How We Read Your Account Data to Find What Sells

When we take over a new client account, the first thing we do is not brainstorm ideas. We read the data: ad results in Meta and Google, organic numbers on the page, and most important, the client’s actual sales. The goal is to answer one question: what actually sells, who buys it, and which content brought them?

Each source alone gives a partial picture. Ads Manager knows who clicked and messaged, but not who bought in the store. The page knows what was saved and shared, but not whether it turned into a sale. The client knows what sold, but not always where the customer came from. Put the three side by side and the real picture appears.

The three sources we read

1. Ad accounts

We go back at least three to six months where available. We sort ads by the number that matters, such as cost per conversation or cost per purchase, not by spend or reach. Then we open the top ads and watch the videos themselves: the hook, the angle, who is talking, and how long it runs.

2. Organic numbers

On Instagram, Facebook and TikTok we care more about saves, shares and sends than likes, because they mean the content had real value for that person. A post sent to many friends often touched something worth turning into an ad.

3. The client’s sales data

This is the source most people forget. We ask for the best-selling products, the days and times sales peak, and a sample of WhatsApp conversations if possible. Conversations show what customers ask before buying, which ad brought them, and why some did not complete. If website tracking is not set up properly, fixing it comes first. Details are in conversion tracking made simple.

The questions we ask the numbers

Numbers only talk when you ask them something specific. These are the questions we work through on every account:

1. Which five ads brought the cheapest results, and what do they share? The same angle, person or length? That shared thread is the first clue for strategy.
2. Which product gets attention, and which one sells? Sometimes one product gets views and comments while sales come from another that nobody advertises.
3. Where do people stop? Many views and few messages points to the video or offer. Many messages and few sales usually points to the reply or the price.
4. Who actually buys? Breaking results down by age, gender, area and platform sometimes shows the buyers are not who the client imagined.
5. When do they buy? Some businesses see sales cluster on certain days or times, which affects content timing and budget.
6. What has tired? Ads that were strong and now cost more, a sign the audience has seen them enough.

Example: a women’s clothing store in Amman

Say a women’s clothing store in Amman sells in-store and on Instagram, and the owner believes evening dresses are her strongest line because those posts get the most likes. Reading the data could show a different picture:

• Dress posts get lots of likes, but few people ask the price.
• A simple video of a staff member trying on an everyday work outfit brought far more conversations, and most questions were “Do you have larger sizes?”
• Conversations rise at the end and start of each month, around payday.
• Most buyers come from specific areas of Amman, not every area the ads reach.

The plan changes completely. Instead of spending on evening dresses, we build content around workwear, make dedicated videos for larger sizes because the market asked, concentrate budget around payday, and narrow geographic targeting to the areas that buy. The dresses do not disappear. They stay as page content that keeps the store’s image.

Common mistakes when reading data

• Judging by likes and views. Those measure attention, not sales. We explain why in likes aren’t sales.
• Comparing ads with different objectives. A reach ad cannot be compared with a messages ad.
• Judging small samples. An ad with three cheap messages is not a winner yet. There is not enough data.
• Ignoring the reply. If ads bring messages and sales do not rise, read the conversations before blaming the ad.
• Trusting platform numbers alone. Platforms credit themselves in different ways, so we always compare against the client’s sales.

Big numbers need reading too. A paid ad for Phobia, the horror experience, reached 3.5 million views, 20,000 shares and over 42,000 likes. Shares are a strong sign the content landed, but after any number like that we ask the same question: how many people messaged or booked in that period? That is where cost per conversation connects reach to sales.

How we handle this at Wave

In the first week with any client, we request access to the ad accounts and pages, plus sales data and a sample of conversations. We produce a one or two page summary covering the best ads and what they share, the products that actually sell, and where customers drop off. That summary is the foundation for the strategy, the angles and the first month’s plan. We then repeat the same reading every month in the report.