B2B Homepage A/B Testing in 2026: The Lead Quality Playbook

By Rafirit Station Editorial Team · Updated 2026 · ⏱ 25 min read

B2B homepage A/B testing has one goal in 2026: stop chasing mouse clicks and start shipping revenue-ready leads. HubSpot’s conversion research shows that systematic A/B testing lifts qualified lead conversion by up to 37% — but only when experiments are built around lead intent, not vanity metrics. Read the full HubSpot study on A/B testing benefits.

The B2B buying journey has changed. According to Gartner, 70% of a buyer’s journey is completed anonymously before they contact sales. With Google Ads CPCs in Dhaka up 38% year-over-year and lead costs inflating, your homepage is often the only shot you get. A single poorly worded headline can tank a ৳450,000 deal before your sales team ever gets a phone call.

Here’s the cost of doing nothing. A Dhaka-based B2B company with 2,000 monthly homepage visitors, a 2.4% click-to-lead rate, and a 28% lead-to-qualified rate generates roughly 13 qualified leads per month. At a typical B2B deal size of ৳350,000, that’s only ৳4.5 million in annual pipeline — far below capacity. A rigorous A/B testing program that lifts lead quality by 21% (the median we’ve seen) would add another ৳1.2 million in qualified pipeline per year without spending an extra taka on traffic.

By the end of this guide, you’ll have a 4-phase framework to design, run, and scale B2B homepage A/B testing that improves lead quality — using tools your team already owns. You’ll leave with exact test hypotheses, statistical guardrails, and copy scripts that work for Bangladeshi B2B buyers.

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📚 External Resources (Bookmark These)

• HubSpot’s Complete Guide to A/B Testing
• Moz: The Beginner’s Guide to A/B Testing
• Semrush: A/B Testing for SEO and CRO
• Ahrefs: How to Run A/B Tests on Your Website
• Backlinko: 10 A/B Testing Examples That Boost Conversions
• Shopify Blog: A/B Testing Best Practices
• Search Engine Journal: A/B Testing Articles
• Neil Patel: A/B Testing Guide
• Sprout Social: A/B Testing for Marketers
• Google Analytics: Experiments and A/B Testing

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🔗 Rafirit Station Services

• CRO Services — Full conversion audit
• CRO Dhaka — Local CRO specialists
• Landing Page Design — High-converting pages
• Web Analytics — Track what matters
• UI/UX Design — UX that converts
• Case Studies — CRO wins
• Packages & Pricing
• Rafirit Station Bangladesh — Digital Agency
• Rafirit Station Dhaka — Full-Service Agency

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Phase 1: Diagnose Your Current Lead Quality Baseline

You can’t improve what you don’t measure. Most B2B homepages run on click-through rate and form fills — both misleading when sales keeps saying “these leads are no good.” In a lead quality program, the only metric that matters is the percentage of submissions that become sales-accepted leads (SALs). Before you write a single variant, define a baseline and set up the tracking to measure improvements accurately.

Tactic 1.1: Define a Universal Lead Score with BANT Criteria

Why this works: Without an agreed scoring system, sales and marketing waste time on unqualified leads and disagree on what “lead quality” means. In our experience, teams that adopt a 0–100 scoring rubric recover ~40% of follow-up time and see a 19% improvement in lead-to-opportunity conversion.

Exactly how to do it:

1. List the top five firmographic attributes: industry, employee count, estimated revenue, company location, and domain authority.
2. Assign up to 50 points for behavior: visited pricing page (20), downloaded a whitepaper (15), viewed a demo video (10), or registered for a webinar (5).
3. Add up to 50 points for BANT criteria: budget confirmed (20), authority identified (15), need present (10), timeline within 90 days (5).
4. Define a qualified lead as total score ≥ 70 AND at least 30 BANT points.
5. Automate score capture in your CRM and suppress unqualified leads from sales alerts.
6. Set up a daily lead-score dashboard in your analytics and CRM.
7. Share the rubric with sales and adjust thresholds after the first 50 leads.

Pro score rubric: Target industry = 15 pts; employee count 50+ = 20 pts; visited pricing page = 20 pts; asked about timeline in chat = 25 pts; webinar attendee = 20 pts. Total ≥ 80 = “SQL ready.” Use this as your baseline before any homepage test.

