We’ve been looking at our AdX advertiser vertical trends each month. Below is the snapshot through April. Auto took a big hit, and Travel continued to drop down and almost out of our top 25.

Looking at data from one of our demand partners, Index Exchange, you can see some positive movement in their spend trends. This data is from their most recent May 5th newsletter:

The IAB recently conducted surveys from the publisher and advertiser sides of our industry, that provide some interesting information. The first survey was administered for a publisher / sell-side leadership group. Not surprising, the common thread here is that the group polled sees the advertising categories that will be hit the hardest are those that require consumption outside of the home. Our AdX data for April lines up with the expectations for Travel and Auto, but surprisingly Apparel had a noticeable surge for us – contrary to the sentiments of these publisher leaders.

The question asked: Please select the top five categories you project will be the hardest hit / have the greatest negative impact against your original 2020 plan?

The second IAB survey is from an advertiser / buy-side leadership group. The slide I found most interesting shows how buying patterns have been shifting from the second half of March through April and beyond.

The first question asked: Are you making any short-term (Mar-Jun) advertising spend changes as a result of Coronavirus?

The second question asked: Are you making any Q2 (Apr-Jun) changes to advertising spend as a result of Coronavirus vs. planned investment you had originally planned?

We have an interesting perspective from one of our ad quality vendors, Confiant. In their May 4th newsletter, they shared a view of weighted CPMs over time. Confiant analyzed 15 billion impressions from March 1st to May 4th; the dips they are showcasing align with what we’re seeing and general market trends as well.

The overall takeaways from the Confiant data:

• CPMs have dropped ~ 30% since early March. 
• Mid-April appears to be “rock bottom” as CPMs started rebounding in the second half of the month.
• Year over Year: May 2019 CPMs increased by ~16% versus March 2019. For the same time frame this year, the Confiant data shows a drop of ~ 30% compared to March 2020 CPMs.
• CPMs trended up ~ 5% from that “rock bottom” point.

At Freestar, we will continue to monitor performance, provide resources, and look for new revenue opportunities to continue to diversify our offering. Based on the trends we’ve seen through the second half of April, we expect to continue to make gains through the remainder of Q2. There is more optimism on how the second half of the year finishes and as the USA starts opening business back up, we expect that advertiser sentiments will be shifting more positively as well.

The post A Look Into April first appeared on Freestar.

The Shining Light on the CPM Index

To kick things off, we want to highlight our CPM Index. You can find the index within our dashboard, and below you’ll see a quick gif (do you pronounce it “jif” or a hard G like “gift”?).

The index is a quick snapshot of how specific publisher verticals are doing at Freestar. It’s a good way for you to see how you are doing in your competitive set, or just to get a sense for how other publishers are doing.

Q1 2020 Supply or: How I Learned to Stop Socializing and Love Coughing into my Elbow

We have been hearing from a lot of our clients asking us to share more granular network-wide data, so we want to start with how the first quarter looked from the supply/publisher perspective. These are unprecedented times (something I find myself saying a dozen times weekly), so we want to provide you with some additional information to help understand how the landscape is looking, and make it clear that you are not alone in this struggle.

For the analysis below, we took a look at our top 100 websites, and then dove a bit deeper on a cohort of 40 of those websites that had either a 15%+ increase or decrease in CPMs going from February to March. The data in this section is based on filled impressions and revenue from our aggregated demand partner data.

Comparing January to February:

• 11 of the 100 total sites had a decrease in CPMs
  • 4 sites had a 15%+ decrease in CPMs
    • 2 are in the Gaming vertical
• 88 of the 100 total sites (1 site was not live in January) had an increase in CPMs
  • 48 sites had a 15%+ increase in CPMs
    • 15 are in the Entertainment vertical
    • 4 are in the Sports vertical
    • 3 are from each of the Utility, Health, Education, and eCommerce verticals
• 48 of the 100 total sites had a decrease in daily average impressions
  • 23 sites had a 15%+ decrease in daily average impressions
    • 6 are in the Entertainment vertical
    • 5 are in the Sports vertical
    • 3 are from each of the Finance, and Health verticals
• 51 of the 100 total sites had an increase in daily average impressions
  • 25 sites had a 15%+ increase in daily average impressions
    • 8 are in the Entertainment vertical
    • 3 are from each of the Gaming, and Education verticals

