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How It Works & Who It’s For

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How It Works & Who It's For

For simple user queries, a search engine can reliably find the correct content using keyword matching alone.

A “red toaster” query pulls up all of the products with “toaster” in the title or description, and red in the color attribute.

Add synonyms like maroon for red, and you can match even more toasters.

But things start to become more difficult quickly: You have to add these synonyms yourself, and your search will also bring up toaster ovens.

This is where semantic search comes in.

Semantic search attempts to apply user intent and the meaning (or semantics) of words and phrases to find the right content.

It goes beyond keyword matching by using information that might not be present immediately in the text (the keywords themselves) but is closely tied to what the searcher wants.

For example, finding a sweater with the query “sweater” or even “sweeter” is no problem for keyword search, while the queries “warm clothing” or “how can I keep my body warm in the winter?” are better served by semantic search.

As you can imagine, attempting to go beyond the surface-level information embedded in the text is a complex endeavor.

It has been attempted by many and incorporates a lot of different components.

Additionally, as with anything that shows great promise, semantic search is a term that is sometimes used for search that doesn’t truly live up to the name.

To understand whether semantic search is applicable to your business and how you can best take advantage, it helps to understand how it works, and the components that comprise semantic search.

What Are The Elements Of Semantic Search?

Semantic search applies user intent, context, and conceptual meanings to match a user query to the corresponding content.

It uses vector search and machine learning to return results that aim to match a user’s query, even when there are no word matches.

These components work together to retrieve and rank results based on meaning.

One of the most fundamental pieces is that of context.

Context

The context in which a search happens is important for understanding what a searcher is trying to find.

Context can be as simple as the locale (an American searching for “football” wants something different compared to a Brit searching the same thing) or much more complex.

An intelligent search engine will use the context on both a personal level and a group level.

The personal level influencing of results is called, appropriately enough, personalization.

Personalization will use that individual searcher’s affinities, previous searches, and previous interactions to return the content that is best suited to the current query.

It is applicable to all kinds of searching, but semantic search can go even further.

On a group level, a search engine can re-rank results using information about how all searchers interact with search results, such as which results are clicked on most often, or even seasonality of when certain results are more popular than others.

Again, this displays how semantic search can bring in intelligence to search, in this case, intelligence via user behavior.

Semantic search can also leverage the context within the text.

We’ve already discussed that synonyms are useful in all kinds of search, and can improve keyword search by expanding the matches for queries to related content.

But we know as well that synonyms are not universal – sometimes two words are equivalent in one context, and not in another.

When someone searches for “football players”, what are the right results?

The answer will be different in Kent, Ohio than in Kent, United Kingdom.

A query like “tampa bay football players”, however, probably doesn’t need to know where the searcher is located.

Adding a blanket synonym that made football and soccer equivalent would have led to a poor experience when that searcher saw the Tampa Bay Rowdies soccer club next to Ron Gronkowski.

(Of course, if we know that the searcher would have preferred to see the Tampa Bay Rowdies, the search engine can take that into account!)

This is an example of query understanding via semantic search.

User Intent

The ultimate goal of any search engine is to help the user be successful in completing a task.

That task might be to read news articles, buy clothing, or find a document.

The search engine needs to figure out what the user wants to do, or what the user intent is.

We can see this when searching on an ecommerce website.

As the user types the query “jordans”, the search automatically filters on the category, “Shoes.”

This anticipates that the user intent is to find shoes, and not jordan almonds (which would be in the “Food & Snacks” category).

By getting ahead of the user intent, the search engine can return the most relevant results, and not distract the user with items that match textually, but not relevantly.

This can be all the more relevant when applying a sort on top of the search, like price from lowest to highest.

This is an example of query categorization.

Categorizing the query and limiting the results set will ensure that only relevant results appear.

Difference Between Keyword And Semantic Search

We have already seen ways in which semantic search is intelligent, but it’s worth looking more at how it is different from keyword search.

While keyword search engines also bring in natural language processing to improve this word-to-word matching – through methods such as using synonyms, removing stop words, ignoring plurals – that processing still relies on matching words to words.

But semantic search can return results where there is no matching text, but anyone with knowledge of the domain can see that there are plainly good matches.

This ties into the big difference between keyword search and semantic search, which is how matching between query and records occurs.

To simplify things some, keyword search occurs by matching on text.

“Soap” will always match “soap” or “soapy ”, because of the overlap in textual quality.

More specifically, there are enough matching letters (or characters) to tell the engine that a user searching for one will want the other.

That same matching will also tell the engine that the query soap is a more likely match for the word “soup” than the word “detergent.”

That is unless the owner of the search engine has told the engine ahead of time that soap and detergent are equivalents, in which case the search engine will “pretend” that detergent is actually soap when it is determining similarity.

Keyword-based search engines can also use tools like synonyms, alternatives, or query word removal – all types of query expansion and relaxation – to help with this information retrieval task.

NLP and NLU tools like typo tolerance, tokenization, and normalization also work to improve retrieval.

While these all help to provide improved results, they can fall short with more intelligent matching, and matching on concepts.

Semantic Search Matches On Concepts

Because semantic search is matching on concepts, the search engine can no longer determine whether records are relevant based on how many characters two words share.

Again, think about “soap” versus “soup” versus “detergent.”

Or more complex queries, like “laundry cleaner”, “remove stains clothing”, or “how do I get grass stains out of denim?”

You can even include things like image searching!

A real-world analogy of this would be a customer asking an employee where a “toilet unclogged” is located.

An employee with only a pure keyword-esque understanding of the request would fail it unless the store explicitly refers to their plungers, drain cleaners, and toilet augers as “toilet uncloggers.”

