レイジクリック

レイジクリックとは

Rage clicks, also known as frustration clicks, occur when a user repeatedly clicks or taps on an element within a software application because of an inability to achieve their desired outcome. These rapid clicks often signal user frustration with a technical issue, confusing design, or a non-responsive element.

While a single click may not indicate a problem, a flurry of clicks in a short period on a specific element strongly indicates user frustration. By identifying and addressing the root cause of rage clicks, software application companies can improve the user experience (UX) and potentially reduce churn.

レイジクリックがソフトウェア企業の懸案事項かつ重要課題である理由

レイジクリックを認識して対処することの重要性にはいくつかの理由あります。レイジクリックは、以下のような重大な悪影響を及ぼし得るユーザーの不満を示唆しています。

Reduced user satisfaction: When users encounter repeated technical issues, confusing interfaces, or unresponsive elements, their satisfaction plummets. This frustration can snowball, leading users to abandon the software altogether (churn).
Increased churn: Persistent frustration can lead users to seek alternative solutions. Losing users due to poor UX directly impacts a company’s bottom line.
Damaged brand reputation: Frustration can be contagious. Users with negative experiences might share their complaints, potentially damaging the software’s reputation and deterring new users.

一方、レイジクリックは改善の貴重な機会でもあります。

Uncovered usability issues: Rage clicks often highlight specific pain points within the interface. By analyzing where these clicks occur, companies can identify areas where the interface is confusing or unclear, improving overall usability.
Prioritized bug fixes: Rage clicks can pinpoint technical problems. Analyzing this data can help prioritize bug fixes and ensure a smoother UX.
Improved product development: Understanding user frustration points through rage clicks can inform future product development efforts. By identifying areas where users struggle, companies can prioritize features and functionalities that address these pain points, leading to a more user-friendly experience.

要するに、レイジクリックは諸刃の剣です。レイジクリックはユーザーの不満を表し、ユーザーが別のソリューションを求める結果になりかねません。ただし、レイジクリックからはUXに関する貴重なインサイトも得られます。このインサイトを活用してソフトウェアアプリケーションを改善し、最終的にはユーザーの満足度とリテンションを高めることができます。

レイジクリックの種類

ユーザーの意図に基づくレイジクリックの正式なカテゴリーは存在しませんが、以下の3つの一般的な事例があります。

Clicking an unresponsive button: This could be due to bugs, slow loading, or network issues. (e.g., repeatedly clicking “Add to Cart” that doesn’t respond). It’s also possible the function for the clicked element hasn’t yet been implemented, but it appears as visible and active in the user interface (UI).
Clicking on the wrong element: This might happen if elements are too close together or the visual design is cluttered (e.g., clicking a nearby ad instead of a “Close Ad” button). “Accidental” clicking can also occur when UI elements simply look like they should be clickable but are not.
Clicking repeatedly out of frustration: Error messages with unclear instructions or unresponsive elements can lead to this. (e.g., repeatedly clicking “Submit” after an error message with no guidance on how to fix it). Another cause is a UI that doesn’t respond quickly to indicate the user’s initial click was registered, prompting the user to click again and again.

レイジクリックのよくある原因

レイジクリックを引き起こす要因は以下のとおりです。

Slow loading times: No one likes to wait. If a page or element takes too long to load, users might click repeatedly in frustration.
Broken links: Clicking on a link that leads to a dead end or an error page is a surefire way to induce rage clicks.
Unclear error messages: Error messages should be clear, concise, and actionable. Confusing or unhelpful error messages leave users frustrated and unsure of how to proceed.
Confusing interfaces: A cluttered UI with unclear hierarchy or misleading design elements can make it difficult for users to find what they’re looking for, leading to rage clicks.

Regardless of the cause, rage clicks can result in lost productivity and decreased digital adoption of the software.

レイジクリックとユーザーエラーの見分け方

ユーザーのミスとレイジクリックを区別することは重要です。その方法は以下のとおりです。

Analyze click patterns: Look for repetitive clicks in a short timeframe on the same element. (e.g., repeatedly clicking “Submit” within seconds suggests rage, not careful review.)
Consider user behavior: Did the rage click occur after a specific action, like entering incorrect information? (e.g., typos followed by repeated “Submit” clicks likely indicate user error.)
Review session recordings: Recordings provide context. Hesitation before repeated clicks suggests confusion (user error), while rapid clicks likely indicate rage (technical issue or unresponsive element).

ここでは、その違いを説明する具体的例をご紹介します。

Rage click: A user tries to click a “Play Video” button, but nothing happens. They click it repeatedly because the video isn’t loading due to a slow internet connection. (This is a rage click because the button appears functional but has a technical issue preventing it from working.)
User error: A user fills out a registration form and accidentally enters an invalid email address. They click “Submit” and see an error message, so they correct the email address before submitting again. (This is likely user error because they made a mistake, corrected it after receiving feedback, and then submitted the form successfully.)

