In a new Adobe study of 1,000+ US consumers, we uncovered some surprising patterns in how — and when — people shop online.

Key findings:

• Night owls spend the most: Just 9% of shoppers browse late at night (10pm–3:59am) — but they spend more per year than any other group, averaging $3,684 annually.
• Gen Z acts on impulse 25% more often than older generations.
• Saturdays see the most browsing, but Sundays drive the highest spending.
• New Yorkers spend nearly 9% of their income online each month, more than any other state.
• Millennials spend more online per month than any other age group ($282/month), while boomers spend $216.
• Apple users spend 87% more time shopping online than Android users.
• PC users spend 23% more time shopping than Mac users.
• Early birds are 31% more likely to impulse buy than wind-down shoppers.
• Nearly 1 in 10 shoppers make purchases on payday.
• Night owls are the most likely to respond to urgent, limited-time offers and to shop right after being paid.

A scroll at sunrise. A splurge after payday. A cart full of impulse buys at 2am.

These aren’t edge cases — they’re buying signals. And if your ecommerce strategy doesn’t account for them, you’re likely missing your most valuable customers.

What is ecommerce analytics?

Ecommerce analytics isn’t just about tracking traffic or counting conversions anymore.

Today, it’s about understanding why people buy — and when.

It’s no surprise that Gen Z clicks through more impulsively. That night owls generate more revenue per shopper. That a surge in first-time purchases might be tied to payday — or a UX tweak you rolled out last week.

Modern ecommerce analytics combines behavioral signals, contextual triggers, and real-time data activation. It helps you:

• Identify which products drive high lifetime value
• Spot drop-offs in the purchase journey as they happen
• Attribute campaign impact across platforms, not just channels
• React to changing shopper behavior across time of day, device, and region

And increasingly, it must be accessible beyond the analytics team. Merchandisers, product owners, and growth marketers all need to see what’s working — and what’s not — without waiting on dashboards.

That’s why platforms like Adobe Customer Journey Analytics, specifically Product Analytics are changing the game. They replace siloed metrics with a single, event-based view of the customer journey — from first touch to repeat purchase — and make insights self-serve.

Why traditional analytics tools fall short.

Most ecommerce teams still rely on legacy tools built for an earlier era — one where customer journeys were linear, web-only, and easy to segment by campaign or channel.

But today’s shoppers don’t follow a straight path. They jump from Instagram to your site, abandon a cart on mobile, then return days later via a promotional email — often on a different device.

Traditional analytics platforms weren’t built for this.

Here’s where they fall short:

• Session-based models miss the full journey: They can’t connect touchpoints across devices or channels, leaving gaps in attribution and insight.
• Data lives in silos: Web analytics, CRM, email performance, and product usage data are rarely unified — making it hard to see the complete picture.
• Dashboards are static: Business users can’t explore beyond pre-set reports, and answering new questions often requires tagging changes or data exports.
• Timing isn’t built in: Most tools treat all users the same, regardless of when they shop or how their behavior changes throughout the day or week.

The results? Missed revenue opportunities, reactive decision-making, and a disconnect between insight and action.

That’s why leading ecommerce brands are turning to Adobe Customer Journey Analytics and Product Analytics — to shift from fragmented views to unified, real-time insights that drive business outcomes.

Why this matters now.

Shoppers don’t just convert because of what you offer. They convert when it’s the right moment — on the right device, with the right prompt.

That "right moment" isn’t the same for everyone.

Some wait until payday. Others browse after midnight. Some convert quickly, while others take days. And the signals that reveal these patterns — like, session timing, campaign exposure, and funnel drop-off — are already in your data.

The problem? Most analytics tools weren’t built to surface those signals, let alone make them actionable.

Adobe Product Analytics changes that.

By combining guided analysis with real-time customer journey data, ecommerce teams can:

• Identify high-value segments based on actual behavior
• Understand friction in time-sensitive journeys
• React faster with campaign-ready audiences

In a crowded market, timing and insight are your advantage. With Product Analytics, you can use both at scale.

Applying these insights: What leading ecommerce teams do differently.

Understanding when and why people buy is only useful if you can turn those insights into action. That’s where Product Analytics makes a measurable difference.

