Mobile conversion optimization for ecommerce

Mobile conversion rates lag desktop CVR by 40-60% for most ecommerce brands. Given 70% of traffic is mobile, that gap is where most revenue gets left on the table.

Speed first, always

Every 1 second of mobile load time equals 7% CVR drop. LCPunder 2.5 seconds. INP under 200ms. CLS under 0.1. Hit these or nothing else matters.

Tap target sizing

Every clickable element 44x44 pixels minimum. Padded click areas around small text links. Buttons with enough margin that fat-finger mis-taps do not happen.

Mobile payment priority

• →Apple Pay↗, Shop Pay, Google Paybuttons above traditional checkout.
• →Single-page checkout (no multi-step flows on mobile).
• →Auto-fill for all standard fields.
• →SMS confirmation option instead of email-only.

Sticky elements

Sticky add-to-cart on product pages. Sticky checkout CTA on cart pages. Sticky phone number for service businesses. Persistent CTAs convert 15-30% higher on mobile.

Frequently asked questions

Is this approach right for early-stage companies?

Most frameworks in this space assume a certain level of operational maturity, dedicated team members, established measurement infrastructure, some history of experimentation to build on. Pre-seed and seed-stage companies often lack these prerequisites and need a lighter-weight adaptation. For brands doing under $3M in annual revenue, focus on three or four of the principles that matter most for your specific business model rather than trying to implement the full framework at once. Rigor matters more than coverage at this stage.

How does this work for B2B versus B2C businesses?

The underlying principles around mobile conversion optimization apply across both contexts, but execution differs meaningfully. B2B crotypically has longer sales cycles, multiple stakeholders per deal, and consideration periods measured in months rather than minutes. Measurement frameworks need longer windows. Attributionbecomes more complex. The same core strategic logic applies, but the tactical implementation looks different. We've worked extensively in both contexts and can flex the approach accordingly.

What changes when we integrate this with existing systems?

Every implementation requires integration work, systems don't exist in isolation. Analytics platforms, CRM, email systems, ad accounts, BI tooling all need to talk to each other for this to work at scale. Plan for 2-4 weeks of integration work at the start of any implementation. Shortcutting this phase creates data quality issues that compound and undermine the entire program over 6-12 months. We've seen teams skip integration work to move faster, only to spend 6 months later reconciling measurement discrepancies that could have been prevented upfront.

When should we reconsider the approach?

Every 6 months, run a structured review against the principles outlined here. Ask whether the market has shifted meaningfully, whether your business model has evolved, whether competitive dynamics have changed. Frameworks should evolve with context. A rigid commitment to any specific approach, including ours, eventually becomes the problem rather than the solution. The teams that outperform long-term are the ones that update their operating model based on evidence, not the ones that defend past decisions.