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Guide Your App Store Optimization

Are you on Mobile App Space and facing difficulty in marketing your app…?

Do you want views on your App among 3 million mobile apps already available?

Do you want your App to come in suggestions when keyword is typed?

Are you facing difficulty in “App Store Optimization”?

In the highly saturated market and on the humongous platform of Google store, Apple store and other App stores, people usually make mistakes in understanding ASO and blindly follow the myths. It’s important to understand the various key-points and methods for marketing your app. On your way of making your apps more visible and grab more app users and then convincing them to download your application. In other words, improving your ASO rankings and deliver more traffic to your app store page, avoid following mistakes:

Myth #1: You Need To Change Your Title Too Often
Reality #1: Pick a title and stick with it.

Avoid frequently changing your app’s title in hopes of improving ASO ranking. Your title is the single most important aspect of app store optimization, but repeatedly changing your title will not help your ranking. In fact, doing this may be detrimental to your ASO. As more and more users begin downloading your app and leaving reviews, your app will naturally move up in the rankings. If you keep changing the title, however, it will be more difficult for users to spread the word about your app. Instead, pick a good ASO title from the start and stick to it.

1. Make it short — 25 characters. 10-productivity-wizard-in-app-store (2)
A short title is one THAT users can read in a single screen. Lengthy titles will get cut off. For the single most important piece of search metadata in the app store, you don’t want it to get chopped.

The app below — Productivity Wizard — only has part of their title featured in the screen. They would be better off not producing such a lengthy title. Because I can’t see it from my app browse screen, I’m less likely to download it.

2.Make it creative.
Why creative? Searchers are either categorical or navigational. A user who has heard of or seen your app will be conducting a navigational search to access it. If this title is creative, it is more likely to be cheap mlb jerseys remembered — and thus to be successfully searched for.
A navigational search is something like “Angry Birds” or “Evernote” as opposed to categorical queries such as “bird game” or “note taking app.”

3. Make it unique.
Lack of unique title means you are going to lost in the crowd is similar to creative, but with a twist. Creativity is something that will stand out to the user. You don’t want your app to get lost in the morass of bandwagon apps like Flappy Pig, Flappy Wings, Flappy Fall, Flappy Hero, Flappy Monster, Flappy Nyan, etc. ad nauseam. Bandwagon apps are rarely as successful as the titan they were following.
A navigational search for a “flappy” app produces 2,193 results. Lack of a unique titles means you’re going to get lost in the crowd.

5-search-for-flappy (2)

Myth #2: Stuff Your Title or Description with “Keywords”
Reality #2: Use a Keyword, but don’t keyword stuff.

Keyword stuffing will negatively affect your ASO just as much as it would affect the SEO of a website. Repetitive use of keywords in a title or description in order to increase ASO won’t help your app move up in the rankings. Your app could actually end up suspended if you attempt to stuff it with keywords. Instead, use keywords naturally throughout your title and description. Again, to reference my point above, don’t stuff it. But use keywords to enhance ASO.

 

App titles that contained keywords had a 10.3% higher ranking than those without it. 10.3% doesn’t sound like a lot. However, if it’s as easy as popping a keyword in the title, why not?

Let’s go back to the data that We surveyed in the beginning. Remember how many users search for apps?

8-app-discovery

 

 

 

 

 

 

 

 

 

Myth#3: It Is All About Downloads & Ratings.
Reality#3: Ratings Are Important, But Not the End All.

Judging from ads and press releases, you might be misled to think that ratings are the key performance indicators you need to track to measure success. Ratings, of course, are a good signal of how customers consider your efforts; the download number is a signal of success. However, then?

Then you need a long-term digital strategy that involves all wholesale nfl jerseys China aspects of app publishing and distribution. Ratings do impact on user’s perception; they do not affect app store rankings. Five stars make a good impression; they do not make your ranking.

The truth is, while app ratings are important, they aren’t as significant as most people think in affecting an app’s rankings.

To uncover the truth behind the impact of rating, Inside Mobile Apps conducted a study. They first examined a random sampling of the easy search terms (1-25 results), medium search terms (26-100 results), and competitive search terms (101+ results) to see how each app ranked based on the ratings, both in iOS and Google Play’s search.
Here is what they came up with for iOS rate/rank comparison:

10-average-rating-by-position-iOS

“Google Play’s search algorithm seems to take a more meritocratic approach to app discovery and visibility, letting higher quality apps rise to the top.”

11-avg-search-rating-by-position-google-play

 

Myth#4: Being On the App Store Is Enough
Reality#4: It Needs a Lot Of Downloads To Get Recognized.

This is a die-hard myth: now that you are on the app store, hidden somewhere, you do not really need other work. Everything will happen as some sort of magic, and downloads will flow as a mere consequence of you being there. Some still believe that as long as your app is there, people will find it. You do not need to advertise it; you do not even need to update it.

The truth is, with millions of apps available, it will take much collateral work to avoid failure. ASO is just one piece of the puzzle, and the competition is so fierce that you will need more ‘traditional’ marketing methods to sell it (from social media marketing to content marketing, advertising and PR).

In above we discussed ratings have a less-powerful impact than we might think. But the impact of downloads is usually underestimated.

It’s a tough deal, because in order to get more downloads, you need more downloads. Let the data speak.

13-how-downloads-correlate-to-search-rankings                                   Apps with more downloads simply rank higher. That’s all.

Download velocity depends a lot on how your app does from a marketing standpoint.

path to popularity:

14-ASO-charts

Myth#5: DESCRIPTION IS NOT THAT IMPORTANT
Reality#5 Description is very important.

When you try to sell something, the first thing you do is to describe the value of your product, the uniqueness of its features. Easy, not? Well, not for many developers that still believe the description is an ‘extra’, not a mandatory element of the app store presence. This is a dangerous myth, and it can kill your efforts, leaving you app into oblivion.

Description is probably the second major element in ASO, right after the title. While not directly linked with rankings, it has a great role in the store algorithm. Don’t try to stuff it with keywords, just focus on the natural incorporation of keywords in what you are describing. Moreover, remember that apps now show up in Google’s result pages too.

 

Mistake #6: Quality Of Your Screenshots Does Not Matter
Reality #6:: Screenshots Play Important Role

Quality Screenshots are equally important for the App Marketing on App Store.

Better UI and high end Picture Quality also convince user to download app and feel the features that has been shown in Screenshots.

Στιγμιότυπο-οθόνης-29

Success in App Store is avoiding these myths and it is what drives potential users to install an app. Think of your app page as a storefront on the busiest boulevards in your area and apply each part of our guide to improve your ASO rankings and deliver more traffic to your app store page.

For Further help and queries, say Hello to us on hello@mantralabsglobal.com

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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