# Automating ASO: Building a No-Code Keyword Research System with AI

- Link: https://phiture.com/asostack/automating-aso-keyword-research-with-ai/
- Published: 2025-03-20T12:32:54+01:00
- Author: Wolfgang Peters

###     Table of Contents

 * [Process Overview:](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#process_overview)
 * [Step 1: Collecting Keywords](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#step_1_collecting_keywords)
 * [Step 2: Filtering and Shortlisting Keywords with AI](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#step_2_filtering_and_shortlisting_keywords_with_ai)
 * [Step 3: Refining Keyword Scores with AI](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#step_3_refining_keyword_scores_with_ai)
 * [Adaptive Scoring System](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#adaptive_scoring_system)
 * [Critical Success Factors](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#critical_success_factors)
 * [The AI-Driven ASO System](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#the_ai-driven_aso_system)
 * [Balancing Automation & Manual Oversight](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#balancing_automation_manual_oversight)

![Image with illustration and title ](https://phiture.com/wp-content/uploads/2025/
03/Building-a-keyword-automation-MVP-with-Make-and-Google-Apps-Scripts.png)

App Store Optimization (ASO) is essential for increasing an app’s visibility. One
of the key elements of ASO is selecting the right keywords, but this process can
be time-consuming and complex. To solve this, we developed an automated system using
no-code tools to streamline keyword research with AI. This article outlines how 
traditional ASO tasks can be enhanced through automation, creating a foundation 
for more sophisticated optimization approaches.

 

Contents:

 * [Process Overview:](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#process_overview)
 * [Step 1: Collecting Keywords](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#step_1_collecting_keywords)
 * [Step 2: Filtering and Shortlisting Keywords with AI](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#step_2_filtering_and_shortlisting_keywords_with_ai)
 * [Step 3: Refining Keyword Scores with AI](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#step_3_refining_keyword_scores_with_ai)
 * [Adaptive Scoring System](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#adaptive_scoring_system)
 * [Critical Success Factors](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#critical_success_factors)
 * [The AI-Driven ASO System](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#the_ai-driven_aso_system)
 * [Balancing Automation & Manual Oversight](https://phiture.com/asostack/automating-aso-keyword-research-with-ai/#balancing_automation_manual_oversight)

### Process Overview:

There are three major steps to go from one app name to a shortlist of the most relevant
and promising <100 keywords, leveraging keyword research with AI to focus on the
right opportunities.

 1. **Research** – Collect keyword data from multiple sources.
 2. **Evaluation** – Consolidate and analyze keywords.
 3. **Refinement** – Fine-tune scoring and prioritization.

The tools we use include data from Google Search Console, AppTweak, and Apple Search
Ads, with automation powered by Make.com (a no-code automation tool) and Google 
Apps Scripts (a cloud-based JavaScript platform that integrates with Google services).
The entire process takes less than an hour, with most time spent on API calls and
AI processing.

### **Step 1: Collecting Keywords**

![Automated keyword collection workflow](https://phiture.com/wp-content/uploads/
2025/03/unnamed.png)

Make.com scenario visualization: Automated keyword collection workflow pulling data
from multiple sources including Google Search Console, AppTweak, and Apple Search
Ads. Each branch represents a separate data source integration, with subsequent 
nodes handling data transformation and sheet population.

In the first step, we use a Make.com scenario to populate a Google Sheet with keyword
suggestions from multiple sources. These sources include search terms from Google
Console, suggestions from AppTweak, insights from Apple Search Ads, and AI-driven
research. Additionally, we incorporate AppTweak and Apple Search Ads data not only
for the focus app but also for its competitors.

This saves time and ensures a comprehensive starting list.

### **Step 2: Filtering and Shortlisting Keywords with AI**

This initial collection generates thousands of keywords, including many duplicates,
misspellings (which we consider to some extent), and irrelevant terms. To refine
this list, we use Google Apps Scripts to consolidate and systematically narrow it
down. After the first consolidation step, we typically end up with 1,000-2,000 keywords,
depending on the number of competitors included and their activity on Apple Search
Ads.

![kwo script](https://phiture.com/wp-content/uploads/2025/03/KWO-scripts--300x154.
png)

Google Apps Scripts implementation showing the keyword consolidation function. The
script processes raw keyword data across multiple sheets, handling duplicates and
preparing the data for AI-assisted analysis. This represents the first step in our
automated consolidation pipeline.