But, we would hope, the employee is wise enough to make the connection between the various terms and direct the customer to the right aisle.

(Perhaps the employee knows the different terms, or synonyms, a customer can use for any given product).

A succinct way of summarizing what semantic search does is to say that semantic search brings increased intelligence to match on concepts more than words, through the use of vector search.

With this intelligence, semantic search can perform in a more human-like manner, like a searcher finding dresses and suits when searching fancy, with not a jean in sight.

What Is Semantic Search Not?

By now, semantic search should be clear as a powerful method for improving search quality.

As such, you should not be surprised to learn that the meaning of semantic search has been applied more and more broadly.

Often, these search experiences don’t always warrant the name.

And while there is no official definition of semantic search, we can say that it is search that goes beyond traditional keyword-based search.

It does this by incorporating real-world knowledge to derive user intent based on the meaning of queries and content.

This leads to the conclusion that semantic search is not simply about applying NLP and adding synonyms to an index.

It’s true, tokenization does require some real-world knowledge about language construction, and synonyms apply understanding of conceptual matches.

However, they lack, in most cases, an artificial intelligence that is required for search to rise to the level of semantic.

Powered By Vector Search

It is this last bit that makes semantic search both powerful and difficult.

Generally, with the term semantic search, there is an implicit understanding that there is some level of machine learning involved.

Almost as often, this also involves vector search.

Vector search works by encoding details about an item into vectors and then comparing vectors to determine which are most similar.

Again, even a simple example can help.

Take two phrases: “Toyota Prius” and “steak.”

And now let’s compare those to “hybrid.”

Which of the first two are more similar?

Neither would match textually, but you probably would say that “Toyota Prius” is the more similar of the two.

You can say this because you know that a “Prius” is a type of hybrid vehicle because you have seen “Toyota Prius” in a similar context as the word hybrid, such as “Toyota Prius is a hybrid worth considering,” or “hybrid vehicles like the Toyota Prius.”

You’re pretty sure, however, you’ve never seen “steak” and ”hybrid” in such close quarters.

Plotting Vectors To Find Similarity

This is generally how vector search works as well.

A machine learning model takes thousands or millions of examples from the web, books, or other sources and uses this information to then make predictions.

Of course, it is not feasible for the model to go through comparisons one-by-one ( “Are Toyota Prius and hybrid seen together often? How about hybrid and steak?”) and so what happens instead is that the models will encode patterns that it notices about the different phrases.

It’s similar to how you might look at a phrase and say, “this one is positive” or “that one includes a color.”

Except in machine learning the language model doesn’t work so transparently (which is also why language models can be difficult to debug).

These encodings are stored in a vector or a long list of numeric values.

Then, vector search uses math to calculate how similar different vectors are.

Another way to think about the similarity measurements that vector search does is to imagine the vectors plotted out.

This is mind-blowingly difficult if you try to think of a vector plotted into hundreds of dimensions.

If you instead imagine a vector plotted into three dimensions, the principle is the same.

These vectors form a line when plotted, and the question is: which of these lines are closest to each other?

The lines for “steak” and “beef” will be closer than the lines for “steak” and “car” , and so are more similar.

This principle is called a vector, or cosine, similarity.

Vector similarity has a lot of applications.

It can make recommendations based on the previously purchased products, find the most similar image, and can determine which items best match semantically when compared to a user’s query.

Conclusion

Semantic search is a powerful tool for search applications that have come to the forefront with the rise of powerful deep learning models and the hardware to support them.

While we’ve touched on a number of different common applications here, there are even more that use vector search and AI.

Even image search or extracting metadata from images can fall under semantic search.

We’re in exciting times!

And, yet, its application is still early and its known powerfulness can lend itself to a misappropriation of the term.

There are many components in a semantic search pipeline, and getting each one correct is important.

When done correctly, semantic search will use real-world knowledge, especially through machine learning and vector similarity, to match a user query to the corresponding content.

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Featured Image: magic pictures/Shutterstock




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SEO

A Complete Guide to App Store Optimization (ASO)

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A Complete Guide to App Store Optimization (ASO)

A mobile strategy is critical to your business presence, considering the saturation of mobile devices.

This is where app store optimization (ASO) comes into play.

In this article, you’ll learn:

  • What is app store optimization?
  • How does app store optimization work?
  • How do you optimize for Google Play & Apple App Store?

Whether you are new to app store optimization or simply keen to refine your approach to ASO, this post shares practical insights that are proven to maximize app store success.

What Is App Store Optimization?

Downloads, usage, and in-app spending continue to rise, but many users prefer to use a select few apps more consistently.

Discoverability has never been harder, but the rewards of locking in loyal users are bigger than ever – so maximizing visibility in app stores is crucial.

App store optimization (ASO) describes the process of optimizing the listing pages for your mobile app in app stores like Google Play and Apple’s App Store.

You may come across alternative phrases like “app store marketing” or “mobile app SEO,” but they all refer to the same thing.

The goal is to maximize the visibility (and downloads) of your app for relevant searches – basically, SEO for your mobile app rather than your website.

In many ways, the optimization process for ASO is very similar to SEO; in others, not so much.

Ultimately, ASO aims to maximize app installs while product development works on monetization, engagement, retention, etc.

An effective app store optimization strategy keeps new users coming in while your development team (hopefully) keeps existing ones active and spending.

With the right retention rates, app store optimization acquires the new users you need to drive meaningful growth.

The goal of ASO is nearly always app downloads, but supplemental goals can include items such as:

  • Increased brand exposure.
  • Positive app reviews and ratings.
  • More frequent and increased volumes of app reviews.
  • Audience engagement.
  • Additional marketing channel diversification.

How Does App Store Optimization Work?

