Beyond ‘Required’: Rethinking Form Validation Rules for Better Data and Happier Users

Most teams treat form validation as a binary switch: a field is either required or optional, and the browser or backend enforces that line.

But if you’ve ever stared at messy spreadsheets, puzzling over half-complete submissions, fake phone numbers, or abandoned checkouts, you already know: how you validate is just as important as what you validate.

Thoughtful validation doesn’t just prevent empty fields. It:

• Improves completion rates and reduces abandonment
• Produces cleaner, more trustworthy data
• Builds user confidence instead of anxiety
• Cuts down on manual cleanup and follow-up

This is where tools like Ezpa.ge shine: you can design nuanced validation rules, theme them clearly, and sync everything straight into Google Sheets in real time—so you see immediately whether your rules are helping or hurting.

Let’s walk through a more modern way to think about validation—beyond slapping required on every field and hoping for the best.

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Why “Required vs Optional” Is Too Shallow

Most validation strategies start and end with a single question: Do we absolutely need this field to be filled out?

That’s important, but incomplete. There are at least four different dimensions you should care about:

1. Business criticality – Do we literally need this to proceed? (e.g., contact email for support requests.)
2. User sensitivity – Does this field feel risky or private? (e.g., date of birth, salary, medical info.)
3. Data quality requirements – How precise does this need to be? (e.g., zip code vs. full address.)
4. Timing – Do we need this now, or can we ask later in the journey?

When you ignore those dimensions and mark everything as required, you get predictable problems:

• Users drop off when they hit a sensitive but mandatory field.
• People enter junk data just to get past validation.
• Optional-but-unmarked fields confuse users: “Do I have to give my phone number?”
• Your team spends hours cleaning data instead of acting on it.

A better approach: treat validation as a design tool, not just a technical safeguard.

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Step 1: Start With the Outcome, Not the Input

Before touching any validation setting, ask: What decisions will this data drive?

For each field, write down:

• What will we do with this? (Route, qualify, personalize, report, comply, etc.)
• What happens if it’s missing or wrong? (Is the workflow blocked, slightly degraded, or unaffected?)

Then classify fields into three tiers:

• Must-have to proceed – Without this, the workflow literally breaks.
• Nice-to-have for optimization – Improves routing, personalization, or analytics, but isn’t critical.
• Legacy / vanity – No clear use today; kept “just in case.”

Design validation around those tiers:

• Must-have → Required, but with clear explanation and good error handling.
• Nice-to-have → Optional, with smart defaults or progressive prompts.
• Legacy / vanity → Remove from the form. (Or move to a follow-up micro-survey.)

If you want a deeper framework for choosing what belongs on the form at all, pair this with mapping the mental journey in Flow, Not Fields: Mapping Your User’s Mental Journey Before You Design a Single Input.

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Step 2: Use Validation to Respect, Not Pressure

Many abandonment issues aren’t about effort—they’re about trust and privacy.

Research from Baymard Institute found that when sites only mark required fields, users often assume unmarked fields (like phone number or date of birth) are required and either:

• Drop off because they don’t want to share
• Or enter bogus data to get through

Both outcomes are bad for your business and your data.

Design validation to signal respect for user boundaries:

1. Be explicit about what’s required and what’s not

• Use clear labels like Phone number (optional) instead of leaving it ambiguous.
• If most fields are required, add a note at the top such as: “All fields are required unless marked optional,” then mark the exceptions.
• Avoid relying on a lone red asterisk with no explanation—many people don’t know what it means.

2. Explain why sensitive fields are required

If you truly need something sensitive, pair the field with a short justification:

• Date of birth (required) – Used only to verify eligibility. We never share this with third parties.
• Salary range (optional) – Helps us tailor recommendations. Leaving this blank won’t affect your application.

3. Avoid “fake optional” fields

Nothing erodes trust faster than:

• Labeling a field as optional
• Then blocking submission if it’s empty

If a field becomes required only under certain conditions (e.g., “Other” text field after choosing “Other” in a dropdown), use conditional logic instead of blanket validation. Ezpa.ge makes this easy with field logic and step logic that only triggers validation when the condition is met.

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Step 3: Validate Formats Generously, Normalize Behind the Scenes

Overly strict format rules are a quiet conversion killer. Users get frustrated when the system insists on one way to type something they know many ways to express.

Examples:

• Phone numbers that only accept (555) 123-4567 but reject 555-123-4567 or 5551234567
• Dates that require leading zeros
• Currency fields that reject $1,200 but accept only 1200.00

A better pattern:

1. Accept common variations – Allow users to type naturally.
2. Normalize on the backend or via a hidden transformation – Strip spaces, punctuation, or symbols; standardize to your preferred format.
3. Show them the normalized version only if it’s helpful – For example, after blur, lightly format the number instead of throwing an error.

Practical ideas:

• Use regex patterns that focus on the essence of the data (e.g., “10 digits” for a US phone) rather than the exact punctuation.
• For international audiences, avoid local assumptions about formats; consider separate country selectors and context-aware validation.
• Combine validation with helper text: “We’ll format this for you—any standard phone number format is fine.”

When your Ezpa.ge form syncs to Google Sheets, you can also apply real-time cleaning rules in Sheets to catch edge cases and enrich data without punishing users at the point of entry.

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Step 4: Time Your Validation Messages Thoughtfully

Validation isn’t just what you check—it’s when you check it.

There are three common timings:

1. On submit – User completes the form, clicks submit, then sees errors.
2. On blur – User leaves a field; if it’s invalid, you show a message.
3. As they type – Every keystroke triggers validation.

