Today, let's talk about data analysis methods after an ad is available. Facebook's ad data analysis is a very important part of the ad delivery process, and it's about whether your ad can achieve your goal or generate enough turnover for you.When analyzing your ad data, you can refer to the following basic procedures:

The basic analysis method of advertising data

Dimension analysis requires us to be clear about whether the data is reasonable and can be optimized.

Time dimension:We need to be clear at what time period our ads have the highest impressions and click-through rates. Does this time period match our ad delivery time?

Spatial dimensions:Whether our products are suitable for our delivery area, the factors affecting them include climate, temperature, etc. Whether the number of people interested in our products within the launch area meets the desired standard (refer to the CTR General Standard).

Metric dimensions:Check that key metrics meet ideal or expected standards.

Other dimensions:There are other factors that we need to consider in our ad analysis. For example, the layout problem, now about 70% of Facebook users are using mobile devices to log in and use Facebook, we can according to our needs to block a portion of the layout to achieve the goal of reducing advertising costs.

There are four situations in the four quadrant analysis method.

Zone 1 - High click, high conversion

In the case of customer satisfaction, further optimize the announcement, once again improve click-through rate and turn rate. At the same time, expand your new audience.

Zone 2 - Low click, high conversion

At a lower CPR, we need to optimize our advertising documentation, layout, audience, and more to improve CTR.

Zone 3 - Low click, low conversion

CtR and CVR are not up to standard, we need to consider our advertising from the beginning, is there a problem with our products? Or advertising creativity? Or is it a shopping process issue?

Zone 4 - High click, low conversion

With CVR lower, we need to think more about operations. For example, if there is a problem with a site landing page, the product page cannot be trusted by the user.

The image above is the entire process from the time the customer sees our ad to the time of purchase. Problems at any one point can result in the entire process not going smoothly and reducing sales.

Then the main method of reverse analysis is to reverse the push on the premise of customer purchase and assume to consider all aspects of advertising.

For example, what is the problem with a customer who does not add a product to a car purchase after browsing the product's details page?

First of all, we can be sure that customers are interested in our advertising and products. Without adding a shopping cart, we need to consider whether our product pages need to be optimized. Does the product details page match the ads? Can the product add more size, more color?

Data analysis is just a means, a method. Our ultimate goal is to say, "Find the right audience for your product, find the right product for your audience." You can only make good ads if you have a thorough understanding of both the product and the audience.