Copywriting and creating ad copies that make users buy is definitely an art, but that doesn’t mean there’s no science to it!
The optimal ad body for performance will vary immensely from advertiser to advertiser, which is why the best option is to analyze your own data, instead of looking at broad averages, or companies with wildly different audiences.
In Confect Analytics, you can look at how different aspects of the ad body correlate with a higher, or lower performance, so you can write much more effective copies in the future.
Dimensions are the different ways you can analyze your ad body in Confect.
This ranges from looking at how specific words correlate with performance, or whether it’s better to write more or less text.
The first type of insight you can see is an overview of the words you commonly use in your ad bodies. They are spread out on the horizontal and vertical axis based on their performance (Return On Ad Spend in this example) and the number of impressions.
In that way, the words in the bottom left are correlated with poor performance and have only been used a few times, while words in the top right correlate with high performance and have received many impressions.
Please note that when optimizing for “Return” and “Rate”, higher means better performance, but when using “Cost Per X”, the lower the better. Always take the metric into consideration when analyzing insights.
In this example, we want the highest return possible, so the best words will be in the upper half. If we used cost per purchase, we want it as low as possible, so ideal words will be located in the bottom half.
The next type of insight you will encounter is the number of lines, characters, words in the ad body or number of words per line. These insights are aimed at helping you get the ad copy length right for your brand and audience.
You can see how writing different amounts of text in the ad body impacts performance.
Note that a line means pressing the enter key and starting a new line.
Even though these 4 insights seem very similar at first glance, they are independent. Combining them allows you to find out whether you should write more lines with only a few words and characters, fewer lines with more words and characters, or any other combination.
If you’re wondering whether you should consider using a specific word in your ad designs, this is the insight to help you with that.
It allows you to make different comparisons of using a specific word and not using it, or see the difference between using multiple other words.
You can add more words to the comparison by separating them with the | sign. For example: “sports | adventure”.
A common copywriting question is whether the ad body should include emojis, and whether or what type of punctuation marks will work best.
You can easily settle this by analyzing how the number of emojis correlates with performance, and get an answer to the question of how many emojis should be used, or whether they should be used at all.
In a similar way, you can analyze how the question mark and exclamation point stack up against each other.
Perhaps you should only be using one of them in your ad bodies, perhaps you should be using them both, or perhaps it’s better to leave them out completely.
The last type of insights in this list is the ability to compare how the presence of tags (@user), hashtags (#hashtag) and links in the ad copy influences performance.
Tags, links and hashtags may not be something many advertisers use in their ad bodies, however it might cause significant improvement in your specific case.