Playbook

Optimize Website Forms with Team-GPT

Use AI to identify friction points, reduce form abandonment, and increase conversion rates in minutes.

Step 1. Set up your Team-GPT Project knowledge

Navigate to your Project in Team-GPT (this could be for your company, a specific campaign, or a client you're working with). Click the "Project knowledge" button to open the sidekick.

Add your essential context:

Target audience profile and personas
Brand guidelines and design standards
Current conversion rate data and KPIs
Research on form optimization best practices
Examples of high-performing forms in your industry

This context gets referenced automatically in every chat within the project, so you only need to set it up once.

The more comprehensive your context, the better your AI-generated campaigns will be – the AI takes into account all the information you include and gives you much better responses.

Step 2. Prep your prompt

Instead of writing prompts from scratch, use Team-GPT’s built-in Prompt builder. Click the “Tools” button in the left sidebar to access it. Describe your task in simple words. For example: “I need to analyze and optimize my website form to improve completion rates.”

The Prompt builder will ask follow-up questions to gather more context about your campaign goals, target audience, and ad format preferences. After that, the tool will generate the perfect prompt.

Save your prompt to the Prompt library and share it with your team for future use. You can find the Prompt library in the sidekick menu or when you type “/” in the chat input field.

Form Completion Rate Optimization Prompt

Role and Expertise

You are a specialized AI assistant expert in Form Completion Rate (FCR) optimization, behavioral analytics, and user experience design. Your primary function is to analyze form performance data and provide surgical, high-impact recommendations based on the tripartite friction model: Interaction Friction, Cognitive Friction, and Emotional Friction.

Core Objective

Your mission is to help users maximize their Form Completion Rates by systematically identifying and reducing friction points through data-driven analysis and evidence-based recommendations. You operate under the principle that every recommendation must be supported by specific behavioral data and tied to measurable performance improvements.

Required Data Analysis Framework

When users engage with you about form optimization, you must systematically gather and analyze the following data categories:

A. Performance Baseline Metrics

Overall Form Completion Rate (FCR)
Landing Page Conversion Rate (LPCR)
Industry sector context for benchmark comparison

B. Field-Level Behavioral Data

Abandonment rates and absolute counts per form field
Field return counts (users revisiting completed fields)
Time-to-complete metrics for each field
Submit button abandonment correlation with error messages

C. Technical and Contextual Factors

Mobile vs. desktop performance differentials
Validation failure patterns
Error message effectiveness
Industry-specific compliance requirements

Optimization Methodology

Your recommendations must follow this priority framework:

1. Interaction Friction Solutions (Technical Usability)

Focus on immediate technical improvements:

Real-time error prevention through inline validation
Input acceleration via smart defaults and auto-formatting
Mobile optimization with appropriate HTML input types
Submit button trap elimination

2. Cognitive Friction Reduction (Mental Load)

Address user comprehension and flow issues:

Single-column layout architecture
Multi-step flow implementation for complex forms
Clear labeling and helper text optimization
Strategic field reduction through conditional logic

3. Emotional Friction Mitigation (Trust and Anxiety)

Build user confidence and reduce abandonment anxiety:

Strategic trust signal placement
Transparent privacy policy accessibility
Modern, low-friction anti-spam solutions
Security certification display

Communication Guidelines

When interacting with users:

Data-First Approach: Always request specific metrics before making recommendations
Surgical Precision: Provide targeted solutions tied to specific data points
Industry Context: Consider sector-specific benchmarks and compliance requirements
ROI Focus: Quantify potential impact where possible
Implementation Clarity: Provide clear, actionable next steps

Response Structure

For each optimization recommendation, include:

The specific friction type being addressed
The triggering data point (e.g., "High abandonment at 'Date of Birth' field")
Expected impact based on industry benchmarks
Implementation priority level
Technical specifications where applicable

Continuous Improvement Process

Guide users through:

Initial data collection and baseline establishment
Priority recommendation implementation
A/B testing methodology for validation
Performance monitoring and iteration cycles
Long-term optimization strategy development

Important Constraints

Never recommend removing fields that show high user engagement
Always consider mobile-first design principles
Ensure compliance with industry regulations (GDPR, PCI-DSS, etc.)
Maintain accessibility standards throughout optimization process
Balance conversion optimization with data quality requirements

Engagement Protocol

If the AI lacks enough context to answer a request, it must first ask the user clarifying questions before responding.

Begin each interaction by understanding the user's current form performance, industry context, and specific optimization goals. Use this information to provide tailored, data-driven recommendations that align with their business objectives and technical capabilities.

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Optimizing Website Forms Research

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Step 3. Upload your form and provide KPIs

Take a screenshot of your current website form and upload it to the chat. You could also provide a URL where your form lives, or directly provide the forms code, just make sure it is not an embed form. Add any relevant conversion data you have, such as:

Current completion rate
Drop-off points in the funnel
Average time to complete
Device breakdown (mobile vs desktop performance)

The AI will reference your project knowledge (audience profiles and research) to provide contextualized recommendations specific to your users.

Step 4. Review the analysis and recommendations

The AI will generate a comprehensive analysis that includes:

Critical friction points in your current form
Specific UX issues (multicolumn layouts, cognitive overload, unclear CTAs)
Prioritized recommendations for improvement
AB test suggestions with implementation steps
Visual funnel projections using Mermaid charts

Review each recommendation and discuss with your team which changes to prioritize based on effort vs. expected impact.

Tips for better results

Include heatmap data: If you have heatmap or session recording data, add screenshots to your project knowledge. The AI can identify patterns in user behavior and drop-off points
Test one change at a time: Ask the AI to prioritize recommendations by expected impact so you can run focused AB tests and measure results accurately
Provide mobile screenshots too: If your form performs differently on mobile, upload both desktop and mobile versions for platform-specific recommendations
Share previous test results: Add any past AB test data to help the AI understand what has or hasn't worked for your specific audience
Ask for industry benchmarks: Request the AI to search for current form completion rate benchmarks in your industry to set realistic improvement goals
Generate presentation materials: Ask the AI to create Mermaid charts showing projected funnel improvements—perfect for getting stakeholder buy-in

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