How to Scrape and Analyze App Store Reviews

App Store reviews provide direct feedback from users about their experience with iOS applications. These reviews often describe aspects such as app performance, usability, feature expectations, bugs, and reactions to recent updates.

Analyzing App Store reviews helps identify common user frustrations, detect recurring technical issues, and understand how changes in the app affect user satisfaction over time.

Kimola allows you to collect and analyze App Store reviews without requiring any technical setup. By automatically gathering publicly available user feedback and converting it into structured insights, Kimola helps teams better understand user sentiment and product experience.

App Store reviews can be collected by adding an App Store app link to Kimola. This tutorial explains the data collection process and how the resulting dataset can be analyzed within the platform.

Let's begin.

Automatically Scrape App Store Reviews

Automatic scraping allows you to analyze App Store reviews by providing the URL of an app page. There is no need to manually collect reviews or prepare a dataset beforehand.

When an App Store link is added, Kimola automatically detects the platform, collects publicly available user reviews, and prepares the dataset in the background. The collected data is then processed using Kimola’s report creation workflow based on the selected analysis settings.

This method is useful when analyzing overall user sentiment, identifying recurring usability issues, or comparing multiple apps within the same category.

Follow the steps below to create your first report.

Step 1: Get the App Store App Link

Open the App Store page of the app you want to analyze and copy the URL from your browser’s address bar. Make sure the link points directly to the app page rather than a search results page or category listing.

You can analyze reviews from a single app to understand overall user feedback, or include multiple app links to compare user experiences across different applications.

Including multiple apps in the same report can help identify differences in usability, performance issues, and feature expectations across competing apps.

Step 2: Enter the Link into Kimola

Open the Kimola Dashboard and locate the Create your report section on the home screen. Paste the App Store link you copied in the previous step into the input field, then select Start to proceed.

If you want to analyze reviews from more than one application, you can include multiple App Store links. This is useful when comparing similar apps within the same category or evaluating user feedback across competing products.

To include multiple apps, select Add Multiple and enter each App Store URL on a separate line.

After the links are submitted, Kimola automatically validates each URL to ensure that the pages are supported and accessible. If any issues are detected, a validation screen highlights the problematic links so they can be corrected or removed before continuing.

Once the links are confirmed, Kimola collects the available App Store reviews from the selected apps and prepares a dataset that will be used to generate the report.

Step 3: Select the Report Size

Kimola then prompts you to define how many reviews will be collected and analyzed. Using the slider, you can set the dataset size that will be used to generate the report.

When multiple app links are included, Kimola distributes the selected dataset size across all apps. If one app contains fewer available reviews than required, the remaining portion is automatically filled using reviews from the other apps.

This ensures that the dataset reaches the selected size whenever sufficient data is available while also helping you manage query usage more efficiently.

Understanding App Store Review Data

App Store reviews typically contain short, direct feedback about user experiences with iOS applications. Users often comment on app performance, stability, interface usability, feature expectations, and issues encountered after updates.

Reviews are usually accompanied by a star rating that reflects the overall satisfaction of the user. In many cases, written feedback explains the reasons behind the rating, providing additional context about user expectations and frustrations.

When App Store reviews are collected automatically, Kimola captures the main fields available on the platform, including review text, rating, date, and source URL. These fields can later be used to filter feedback by rating level, analyze sentiment trends over time, or identify recurring themes in user feedback.

Analyze App Store Reviews

Once the App Store reviews are collected, they can be analyzed to identify recurring themes, sentiment patterns, and common usability issues. This process transforms unstructured user feedback into structured insights that help teams understand how users experience the application.

Kimola applies the same report creation workflow regardless of how the data is collected. The following steps focus on configuring the analysis so that the results align with your research or product objectives.

Choose Interpretations

In addition to standard sentiment analysis and classifications, Kimola allows you to apply higher-level interpretations to App Store reviews. These interpretations help identify patterns such as common usability issues, feature requests, user expectations, and feedback related to app updates.

Selected interpretations appear under My List, where they can be reviewed and adjusted before the report is created.

Interpretations do not consume queries from your plan. Instead, they use GPT Credits, which are available as an add-on. GPT Credits do not expire and can be purchased whenever additional analysis capacity is needed.

Review Report Settings

Before running the analysis, Kimola displays the Review screen where the report configuration can be verified.

The Report Title field shows the name of the report. When the report is created from a single App Store link, the title is generated automatically based on the app name. If multiple app links are included, the title must be entered manually before continuing.

The Source / Dataset field indicates the data source used for the report, such as the App Store links added during the data collection step.

The Report Output dropdown allows you to select the language used for the analysis results. This setting determines the language of sentiment labels, themes, summaries, and interpretation outputs.

The Required Query section shows how many queries will be used to generate the report and how they are distributed across the data collection and analysis steps. This preview allows you to review expected query usage before starting the report.

Create the Report

Select Create Report to start the process. Kimola collects the selected App Store reviews, applies the configured analyses, and generates the report automatically.

Once the report is ready, it appears in the Reports section of the dashboard. From there, you can explore the analysis results, organize the report under a Project, or export the outputs for further use.

Reports can be exported in several formats depending on your workflow, including Excel, PowerPoint, and PDF. Reports can also be shared via email for scheduled or on-demand distribution.

Conclusion

App Store reviews provide valuable feedback about how users experience iOS applications in real-world conditions. By analyzing this feedback, teams can identify usability issues, understand user expectations, and detect patterns in customer satisfaction.

Kimola simplifies the process of collecting and analyzing App Store reviews by automatically gathering publicly available data and transforming it into structured insights. This enables teams to focus on interpreting user feedback and improving the product experience.

All App Store review data should be collected and analyzed strictly for research and internal decision-making purposes. Review content must not be redistributed, republished, or used in ways that violate App Store policies or applicable copyright regulations. Users are responsible for ensuring compliance with all platform-specific terms.