Time Analysis – RetentionX

Imagine you're selling ice cream. In summer, business is booming. In winter, sales slow down. Would you try to make up the difference by pouring more money into advertising? Probably not. You'd recognize that demand is naturally higher in summer and adjust your strategy accordingly.

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The same principle applies to your online store. Customer demand isn't constant throughout the day, week, or year. Some hours consistently generate more orders, while certain days naturally outperform others. Yet when sales slow down, many brands respond by increasing marketing pressure instead of asking whether they're simply pushing against natural purchasing patterns. Instead of fighting demand, why not use it to your advantage?

With Time Analysis, you can identify when your customers are most likely to buy and use those insights to make smarter decisions about campaign timing, promotions, and advertising budgets.

How to Analyze Purchasing Patterns

Time Analysis helps you uncover recurring purchasing patterns across different hours, days, and months. Start by selecting the KPI you'd like to analyze, then choose how to break it down:

Hours of the Day
Days of the Week
Days of the Month
Months of the Year

All time intervals are based on your store's time zone.

You can also filter your results by customer segment.

Rather than showing cumulative totals, the report reveals average performance for each recurring time interval within your selected period. For example, if you acquired 120 new customers across four Fridays, the report shows an average of 30 new customers per Friday, not 120.

The key is to identify recurring patterns, not isolated peaks. One particularly strong Friday might be an exception, but consistently strong Fridays reveal a pattern worth exploring.

Start With Your Goal

Want to understand when you acquire the most customers? Look at New Customers. Interested in your busiest shopping periods? Compare Orders and Revenue. And if profitability or long-term customer value matters most, explore CM1 and LTV. The periods generating the most orders aren't necessarily those generating the most value.

New Customers	Average number of customers who placed their first order during the selected time interval.
Orders	Average number of orders placed during the selected time interval.
Items	Average number of items sold during the selected time interval, before returns.
Gross Revenue	Average gross revenue generated during the selected time interval, excluding taxes, shipping revenue, and discounts by default. You can adjust this in the Report Settings, which triggers a full data re-import and recalculation.
Net Revenue	Average net revenue generated during the selected time interval, after refunds.
Product Returns	Percentage of items returned from orders placed during the selected time interval.
Gross Margin	Percentage of net revenue remaining after deducting COGS from orders placed during the selected time interval, calculated as (Net Revenue - COGS) / Net Revenue × 100.
AOV	Average Order Value of purchases placed during the selected time interval, calculated as Gross Revenue / Orders.
LTV	Average realized lifetime value of customers who placed their first order during the selected time interval, based on Net Revenue minus COGS.
CM1	Average contribution margin generated during the selected time interval, calculated as Net Revenue minus COGS.

Turning Insights Into Action

Knowing when customers buy is useful. Knowing what to do with that information is even better.

1. Catch Peak Demand

Use recurring purchasing peaks to inform when you send emails, launch products, or communicate with your customers.

For example, if Friday evenings consistently generate the most orders, schedule your next email campaign around that window to capture existing demand.

Keep in mind that the best time to reach customers isn't necessarily the exact time they purchase. Experiment with different send times to find what works best for your audience.

2. Tailor Your Offers

The busiest periods aren't always the most valuable. Imagine Fridays generate the most orders, but Sunday shoppers consistently spend more per purchase.

Use these insights to tailor your promotional strategy. For example, higher-value shopping periods could be ideal for highlighting premium products, bundles, or personalized recommendations, while quieter periods might benefit from different incentives.

Compare Orders, AOV, Gross Margin, and CM1 to identify opportunities that go beyond sales volume alone.

3. Optimize Ad Timing

Align your advertising strategy with recurring purchasing patterns to make better use of your budget.

For example, imagine that 80% of your sales happen between 10 am and 10 pm. Rather than distributing your budget evenly across all 24 hours, consider testing higher spending during periods of stronger demand.

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