AI-Assisted Form Fill Analysis

Exporting form submissions and running them through AI analysis is a fast, low-effort way to demonstrate lead volume and quality to skeptical clients — and to surface content and keyword opportunities you'd otherwise miss.

The core insight: most clients don't know how many real leads they're getting, or what those leads are worth. A structured analysis turns raw form data into a compelling story.

When to Use This

• Client is skeptical of ROI or questioning the value of digital marketing spend
• You need to demonstrate results before attribution infrastructure (e.g., CallRail) is fully in place
• You want to identify content gaps based on what prospects are actually asking about
• You need to build a recurring lead reporting cadence

Process

1. Export Form Data from Gravity Forms

1. In the WordPress admin, hover over Forms → click Import/Export
2. Select the relevant form (typically the main contact form)
3. Choose Export Entries, select all fields, and set a date range (e.g., current year to date)
4. Download the CSV

2. Analyze with ChatGPT

Upload the CSV to ChatGPT and prompt it to:

• Categorize entries into: legitimate leads, spam/empty, job inquiries, internal tests
• Summarize the legitimate leads: what are people asking about? What services, locations, or concerns come up most?
• Identify keyword themes from the message field — these map directly to content opportunities
• Flag source attribution — entries with UTM parameters or gad_source values indicate ad-driven inquiries

Example prompt structure:

Here is a CSV export of contact form submissions from [client] for [date range].
Please:
1. Categorize each entry as: legitimate lead, spam, job inquiry, or internal test
2. Count each category
3. Summarize the top themes and questions in the legitimate leads
4. List any entries that appear to have come from paid ads (look for UTM or gad_source fields)

ChatGPT will produce a categorized table and summary. Typical output for a mid-size senior care client: ~138 legitimate leads, ~28 spam, ~3 job inquiries out of 200 total entries.

3. Extract Content and Keyword Opportunities

Ask a follow-up prompt:

Based on the themes in the legitimate leads, suggest 10 blog post topics
that directly address what these prospects are asking about.
For each topic, include: title, target keywords, recommended word count, and
whether an FAQ section would be appropriate.

Common high-value themes that emerge for senior care clients:
- Availability / "do you have openings?"
- Cost and payment options (Medicaid, private pay, disability)
- Difference between assisted living and memory care
- How to schedule a tour
- What the move-in process looks like

These themes become the basis for a content plan. See ^[1] for how to publish and promote these posts.

4. Build the Revenue Framing

Once you have a legitimate lead count, calculate the potential revenue to reframe the conversation from cost to opportunity:

Monthly revenue per resident × 12 months = annual value per resident
Annual value × number of legitimate leads = total addressable revenue pool
Total pool × 10% conversion = conservative revenue opportunity

Example (Adavacare):
- 138 legitimate inquiries in one year
- ~$6,000/month per resident × 12 = $72,000 annual value
- 138 × $72,000 = ~$10M total pool
- 10% conversion = ~$1M annual revenue opportunity

This reframes the client conversation: the question isn't whether digital marketing is working — it's whether you're capturing your share of existing demand.

5. Set Up Recurring Reporting

Configure Gravity Forms to export automatically or set up a Google Sheets integration so lead data accumulates in a shared sheet. Options:

• Gravity Forms + Zapier/Make: push each new entry to a Google Sheet row automatically
• Manual weekly export: pull the CSV, append to a master sheet, run the AI analysis on new entries only
• Gravity Forms Notifications: ensure the client is receiving email notifications for every submission (check the notification settings — it's common for these to be misconfigured or going to a former employee)

Deliver a weekly or monthly summary to the client that includes:
- Total inquiries (legitimate vs. spam)
- Top themes / what prospects are asking about
- Which locations or services are generating the most interest
- Source breakdown (organic, ad-driven, direct)

Combining with Search Query Analysis

For a more complete picture, pair the form fill analysis with a Google Search Console export:

1. In GSC → Performance → export queries to Google Sheets
2. Upload to ChatGPT alongside the form data
3. Ask: "What are the top non-brand queries driving impressions but not clicks? How do these compare to the themes in the form fills?"

This reveals the gap between what people are searching for and what the site currently ranks for — and validates the content plan. See ^[2] for the full GSC workflow.

Notes and Caveats

• Verify AI-generated numbers: ChatGPT will sometimes fabricate statistics when asked about costs, market data, or industry benchmarks. Always prompt it to flag estimates and verify any specific figures before including them in client-facing materials.
• Use Gemini for keyword research: Gemini has access to Google's data and gives more accurate search volume and keyword difficulty estimates than ChatGPT for SEO-related queries.
• Source attribution in form data is incomplete: entries without UTM parameters don't mean they didn't come from ads — it means tracking wasn't set up properly. This is the gap that CallRail + DNI solves. See ^[3].
• 200 unread form entries is a red flag: if a client's inbox shows hundreds of unread submissions, confirm that notification emails are configured and going to the right person. Leads sitting unread are a churn risk.

Related

• ^[3]
• ^[1]
• ^[2]
• ^[4]
• ^[5]