They reach your site. Why don’t they buy?

Start with the ad they saw.

Open it on your phone. Look at the product, price, offer and promise, then follow the link. Does the landing page deliver what the ad led you to expect? Having to find the product or promotion all over again is a break worth investigating. It does not, by itself, explain every lost order.

Consider an illustrative example: a retailer advertises fast delivery but only reveals the delivery date after the shopper enters an address. This is not a client case. Someone can want the product and still leave because they cannot tell whether it will arrive in time. A more dramatic ad does not answer that question.

Find the point where the journey breaks.

A visit, a product view, an item added to the basket, checkout started, an order. Examine those steps by device, traffic source and product. The comparison is useful only when tracking is consistent and there is enough activity. A short period or a change in measurement can distort the picture.

If people never reach the advertised product, check the destination and page loading. If they view it but do not add it to the basket, examine the offer and unanswered questions. If they start checkout and leave, inspect delivery costs, payment and errors. These are starting hypotheses. A chart cannot tell you the precise reason someone left.

Bring customer questions into the discussion. What do people ask before ordering? What can they not find? Appropriately collected session recordings can help identify friction, while customer conversations can explain concerns that an analytics report cannot name.

Check what counts as a sale.

A Google Ads conversion can represent a purchase, a phone call or another configured action. Check which actions feed the column you are using. Google describes these distinctions in its conversion measurement documentation. A large conversion count does not automatically represent revenue.

Compare the report with store orders. Check the period, currency, cancellations and returns. Platform attribution and money received will not necessarily match. A budget decision needs a clear understanding of the numbers being compared.

You know the store. Your customer doesn’t.

You have used the site hundreds of times. You know where the filter is, what each variant means and that delivery is calculated at the end. While testing, you fill in missing information from memory. A new customer has to find the answer or take the risk.

Ask someone unfamiliar with the store to choose a product for a specific purpose. Let them talk through the choice. Don’t point to the right button. The moment you want to say ‘look, it says it here’, you have found something worth fixing. Write down what they were looking for in their own words. The problem may be the label, the location or the missing answer itself.

Notice what they need before deciding. You might have spent hours on the description while they simply want a photo showing the product beside a familiar object. More copy won’t explain its size as well as the right picture.

Visits arrive with different intentions.

Someone following an interesting video may still be deciding whether they need the product. Someone searching for the exact model is closer to choosing. An increase in exploratory traffic can lower the overall conversion rate without anything having broken at checkout.

Separate the questions. Is the ad reaching suitable people? Does it prepare them for what follows? Does the page help them buy? Problems can exist in several places at once. Follow a complete journey before reaching a conclusion about the store.

Take a clearly identified test order through the entire process and keep it separate in reporting. The button might work while the confirmation email fails. A payment might succeed while the order never reaches the team’s queue. At that point, the problem is consuming staff time and customer trust as well.

Choose a change you can follow.

Write down your hypothesis, the proposed change and the signal you expect. For example: shoppers discover delivery timing too late; put that information beside the order button; follow progression to checkout and questions about delivery. Keep orders, margin and returns in view too.

Changing the offer, ads, product page and budget together makes it harder to explain what caused a difference. A controlled experiment helps when traffic allows it. Otherwise document the changes and treat before-and-after comparisons as clues, not causal proof.

This week, choose the product you advertise most. Follow its journey on your phone, from the ad to the step before payment. Write down the questions the page leaves unanswered. That gives the team something much more useful to work on than a general request for more creative.