If you’re new to app store optimization, it might help to think of it as SEO for your mobile app.

Except, rather than optimizing a website to show in search engines, you’re optimizing your mobile app listings for the relevant app stores.

In this sense, you could argue ASO is more like optimizing a Google Business Profile to show in Maps and local results.

The other key difference is you’ve got two major mobile app stores to optimize for: Google Play and Apple’s App Store.

These aren’t the only two app stores worth considering, especially if you’re developing apps for other devices (TVs, games consoles, etc.), but they are the biggest – by far.

According to Statista insights from Q3 2022, here are the top three app stores based on the number of available apps:

  • Google Play: 3.55 million.
  • Apple App Store: 1.64 million.
  • Amazon Appstore: 0.48 million.

As a result, most ASO guides focus on optimizing app listings for Google Play and Apple App Store. Aside from being the top two platforms, the optimization process is a little different for each.

This is mostly due to each app store having its own algorithm – much like different search engines.

In practice, most app store algorithms are more alike than they are different. So, the basic principles of app store optimization apply to all of them. However, some stores may use the odd ranking signal that others don’t.

To keep this guide simple, we’ll start by running through the most common ranking signals for app stores, in general.

Then, we’ll take a closer look at Google Play and Apple App Store to see how they’re different.

Organic Optimization: Your ASO Foundation

The key ingredient missing from many ASO marketing delivery approaches is organic search optimization and integration of app stores within the broader organic marketing mix.

There is more overlap between ASO and SEO than direct competition between the two.

The integration of these areas, and the application of consistent focus on ASO, can support numerous search marketing gains.

You may be surprised to discover that many of the traditional search engine optimization tactics that work for search engine performance, such as Google and Bing, can also be directly applied to ASO.

Examples of this include:

  • App name, title, and URL optimization.
  • Keyword research for ASO.
  • App rating and reviews generation and handling.
  • Deep linking within mobile apps.
  • Indexation of Apps in Google search engine results pages (SERPs).
  • Click-through rate (CTR) optimization.

The biggest marketing mistake, however, when it comes to integrating SEO and ASO is overlooking the role of the website in driving volumes of referral visits directly to your store page and app downloads section.

Your website should be seen as the driving force behind leading people throughout the information-seeking and buying funnel from your main online entity (your website) through to an engaged, ready-to-buy/download audience (your app store).

As content levels are limited within the app stores themselves, the more you can leverage your website content to increase app awareness and discovery to build external app authority and visibility, the greater the value, traffic, and downloads your app will receive.

The Most Important App Store Ranking Factors

Like search engines, app stores don’t reveal the details of their algorithms to the public.

That being said, the following seven ranking factors are key, functional components of all major app stores:

  • App name or title.
  • App descriptions (including keywords).
  • Installs.
  • Engagement.
  • In-app purchases and events.
  • User reviews.
  • Updates.

You can break these ranking factors into three categories: discovery, conversion, and validation.

Discovery signals help app stores connect your app with relevant searches. This includes your app name /title, description, keywords, and other contextual signals.

Conversion signals tell app stores that your listing compels users to download your app – a strong indicator that your listing should show for more relevant searches.

Finally, you’ve got validation signals (engagement, in-app purchases/events, reviews, reports/flags, etc.). These help app stores determine whether users get a positive experience after installing your app.

Positive validation signals (strong engagement, positive reviews, etc.) are an even stronger indicator that app stores should show your app to similar users.

What Do Users Want From An App Store Listing?

Optimizing your app listing for visibility is one thing; getting users to actually download your app is something else entirely.

The catch-22 here is that installs directly impact your ranking in app stores.

The more people install your app, the higher it should rank. This, in turn, should result in more installs, higher rankings once again – and so forth.

So, what are the key factors on your mobile app page that determine whether users hit the install button?

  • App icon: On most app stores, your app icon is the most visually prominent element on results pages and recommendation lists.
  • App details: This includes your app name/title and, usually, some short descriptive text explaining the purpose of your app.
  • App rating: Most platforms show the average rating/review score for your app in search results and at the top of your app listing page.
  • App description: With Google Play and the App Store, users can see a brief description on your listing page and they can click to see the full description – so that first sentence or two is crucial.
  • Visuals: This includes any feature images, screenshots, and demo videos that you can add to your listing, showcasing the key benefits and user experience of your app.
  • User reviews: Unless users are already familiar with your app, they’re probably going to browse through some reviews from existing users.

Here, you can see this in action.

Screenshot from Google Play, February 2024App Store Optimization Elements for ASO

Much like SEO, app store optimization is a careful balance of optimizing to maximize visibility in app stores while prioritizing the needs of your users.

Google Play Vs. App Store: Key Differences

Google Play and the App Store are more similar than different when it comes to app store optimization.

Firstly, the ranking factors are very similar, and the differences are mostly technical – for example, Google and Apple handle keywords differently.

Here’s a quick summary of the main ranking factors for Google Play and the App Store.

App Store Google Play
Listing Listing
App name App title
Subtitle Short description
Long description
Keywords (app name, keyword field) Keywords (all inputs), incl. keyword density
Ratings & reviews Ratings & reviews
Listing CTR Listing CTR
App performance App performance
Downloads Downloads
Engagement Engagement
Uninstall rate Uninstall rate
In-app purchases In-app purchases
Updates Updates

As you can see, there’s not much of a difference here – in fact, most of your time will be spent on things like specifications for icons, videos, and other assets for each app listing.

As a general rule, Apple is more strict with its developer guidelines and it’s usually harder to get an app approved for the App Store.

So, if you’re promoting iOS and Android apps, optimizing your listings for Apple’s guidelines will often satisfy both app stores while maintaining consistency and reducing workload.

