How to Review Your Own Store's Traffic After Prime Day

The U.S. Prime Day event ran June 23 through June 26. If your own store ran a promotion, changed its product pages or received a different mix of visitors during those days, now is a useful time to review the records.

Start with your store's actual question. Did an offer attract suitable shoppers? Did people understand the product? Did the extra orders leave enough money after the costs you chose to include?

A traffic spike cannot answer those questions on its own. You need a defined group of visits, a fair comparison and a clear connection between the activity you observed and the orders you recorded.

Preserve the campaign before changing it

Save the details of what your store ran from June 23 through June 26. Include the promoted products, campaign links, offer terms, spend and any changes made during the period.

Record interruptions too. An unavailable variant, delayed delivery notice or broken discount can help explain a result that otherwise looks like weak demand. Include when each issue started and when it was resolved.

Keep team test orders identifiable and remove them from the customer analysis. Check whether cancellations and refunds are already reflected in the order total or need a separate adjustment.

The OSPRY view of online stores can help organize the visitor questions. Keep your own campaign record beside the report so the numbers retain the context of what people actually saw.

Choose a comparison that answers the question

An equivalent set of weekdays is a reasonable place to begin, but it is not automatically a fair baseline. Check whether the comparison period had similar stock, advertising, offers and measurement settings.

Also inspect the calendar and your business's own trading pattern. A holiday, a large email send or a one-off wholesale order can make an otherwise convenient comparison misleading.

Write down the difference you cannot remove. If campaign spend doubled, say so. If a popular product became available only during the event window, keep that in the explanation. Do not describe the comparison as a clean experiment when several conditions changed together.

You can still learn from imperfect data. The aim is to make a decision with visible limits, rather than hiding those limits behind a confident percentage.

Separate the audience into useful groups

Begin with the groups your records can actually support. That might include campaign visitors, direct visits, observed returning visits or people arriving on a particular product page.

Keep known customers separate from new-customer acquisition where you have reliable purchase history. An offer that brings back existing customers may be valuable, but it answers a different question from an offer intended to attract first-time buyers.

Avoid adding groups that only sound precise. If you cannot reliably connect two sessions to the same person, do not report a confirmed return. If the source is missing, retain an unknown-source group rather than assigning it to the channel you hope worked.

Review the systems connected to your workflow to establish where source, customer and order information come from. Note the joins you can substantiate and the gaps you cannot close.

Follow the path from entry to order

For each important group, inspect the path from the landing page to the next meaningful step. Look at product views, relevant guides, cart additions and completed orders where those observations are available.

Ask a question at each transition. Did the campaign land on the promised product? Did people reach the information needed to choose a variant? Did a discount work at checkout? Which product or path deserves a closer inspection?

Do not assume every exit is a failed purchase. The visitor may have been comparing, checking information or completing a task outside your observed records. Use the pattern to identify an investigation, then review the page and support context.

Your review should produce a small set of specific findings. "Shipping terms are difficult to find on the promoted page" gives the team something to address. "Customers are not engaged" leaves them guessing.

Work through revenue and costs together

Consider this hypothetical example. These are invented numbers for illustrating the calculation, not results from a merchant or from Prime Day.

In the comparison period, a store records 1,200 eligible observed sessions and 24 associated orders. Revenue is $2,400. In the event period, it records 1,600 eligible observed sessions and 36 associated orders, producing $3,240 in revenue.

Using orders divided by those eligible sessions, the observed order rate moves from 2% to 2.25%. Average revenue per order moves from $100 to $90. The promotion produced more orders in the example, but the store collected less per order.

Now include a deliberately simplified cost model. Assume product, fulfillment and transaction costs total $60 per order in both periods. Those costs are $1,440 for the comparison period and $2,160 for the event period.

That leaves $960 and $1,080 respectively before advertising and the other costs excluded from this example. If advertising cost $300 in the comparison period and $600 during the event, the remaining contribution is $660 versus $480.

Revenue rose, while contribution after the included costs fell by $180. The calculation does not prove the promotion was a mistake. It tells the owner which trade-off needs a decision. Customer retention, later purchases, overhead and other excluded costs would require additional evidence.

Check whether measurement changed during the run

Before explaining a shift in browsing behavior, confirm that the store recorded both periods consistently. Review tag changes, campaign naming, checkout changes and the consent choices offered to visitors.

If collection changed halfway through, split the period or label the limitation. A difference in recorded activity can come from a different observation process as well as from different shopper behavior.

Your consent configuration belongs in this review because it helps explain what the reports can show. Preserve the distinction between completed order records and the browsing events available around them.

Do a small reconciliation. Choose a few clearly identifiable test paths or authorized records and trace them between systems. An unexplained discrepancy is a reason to investigate before declaring that a channel or product won.

Leave room for the result to mature

On June 28, some observations are still provisional. Your own customers may have unanswered support questions, pending cancellations or purchases they have not yet completed.

Set a follow-up review date appropriate to the question. A broken purchase path needs immediate attention. A question about returns or repeat purchasing needs a period that can actually contain those outcomes.

Keep the original event group fixed for that later review. Adding unrelated new visitors to it would change the population while making the result appear to mature naturally.

If you follow up with known contacts, use the relationship and recorded preferences to decide what is appropriate. An unfinished browsing path alone does not tell you that a person wants a message.

Finish with one decision and an owner

Write a short conclusion with the observation, limitation, action and owner. For example: "The promoted desk page received more eligible visits, but shoppers repeatedly opened the delivery guide. We found that the delivery area was unclear. The store manager will clarify it and review the same path next week."

That conclusion is modest enough to test and specific enough to act on. It avoids attributing every change to Amazon's event or treating an increase in visits as a finished business result.

For your next review, use how OSPRY works to frame the path from visitor context to a practical decision. Bring the campaign details, the order records and the uncertainty with you. Choose the next change because the evidence supports it, then record what you will check afterward.