Comparing February to March:

• 82 of the 100 total sites had a decrease in CPMs
  • 33 sites had a 15%+ decrease in CPMs
    • 10 are in the Entertainment vertical
    • 8 are in the Sports vertical
    • 3 are from each of the Education, Finance, and Gaming verticals
• 18 of the 100 total sites had an increase in CPMs
  • 7 sites had a 15%+ increase in CPMs
    • 2 are in the Education vertical
• 54 of the 100 total sites had a decrease in daily average impressions
  • 33 sites had a 15%+ decrease in daily average impressions
    • 7 are in the Entertainment vertical
    • 5 are in the Sports vertical
    • 4 are from each of the eCommerce, and Hobby verticals
• 46 of the 100 total sites had an increase in daily average impressions
  • 28 sites had a 15%+ increase in daily average impressions
    • 10 are in the Entertainment vertical
    • 4 are in the Finance vertical
    • 3 are in the Sports vertical

Of the 40 sites, with a 15%+ decrease/increase in CPMs comparing February to March:

• 21 sites had a decrease in daily average impressions January to February
  • 11 of which are a 15%+ decrease in daily average impressions
• 18 sites (1 site was not live in January) had an increase in daily average impressions January to February
  • 11 of which are a 15%+ increase in daily average impressions
• 15 sites had a decrease in daily average impressions February to March
  • 10 of which are a 15%+ decrease in daily average impressions
• 25 sites had an increase in daily average impressions February to March
  • 19 of which are a 15%+ increase in daily average impressions

2020: A Demand Odyssey

You can always see how demand partners are performing on your site by building out a report in our dashboard.

Below is an aggregated view of how CPMs change by network by month. If this were any other year, this data would look incredibly confusing. The typical expectation is that CPMs rise each month of each quarter, but of course we are in unprecedented times (that’s two for this article). I’d say the actual outliers here are the networks that were able to keep CPMs positive MoM regardless of what was happening in the world. Sharethrough, DistrictM, ROI Media, and Smart Ad Server all stayed positive for each of these CPM comparisons

Here are our top 25 demand partners, comparing CPM for the quarter:

As Google Ad Exchange (AdX) is typically a top buyer for most publishers, we want to take a deeper dive into their advertiser verticals to see how those trends shook out. Surprisingly to me, the auto vertical stayed relatively strong via AdX given the nature of the world; unsurprisingly, Travel buying took an incredibly hard hit moving from 5th to 7th to 13th.

Here’s the top 10 AdX advertiser verticals by spend by month in Q1 2020:

Next up, we want to look at the top 20 AdX advertiser verticals, and how their CPMs have shifted MoM. The verticals are stack-ranked by most spend in the quarter. Again, we see expected CPM growth from January to February, and then the COVID-19 impact in March.

The last look we took was into the top AdX buyers. The “holy trinity” here of Google, Amazon, and Facebook all dropped their CPMs by 30%+ in March (compared to February), which is where the majority of the CPM softness came from with AdX.

We are keeping a close eye on performance in April. Where possible, we are making changes to ad stacks to adapt to the changes in the market, and keeping in close contact with our demand partners. Our programmatic demand team and our CEO, Kurt Donnell, are having regular conversations with upper management at our top demand partners. In addition to the ad stack, our yield managers are regularly working on experiments and sharing findings, and we are exploring ways to further expand our ad product offering.

We can’t control the weather, but we are your umbrella. Sorry, I had to end with something cheesy.

The post Eyes Wide Open first appeared on Freestar.

A little bit of auction theory is necessary to understand why first price auctions are the best bet for network partners in the header. These results are proven rigorously elsewhere, here we state just what is needed to develop an intuitive understanding of how bidders (should) behave in different types of auctions.