Now, let’s take a closer look at app store optimization for Google Play and, then, the App Store.

App Store Optimization For Google Play

To give your app listing the best possible start, you’ll want to dedicate the most time to the following nine elements:

  • App title.
  • App category.
  • App descriptions.
  • App icon.
  • Feature graphic.
  • Screenshots.
  • Promo video.
  • App rating and reviews.
  • Google Play Android Vitals.

We’ll take a closer look at optimizing each of these elements, but always refer to official Google guidelines while managing app listings for Google Play.

App Title

Optimizing your app title for Google Play will feel familiar if you’re used to optimizing website titles for search.

You want to start with the product/branded name of your app and then include a brief description – in no more than a few words – using your primary keyword.

Google Play SearchScreenshot from Google Play, February 2024Google Play Search

You can use up to 30 characters in your app title, but try to keep it as short and punchy as possible.

Prioritize accuracy over keyword targeting and highlight the key benefits of using your app.

App Category

Selecting the right category for your app is essential for matching with relevant searches.

For example, let’s say you’re promoting a heart rate monitoring app. In this case, “Health and Fitness” is the most appropriate category.

Google Play example 2Screenshot from Google Play, February 2024Google Play example 2

When users specifically search for “heart rate monitor,” the keywords in your title are a stronger signal.

However, your app category can help your app show for more general searches like “health and fitness apps” or “productivity apps.”

Crucially, users can also browse categories in the Google Play store to discover new apps without searching.

Google Play Categories ExampleScreenshot from Google Play, February 2024Google Play Categories Example

For more info on selecting the right app category for Google Play, take a look at this Play Console Help page.

Short & Long Descriptions

In Google Play, your app listing includes two descriptions: A short description that shows under the About this app preview and a full description that users can reveal by clicking on the arrow highlighted below.

Google Play Descriptions - ExampleGoogle Play Descriptions - Example

You can use up to 80 characters for your short description and 4,000 characters for your full description.

In your short description, try to describe the core functionality of your app in the most compelling way possible.

Accuracy is key here, but you want to convince users to install your app – so highlight the most attractive benefits.

Your full description provides a more in-depth summary of what your app offers.

Remember that most people won’t click through to read the full description, and those who do are looking for information, not a sales pitch.

You’ll find Google’s official guidelines for creating app descriptions under the “App descriptions” section of this Play Console Help page.

App Icon

App icons show on the left side of search listings in Google Play and the top-right of app listing pages.

Google Play App Icon ExampleGoogle Play App Icon Example

These are the most prominent elements on app store results pages.

Ideally, you want an app icon that either visually describes the role of your app or leverages your brand image as a differentiator.

Designing a unique icon is more challenging if your app has a specific purpose and many competitors – e.g., a heart monitoring app.

Google Play example 3Google Play example 3

If this applies to your app, use design principles like contrast to make your listing stand out from other results.

Notice how Pulse App’s Heart Rate Monitor app stands out from the other listings above?

This is thanks to a combination of simple iconography with strong contrast, using a black background to stand out from the white Google Play results page.

Compare this to the REPS app, which uses similar iconography without a black background, and the Bodymatter app, which uses a black background but a more complex design.

Google Codelabs has an excellent tutorial on designing and previewing app icons. It includes best practices and tips for making an icon that stands out on results pages and the latest Android features, such as adaptive icons.

Feature Graphic And Promo Video

Feature graphics show on your app listing page and can also show for branded searches, paid ads, or recommendation sections on Google Play.

Until recently, you could only use images as featured graphics, but you can now use promo videos in their place.

Google Play Feature Screenshot from Google Play, February 2024Google Play Feature

This is one of the most visible assets on your Google Play listing, so use feature graphics to capture attention and showcase the best of your app.

Google suggests:

“Use graphics that convey app or game experiences, and highlight the core value proposition, relevant context, or story-telling elements if needed.”

You’ll find more guidance on creating feature graphics under the Preview assets section of this Play Console Help page.

App Screenshots

App screenshots show in the same horizontal panel as feature graphics on your app listing page.

They’re designed to showcase the best features of your apps while showing users what the in-app experience looks like.

Google Play Screenshot ExampleScreenshot from Google Play, February 2024Google Play Screenshot Example

You can include descriptive text in your screenshots to emphasize the key benefits of your app’s most important features.

Keep things descriptive, though.

Google prohibits the inclusion of performative or ranking text in screenshots, such as “app of the year” or “most popular…” and promotional information like “10% off” or “free account.”

If your app supports multiple languages, you’ll need to provide screenshots for each language version, including any translated descriptive text.

See the screenshots section of this Play Console Help page for more info.

App Ratings & Reviews

App ratings show prominently in results and at the top of the app listing pages in Google Play. Besides this, you’ve also got a prominent Ratings and reviews section as the largest element on your listing page.

Google Play Rating ReviewsScreenshot from Google Play, February 2024Google Play Rating Reviews

Aside from being a ranking factor, app ratings and reviews are one of the biggest trust factors that help users choose which apps to install.

You don’t need perfect review scores but a positive (3.5+ stars) is a great asset for rankings and installs.

Your review profile also allows users to view the feedback left by others – and how you respond. Once again, how you deal with user problems is often more important than the scores or feedback itself.

You’ll need a framework in place for generating regular reviews and replying to them, engaging with reviewers, and solving user issues.

Your replies are also visible, so avoid generic responses – show new, potential users how good you are at dealing with problems.

In fact, don’t take inspiration from Google’s own support team for Google One. Privacy is great, but the tone of the reply below is more dismissive than helpful, and the exact same response appears throughout replies.

