Content discovery is inherently linked to good online experiences. The content discovery process involves users searching for relevant content on your owned digital channels. This typically means your website, but can also include user portals, content hubs, social media, and every place where your content lives.
Search plays a key role in content discovery, especially when you want to drive conversions. After all, up to 30% of visitors use a search box when offered. And those users are more motivated, meaning they’re willing to make a purchase.
But what does good content discovery look like? A great content discovery experience is powered by a few things: unified search, personalization, and recommendations.
Artificial intelligence and machine learning play a key role in the latter two, because it can tailor search results to the individual user and the individual search. AI anticipates the searcher’s next move using behavioral data, user intent, user search history, and more, and serves up personalized results and suggestions.
Below, we’ll dive into the details of content discovery, explain how AI-powered search helps improve it, and list 10 key features to look for when scoping out a content discovery tool.
What Is Content Discovery?
Content discovery is the process of surfacing relevant digital content for each individual user based on their specific needs. It’s about delivering this content quickly, regardless of where it lives on your public-facing website or internal intranet.
Great digital experiences are driven by great content, which typically comes from many different sources. Content lives on your website, social networks, intranet, and other service providers which tend to be siloed from each other. Content and data may also be housed within internal repositories including your CMS, CRM, or ERP. Plus, content comes in many forms, like video content, Excel spreadsheets, PowerPoints, blog post, Google docs, and more.
Centralizing this content within a searchable repository is the first step in making it easier to access and discover. AI and machine learning further facilitate content discovery by using automated techniques to find and deliver content based on user intent — even when that intent isn’t explicitly stated.
Key Components Of An AI-Driven Content Discovery Tool:
- Machine learning to analyze the content and structure of documents and user queries to understand the intent behind each search.
- Relevancy algorithms to score and rank content based on how well it matches the user and the user’s needs.
- Query understanding to learn from user behavior over time and improve the relevancy of content for future searches.
- Metadata to obtain additional information about a content item or source which helps with indexing and makes information easier to find.
- AI-based personalization for intuitive recommendations using an individual’s content consumption patterns, related content, and things like user demographics/location.
Why Is Content Discovery Important?
Content discovery helps align a user’s search experience with their (high) expectations for receiving relevant results no matter where they search — your website, intranet, product page, customer portal, or sales platform (to name a few places).
Nearly 90% of customers say they’ll pay more for a better customer experience — this amounts to a price premium as high as 18% — just because a customer is happy with their online experience.
Companies like Google, Amazon, and Walmart set a high bar when it comes to good user experience for search. These companies understand that great content amounts to more audience engagement and better serves consumers.
They excel at delivering high quality content that resonates with individual users, subscribers, and consumers based on their specific needs. This type of consumer experience is informing B2B expectations, with over 70% of B2B buyers demanding personalized buying experiences from sellers and vendors.
A content discovery tool that’s powered by AI helps you meet these expectations by:
- Improving the impact of your content (e.g., content velocity) and facilitating better content discoverability.
- Continuously learning from user behavior to boost content relevancy over time.
- Closing the gap between visit and conversion by surfacing the right content recommendations quickly.
- Improving the self-help success rate of users and customers by helping people effectively serve themselves.
- Using intent to match content with each individual user.
- Connecting content from disparate sources (e.g., content silos) to create a unified index of content.
How A Unified Index & AI Can Elevate Content Discovery
From Splintered to Seamless
Before you can make your content fully discoverable, you need to unify your content sources. That means creating a unified index of content which AI uses — along with behavioral user data — to deliver personalized results.
A unified index combined with the power of AI can help enhance content discovery by avoiding content silos that create fragmented search experiences.
Fragmented search happens when customers, employees, users, viewers, marketers, etc. — need information from an online source or repository, but they’re not sure where that information sits. It could be in their email or in Slack or in the company’s CMS.
They’ll start their content research from one of those various silos. Maybe their email, but because the content isn’t centralized in one place, the search results are limited and often incomplete. So, they begin a new content search, perhaps from within Slack and again across the company intranet.
