Are you getting the best revenues from your Header Bidding Process?
Header Bidding has been a hot topic of discussion since a couple of months and has been turning a lot of heads in the programmatic advertising industry. As we explained in our previous introductory blog to header bidding and why publishers across the world are going gaga over it, header bidding has come across as a popular choice for accepting bids across publishers and it creates a win win scenario with all parties involved in the process. If you are thinking of shifting towards header bidding or are already using that process, you would by now be well acquainted with the process and how millions of bids need to be compared and analyzed in real time to arrive at the best possible win bid rates. This is where Pre-Bid Analytics comes into play.
A strong analytics platform helps you analyze and manage your header bidding process better. It helps you identify where you are losing out on your revenue stream, which bids are working for your website and which are not, which ad spots are getting the maximum bids and measure bid performance across various time periods. Using such advanced analytics tool, you can also drill down the data to figure out the cause behind any inconsistencies and ensure better publisher revenues.
Let us explain this better by using an example with visual charts to see how analytics can help you decipher the reason behind revenue drop for a publisher.
Step 1: Compare the revenues across a week, generated by top bidders to see if there has been a drop in the incoming bids.
Fig 1.0: In the above image you can see the daily revenue for a week broken down by top bidders
As you can see the revenue generated was lowest on 7th April and there was a considerable drop in bids by the top 3 bidders. This could be due to many reasons, the bids might be taking too much time to process, resulting in higher latency; the win bid price bucket might have been altered; or maybe the bidders were unable to bid due to some error. Let us take a closer look at these possibilities and find out the exact cause behind this drop.
Step 2: Create a bar chart showcasing bid count by latency to see if bidders faced timeout or other issues causing delays
Fig 1.1: In the above image you can see the bid counts in the previous week sorted by latency buckets
As you can see, most of the bids fall under regular time buckets, thus latency is not the cause behind the revenue drop.
Step 3: Analyze the bids and their win rate by bid price buckets for 7th of April
Fig 1.2: In the above image you can see the bids for 7th April sorted by their bid price buckets
As you can see, maximum bids placed on 7th April were in the price bucket of $0-$0.5, thereby leading to no wins. Hence, the number of bid wins was significantly lower for this date.
Step 4: Let us now focus on the top bidders and see if they faced any errors while bidding
Fig 1.3: In the above image you can see the bid count and bid error rate for the top bidders
From the above chart you can see that for the top bidders, almost 30-50% error rate was observed. Considering all the above findings, you need to lower your price floor bucket and check out why the errors were faced by your top bidders by contacting you header bidding partners and fixing the issues.
By closely monitoring and analyzing your received bids, you can hence ensure a smooth header bidding process with higher returns. Get more insights into advanced analytics for your header bidding process now with Sigmoid and request for your free demo now!