In second price auctions, the best strategy for a bidder is to bid their true value for the available impression. This is because when they win, they pay the next highest price (often +$0.01 cpm). This means that the winning bidder is always paying less than their true value, thus capturing a “surplus” (the difference between what the impression is worth and what they paid). This is a very easy strategy for a bidder since they do not need to estimate what any competitors in the auction are doing as that information does not inform their bid price.

The short answer is that second price auctions are great for traditional/non-header real time bidding (RTB), and first price auctions should be used by network partners in header bidding.

In contrast, the best strategy for a bidder in a first price auction is to bid less than their true value for the impression (to “shade” their bid). This is what is required to capture that same surplus that comes naturally in second price auctions. It is typically difficult to shade well. This is because the amount a bidder can shade and still win is dependent on how much other bidders are bidding in the auction. As a side note, algorithmic shading is likely the best way to proceed. One can adjust the amount of shading to optimize a win rate in a relatively straightforward way using only log level data without having to estimate other bidders bids. One benefit of first price auctions is that the winner has direct control over the clear price* in the auction. This will be important when we discuss how to improve win rates in header bidding.

Second and First Price Auctions in Practice

For non-header real-time bidding (RTB), second price auctions are fantastic as long as they are transparent**. Second price auctions are easier for both the publisher (setting floors) and the advertiser (there is no need to estimate others’ bids) to compete optimally. Additionally, it turns out that the expected revenue for the publisher is the same in first and second price auctions (under certain conditions that may not hold in practice…), suggesting that we do not lose anything by using the second price mechanism compared to many alternatives.

With header bidding, however, the single RTB auction has been replaced with a sequence of auctions (Fig. 1). There is a round of individual auctions in the header where each demand partner runs an internal auction and returns the clear price of that auction. The largest returned clear price from all of the demand partners is then selected by the publisher’s header bidder wrapper and sent to DFP (or possibly another exchange as the final auction) for competition against AdX and EBDA in a second price auction. This means for an advertiser to win an auction via header bidding, several things must occur. First, that advertiser has to be the highest bidder in their network. Second, the clear price from their network has to be larger than that of all other networks competing in the header. Finally, the clear price from their network has to be higher than the highest bids in AdX and EBDA (assuming guaranteed contracts or other higher priority line items do not take the impression). Note in particular the last two criteria are not dependent on the advertiser’s original bid unless their network is running a first price auction.

As an example in the next section will show, the win rate of header partners is lower and publishers tend to receive less revenue when the network partners use a second price auction to determine the clear price of their internal auction. This suggests that all network partners should use a first price auction when competing in a header auction, and publishers should choose to work with network partners who use first price auctions in the header.

Figure 1 Example of data flow in header bidding from the bid request until the winner is selected. The gray dashed boxes show the two sets of auctions that occur sequentially during the header bidding process. Note that Pubfig, Freestar’s proprietary wrapper is referenced as receiving the bids from the header. Outside of Freestar this wrapper might be Prebid or another wrapper but the data flow will remain the same.

The differences in outcomes between the use of a second price and first price auction by networks competing in the header

This is intended to drive intuition about the impact of auction mechanism choice on both publishers and advertisers. It should not be considered a rigorous proof.

Fig. 2 shows the flow of revenue from two hypothetical networks competing in the header through competition in a final auction against a hypothetical AdX bid. The first part of the figure (1) shows the two highest bids from each network during their own internal auction (this data is not usually observed but is required to understand what the clear price is for each networks internal auction). Each network will return the clear price from their own auction to compete in the header. The next part (2) indicate the auction mechanism (first or second price) the networks use to determine the clear price along with the associate clear price. The part after (3) shows the bids for the final auction (the winner of the header with their associated clearing price competing against a hypothetical AdX bid). Finally, the last part of the figure (4) shows the auction winner and revenue received by the publisher in either case. For simplicity EBDA, fees, changes in bidder strategy****, and price floors are omitted in this example and from further discussion.