Google Play Review ExampleScreenshot from Google Play, February 2024Google Play Review Example

This feedback can also help you develop a stronger product, and users often edit their reviews, following updates or resolved tickets.

Always remember: Long-term revenue is the goal, which starts with quality app experiences, engagement, and retention.

Google Play Android Vitals

Google provides an extensive toolkit for optimizing your mobile app. Its Android vitals initiative sets out the most important usability metrics that affect the visibility of your app on Google Play.

If you’re used to optimizing websites for search, this will sound a lot like Google’s Core Web Vitals.

The principle Android vitals is similar in terms of performance affecting your search ranking, but this is a far more extensive initiative than Core Web Vitals, as it stands.

Android vitals are broken into two key components:

Core vitals

All other vitals

To maximize the visibility of your app in Google Play, keep the user-perceived crash rate below 1.09% across all devices and 8% per device, with the user-perceived ANR rate below 0.47% across all devices and 8% per device.

Google Play Bad Behaviour ExampleScreenshot from developer.android.com, February 2024Google Play Bad Behaviour Example

Take a look at the official Android vitals documentation page for more information.

App Store Optimization For App Store

For the App Store, we’ve also got nine key elements to optimize, but they’re not quite the same as Google Play:

  • App name.
  • App subtitle.
  • Categories.
  • Keywords.
  • Description.
  • App icon.
  • App previews.
  • Screenshots.
  • App ratings and reviews.

One of the key differences here is how the two platforms handle keywords. While Google analyzes your whole listing for keywords, Apple provides a single field for you to add keywords.

Again, always refer to official Apple documentation when optimizing listings for the App Store.

App Name

In the App Store, your app name simply provides a recognizable and memorable name for your mobile app.

You don’t need to worry about keywords or descriptive text here – that comes later.

App Store NameScreenshot from App Store, February 2024App Store Name

For now, concentrate on coming up with an app name that’s easy to remember and spell while somewhat describing what your app does.

Apple offers the following advice:

“Choose a simple, memorable name that is easy to spell and hints at what your app does. Be distinctive. Avoid names that use generic terms or are too similar to existing app names.”

You can use up to 30 characters for your app name in the App Store, but try to keep it as short and punchy as possible.

App Icon

As with most app stores, the app icon is one of the most prominent elements as users browse the iOS app store. Apple provides extensive design guidelines for app icons and it’s more strict than most.

App Store IconScreenshot from App Store, February 2024App Store Icon

So, if you’re promoting your app across the App Store, Google Play, and any other platforms, you might want to start with Apple first. In most cases, this makes it easiest to maintain a consistent design across all platforms.

Generally speaking, the same design principles apply. Keep it simple and impactful with intelligent use of iconography, color, and contrast.

Look at your competitors and try to come up with something that stands out from the other apps your target audience is likely to see.

Subtitle

Your app subtitle provides a brief description below the app name. Use this to highlight the purpose and benefits of your app in the most compelling way possible.

App Store SubtitlesApp Store Subtitles

This is your first opportunity to excite potential users about your app, so try to make an impression here. You’ve only got 30 characters to work with, which means punchy subtitles tend to do best.

You’ll want to test and refine your subtitles over time, paying close attention to CTRs and installs as you try different variations.

Categories

As with Google Play, categories are key for discoverability in the App Store.

You can assign primary and secondary categories for iOS apps to help users find your app; the primary category has the strongest weight. – so choose the most relevant one.

App Store Categories Screenshot from App Store, February 2024App Store Categories

Apple provides extensive guidance for choosing app categories. Make sure you follow Apple’s guidance because selecting the wrong categories violates the App Store guidelines.

In some cases, you may find multiple categories that match your app.

For example, if you’re running a photo-sharing social media app, you could select either Photo & Video or Social Networking as your primary category.

In such cases, Apple suggests considering the following:

  • Your app’s purpose: Your primary category should be the one that best describes your app’s main function or subject matter.
  • Where users look for an app like yours: Understanding your audience will help you identify the category in which they will likely look for your app. Will they consider your app more of a social network or a photography app?
  • Which categories contain the same type of apps as yours?: Research how similar apps are categorized — users may already know to visit these categories to find this type of app.

If multiple categories accurately reflect the purpose of your app, you’re unlikely to run into any violation issues.

At this point, it’s more a question of which category matches the search and everyday use of your app – not only to maximize visibility but also to set the right expectations for users who install your app (think engagement and retention).

Keywords

While Google Play looks for keywords throughout your app listing (similar to how Google Search analyses web pages), the App Store provides a dedicated keywords field.

You can use up to 100 characters to add keywords (separated by commas – no spaces) to help users discover your app. Apple offers the following advice for choosing keywords:

“Choose keywords based on words you think your audience will use to find an app like yours.

Be specific when describing your app’s features and functionality to help the search algorithm surface your app in relevant searches.”

Apple also recommends considering “the trade-off” between ranking well for less common terms versus ranking lower for popular terms.

The most popular keywords may generate a lot of impressions and traffic, but they’re also the most competitive, which can impact CTRs and installs.

App Description

Your app description should provide a short, compelling – and informative – description of your app, highlighting its main purpose and benefits.

Similar to Google Play, you can use up to 4,000 characters in your app description, but users can only see the first two lines (and most of the third) without clicking to see more.

Apple suggests the following:

“Communicate in the tone of your brand, and use terminology your target audience will appreciate and understand. The first sentence of your description is the most important — this is what users can read without having to tap to read more.”

App Store Description ExampleScreenshot from App Store, February 2024App Store Description Example

If you want to update your app description, you’ll have to resubmit your app listing, so it’s important to try and get this right and only make considered changes.

You can also add up to 170 characters of promotional text to the top of your app description.

