How to identify volume fluctuations with existing suppliers?
Think of an ad-exchange as a digital stock exchange, where impressions are shares. It’s a marketplace that auctions ad impressions in real time. The volume of these ad impressions served per millisecond is huge. After the winning bet is decided, in a fraction of a millisecond your browser loads and serves you an ad impression.
Volume fluctuations in publisher’s ad impressions is a common scenario. It is important to keep a check on them and take action as necessary whenever fluctuations are observed. Fluctuations could be due to website layout getting changed, interested advertisers being blocked, integration issues etc. This impacts both the publisher’s revenue and rating and also bidder’s inventory.
For the buyers, it is meaningful to know the volume of inventory they have on the offer. The reasons could be many – the source/ad domain may be going off the market, source may be undergoing maintenance etc. A buyer may dig in further into dimensions and metrics for further analysis.
This kind of analysis will give buyers the power to take timely actions and change their programmatic algorithm to target high quality impressions.
In this blog, we’ll diagnose volume fluctuations with existing advertisers. To understand this, we have to go through the publisher’s data during the given period and compare them against other metrics.
You can use the comparison capabilities of our dashboard to explore data over time to diagnose volume fluctuations with existing suppliers.
Step 1: To view volume by the source, we’ll expand the Publisher’s name and view it against related metrics like impressions, total_bids and uniques.
Fig 1:In the above image, we’ve expanded the Bidder’s name and we’re viewing it against various metrics
Step 2: In order to determine how to the volume is changing over time – we’ll need to compare a time frame (week over week in this case). Once you activate the custom comparison with previous week, you can sort by the delta impression metric to see which sites have the greatest percentage increase or decrease in volume over the previous time period.
Fig 2: In this image, we’re comparing the data and understanding the cause for fluctuations in the data.
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