Rachayeta Singla Rachayeta Singla
Rachayeta Singla is the Content Marketing Manager at Sigmoid, working on content marketing domains, inbound marketing and PR operations.

Rachayeta Singla
She works as Content Marketing Manager in Sigmoid.

Scalable Solution for increasing header Bidding Demands

Header Bidding aka open bidding process for all bidders bidding at the same time has opened up a whole new pandora’s box of having to analyze millions of ad requests in real time and allotting the ad spot to the right winner. But finding out the winner among the rapidly increasing number of bids is pushing up the cost of analyzing all the bids and their responses. DataXu, for example, saw a 100% increase in bid queries in 2016 as compared to the usual 40% year on year growth of bids received annually. As a result, ad exchanges and publishers have had to invest additional resources to handle the increasing bid traffic.

Due to sudden growth spurts in the bid queries, scalability has become a major issue for ad exchanges following the header bidding model. Scaling the hardware to handle the increasing rush in bid queries has proven to be an expensive and a gruelling task. One must ask, if there are hacks or alternate ways to process this huge amount of traffic without feeling a strain on your pockets?

Let us take a look at the below example that can help you optimize your bid analytics requirements by scaling efficiently only when required.

Step 1 : Select an advertiser who runs different campaigns targeted for different genders

data filtered header bidding

Fig 1.0: The above image shows all data filtered for the selected Advertiser Name

Step 2 : Select a campaign ID to filter all data for that campaign and compare all traffic received for that campaign for Females vs Males

selected Campaign ID header bidding

Fig 1.1: The above image shows all data filtered for the selected Campaign ID

In the above image you can see this campaign preferred female users over male users. Let’s filter off all male targeted traffic and increase female targeted traffic for that advertiser

Step 3 : Compare the next day’s data to check for results

campaign ID data header bidding

Fig 1.2: The above image shows campaign ID data when compared between 1st Dec – 2nd Dec and 29th Nov – 30th Nov

Now let us compare the next day’s data with the previously chosen date when the changes in traffic were implemented. As you can see there is a huge increase in the number of wins for the selected advertiser bidding for the female users.

This is one of the many interesting and efficient ways to deal with more queries per second without scaling up the hardware. Try it out if you haven’t already. For more such tips and tricks, keep checking our blog or ask for a free demo today!

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