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Transform your digital assets with advanced image tagging strategies

The Contentstack TeamJun 07, 20248 min read

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Discover how strategic image tagging increases efficiency and image searchability. Image tagging makes your images accessible and searchable while enhancing the user experience. Request a free demo to understand the best practices for manual and automated tagging.

Highlights

You’ll learn about:

The benefits of image tagging:

  • Enhances searchability and user experience
  • Organizes digital assets
  • Improves SEO

The best practices for image tagging:

  • Maintain consistency and balanced tag quantity
  • Use a mix of broad and specific tags for functionality

The dos and don'ts of manual and automated image tagging:

  • Manual tagging requires consistent taxonomy and periodic review
  • Automated tagging enhances speed and accuracy but needs human oversight

Optimize your image tagging strategy today to streamline workflows and improve searchability.

Keep reading to learn more!


As a business owner, you’re probably familiar with the domino effect a single misplaced image or file can trigger. Disorganized digital images lead to more than just inconveniences—they disrupt your entire workflow so much that it can cost you $11,000 per year for each and every employee working with your creative assets. Not to forget the extra 10-15% of the time lost creating new images because you and your team couldn’t locate the original image. 

Whether you’re an e-commerce store or an IT company, getting a handle on organizing your digital assets sets you apart from others. That’s where image tagging comes in. If you want to prevent your team from searching every keyword they can think of and looking through every possible folder for a digital image only to return empty-handed, start tagging your images. 

Here is what image tagging constitutes—and how it can streamline your workflow and bring order to the digital image chaos created across your company.

What is image tagging?

Image tagging is the process of labeling your digital images using keywords or meta tags. These tags include useful information about the image, such as the photo’s subject, data and location. 

For instance, an image showing how a headless content management system (CMS) works will likely have the tag “headless CMS” or “API integration” and the like. But, it may have tags related to specific content types like 'blog post' and 'landing page.' It can even have category tags such as 'marketing,' 'sales,' and 'product development.' These tags organize and classify your images within the Headless CMS environment. 

Without an associated image tag, the photo will be in your cloud and cannot be searched other than its name (if the image has a name). Worse, you must know its location and manually navigate to access it. 

When you tag images, the information converts to metadata, which you can use for web accessibility, search engine optimization and content management

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What are the benefits of image tagging?

Enhances searchability

Tagging images gives search engines and your website a map they can follow. When you organize your images with labels, it becomes easier for users to find images of what they need.

Improves user experience

Well-tagged images help users quickly spot visuals that resonate with their interests, keeping them engaged and satisfied.

Helps you organize your content

When you manually tag your images, you neatly stack them in a well-organized wardrobe. By labeling images with descriptions like 'product,' 'event,' or 'team,' you create a virtual filing system that lets you access the right image.

Increases SEO

Image tags contribute to search engine optimization. How? It provides additional context to search engine crawlers. Relevant image tags and metadata improve the visibility of your visual content in search results, driving organic traffic.

Using descriptive and relevant keywords in your image tags improves your SEO. Ensure each tag describes the image's content with keywords that potential users will likely search for. In addition, alternative text (alt text) makes your images accessible and enhances SEO. Alt text should be concise, descriptive and keyword-rich, providing a clear explanation of the image for search engines and users 

Relevant keywords and alt text make your images more discoverable in search engine results pages (SERPs).

Makes your images accessible

Accessibility is about making sure everyone feels welcomed. Descriptive tags like alt text help users and employees understand images and ensure that everyone, regardless of ability, can fully enjoy and engage with your visual content.

How does image tagging work?

The manual image tagging process implies a person or a group of people who look at the image and input what they see on the image. For example, if a photo shows a girl sitting on a beach chair drinking coconut water, the team can add image classification tags such as:

  • Woman
  • Beach
  • Chair
  • Beach chair
  • Coconut water

On the other hand, ‘automated image tagging’ uses a sophisticated way of interpreting images, such as machine learning and artificial intelligence technology. Automatic image tagging software is trained on a set of images. Using that knowledge, the picture tagging software labels your images with descriptive tags. Upon seeing an image, it first identifies the object and labels it.

The automated tagging software primarily analyzes the composition of your photo and identifies various objects. It compares the pixel against objects trained and stored in its memory.

If you’re using Contentstack, you can achieve automated image tagging with Contentstack webhooks, AWS Lambda and AWS Rekognition.

Auto Image Tagging CMS

Using Contentstack webhooks, AWS Lambda and AWS Rekognition, you can accurately identify the content of your images. What sets AWS Rekognition apart is its ability to provide correct facial analysis and recognition. 

To set up an image auto-tagging in Contentstack, read this step-by-step guide

Manual image tagging vs automated image tagging

AspectManual image taggingAutomated image tagging
ProcessTags are assigned by individuals based on interpretation and understanding of the content.Image tagging software automates and generates tags using algorithms, AI or machine learning models without human intervention.
AccuracySubject to human error and inconsistency in tagging, influenced by individual interpretations.Generally more consistent and accurate as it follows predefined algorithms and rules.
CostCan be costly in terms of labor and time, especially for extensive tagging requirements.Initial setup costs for implementing automated systems may exist but provide long-term cost savings.
Time efficiencyTime-consuming, especially for large volumes of content, as each item needs manual input for tagging.The faster process for handling large datasets is due to automation capabilities.
ConsistencyProne to inconsistencies in tagging style and application across different taggers.Maintaining a consistent image tagging process.
FlexibilityOffers flexibility in adapting tags based on nuanced or specific requirements, allowing for custom tagging approaches.Less adaptable to unique or context-specific tagging needs compared to manual tagging.