 Next, we leverage keyword research with AI to further refine the list by:

 * Removing irrelevant competitor terms
 * Filtering out overly generic, low-intent keywords
 * Eliminating “brand-heavy” keyword phrases, except when the brand name directly
   represents the app’s function

We introduced this last step after early iterations resulted in a keyword list dominated
by “brand + function” phrases. To better capture user intent, we prioritize keywords
that users are likely to search for when looking to download and use the app. This
includes:

 * Category-related terms
 * Generic but relevant product/service keywords
 * Keywords related to user problems
 * Terms reflecting app features and functionality

To improve AI understanding, we provide a prompt with these criteria along with 
examples of high-quality keywords across different apps.

This process typically reduces the list to fewer than 200 keywords or phrases. At
this stage, we pull keyword metrics from AppTweak and prompt AI to assess each keyword’s
relevance based on four key dimensions:

 1. **Name alignment** – How well the keyword matches the app’s brand
 2. **Category alignment** – Relevance to the app’s primary category and functions
 3. **Value proposition alignment** – How closely it relates to the app’s core features
    and benefits
 4. **User motivation alignment** – How well it addresses user needs and intent

These dimensions collectively form a comprehensive relevance score, helping to filter
out irrelevant terms and prioritize those most likely to attract high-quality users.

Final keyword prioritization is based on multiple factors, including:

 * Relevance score (weighted most heavily)
 * Search volume
 * Keyword difficulty
 * Current ranking
 * Historical install data

Each factor is normalized and weighted to generate a final priority score, which
classifies keywords as high, medium, or low priority.

### **Step 3: Refining Keyword Scores with AI**

### **Adaptive Scoring System**

The difficulty, search volume, and other keyword metrics vary significantly between
apps and markets. A fixed scoring system failed to produce consistently good results.
Similarly, arbitrarily designating the top 10 keywords as high priority did not 
yield the desired outcomes.

Our solution is to allow AI the flexibility to refine the scoring system dynamically.
The AI can adjust priority bands and reweight factors (e.g., increasing the influence
of search volume while decreasing the weight of difficulty) based on the specific
context. The prompt instructs the AI to analyze patterns in the initial scoring,
identify potential issues, and make adjustments to improve accuracy.

While we’ve implemented logging to track these changes, the AI’s decision-making
process remains somewhat opaque due to its complexity. Nonetheless, this adaptability
has significantly enhanced the final results.

### **Critical Success Factors**

Through iterations, we’ve identified key factors that separate mediocre from high-
quality results:

 1. **Ensuring AI understands the brand
     
    User motivations, though only briefly mentioned
    earlier, play a crucial role in assessing keyword relevance. Properly capturing
    these motivations significantly impacts scoring and prioritization.
 2. **Granting AI flexibility in scoring adjustments****

 

 * Dynamic adjustments to weights and priority bands are essential to accommodate
   varying metrics across different apps and markets.
 * This adaptability allows the process to evolve alongside advancements in AI, 
   improving over time.

### **The AI-Driven ASO System**

AI plays three distinct roles in this process:

 1. **Initial Keyword Research** – Identifying relevant keywords based on user intent
    and app functionality.
 2. **Relevance Scoring** – Evaluating keywords using our four key dimensions.
 3. **Score Refinement & Prioritization** – Adjusting scores based on market-specific
    keyword metrics to ensure practical prioritization.

Each stage builds upon the previous one: relevance scoring integrates insights from
the research phase, while final prioritization combines relevance scores with real-
world data. This structured approach maintains consistency while ensuring that each
step adds meaningful value.

As we refine our prompts and leverage newer AI models, we continue to see improvements
in accuracy and efficiency. While the system is not yet perfect, it is already transforming
our approach to ASO. We are confident that future iterations will further enhance
precision and scalability.

### **Balancing Automation & Manual Oversight**

Although this MVP includes manual verification steps, this is intentional. These
steps allow us to review and validate each stage of the process, ensuring accuracy
and reliability. Once we achieve a consistently high-quality output, full automation
will be a straightforward next step.

Next, we move on to **keyword placement and analysis**, where we strategically integrate
the prioritized keywords and evaluate their impact.

 

**Before You Go**

 * Curious to learn more about how we craft great user journeys for our clients 
   at Phiture? Discover how our **PressPlay** platform leverages AI to automate 
   A/B testing and enhance App Store Optimization in our [PressPlay overview](https://phiture.com/pressplay/).
 * Explore how AI is integrated into ASO strategies by reading about our **ChatGPT
   Plugin for ASO**, an experiment from our AI Labs, in this [blog post](https://phiture.com/asostack/aso-plugin-chatgpt/).
 * Learn about the **ASO Stack**, Phiture’s innovative framework that holistically
   considers all factors impacting app visibility and ranking in the app stores,
   in our [ASO Stack overview](https://phiture.com/asostack/).