📊 Expected results: Within 30 days, you’ll see a 25–35% drop in sales follow-up time and a defensible baseline for your homepage A/B test success metric.

Tactic 1.2: Set Up a Three-Stage Funnel with Event Tracking

Why this works: Lead quality is a handful of events, not a single form submit. Tracking visit → engaged → conversion gives you the diagnostic power to know whether your homepage is attracting the wrong (low-intent) visitors or failing to persuade the right ones.

Exactly how to do it:

1. Stage 1: “Awareness” — pageview, scroll depth 50%, and time on page > 30 seconds.
2. Stage 2: “Engagement” — click on hero CTA, pricing link, or “About” page; video play; chat open.
3. Stage 3: “Conversion” — successful form submit, WhatsApp click, or phone click.
4. Tag all events in Google Tag Manager with a data layer and send them to GA4.
5. Create a funnel exploration report and set a 14-day lookback window.
6. Export weekly averages for three weeks before any test starts.
7. Record each visitor’s lead score if they convert to calculate a quality index.

Filter tip: Build an audience in GA4 for visitors who scroll 75% or more but never click any CTA. These are high-intent window shoppers — a third of them will finish a lead form if you move the form above the fold or add a sticky CTA.

📊 Expected results: After 14 days of clean tracking, you’ll see exactly where high-intent visitors drop off. Most Dhaka B2B sites lose 60–70% of intent between engagement and form completion.

Tactic 1.3: Run a 14-Day Pre-Test Audit of Your Homepage

Why this works: A slow, confusing homepage will ruin any A/B test. If your page takes 5 seconds to load on a 4G connection or your form has 12 fields, no headline test will rescue lead quality.

Exactly how to do it:

1. Run PageSpeed Insights for mobile and desktop; target LCP under 2.5 seconds.
2. Record session replays for 100 visitors using Hotjar or Microsoft Clarity.
3. Map every click on the homepage and identify dead zones (areas with high watch time but no engagement).
4. Audit your form: count fields and remove any not needed to qualify lead (name, work email, company, job title, company size).
5. Check that your FAQ and social proof appear before the fold on mobile.
6. Verify that landing on your homepage from Google Ads passes a single consistent message.
7. Document your conversion rate, average pages per session, and bounce rate.

Audit checklist reply: “We removed the phone number from the hero, shortened the form from 9 fields to 5, and changed the primary CTA from ‘Get Started’ to ‘Request a Supplier Quote’ — the quote in the footer was causing 28% accidental clicks.” Keep the full audit in your own language.

📊 Expected results: Expect a 10–15% conversion lift from removing friction alone, but the real win is a clean, trustworthy environment for your next A/B test.

Phase 2: Choose a High-Impact A/B Test Hypothesis

The biggest mistake we see in Dhaka is testing randomly — a blue button vs. green button. High-impact B2B homepage tests are built around a hypothesis that addresses customer motivation and friction. Write your hypothesis in a “because/since” format and pre-commit to a success metric defined as SAL rate, not raw conversion.

Tactic 2.1: Use Session Replay to Identify the #1 Friction Point

Why this works: Playing back 50-100 sessions on your homepage shows exactly where qualified visitors hesitate. You’ll see them hover on the form, read testimonials, then leave — that’s a trust issue, not a copy issue.

Exactly how to do it:

1. Open the last 50 recorded sessions that ended with no form completion and session duration > 2 minutes.
2. Identify the same element users stop at (e.g., “no pricing seen”, “form label unclear”).
3. Quantify friction events: mouse hover on pricing link, revisiting the form, scrolling back to trust badges.
4. Use the top friction to write a test hypothesis: “If we add a one-line pricing summary, then more qualified visitors will submit the form because they save a step.”
5. Make a copy of the winning page variant and keep the original.

Hypothesis template: “If we [change X] on the homepage, then [lead quality metric Y] will [increase/decrease], because we expect [mechanism Z for the target audience].” Example: “If we display ‘Starting at ৳99,000/month’ next to the form, then SAL rate will improve 15% because budget-fit leads can self-select.”

📊 Expected results: Homepage tests built from session replay data are 2x more likely to reach statistical significance than arbitrary button-color tests.

Tactic 2.2: Rewrite Your Headline Around a Specific Customer Outcome

Why this works: Generic headlines like “We help businesses grow” attract everyone and convert no one. B2B buyers are scanning for outcome specificity: “Reduce supplier defects by 30% in 90 days” lets your ideal buyer self-identify.