Here are five ways modern ecommerce teams are using data to drive growth — and how Product Analytics supports each one:

1. Segment by timing, not just demographics.

Most analytics platforms group users by age, geography, or channel. But real buying behavior often depends on when people engage.

Use case: Build segments for morning browsers, late-night buyers, and payday-driven shoppers. Then compare funnel conversion, cart abandonment, or revenue per session across each group.

How Product Analytics helps: Filter events by timestamps, session start times, or days of the week. Apply usage trends and funnel views to compare performance across time-based cohorts.

2. Identify and fix friction in high-intent journeys.

You already know which steps should lead to conversion — but where do users drop off?

Use case: Analyze add-to-cart to checkout flows and compare free versus logged-in users. Use the insight to streamline your checkout or trigger follow-up emails.

How Product Analytics helps: Run guided funnel analyses with pre-built visualizations. Toggle between lifetime or session-based journeys, and compare conversion rates across user segments.

3. Measure the impact of campaigns and feature releases.

You launch campaigns to drive action — but can you prove the outcome?

Use case: After a reactivation campaign targeting dormant users, measure whether it led to more media starts or purchases.

How Product Analytics helps: Use impact analysis to compare behavior before and after campaign exposure. Segment by clickthrough or first-use action to quantify the lift.

4. Spot patterns in user growth and retention.

Are you gaining active users — or just cycling through one-time buyers?

Use case: Track net user growth week by week, and then drill into dormant cohorts to understand why they didn’t return.

How Product Analytics helps: Use the active and net user growth views to visualize retention over time. Export segments to Adobe Real-Time CDP to activate reengagement journeys.

5. Go from insight to action — without waiting on data teams.

Your team shouldn’t have to file tickets to explore what’s happening in the data. You need to act in the moment.

Use case: A merchandiser spots low conversion on a popular product. They quickly build a segment of users who viewed but didn’t buy — and launch a targeted email promotion.

How Product Analytics helps: Use any guided view to define a segment, and then push it directly to Adobe Journey Optimizer for activation — without leaving the interface.

How Adobe Product Analytics helps ecommerce teams move faster.

You don’t need more dashboards. You need faster answers — and clearer next steps.

That’s exactly what Adobe Product Analytics delivers.

Built on top of Customer Journey Analytics, Product Analytics gives ecommerce teams a guided way to explore their most important metrics — without needing a data science degree or endless tagging tweaks.

Instead of raw tables or static reports, teams get pre-configured analysis views designed for ecommerce decision-making:

• Funnel analysis: See where shoppers drop off, compare free versus paid users, and identify friction in your checkout or sign-up flow.
• Impact analysis: Measure the performance lift of a new feature, email campaign, or UX update.
• User growth: Track new, retained, and dormant customers across daily, weekly, or monthly cycles.
• Usage trends: See how customer behavior changes over time — and spot shifts early.
• Audience activation: Instantly turn any segment into a live audience in Adobe Real-Time CDP or Adobe Journey Optimizer — no rework required.

Everything is powered by Customer Journey Analytic’s event-based data model, which means you’re not limited to session data or rigid schemas. You can filter by product views, cart adds, campaign clicks, and even time of day or device type — right inside the interface.

And when a guided view sparks a new question, you can open it directly in Analysis Workspace to explore further, share with a teammate, or drill into specific customer segments.

The result: Ecommerce teams don’t just see what’s happening — they can understand why, and act on it immediately.

Your ecommerce advantage: data you can act on.

Shopping behaviors are changing. Personalization expectations are rising. And the only way to keep up is with analytics that work at the speed of your customers.

Adobe Product Analytics gives ecommerce leaders a way to:

• React to real-world behavior shifts, from payday spending spikes to late-night splurges.
• Run experiments and know if campaigns are working — without waiting on analysts.
• Align product, marketing, and analytics teams around the same real-time data.

It’s not just ecommerce data — it’s ecommerce momentum. Powered by Adobe Customer Journey Analytics.

Want to learn more about Product Analytics? Explore the full capabilities here or book a demo.

Shop O’clock is an original research initiative from Adobe. In June 2025, we surveyed over 1,000 U.S. consumers about their online shopping habits and analyzed behavioral trends across demographics, locations, devices, and times of day. The findings in this report are based on self-reported data combined with aggregated behavioral patterns. All figures are reflective of the survey sample and may not represent the entire U.S. population.

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