A/B testing, heatmaps, and conversion optimisation — made in India
VWO is India's own A/B testing and conversion optimisation platform — built in Delhi, used globally by 2,500+ companies. It bundles A/B testing, multivariate testing, heatmaps, session recordings, and funnel analysis in one dashboard. For Indian product and growth teams starting their experimentation journey, VWO's combination of affordability, INR billing, and local support makes it the most practical entry point before considering enterprise alternatives like Optimizely.
VWO (Visual Website Optimizer) is a conversion optimisation platform founded in 2009 in New Delhi by Sparsh Gupta and Paras Chopra. It's one of India's most successful B2B SaaS companies — used by 2,500+ brands globally including Ubisoft, eBay, Hyundai, and hundreds of Indian enterprises including Myntra, MakeMyTrip, and Nykaa.
At its core, VWO lets product and growth teams run A/B tests on websites and apps without requiring a code deploy for every variation — a huge advantage for teams where engineering bandwidth is the bottleneck. The visual editor lets you change text, images, and layouts through a point-and-click interface. For mobile apps, VWO's SDK enables server-side experiments that are more powerful for product teams than simple UI changes.
Beyond A/B testing, VWO has evolved into a full conversion optimisation suite — heatmaps, session recordings, form analytics, and funnel reports are all included depending on your plan. This makes VWO a competitor to both Optimizely (for experimentation) and Hotjar (for user behaviour analysis).
Point-and-click editor to create test variations without code. Change headlines, CTAs, images, and layouts. Automatically calculates statistical significance. Non-technical PMs can run tests independently.
SDK-based experiments for mobile apps and backend logic. Run experiments on pricing, algorithms, or feature rollouts without UI changes. Connects to Mixpanel/Amplitude for analytics integration.
Click maps, scroll maps, and session recordings to understand user behaviour. See where users click, where they drop off, and what they ignore — essential context for designing good experiments.
Test multiple elements simultaneously (e.g., headline + CTA + hero image) and identify the winning combination. More efficient than running sequential A/B tests for pages with multiple variables.
VWO pricing is based on monthly tracked users (MTUs) — visitors who interact with your site/app during a month. INR billing available, which removes forex complexity for Indian teams.
For up to 10K MTUs. A/B testing, multivariate, and URL redirect tests. Heatmaps and session recordings at add-on pricing. Good starting plan for Indian startups with moderate traffic.
50K MTUs. All Growth features + session recordings, heatmaps, and form analytics included. Server-side testing available. Most mid-market Indian brands with active experimentation run this plan.
100K+ MTUs. Dedicated account manager, custom integrations, SLAs. Multi-year contracts with significant discounts. Indian enterprises typically negotiate 30–40% off list pricing.
💡 INR advantage: VWO invoices in INR with standard Indian GST — no reverse charge complexity. At ₹16,000–33,000/month for the most useful plans, VWO is significantly cheaper than Optimizely ($50,000+/year) for teams running fewer than 50 experiments per year.
Engineering-first experimentation platform. Better for server-side and mobile experiments. Free tier is generous. Preferred by developer-led product teams.
Open-source. Includes A/B testing + feature flags + analytics free up to 1M events. Self-hostable. Best for early-stage teams wanting everything in one tool.
Enterprise-grade experimentation platform. Significantly more powerful — and significantly more expensive. Worth it only at Series C+ or 100+ experiments/year velocity.
<head>. Use VWO's Chrome extension to verify it's loading on every page before setting up any tests. A misconfigured snippet is the most common reason experiments don't work.We help Indian product teams set up their first A/B testing programme — from tool selection to experiment design to reading results correctly.
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