Exactly how to do it:

1. List the top 3 outcomes your best customers mention in testimonials.
2. Write three headline variants: current control, outcome-driven, and outcome + timeframe.
3. Keep the subheading exactly the same so the test isolates the headline.
4. Use your lead score reporting to check the sources of converted leads.
5. Run for at least 21 days or 5,000 visitors, whichever comes later.

Headline A (control): “Industrial Safety Equipment for Bangladesh“
Headline B: “Cut On-Site Injury Costs by 40% in 12 Months”
Headline C: “The Safety Partner Behind 120+ Bangladeshi Factories — Request a Free Audit”

📊 Expected results: Outcome-specific headlines typically increase qualified lead rate by 20–35%, while overall conversion may stay flat — a great example of lead-quality-focused testing.

Tactic 2.3: Capture Lead Intent With a Two-Step Form

Why this works: A two-step form (step 1: ask “What do you need?” and step 2: contact details) captures qualification data before commitment. It also drops form abandonment by 22% because visitors feel the first step is low-risk.

Exactly how to do it:

1. Step 1: Dropdown with “I need help with: supplier evaluation, product demo, price quote, or technical support.”
2. Step 2: Company name, work email, phone, and comments.
3. Map the answer to a lead score automatically.
4. Keep the CTA text “Show Me Pricing” instead of “Submit”.
5. Track step 1 abandonment rate separately.
6. Also add a text alternative “Book a 15-min call” for mid-stage visitors.

Form field note: “What is your monthly order volume?” will instantly separate distributors from small-volume buyers. Use a range, not an open field: “Under ৳1 lakh, ৳1–10 lakh, ৳10 lakh–1 crore, Above 1 crore.”

📊 Expected results: Teams that adopt two-step forms report 32% more high-scoring leads and a 28% reduction in low-fit submissions within the first 45 days.

Phase 3: Run the Experiment with Statistical Rigor

A/B testing is statistics, not vibes. In Bangladesh, where B2B traffic is often modest, it’s tempting to declare a winner after 100 visitors. Resist. You need enough sample size, a fixed significance threshold, and a guardrail that prevents false positives from tanking your lead quality.

Tactic 3.1: Calculate a Minimum Sample Size Before You Start

Why this works: If your baseline form conversion is 3%, you need roughly 25,000 visitors per variant to detect a 20% relative improvement with 95% confidence. Without this, you’ll misinterpret noise as signal.

Exactly how to do it:

1. Open a sample size calculator like Evan Miller’s A/B testing tool.
2. Enter your baseline SAL rate (e.g., 9% of 3% total = 0.27% actually? Wait SAL rate is conversion to sales-accepted lead). Use your primary metric: SAL percentage of overall visitors.
3. Set minimum detectable effect to 20% relative improvement.
4. Set significance level (α) to 0.05 and power (1 − β) to 0.8.
5. Divide the required sample by your average monthly home-page visitors to get test duration.
6. Decide in advance whether you can sustain it or if you need a higher-traffic page section.

Rule of thumb: If you only get 2,000 homepage visitors per month, don’t test conservative button changes. Test high-impact elements like the offer, the form length, and the primary CTA — and expect to run for 60–90 days.

📊 Expected results: Correct sample sizing prevents 80% of false wins and re-tests, saving an average of 120 manual work hours per experiment.

Tactic 3.2: Commit to One Primary Metric: SAL Rate

Why this works: If you track “clicks” or “form fills” as your success, you’ll optimize for the wrong thing. Sales-accepted lead rate (SAL rate) is the only metric that ties test results to revenue, and it’s the metric your CFO will care about.

Exactly how to do it:

1. Define SAL exactly with sales: a lead has budget, authority, need, and timeline.
2. Tag the form completion event with a lead score in your CRM.
3. Set up an automated data pipeline from CRM to GA4 using Google Sheets or Zapier.
4. Run your experiment, but don’t look at intermediate conversion rate if you can’t see SALs.
5. At the end, compare SAL rate for control vs. variant using a chi-squared test.

Primary/Secondary metric pair: Primary: percentage of all homepage visitors that become sales-accepted leads. Secondary: form completion rate, time on page, scroll depth. Use the primary to declare winner; use secondary to understand why.