Crucially, you can change this text at any time without having to resubmit your app listing, making this a great place to share the latest news and info about your app – such as limited-time sales, the latest features, or fixes from the last update.

App Previews

App previews are the App Store equivalent of promo videos.

You can add up to 30 seconds of footage to illustrate the key benefits of your app and the experience of using it.

App Store App PreviewScreenshot from App Store, February 2024App Store App Preview

Again, Apple has strict guidelines and specifications for app previews – make sure you tick all the right boxes.

As with most things, if you’re listing your app in the App Store and Google Play, getting your app preview approved for the App Store first should mean you can use the same format for Google Play – as long as you include footage from the Android version of your app.

Screenshots

You can add up to 10 screenshots to your app listing for the App Store.

If you don’t have an app preview, the first one to three screenshots will show in search results, so make sure these highlight the core purpose of your app.

App Store ScreenshotScreenshot from App Store, February 2024App Store Screenshot

In your remaining screenshots, you can focus on the main features or benefits of using your app.

Try to stick to one feature or benefit per screenshot to communicate each purpose clearly.

App Ratings & Reviews

Once again, app ratings and reviews are important for maximizing visibility and installs in the App Store.

If anything, user reviews are more prominent in the App Store than Google Play, but we can’t say whether this has any meaningful impact on downloads.

App Store ReviewsScreenshot from App Store, February 2024App Store Reviews

The same general principles apply here: try to develop a regular stream of reviews and manage a positive app rating.

Again, you don’t need perfect scores, but you do need to respond to user reviews and address potential issues.

Prioritize negative reviews and respond as quickly as possible with responses that deal with issues – avoid generic, unhelpful responses.

Extra App Store Optimization Tips

App store optimization is an ongoing process that needs ongoing attention. Getting your listings approved for app stores is only the beginning.

Maximizing visibility and – more importantly – revenue from your mobile apps requires a complete product development strategy.

Here are some final, additional tips to help you drive long-term success from app store optimization:

  • Know your KPIs: Don’t get distracted by the wrong metrics and KPIs – know what you’re optimizing for and center every decision around your business goals.
  • Prioritize user experience: Visibility is one thing, but you’re not going to maximize it or take full advantage of it if people uninstall your app or rarely use it – so make sure quality product development and UX design are at the heart of your ASO strategy.
  • A/B test key app store elements: Test and optimize the most important elements on your app listings to increase visibility, CTRs, installs, and retention (descriptions, videos, screenshots, reviews, etc).
  • Master each app store’s analytics system: Google Play and the App Store both provide capable (albeit in different ways) analytics systems to help you improve visibility, revenue, and product quality – so make full use of them.
  • Promote your app with ads: Both Google and Apple provide dedicated ad systems for their respective app stores to get your app in front of more eyes.
  • Promote your apps outside of app stores: Use other marketing channels to promote your apps – social media, app directory websites, app review websites, affiliate marketers, tech publications, etc.
  • Localize your app listings: App stores can connect you with global audiences, but only if you optimize your listings for each target language and location (this is called localization) – with translated text, screenshots, videos, etc.

Conclusion

The mobile app industry still shows growth despite smartphone penetration being way past saturation.

Smartphones aren’t the only devices in people’s lives anymore, either.

Apple Vision Pro launched with over 600 compatible apps, opening another space for mobile experiences beyond the confines of traditional smartphones.

App store optimization (ASO) will become more complex as new devices and app stores emerge.

However, the rewards will also grow, and the companies already mastering ASO for today’s app stores will be first in line to benefit as emerging technologies bring new opportunities.

More Resources:


Featured Image: Billion Photos/Shutterstock

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My 5 Favorite Ahrefs Use Cases for Content Marketers

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My 5 Favorite Ahrefs Use Cases for Content Marketers

I’ve used Ahrefs since 2016. I thought I was a power user, but since joining the team, I’ve discovered a bunch more use cases that I can’t imagine living without.

Here are five of my favorite ways to use Ahrefs for content marketing:

Let’s be honest: we all snoop on our competitors to see what’s working (and isn’t). But today, a lot of the most exciting content strategies live outside of the company blog: free tools, app integrations, programmatic content, you name it.

For most websites, you can use the Site structure report in Site Explorer to quickly see how the website is structured, and which parts generate the most organic traffic.

In the example below, we’re looking at Copy.ai’s site structure. We might expect their blog to drive most of their organic search traffic, but according to the Site structure report, it only accounts for 4% of organic traffic. Instead, their /tools subfolder drives almost 60% of their traffic:

Click deeper into the site structure, and you can see the individual pages generating the most traffic. In this case, three tools alone account for an estimated 20% of the entire website’s organic search traffic:

1708502174 559 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502174 559 My 5 Favorite Ahrefs Use Cases for Content Marketers

We can even compare metrics from today to a point in the past and see how their strategy has changed. Compared to a year ago, Copy.ai has grown traffic to its /tools subfolder but removed 195 pages from its blog:

1708502174 652 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502174 652 My 5 Favorite Ahrefs Use Cases for Content Marketers

It’s easy to track the performance of any blog as a whole. Add the URL into Site Explorer, and a second later, you’ll see key metrics:

1708502174 4 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502174 4 My 5 Favorite Ahrefs Use Cases for Content Marketers

But for big blogs (ours has some 2.5k indexed pages), it’s harder to answer questions like:

  • Which authors are driving the most traffic?
  • How does link acquisition differ between SEO content and thought leadership content?
  • Does updating our articles with an on-page SEO tool improve performance beyond just updating them normally?