How does digital asset management (DAM) help in image tagging?

Integrating Contentstack with a digital asset management system like Bynder enhances the image tagging process. The DAM offers a centralized repository that houses all your creative assets, ensuring your team can access any image anytime and reducing search time. This also reduces the risk of misplaced or duplicate files. 

DAM systems come with powerful metadata management features. Metadata, which includes details about the content, format and context of images, can be automatically generated and attached to images for easier tagging.

Most DAM platforms use AI-driven tagging capabilities based on image analysis to automate the entire tagging process. These systems can identify objects, faces, text and other visual elements, providing relevant tags.

Case study: How did Boels Rental integrate Contentstack with Bynder to streamline tagging?

Boels Rental wanted to modernize its careers website but faced limitations with its traditional CMS. Migrating to Contentstack’s headless CMS, Boels team managed their careers website content more effectively. Their developers could control content modeling and editors created engaging job postings for international roles. 

Using tools like Bynder from Contentstack Marketplace, Boels improved their tagging and search capabilities, enhancing overall content management efficiency. 

With a unified content repository, Boels achieved a single content source for cross-site use, improving efficiency and SEO initiatives.

After using Contentstack, Liz Cummins, Head of Digital Marketing Experience at Boels Rental, said:

“In addition to data and client insights, Contentstack supports us in engaging, supporting and informing our many audiences. It’s been helpful to have regular check-ins with Contentstack reps, sharing our pain points and our wish list. We’ve been able to maximize the benefit of a headless CMS platform to amplify content across various communication channels.”

Read the complete case study here.

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Best practices for image tagging

Ensure consistency in tagging

Establishing a regular style for your tags helps users learn and adapt your tagging system. It will prevent your tags from looking messy and unprofessional. Try to use a standardized set of keywords or tags across all images. Standardization makes it easier to search for images. 

Maintain balance in tag quantity

The number of tags you assign for each image is important. Too few tags increase the risk of making your images hard to find. Also, adding too many tags creates confusion and dilutes the relevance of each tagconfusing your users. So, strike a balance in tag quantity. 

Use a mix of broad and specific tags

Use broad and specific tags to enhance image search and functionality. Broad tags, such as ‘nature,’ help to group a wide array of related images, while more specific tags, like ‘sunset beach,’ enable precise filtering and retrieval. This strategy balances the need for comprehensive searches with pinpoint accuracy.

Dos and don'ts of manual and automated tagging

Dos and don’ts of manual tagging

Dos

Don'ts

Do establish a consistent tagging taxonomy.

Don't use overly broad or vague tags without specifics.

Do periodically review and update tags for accuracy. 

Don't over-tag images with too many irrelevant or marginally related keywords. 

Do train team members on best practices and guidelines.

Don't rely on memory or ad-hoc systems for tagging.

Do incorporate feedback from team members to refine tagging standards.

Don't ignore the context and nuances of individual images.

Dos and don’ts of automated image tagging

Dos

Don'ts

Do use AI-driven tools to enhance speed and accuracy. 

Don't blindly trust automated tags without periodic review. 

Do use feedback loops for continuous improvement.

Don't fail to update algorithms based on new data or trends.

Do combine automated tagging with a manual review for precision.

Don't neglect to train AI models on diverse and representative datasets.

Do update AI models with new data for optimal performance.

Don't assume that automated tagging reduces the need for human oversight.

FAQs

What is AI image tagging?

AI image tagging uses artificial intelligence to create automatic descriptive tags for images. Machine and deep learning algorithms help the software to analyze visual content and identify the image's key characteristics, objects and contexts. 

What is auto-image tagging?

Auto-image tagging relates to the process of describing images with tags using software. Using machine learning and AI, the software tags an object within the image.

What do you mean by image tagging in machine learning?

Image tagging in machine learning involves training algorithms to identify objects in an image or give an accurate label to an image. In this process, you feed the machine learning model with images with their correct tags. You then train the model to recognize the logical characteristics and properties of the images so that it can act as a tag for other images.

Learn more

Image tagging is your most loyal friend, especially if you generate or deal with images and digital assets. It increases your user experience and enhances image organization. However, manual tagging is not always simple and easy—on the contrary, it can be very engrossing. That’s why companies prefer using automated image tagging software. 

If you want to experience the power of AI in image tagging, request a free demo today.

About Contentstack

The Contentstack team comprises highly skilled professionals specializing in product marketing, customer acquisition and retention, and digital marketing strategy. With extensive experience holding senior positions in notable technology companies across various sectors, they bring diverse backgrounds and deep industry knowledge to deliver impactful solutions.  

Contentstack stands out in the composable DXP and Headless CMS markets with an impressive track record of 87 G2 user awards, 6 analyst recognitions, and 3 industry accolades, showcasing its robust market presence and user satisfaction.

Check out our case studies to see why industry-leading companies trust Contentstack.

Experience the power of Contentstack's award-winning platform by scheduling a demo, starting a free trial, or joining a small group demo today.

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