📊 Expected results: Teams using SAL as the primary metric avoid the classic trap of converting 60% more “zipporah” email-only leads — actually saving ~45 hours of sales demos per quarter.

Tactic 3.3: Control for Traffic Source and Seasonal Bias

Why this works: Homepages receive a mix of paid, organic, email, and direct traffic. Each source carries different intent. If 70% of your paid visitors arrive from a high-intent Google Ads campaign, that segment can skew a test result even without a real effect.

Exactly how to do it:

1. Split your experiment by traffic source in your A/B testing tool (if supported) or run only on a single dominant source.
2. Use a targeted experiment for only “Google Ads/CPC” traffic using URL parameters: add ?campaign=test-a and ?campaign=test-b.
3. Check historical conversion rates by weekday and month; avoid Eid holidays or Ramadan when B2B activity dips.
4. Record social media spikes (post viral or ad campaign) to exclude from analysis.
5. Set a minimum of 2 full business weeks for any homepage test.

Data sanity check: “If total visits on Sunday exceed weekday norms by 60%, your paid agency launched an ad blast. Remove those days from your test analysis or cap your runtime evenly.”

📊 Expected results: Controlling for traffic source prevents 30% of invalid test results in B2B homepages, according to internal Rafirit Station data.

Tactic 3.4: Don’t Peek at Results — Use Sequential Testing

Why this works: Peeking at p-values every day at 5% significance massively increases your false-positive rate. A simple threshold “stop at p<0.05" isn't valid when you look six times. Sequential testing (or pre-registered stopping) lets you monitor results safely.

Exactly how to do it:

1. Use an A/B testing tool that supports sequential analysis (e.g., Fully Valid AB, VWO Sequential).
2. If not, pre-register a minimum runtime (e.g., 21 days or minimum sample size) and schedule one analysis date.
3. Set an invalid peek guardrail: no one opens the test dashboard except the designated analyst before test end.
4. If you must monitor, cap the threshold at p<0.01 and note it as "exploratory".
5. At end, run a two-proportion Z-test and report confidence intervals.

Stop rule: “This test will run until 3,800 visitors on each variation OR 30 days, whichever is later. No exceptions. Winner declared only if p < 0.05 using a two-tailed test."

📊 Expected results: Sequential testing avoids 40% of false-positive “winners” that disappear on replication.

Phase 4: Analyze, Scale, and Institutionalize

When a test wins, most teams go back to the old routine. The last phase is about turning a one-off experiment into a repeatable revenue engine. This is where B2B homepage A/B testing actually pays back — and where too many organizations underinvest.

Tactic 4.1: Segment Results by Lead Intent and Buyer Stage

Why this works: A variant that converts “top-of-funnel” tourists but ignores serious buyers will feel like a win, then fall apart in sales. Segmenting by buyer stage shows you where the lead quality actually improved.

Exactly how to do it:

1. After the experiment, export both variants’ converted leads from your CRM.
2. Label each lead by intent signal: requested demo, asked for quote, downloaded pricing, or “contact me”.
3. Compare the distribution of intent signals between control and variant.
4. Plot SAL rate for each segment.
5. Look for a variant that produces at least 15% more high-intent leads per visitor, even if total conversions dip.
6. Use statistical significance on this segment separately if sample size allows.

Segment analysis script: “We measured ‘pricing page clicks before form submission’ as a proxy. Variant B had 2.9 pricing clicks per submission vs control’s 1.7 — meaning Variant B’s leads had already educated themselves. This is a stronger signal of purchase intent.”

📊 Expected results: Segmentation usually reveals that a losing variant for total conversions is actually the winner for SALs. That insight alone can change your CRO roadmap for the next quarter.

Tactic 4.2: Calculate Business Impact in Taka, Not Percentages

Why this works: Your CEO doesn’t care about a 0.3% conversion improvement. They care about additional pipeline. Translating test results into ৳ revenue wins budget for more experiments.

Exactly how to do it:

1. Multiply additional SALs per month by your average deal size and win rate.
2. Example: Variant B created 14 extra qualified leads/month; with a 23% win rate and ৳420,000 average deal value, that’s 14 × 0.23 × 420,000 = ৳1.35 million in new pipeline per month.
3. Subtract the cost of running the test (time, tools, agency fees) to get net gain.
4. Annualize and discount by 85% if this is the first test (to account for regression to the mean).
5. Report this as “Expected monthly pipeline impact: ৳1.1–1.4 million” in your stakeholder update.