By analyzing user behavior and click patterns alongside session recordings, companies can better understand whether a user is experiencing genuine difficulty or resorting to rage clicks due to frustration with the software.

Pendoセッションリプレイでは、データの背後にある行動を確認できます。

その方法をチェック

レイジクリックデータを分析するベストプラクティス

Identify trends and patterns: Look for areas where rage clicks are concentrated. This can indicate problem areas in your software application.
Consider user context: Don’t just focus on the clicks themselves. Look at what the user tried to accomplish before the rage click happened.
Correlate rage clicks with other data: Combine rage click data with other user feedback methods like surveys and support tickets to get a more complete picture.

プロダクトアナリティクスでレイジクリックを特定する方法

プロダクトアナリティクスツールは、ユーザー行動データを追跡することでレイジクリックの特定を支援します。

High click rates in a short timeframe: A surge of clicks on a particular element within a defined period can signify rage clicks.
Click location: Identifying areas with excessive clicks can highlight usability issues.
User journeys: Analyzing user journeys can reveal where users get stuck or encounter difficulties, potentially leading to frustration and rage clicks.
Click patterns: Unusual click patterns, such as users clicking back and forth rapidly between two elements, potentially indicating confusion or frustration.

プロダクトアナリティクスでは、このようなデータポイントを分析することで、ユーザー体験の不満からレイジクリックに至るエリアを特定するのに役立ちます。

ソフトウェアアプリケーションを改善するレイジクリックデータの活用方法

ユーザーがレイジクリックを行う理由を理解することで、企業はソフトウェアアプリケーションを改善するための的確な行動を取ることができます。

Fix bugs: Rage clicks can pinpoint technical problems. Analyze this data to identify bugs that must be addressed.
Enhance usability: Areas with high rage clicks likely have usability issues. Redesign confusing interfaces, improve element clarity, and streamline user flows.
Prioritize development: Use rage click data to understand user pain points. Focus development efforts on features that address these frustrations.
Improve onboarding: Rage clicks during onboarding indicate a confusing signup process. Revamp onboarding tutorials and provide clear instructions.

ユーザビリティの指標としてレイジクリックを活用する場合の制限事項

レイジクリックはユーザーの不満を示す貴重な指標ですが、考慮に入れておくべき制限事項があります。

Misinterpreting clicks: Not all rapid clicks are rage clicks. User habits or technical glitches can mimic rage clicks.
Isolating the cause: Rage clicks might be a symptom of a larger issue. Analyzing rage click data alongside other user feedback methods helps pinpoint the root cause.

レイジクリックと併せてユーザーフィードバックデータを活用する方法

UXを包括的に理解するには、レイジクリックなどの定量的データだけでは不十分です。レイジクリックイベントを検出することは必須で、Pendoのようなプロダクトアナリティクスプラットフォームは、クリック数（定量的データ）を記録してレポートを作成できます。ただし、定性的データ（ユーザーフィードバックなど）からはより深い理解が得られます。

Surveys allow users to express their opinions and frustrations directly, validating insights from rage click data. (Pendo makes creating and collecting data from in-app surveys simple.)
User interviews can provide valuable qualitative data about user pain points, shed light on the emotions behind rage clicks, and validate a spike in the quantitative rage click data.
Support ticket data analysis can identify common user issues and areas where users need assistance. A high volume of tickets can corroborate the rage click data for a particular feature or page.

But there’s a third type of data invaluable in understanding user behaviors and sentiment: Visual data. Visual data such as Pendo’s session recordings and playback, heatmaps, and user flow analysis empower product teams to more precisely pinpoint what the user was doing that caused spikes in rage clicks or other usage events. After all, just like you can’t improve what you can’t measure, often you can’t fix what you can’t see.

By combining quantitative, qualitative, and visual data, a product team can:

Triangulate findings: Gain a more complete picture of the UX to ensure improvements address the root cause of frustration.
Solve problems efficiently: A broader understanding allows for more effective prioritization of improvements and new features needed in future development.

