{"id":23982,"date":"2020-07-01T13:31:04","date_gmt":"2020-07-01T11:31:04","guid":{"rendered":"https:\/\/yieldbird.com\/?p=23982"},"modified":"2024-07-22T19:27:13","modified_gmt":"2024-07-22T17:27:13","slug":"how-to-utilise-event-level-data-so-as-to-better-understand-the-demand-side-and-price-your-inventory-more-efficiently","status":"publish","type":"post","link":"https:\/\/yieldbird.com\/research-hub\/how-to-utilise-event-level-data-so-as-to-better-understand-the-demand-side-and-price-your-inventory-more-efficiently\/","title":{"rendered":"How to Utilise Event-Level Data so as to Better Understand the Demand Side and Price Your Inventory More Efficiently?"},"content":{"rendered":"\n

All Publishers using Google Ad Manager can access the reporting tools in order to gain insights about their inventory\u2019s performance. It is a very useful tool for having an overall understanding about both revenue and traffic. However, GAM reports<\/strong> are aggregated at a pretty high level: you can only analyze an ad-unit\u2019s performance averaged over a day (or, in best-case scenario, over an hour). This does not allow you to understand a single auction\u2019s dynamics, i.e. what the distribution of bids was (not only the winning bids, but all of them). Therefore, if you want to price dynamically and effectively<\/a>, a more in-depth view is needed<\/strong>.<\/p>\n\n\n\n

A glimpse of what sort of precious information can be found and used is offered by GAM\u2019s Bid Landscape tool<\/strong> (a sample can be seen in the figure below). However, it is also a tool with its limitations. The only dimension to filter by is via a pricing rule; there is no possibility of either choosing a customized date range, or doing a breakdown of the Advertisers. And this is only the tip of the iceberg in terms of its restrictions.<\/p>\n\n\n\n

\"event-level<\/figure>\n\n\n\n


<\/p>\n\n\n\n

How about\u2026 more Event-level data?<\/strong><\/h2>\n\n\n\n


<\/p>\n\n\n\n

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Recently, Google allowed Publishers to purchase additional Event-level data from Ad Manager<\/a><\/strong>. What this means in practice is that you can analyze several characteristics about each and every auction run in your network: which Advertisers placed bids, what prices were offered, and what was the final winning price – to name just a few. When utilised efficiently, this tool can help tremendously with pricing the inventory in an optimal way.

An interesting and useful insight offered by this data is knowing how many bidders are typically interested in one\u2019s inventory.<\/strong> On the chart below we can see how this differs between countries for a certain part of Yieldbird\u2019s inventory. Apparently, users from the United States are of interest to, on average, twice as many Advertisers as those from Turkey.<\/p>\n\n\n\n

\"event-level<\/figure>\n\n\n\n

Understanding how this number depends on different factors can prove crucial to pricing the inventory efficiently. You can, for example, differentiate your floor prices during the day – higher when most traffic comes from the US, and lower when it comes from Russia.<\/p>\n<\/div><\/div>\n\n\n\n


<\/p>\n\n\n\n

The Floor-price effect\n<\/strong><\/h2>\n\n\n\n


<\/p>\n\n\n\n

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Probably the most important and powerful tool for those Publishers who refuse to yield all control to Google are floor prices<\/a>. However, anyone using them knows that understanding their effects can prove very difficult in a first-price environment<\/strong>. And this is where Event-level data<\/strong> comes to the fore. By plotting all the bids and comparing their distribution before and after the introduction of a floor price, the Publisher can see if the Advertisers have adjusted the bids as planned – placing higher bids in order to maintain ad volume – or if they have remained the same – which would effectively mean that the floor prices have killed some part of revenue.

On the chart below, we can see an example of a well-placed floor price – the bid distribution before setting it (yellow line) was peaking around 0.15; and basically no bids were placed above 0.50. The first day on which the floor price was set (pink line) already shows the bidders\u2019 adjustments. Then, the plot for the next day (black line) shows an even further adjustment – many more bids in the range 0.4-0.8. This will have a direct positive impact on the revenue yielded from the analysed ad unit.<\/p>\n\n\n\n

\"changes<\/figure>\n\n\n\n

Best of all, it\u2019s up to the user to decide what is the scope of the analyzed auctions<\/strong>: e.g. only one particular ad unit, a group of similar countries, or even a zoom into the single most-paying Advertiser.<\/p>\n<\/div><\/div>\n\n\n\n


<\/p>\n\n\n\n

Is Event-level data for everyone?\n<\/strong><\/h2>\n\n\n\n


<\/p>\n\n\n\n

Event-level data<\/strong>, while extremely useful, might not be the right fit for everyone. First of all, it comes at an extra cost (priced individually). Smaller Publishers might not immediately see value in purchasing additional data, as the default GAM data might already seem abundant. Second, making use of terabytes of data requires a lot of processing power, even for simple queries – so an additional paid tool might be necessary. Finally, processing, understanding and utilizing such vast amounts of data typically requires at least a small data science team.

If you feel that detailed auction data might improve your revenue optimization, our team of experienced Yieldbird data scientists will help you every step of the way<\/a>.<\/em><\/p>\n\n\n","protected":false},"excerpt":{"rendered":"

All Publishers using Google Ad Manager can access the reporting tools in order to gain insights about their inventory\u2019s performance. It is a very useful tool for having an overall understanding about both revenue and traffic. However, GAM reports are aggregated at a pretty high level: you can only analyze an ad-unit\u2019s performance averaged over […]<\/p>\n","protected":false},"author":32,"featured_media":23987,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[28],"tags":[],"content-category":[],"class_list":["post-23982","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ad-tech"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/posts\/23982","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/users\/32"}],"replies":[{"embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/comments?post=23982"}],"version-history":[{"count":1,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/posts\/23982\/revisions"}],"predecessor-version":[{"id":33399,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/posts\/23982\/revisions\/33399"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/media\/23987"}],"wp:attachment":[{"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/media?parent=23982"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/categories?post=23982"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/tags?post=23982"},{"taxonomy":"content-category","embeddable":true,"href":"https:\/\/yieldbird.com\/research-hub\/wp-json\/wp\/v2\/content-category?post=23982"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}