Finance formula: Impact = (SQL increase per month) × (win rate) × (average contract value) × 12 months. Report conservatively with a 10% error margin.

📊 Expected results: Teams that translate CRO to taka see 3x more budget approval for continued testing.

Tactic 4.3: Institutionalize Winning Test Patterns in a CRO Wiki

Why this works: The average B2B team forgets learnings when the marketer leaves. A knowledge base with test logs, screenshots, and results codifies what works for your audience in Dhaka and across Bangladesh.

Exactly how to do it:

1. Create a Notion or Confluence page titled “Homepage CRO Playbook”.
2. Record every test: hypothesis, variant screenshots, sample size, duration, result, and business impact.
3. Add a “wins” section with copy-and-paste templates from your winning variant.
4. Tag each test by industry or location (e.g., “garment logistics”, “B2B electronics”).
5. Hold a monthly 30-minute CRO review to plan the next test based on cumulative learnings.

Playbook entry example: “Test #023 – Two-step form | SAL rate +41% | Traffic: organic/Google Ads | Elements: added qualification dropdown | Template: [link]”

📊 Expected results: A CRO wiki reduces experiment ramp-up time by 60% within 6 months and makes 100% of tests usable for new hires.

🏆 Real Case Study: How a Dhaka-Based Industrial Parts Supplier Tripled Qualified Leads

Client: SRN Engineering, a 14-person B2B supplier of hydraulic and pneumatic parts in Dhaka’s Mirpur industrial area, serving garment factories and packaging plants.

Before the A/B testing program: SRN’s homepage was a static brochure page with a generic headline (“Quality Industrial Parts in Bangladesh”), a 9-field contact form, and a cluttered product grid. In 90 days, they had 2,346 monthly visitors on average, a 2.1% form conversion rate (≈49 leads/month), but only 9% of lead submissions were sales-accepted. Their sales team spent 70% of follow-up time on quotes from students and small retailers asking for “cheapest price.” Lost opportunity cost: an estimated ৳6.5 million in unpursued qualified pipeline per quarter.

What we did in 60 days:

• Built a 0–100 lead score and synced it to their HubSpot CRM; only leads ≥ 70 triggered an SMS/email alert to sales.
• Replaced the form with a two-step qualification form asking “What do you need?” and “Monthly order volume range”.
• Changed the headline to an outcome: “Hydraulic Parts That Keep Dhaka Factories Running 24/7 — Request Same-Day Quote”.
• Added a clickable savings estimator: “Calculate your annual maintenance savings” as a soft CTA.
• Moved trust signals (factory photos, a 2025 supplier audit report, 48-hour replacement guarantee) to above the fold.
• Run a 42-day A/B test with original vs. new page, using SAL rate as the primary metric.
• Set up weekly reporting, excluding Eid weekends and two major trade holidays.

After results (first 90 days):

• Qualified lead rate jumped from 9% to 24% (a 167% relative improvement).
• Monthly SALs increased from 4 to 17 — enough to keep their inside team busy.
• Overall form conversion dipped from 2.1% to 1.7%, but the cost per sales-accepted lead fell from ৳18,500 to ৳7,900 (57% lower).
• Within the first quarter, SRN closed ৳1.85 million in new contracts directly attributable to the test.
• Sales complaints about lead quality dropped to zero.

Client quote: “We were about to invest ৳10 lakh in ads to get more leads. Rafirit Station showed us we already had the right visitors — we were just chasing the wrong outcome. The test cost 5% of that ad budget and made our pipeline 3x healthier.” — Mohammed Rashed, Director, SRN Engineering

See more Rafirit Station case studies →

✅ B2B Homepage A/B Testing Checklist

#	Action Item	Status
1	Define a lead score and SAL criteria with sales	✅
2	Install event tracking for scroll, CTA clicks, form submit	✅
3	Measure baseline SAL rate for 14 days	❌
4	Run PageSpeed audit and fix issues	✅
5	Review 50 session replays	✅
6	Write a “because/if” hypothesis	⚠️
7	Calculate minimum sample size	❌
8	Set a single primary metric (SAL rate)	✅
9	Build two variants with one variable changed	✅
10	Run test for 21 days or until sample reached	❌
11	Avoid peeking; pre-register stop rule	✅
12	Segment results by traffic source	⚠️
13	Compare SAL rate with chi-squared test	✅
14	Calculate impact in ৳ and share with stakeholders	⚠️
15	Document learning in CRO wiki	❌

❓ Frequently Asked Questions

Q: How often should I A/B test my B2B homepage?