Each has tradeoffs:

• Only on submit can feel harsh: you make it to the end, then get hit with a wall of red.
• Instant validation while typing often feels jumpy and stressful, especially for complex rules (e.g., passwords).
• On blur with smart rules tends to be the sweet spot.

A practical pattern that works well in most forms:

• Validate required-ness on blur – If they leave a required field empty, show a gentle prompt.
• Validate complex formats on blur – Email, password, ID numbers.
• Summarize remaining issues on submit – If anything’s still wrong, show a concise list at the top and link each item to the relevant field.

Guidelines for humane validation messages:

• Make the tone helpful, not scolding.
  • Prefer: “Add a valid email so we can send your receipt.”
  • Avoid: “Invalid email address.”
• Keep messages close to the field and use clear, accessible color and icons.
• Remove error states immediately once the field is fixed.

If you want to go deeper into the UX of these moments, pair this article with Error States that Convert: Turning Validation Messages into Micro-Moments of Trust.

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Step 5: Use Progressive Validation Across the Journey

Not every rule needs to fire on the first screen.

Instead of front-loading every requirement, think in stages:

1. Entry stage – Remove friction so people can raise their hand.
2. Qualification / enrichment stage – Ask for more detail once intent is clear.
3. Verification / consent stage – Apply stricter validation when commitment is high.

Examples:

• Newsletter signup

  • Stage 1: Email only (required, validated for deliverability).
  • Stage 2: After confirmation, optional profile form (industry, role, company size).

• Customer intake

  • Stage 1: Short intake form with only essential routing fields.
  • Stage 2: Follow-up form or micro-survey to collect deeper context.

• High-stakes flows (payments, healthcare, hiring)

  • Stage 1: Basic identifiers.
  • Stage 2: Verified identity, consent, and legal agreements with rigorous validation.

For these higher-risk scenarios, validation intersects with regulation and compliance. If that’s your world, you’ll get a lot of value from High-Stakes, Low Friction: Designing Verification and Consent Flows That Users Actually Finish.

Ezpa.ge’s multi-step forms and custom URLs make it straightforward to split flows this way—each step can have its own validation intensity, messaging, and even branding if you’re running multi-brand systems.

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Step 6: Align Validation With Your Data Model

Validation rules shouldn’t live in isolation. They should reflect the data structure you actually need downstream.

Questions to ask your ops or analytics partners:

• Which fields need to be unique identifiers (e.g., email, customer ID)?
• Which fields drive routing rules (e.g., region, plan type)?
• Which fields are used to join datasets (e.g., account number, location code)?
• Where do we often see data quality issues that require manual cleanup?

Then tune validation rules accordingly:

• Use stricter validation on keys and routing fields.
• Use softer validation (or optional fields) for purely descriptive data.
• Mirror your downstream constraints: if your CRM can’t handle emojis in names, catch that early.

If your forms feed into Sheets, take advantage of workflows like those in From Spreadsheet Chaos to Source of Truth: Structuring Google Sheets for Scalable Form Data to:

• Standardize picklist values
• Enforce consistent formats with data validation
• Flag suspicious entries for review instead of blocking users upfront

This combination—sane front-end validation + smart sheet rules—lets you be generous at the point of entry while still ending up with reliable, report-ready data.

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Step 7: Test Validation Like You Test Layouts

Teams often A/B test headlines, button copy, and field order—but leave validation rules untouched.

That’s a missed opportunity.

Variables worth testing:

• Marking both required and optional vs. only required
• Strict vs. flexible input formats
• Early vs. late validation timing
• Different tones of error messages

Metrics to watch:

• Completion rate – Do more people finish the form?
• Average time to complete – Are you speeding people up or slowing them down?
• Error rate per field – Which fields cause the most friction?
• Downstream data quality – Are there fewer fake values, fewer support tickets, less cleanup?

If you’re already using Ezpa.ge with Google Sheets, you can run lightweight experiments and track them live, similar to the workflow outlined in our piece on Real-Time AB Testing with Google Sheets: Ship Form Experiments in a Single Afternoon.

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Bringing It All Together

Rethinking validation is about shifting from gatekeeping to guiding.

When you:

• Start from the decisions your data must support
• Respect user privacy with clear, honest signals
• Validate formats generously and normalize behind the scenes
• Time error messages to be helpful, not jarring
• Spread validation across the journey instead of cramming it into one screen
• Align rules with your actual data model
• And treat validation as an experiment, not a one-time config

…you end up with forms that:

• Feel calmer and more trustworthy
• Generate cleaner, more actionable data
• Reduce internal friction instead of creating it

That’s the real promise of going beyond “required.”

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Where to Go From Here

You don’t need to rebuild your entire form system overnight. Start small:

1. Pick one high-traffic form.
2. Audit every field: Must-have? Nice-to-have? Legacy?
3. Loosen any overly strict format rules and move normalization to Sheets or your backend.
4. Rewrite your error messages to be clearer and kinder.
5. Ship, then watch the data for a week.

If you’re using Ezpa.ge, you can:

• Adjust validation and logic in minutes
• Sync results live into Google Sheets
• Layer on lightweight experiments without waiting on engineering

Your forms are often the first serious conversation your product has with a user. Make sure your validation rules sound like a helpful guide—not a bouncer.

Ready to try this on a real form? Log into Ezpa.ge, pick your most important form, and spend 30 minutes revisiting just three things: which fields are truly required, how you explain them, and how strictly you validate formats. The improvement—in both user happiness and data quality—will show up faster than you expect.