Enter Portfolios. Portfolios allow you to group a list of URLs together and view their aggregated metrics. I use one portfolio for tracking the performance of my articles:

1708502174 611 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502174 611 My 5 Favorite Ahrefs Use Cases for Content Marketers

And another for tracking recent articles published by my team:

1708502174 920 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502174 920 My 5 Favorite Ahrefs Use Cases for Content Marketers

And another still for monitoring the search performance of some of the biggest “parasite SEO” publishers (to see whether or not Google is really doing anything to combat it):

1708502175 763 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502175 763 My 5 Favorite Ahrefs Use Cases for Content Marketers

In every case, you can click into your portfolio and see the same detailed metrics you’re used to from Site Explorer:

1708502175 207 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502175 207 My 5 Favorite Ahrefs Use Cases for Content Marketers

Portfolios has become my default way of using Ahrefs, and there are tons of use cases:

  • Compare articles written by freelancers, in-house terms, and (dare I say it) AI tools
  • See which article topics drive the most traffic
  • Analyze the performance of different content types (helpful for separating out the impact of search content and thought leadership content)
  • Monitor the performance of key competitor articles
  • Measure the impact of newly updated or rewritten articles
  • Track experiments (create one portfolio as a control and another for the articles you want to experiment on)

The hardest part of keyword research (at least for me) is always generating seed keywords.

When you have a few terms to explore, it’s easy to find long-tail variations, matching terms, related terms, you name it. But coming up with those first few topics? Not always easy, and it becomes even harder once you’ve exhausted obvious topics.

But now, we can just use a little AI brainstorming power to turn a blank page into dozens and dozens of seed keywords. In Site Explorer, just ask our little AI friend for help:

1708502175 13 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502175 13 My 5 Favorite Ahrefs Use Cases for Content Marketers

Let’s use the bog standard keyword “content marketing” as an example. Here are technical and specialized terms related to content marketing:

1708502175 654 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502175 654 My 5 Favorite Ahrefs Use Cases for Content Marketers

Here are emerging trends:

1708502175 42 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502175 42 My 5 Favorite Ahrefs Use Cases for Content Marketers

And now controversial and debate-generating keywords (“quality vs quantity”—going right for the meaty topics):

1708502175 266 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502175 266 My 5 Favorite Ahrefs Use Cases for Content Marketers

With our big list of seed keywords, hit “Search” and we’ll see the estimated search volume, keyword difficulty, and a bunch of other data points for our ideas. Click the Matching terms or Related terms reports and our list of possibilities will grow massively:

1708502176 245 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502176 245 My 5 Favorite Ahrefs Use Cases for Content Marketers

Not every idea will be a home run in terms of significant search volume, but many will—and they might be ideas you wouldn’t otherwise have considered.

It’s pretty tricky to refine a list of 300 target keywords to a realistic selection of article ideas. Many keywords will have overlapping intent, others might be subtopics that make more sense to mention as part of another topic. Tricky!

Here we’ve used AI to brainstorm seed topics and used the Matching terms report to find even more ideas. We’ve wound up with 1,622 keyword ideas in about 30 seconds of research:

1708502176 487 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502176 487 My 5 Favorite Ahrefs Use Cases for Content Marketers

Great, but also totally overwhelming. But we can make life much easier by using the Cluster by Parent Topic tab.

Parent topic aims to cluster keywords with similar or the same search intent, so you can target them all on one page instead of many.

If we wanted to target the keywords “content marketing audit” and “content audit definition”, we could instead target the parent topic “content audit”—and also rank for “content marketing audit” and “content audit definition”.

Three keyword rankings, one article.

In the image below our 1,622 keywords are grouped by their parent topic. We’ve gone from 1,622 keywords to just 162 clusters—much more manageable:

1708502176 236 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502176 236 My 5 Favorite Ahrefs Use Cases for Content Marketers

Just one of these clusters, content audit, contains 43 keywords. So by writing one article targeted at content audit, we stand to rank for 43 of the keywords we were interested in:

1708502176 706 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502176 706 My 5 Favorite Ahrefs Use Cases for Content Marketers

Competitive SERPs are usually a never-ending game of content optimization and updating. Competitors publish new articles, or update their existing ones, and you have to update your content to avoid sliding down the rankings.

When you formulate your plan for updating an article, it’s useful to see exactly how competitors have updated their articles.

Here’s the organic traffic graph for Zapier’s most popular blog post, How to Use ChatGPT. We can see a huge increase in organic traffic starting in November 2023:

1708502176 649 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502176 649 My 5 Favorite Ahrefs Use Cases for Content Marketers

This begs an obvious question: what happened in November? What caused the massive traffic increase? Is it something that we can learn from?

Well, good news: we can use the Page inspect report to find out.

By default, you can see the current HTML and page text for your chosen URL:

1708502176 871 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502176 871 My 5 Favorite Ahrefs Use Cases for Content Marketers

But more importantly, we can use Page inspect to compare the on-page text at specific points in time: like just before and after their big traffic surge in November 2023. In a couple of clicks, we can actually see if Zapier updated the page in a way that might have triggered the traffic increase.

In this case, we can see entirely new sections of text that were added to the article around the time of the traffic increase, like this collection of “how to” content:

1708502176 729 My 5 Favorite Ahrefs Use Cases for Content Marketers1708502176 729 My 5 Favorite Ahrefs Use Cases for Content Marketers

There are plenty of factors that can improve search performance, but this is a powerful way of isolating the impact of on-page changes. If we were writing an article on the same topic, or refreshing an article Zapier had dethroned, this is exactly the kind of section I would consider adding.

Final thoughts

I’ve used Ahrefs for keyword research, link building, and reporting since forever, but these new-to-me workflows have made my life much easier. If you’re a content marketer, they might help you too.

Got any interesting Ahrefs workflows to share? Let me know on X or LinkedIn!