ここでは、3種類のデータを組み合わせる例をご紹介します。たとえば、オンライン販売者が、プロダクトフィルターメニューにレイジクリックが集中していることに気付いたとします。これは、ユーザーがプロダクトのフィルタリングに苦労している可能性を示唆する定量的データです。オンラインアンケートの参加者は、フィルターオプションが雑然としていて、直感的でないため、ユーザーが混乱を感じていることを確認しています。セッションの記録には、ユーザーが意図した結果が得られないオプションを繰り返しクリックしている様子が表示されています。このような定量的、定性的、視覚的なデータの組み合わせにより、販売者が再デザインすべきUIのエリアを特定できます。

Pendoは、これらすべてのデータポイントを1か所に集約し、このプロセスを合理化し、プロダクトチームがソフトウェアアプリケーションを改善するためのデータ主導型の意思決定を行えるツールを提供します。

レイジクリックデータを活用したプロダクトの改善事例

社名は挙げなくとも、企業がレイジクリックデータをうまく活用してプロダクトを強化した実例は数多くあります。

An e-commerce platform noticed a high concentration of rage clicks on their mobile checkout page. By analyzing rage click data and user recordings, they discovered a confusing button layout that caused users to click repeatedly on the wrong element. Redesigning with a clearer button hierarchy significantly decreased rage clicks with a corresponding increase in completed checkouts.
Mobile banking app users trying to transfer money were abandoning their transactions at high rates. Rage click data and user recordings revealed that requiring users to scroll down to find the “Confirm” button was the cause of the frustration and rage clicks. Repositioning the button for better visibility led to fewer rage clicks and a smoother money transfer experience.
A video editing software app saw a spike in rage clicks in its timeline editing feature. Analyzing rage click data, user feedback, and session recordings confirmed users were frustrated by the difficulty of zooming in and out on specific timeline sections. The product team implemented more intuitive zooming, addressing user pain and reducing rage clicks.

上記はほんの一部で、何百とおりもの事例が存在します。さまざまな業界の企業がレイジクリックデータを活用して、ユーザビリティ上の問題を特定し、ユーザーの不満を理解し、プロダクトを改善しています。

Pendoセッションリプレイの実例を見る

デモを依頼

レイジクリックの回避にPendoが理想的である理由

レイジクリックを完全になくすための単一のソリューションはありませんが、Pendoのプロダクトアナリティクスプラットフォームは、ユーザー行動を理解し、不満が生じているエリアを特定し、最終的にソフトウェアアプリケーションでのレイジクリックを減らすために特別に設計された堅牢なツールスイートを提供します。

Pendo goes beyond just clickstream data. By combining quantitative, qualitative, and visual data, Pendo facilitates a deeper understanding of the root causes of rage clicks within your application.

Quantitative data: Pendo’s product analytics tools capture user events and event properties. This data can include clicks, scrolls, form submissions, error messages, and other relevant user interactions within your application. By analyzing patterns in this quantitative data, you can identify areas with high concentrations of clicks or rapid clicks in a short timeframe, which can indicate rage clicks.
Qualitative data: Pendo integrates seamlessly with user feedback tools, allowing you to collect in-app surveys and feedback alongside quantitative data. This qualitative data provides valuable insights into user thoughts and emotions behind their actions, helping you understand the “why” behind the rage clicks.
Visual data: Pendo offers a robust suite of visual data tools:Session recordings: Capture every user interaction, allowing you to see what users are clicking on, where they hesitate, and where they get stuck. Session recordings can be particularly helpful in understanding the context behind rage clicks and visualizing the user’s journey leading up to the frustrated clicks.
Heatmaps: Provide a visual representation of where users click on a page, helping to identify areas with excessive clicks that might indicate rage clicks. Heatmaps can highlight confusing layouts, unclear elements, or places users might accidentally click.
User flow analysis: Helps visualize user journeys and identify potential drop-off points or areas where users get stuck, potentially leading to rage clicks. By analyzing user flows, you can see where users abandon specific tasks or encounter difficulties that might lead to frustration and rage clicks.

Beyond just user data capture and delivery, Pendo empowers you to take action. The platform offers advanced analytics tools that help you segment user data, filter rage clicks by specific user groups or features, and identify trends over time. This allows you to prioritize improvements and focus your development efforts on areas with the highest impact on reducing rage clicks and improving user satisfaction.

Pendo enables a data-driven approach to product development. By continuously monitoring rage clicks and user behavior, you can identify usability issues early on and iterate on your software to create a more user-friendly experience. This proactive approach helps prevent rage clicks before they become a major user pain point.

つまり、レイジクリックはユーザーにとっては苛立たしいものですが、プロダクトを改善する貴重なインサイトも提供してくれるものでもあります。Pendoのプロダクトアナリティクスプラットフォームは、ユーザー行動を理解し、不満が生じているエリアを特定し、最終的にソフトウェアアプリケーションでのレイジクリックを減らすことを目的としたツールスイートを提供します。

Pendoセッションリプレイがプロダクトロードマップにどのように役立つか

ツアーを見る

プロダクトアナリティクスを活用してレイジクリックを回避する方法の詳細

For those looking to dig deeper into rage clicks, request a personalized demo or learn how Cin7 uses Session Replay at their organization.