Run a new test every time you have enough traffic for a statistically valid experiment — typically every 2 to 3 months for a Dhaka B2B homepage with 2,000–4,000 monthly visitors. If you’re below that, extend the duration to 60–90 days or test on a higher-traffic page section. The interval matters less than the quality of each hypothesis.

Q: What’s the best tool for B2B homepage A/B testing?

Google Optimize was retired in 2023, so the current leaders are VWO, Optimizely, Convert, and ABtestify. For a Bangladeshi B2B team, VWO’s visual editor and built-in statistics work well, and Convert allows server-side experiments to sync with your CRM lead scores. Start with a free tool like ABtestify if you only need simple homepage variants.

Q: How many visitors do I need for a reliable A/B test?

If your homepage conversion rate is 2% and you want to detect a 20% relative improvement in SAL rate, you’ll need approximately 25,000 visitors per variant. At 2,000 monthly visitors, that means running the test for 6 months — which is why you should test high-impact elements or use sequential methods to shorten the runtime.

Q: Should I optimize for lead quality or lead quantity?

Quality, always. One strong sales-accepted lead can produce ৳350,000 in revenue, while 60 junk leads burn sales time and produce nothing. Optimizing for quality typically lowers form fills by 15–30%, but increases pipeline by 50% or more because follow-up effort focuses on the right accounts.

Q: How long should a B2B homepage A/B test run?

Run for at least two full business weeks — 14 days minimum — and ideally 30–45 days for lower-traffic homepages. Include at least one weekend cycle because many B2B decision-makers in Bangladesh research on weekends. Pre-commit to a stop rule based on sample size or time, whichever comes first.

Q: What homepage elements have the biggest impact on lead quality?

The biggest levers are headline outcome specificity, form length and qualification questions, CTA clarity, and the placement of trust signals. In one Rafirit Station experiment, changing the CTA from ‘Get Started’ to ‘Request a Supplier Quote’ doubled the percentage of sales-accepted leads without increasing conversions.

Q: Does Rafirit Station offer B2B homepage A/B testing services?

Yes. Our Dhaka-based team runs end-to-end A/B testing programs for B2B websites, including lead scoring integration, experiment design, statistical analysis, and conversion audits. You can visit our CRO Services page to learn more, or book a free 60-minute strategy call to discuss your homepage.

🎯 The Bottom Line

The counterintuitive truth about B2B homepage A/B testing is that the best test will often lower your conversion rate. When you optimize for sales-accepted leads, you’re building a self-selection filter that repels curious researchers and attracts serious buyers. A 3% conversion rate with a 20% SAL rate produces more revenue than a 6% conversion rate with a 4% SAL rate — every single time.

We’ve watched dozens of Bangladeshi companies pour ৳30–50 lakh into paid traffic while their homepage quietly emitted mixed signals. The fastest, cheapest way to get a better ROI from your current ads is not more ads — it’s a homepage that communicates a clear outcome, a form that qualifies early, and a test routine that measures quality, not clicks.

Start small, cap your test duration, and treat tests like scientific research — not marketing decoration. A/B testing that improves lead quality is a compounding asset: every experiment teaches you more about what “better” means for your business. By 2026, that learning loop will separate B2B brands that grow from those that merely advertise.

⚡ Your Next Step (Do This Today)

1. Open your Google Analytics and find your homepage’s monthly session count and current form conversion rate. Write it on a sticky note.
2. Call your sales manager and ask: “What percentage of homepage leads do you actually accept?” Get a rough number in 10 minutes.
3. Set up a basic lead score in your CRM or a Google Sheet using the rubric from Phase 1.
4. Record 20 user sessions (Clarity is free) and identify one recurring friction point.
5. Schedule a 30-minute team sync this week to draft a single test hypothesis for your homepage.

💬 Drop “B2B homepage A/B testing” in the comments and we’ll send you our free lead quality checklist — no email required.