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5 Questions Answered About The OpenAI Search Engine

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5 Questions Answered About The OpenAI Search Engine

It was reported that OpenAI is working on a search engine that would directly challenge Google. But details missing from the report raise questions about whether OpenAI is creating a standalone search engine or if there’s another reason for the announcement.

OpenAI Web Search Report

The report published on The Information relates that OpenAI is developing a Web Search product that will directly compete with Google. A key detail of the report is that it will be partly powered by Bing, Microsoft’s search engine. Apart from that there are no other details, including whether it will be a standalone search engine or be integrated within ChatGPT.

All reports note that it will be a direct challenge to Google so let’s start there.

1. Is OpenAI Mounting A Challenge To Google?

OpenAI is said to be using Bing search as part of the rumored search engine, a combination of a GPT-4 with Bing Search, plus something in the middle to coordinate between the two .

In that scenario, what OpenAI is not doing is developing its own search indexing technology, it’s using Bing.

What’s left then for OpenAI to do in order to create a search engine is to devise how the search interface interacts with GPT-4 and Bing.

And that’s a problem that Bing has already solved by using what it Microsoft calls an orchestration layer. Bing Chat uses retrieval-augmented generation (RAG) to improve answers by adding web search data to use as context for the answers that GPT-4 creates. For more information on how orchestration and RAG works watch the keynote at Microsoft Build 2023 event by Kevin Scott, Chief Technology Officer at Microsoft, at the 31:45 minute mark here).

If OpenAI is creating a challenge to Google Search, what exactly is left for OpenAI to do that Microsoft isn’t already doing with Bing Chat? Bing is an experienced and mature search technology, an expertise that OpenAI does not have.

Is OpenAI challenging Google? A more plausible answer is that Bing is challenging Google through OpenAI as a proxy.

2. Does OpenAI Have The Momentum To Challenge Google?

ChatGPT is the fastest growing app of all time, currently with about 180 million users, achieving in two months what took years for Facebook and Twitter.

Yet despite that head start Google’s lead is a steep hill for OpenAI to climb.  Consider that Google has approximately 3 to 4 billion users worldwide, absolutely dwarfing OpenAI’s 180 million.

Assuming that all 180 million OpenAI users performed an average of 4 searches per day, the daily number of searches could reach 720 million searches per day.

Statista estimates that there are 6.3 million searches on Google per minute which equals over 9 billion searches per day.

If OpenAI is to compete they’re going to have to offer a useful product with a compelling reason to use it. For example, Google and Apple have a captive audience on mobile device ecosystem that embeds them into the daily lives of their users, both at work and at home. It’s fairly apparent that it’s not enough to create a search engine to compete.

Realistically, how can OpenAI achieve that level of ubiquity and usefulness?

OpenAI is facing an uphill battle against not just Google but Microsoft and Apple, too. If we count Internet of Things apps and appliances then add Amazon to that list of competitors that already have a presence in billions of users daily lives.

OpenAI does not have the momentum to launch a search engine to compete against Google because it doesn’t have the ecosystem to support integration into users lives.

3. OpenAI Lacks Information Retrieval Expertise

Search is formally referred to as Information Retrieval (IR) in research papers and patents. No amount of searching in the Arxiv.org repository of research papers will surface papers authored by OpenAI researchers related to information retrieval. The same can be said for searching for information retrieval (IR) related patents. OpenAI’s list of research papers also lacks IR related studies.

It’s not that OpenAI is being secretive. OpenAI has a long history of publishing research papers about the technologies they’re developing. The research into IR does not exist. So if OpenAI is indeed planning on launching a challenge to Google, where is the smoke from that fire?

It’s a fair guess that search is not something OpenAI is developing right now. There are no signs that it is even flirting with building a search engine, there’s nothing there.

4. Is The OpenAI Search Engine A Microsoft Project?

There is substantial evidence that Microsoft is furiously researching how to use LLMs as a part of a search engine.

All of the following research papers are classified as belonging to the fields of Information Retrieval (aka search), Artificial Intelligence, and Natural Language Computing.

Here are few research papers just from 2024:

Enhancing human annotation: Leveraging large language models and efficient batch processing
This is about using AI for classifying search queries.

Structured Entity Extraction Using Large Language Models
This research paper discovers a way to extracting structured information from unstructured text (like webpages). It’s like turning a webpage (unstructured data) into a machine understandable format (structured data).

Improving Text Embeddings with Large Language Models (PDF version here)
This research paper discusses a way to get high-quality text embeddings that can be used for information retrieval (IR). Text embeddings is a reference to creating a representation of text in a way that can be used by algorithms to understand the semantic meanings and relationships between the words.

The above research paper explains the use:

“Text embeddings are vector representations of natural language that encode its semantic information. They are widely used in various natural language processing (NLP) tasks, such as information retrieval (IR), question answering…etc. In the field of IR, the first-stage retrieval often relies on text embeddings to efficiently recall a small set of candidate documents from a large-scale corpus using approximate nearest neighbor search techniques.”

There’s more research by Microsoft that relates to search, but these are the ones that are specifically related to search together with large language models (like GPT-4.5).

Following the trail of breadcrumbs leads directly to Microsoft as the technology powering any search engine that OpenAI is supposed to be planning… if that rumor is true.

5. Is Rumor Meant To Steal Spotlight From Gemini?

The rumor that OpenAI is launching a competing search engine was published on February 14th. The next day on February 15th Google announced the launch of Gemini 1.5, after announcing Gemini Advanced on February 8th.

Is it a coincidence that OpenAI’s announcement completely overshadowed the Gemini announcement the next day? The timing is incredible.

At this point the OpenAI search engine is just a rumor.

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