Resolving Content Silos
A unified index solves the problem of performing the same search repeatedly across multiple sources by connecting the content from these sources in one search hub. That content can come from a variety of sources:
- Your website
- A CMS like Sitecore or Adobe Experience Maker
- A knowledge management platform like Confluence
- An intranet
- CRM and service management tools like Zendesk
- Enterprise applications like Salesforce or SAP
- A video and streaming service like YouTube or Vimeo
Your content is then indexed and made searchable from a unified search engine. With a unified search engine, users search for content across your entire content ecosystem — including content stored in the cloud or on-prem — from a single user interface. Since results are sourced from the unified index, query response time is fast.
AI is the ingredient that makes it possible to personalize content results for each user. AI uses data and machine learning to understand the user’s intent and retrieve relevant content.
For example, if you’re a customer support representative and you search for “macbook pro battery life,” the AI uses information about you, combined with machine learning algorithms, to understand your intent. In this case, you need content that will help you answer a customer’s question.
If you’re a salesperson looking for content to share with a prospect, the query “macbook pro battery life” has a completely different intent. In this case, the AI recognizes that you need content to help you sell the product.
In both cases, the AI interprets the user’s intent, then surfaces different content based on context and behavior. So, although both users entered the exact same query, the results are different.
10 Features to Look For in a Content Discovery Platform
At Coveo, we think of successful content discovery in the context of relevance. To achieve it, you need technology that connects your owned content with the people who interact with it. There are 10 key features that drive a best-in-class content discovery platform.
They include:
1) Crawling modules/custom connectors
Pulls content from on-prem and cloud systems and pushes it to the search index (without triggering security warnings). Look for built-in connectors and SDKs that let you create custom connectors to any source of information.
2) Ranking rules management
Provides full visibility of how search results are ranked and lets you combine featured results and ranking expressions in one place. Also lets you control the timing of each rule activation (for example, so you can create timed rules for specific promotions or campaigns).
3) Dynamic facets powered by AI
A search facet is basically a filter that allows users to refine search results. Dynamic facets act on user queries without human intervention, enabling the system to determine not only the most appropriate facet for each individual user, but what values to use in what order (for example, a user searching for “loveseat” will see different facets than one searching for “office chair”).
4) Question answering
Responds to conversational queries and questions with appropriate content (particularly helpful for voice-activated searches that use smart speakers and digital assistants like Siri).
5) AI-powered personalized recommendations
Guides users to the content most likely to resonate with them based on product associations and in-session actions.
6) In-product experience
Delivers in-app contextual help for customer service, sales, and other employees. AI-driven recommendations and results surface within a specific app (e.g., Salesforce, HubSpot, etc.) providing appropriate documentation based on user-specific context (making it much easier to provide real-time support).
7) Headless UI components
Provides a set of headless UI components that can be dropped into any existing web application. This lets you take advantage of the power of content discovery without having to do a complete rip-and-replace of your existing web app.
8) Fast time-to-market
A system that’s easy to set up and configure. You should be able to move from ideation to creation to integration quickly. The ingredients to make this happen include an accelerator for building custom components, a way to unify multiple code bases, and a collection of crowd-sourced components.
9) Critical updates
Cloud-based systems with ongoing updates and frequent releases ensure that you’re always using the latest and greatest features. Providers should give you ample notice of new updates and access to a sandbox so you can test how changes impact your content and user experiences before they’re pushed to production.
10) Data residency control
Allows you to specify where content should be stored and replicated so you can maintain control over your data. In some cases, you may need to keep content within a certain geographic area due to privacy or compliance regulations like GDPR.
Let Intelligent Site Search Improve Your Content Discovery
Intelligent site search uses AI to understand user queries and match them with the most relevant content. This translates to a better content discovery experience for users who are much more likely to find what they need quickly.
To learn more, download our eBook, How to Boost Conversions With Intelligent Site Search which explains how to implement effective search-to-conversion accelerators.
It also covers some of the key points of content discovery, including how to use AI to improve content discovery, implement UX best practices you may be overlooking, and understand why successful searches are twice as likely to convert.