every second and our patience for a poor search experience is lower than ever.
Once upon a time, we mostly thought about search as an empty box with the little magnifying glass icon. You start typing what you want, hit Enter, and hope the engine works its magic. In most cases, it does its job beautifully with nary a hiccup.
This may be the case if you’re looking for the best pizza shop nearby or a quick oil change, but when searches become entangled in complex webs of data, the process becomes more involved.
Successfully completing a complex query requires your search to be broken down into smaller pieces than a general query. This is what search filters are built for. Also known as faceted search, search filters are renowned for making the life of a content manager much easier.
What Is Faceted Search, Anyway?
The most common example of faceted search is on Amazon. As a store for almost everything, a good faceted search tool is essential for Amazon’s success. We, as consumers, can find the exact product we need among thousands of options. It works so well that we hardly notice.
When you’re looking for an espresso machine on Amazon, the search tool will populate a list of ideas to help guide your search. The first instance of faceted search you see is the drop-down menu that helps define the product’s category.
Searching for an espresso machine in the Home and Kitchen department, Amazon will use that filter to determine that you’re probably not looking for a commercial-grade piece of equipment.
Instead, it’ll guide you toward espresso machines meant for enjoying a leisurely cappuccino at home. Faceted search plays an important role in narrowing the espresso search even further.
Each selection you make narrows your choices from thousands of results until you have a page of machines that best fit your requirements.
That’s faceted search.
How Faceted Search Works in Heretto
When you create content in Heretto, it’s tagged with metadata based on categories like:
- Author
- Subject
- Product Model
- Release Version
- Any custom tag you establish
This metadata enables the Component Content Management System (CCMS) to create a faceted search with each metadata tag acting as a filter.
Content managers can easily find all the content in the system that fits the filters they’ve selected. This makes use and reuse of that content faster and easier. Because metadata is information about content, when metadata is thoughtfully developed, finding pieces of content through faceted search is a smooth process.
Just like Amazon’s metadata ensures a seamless search process, content managers who put effort into tagging their content thoughtfully will also reap the benefits of a smooth faceted search experience.
Why Faceted Search Needs Structured Content
A Component Content Management System (CCMS) manages contents in little chunks (components) that give more granular control. Basically, each component can exist by itself as a standalone piece of information.
This makes the components in a CCMS ideal for rearranging, resuing, editing, and repurposing. Because each component is its own thing, the changes made only affect the component being changed.
The key to finding and managing the organization of these components in a CCMS packed with content? Faceted search.
When Is a Standard Google Search Tool Not Enough?
We don’t have anything against Google search. It is perfectly designed for users looking for approximate answers to general questions. You could use a search bar like Google’s to search through your documentation to find all the instances of keywords you’ve entered.
The trouble is, you’ll end up getting pages and pages of irrelevant results to slog through to find the specific content you need. This is especially troubling (and time-consuming) when your content library is massive. A recent Mckinsey study showed that interaction workers spend nearly 20% of their day looking for internal information.
The focus on speed is why Amazon brings you to a faceted search as soon as you’ve identified a broad product category. Amazon requires defined metadata when a product is added to their database. It makes sense because you don’t want to look through thousands of espresso machines. Similarly, Heretto guides content identification by providing both pre-configured metadata and custom metadata that you can define. Your faceted search in Heretto is a tool designed to make your content much more searchable, findable, and easily available for reuse, updates, edits, and more.
How Good Structure Creates a Better Search Experience
Faceted search is a key capability that makes structured content such an advanced way to work. Compared to dealing with unstructured content like MS Word Docs, faceted search in structured content is much preferred to the nightmarish thought of using a text search tool to sift through hundreds of Word Docs.
With faceted search in a CCMS, you’re not only able to find content quickly, you can navigate to it directly and take whatever actions you need. Search should be simple, root to tip, and it all starts with good structure.
Frequently Asked Questions
Why is a Google-style search not ideal for technical documentation? A Google-style search is built for exploration, casting a wide net to give you many potential answers to a broad question. When a user needs a specific answer from technical documentation, like how to find an API key, they aren't exploring. They need the one correct answer immediately, and sifting through pages of semi-relevant results wastes time and causes frustration.
What is faceted search in simple terms? Think about the last time you shopped on Amazon. You probably didn't just search for "blender." You used the filters on the side to specify the brand, price, and features you wanted. That's faceted search. It uses predefined categories, or metadata, to let you quickly narrow down a huge number of options to find the exact one you need.
How does this type of search help my content team, not just my end-users? While faceted search creates a great experience for your customers, it's also a powerful tool for your internal team. Content managers can use the same filters to instantly find specific pieces of information for updates or reuse. Instead of manually searching through endless documents for a certain procedure, they can filter by product model or release version to pinpoint the exact component they need in seconds.
What kind of content structure is needed to make faceted search work? Faceted search relies on structured content. This means your information isn't locked away in large, static documents. Instead, it's broken down into smaller, independent components. Each component is then tagged with metadata, which is simply information about the content, like what product it applies to or who wrote it. These tags are what power the filters in a faceted search system.
My documentation library isn't that big yet. Do I still need to worry about this? Even if your library is manageable now, it will grow. Implementing a good search structure early prevents major headaches later. As you add more products, versions, and articles, a simple keyword search will become less effective and more time-consuming for both your users and your team. Starting with a system built for precision ensures your documentation can scale effectively without becoming a disorganized archive.
Key Takeaways
- Prioritize precision over possibility in your search: Users of technical documentation are on a mission for a specific answer, much like an Amazon shopper. Design your search to deliver precise, relevant results quickly, rather than providing a broad list of possibilities like a Google search.
- Use filters to guide users to the right answer: For large documentation libraries, faceted search is essential. By using metadata to create filters, you help both users and content managers quickly narrow down vast amounts of information to find exactly what they need without manual sifting.
- A great search experience starts with structured content: Advanced features like faceted search are only possible when your content is built in components and tagged with consistent metadata. The findability of your information depends directly on the quality of its underlying structure.