Figure 2 Example of bid prices flowing from network bidding to the publisher under a first or second price auction in header bidding. 1. The two highest internal bids from each network, 2. the clear prices under two auction mechanisms (first or second price) is used by the network partners to determine the header bids resulting in two different outcomes for the auction, 3. the clear price from the highest header bidder competes against AdX (the AdX bid stays the same regardless of the mechanism used by header partners), and 4. the winner of the impression and the amount paid to the publisher under a second price auction (right) and first price auction (left).

Discussion

The second takeaway is that the publisher receives less revenue when their header partners are using second price bids, regardless of whether those partners win or not. This is because when price reduction occurs for AdX (if they win), the clear price is lower than it would be if the header bid had not been reduced. If AdX does not win, the header partner wins at the reduced rate. This again means the clear price is lower than if it had not been reduced.

When network partners use first price auctions to determine the winner both of these shortcomings disappear. If advertisers are dissatisfied with their win rate, they may directly impact their win rate by bidding higher/shading less. This opens up the possibility to bid shade programmatically to capture a specific win rate, which is not possible in second price auctions. Additionally, if the advertiser can bid higher to achieve their goals, publishers capture more revenue thus making header bidding worth the effort and preventing high value impressions from being captured inexpensively in the final auction.

In Conclusion

In this short article we have demonstrated why second price auctions are less ideal when used by network partners in the header. We further demonstrated the benefits of first price auctions compared to second price for both advertiser and publisher in header bidding. It is our recommendation that publishers only add network partners to the header that use a first price auction, and that advertisers only work with network partners who use a first price auction in the header.

Footnotes

* The “clear price” is the price the winner of an auction pays. For first price auctions this is just the highest bid. For second price auctions it is the largest of the second highest bid and the floor price (if it exists), typically plus $0.01 cpm.

** By transparent I mean an auction in which any additional levers are disclosed (e.g., using floors). Even when an auction is billed as second price, sometime less ideal price levers (e.g, soft floors***) are used without being disclosed. There is nothing inherently wrong with soft floors (other than making bidding strategies more complicated), but soft floors make an auction not second price. So if used it should be clear to bidders that they are bidding in a mixed mechanism auction and not a second price auction.

*** Soft floor – A floor that behaves like a regular floor if the winning bid is larger than the soft floor, but otherwise lets the highest bidder win at their bid price. This can be contrasted against the more traditional floor price (“hard floor”) which is the minimum that a publisher will accept for an auction (meaning that bids lower than the floor cannot win). A soft floor turns a traditional second price auction into a mixed mechanism where the auction mechanism is first price for bids lower than the soft floor and second price for bids higher than the soft floor.

****In a first price auction as stated above, bidders would be shading. This means that all bids from network partners would be lower. However, if shading was optimized to a specific win rate the highest bidder from network 1 in Fig. 2 would likely be bidding more than $5 cpm, so the results reported would hold except the final revenue to the publisher would likely be somewhere between a $5 and $10 cpm instead of a $10 cpm. As noted, this point was omitted from the main text for clarity.

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Publishers and their ad operations teams put price floors in place to increase revenue for highly viewable inventory, increase competition between bidders, and help mitigate ad fraud. Using price floors to combat fraud, however, goes hand-in-hand with the long-held belief that ad fraud is perpetrated mostly against lower value impressions. The accepted logic is that by setting a price floor at the lowest CPM you’re willing to accept for your inventory, you’ll not only drive up your eCPM but weed out a lot of the bad ads in the process. Seems like a sound plan, right? Not entirely.

To better understand if setting arbitrary hard price floors could, in fact, weed out bad ads, Freestar dug into our data analyzing 200 million impressions filled by 11 different bidders across our sites. We discovered that each bidder had distinct variances in bad ad frequency by bid bucket. Not only that, each bidder saw spikes in bad ad frequency well above the low CPM price floors set by many publishers.

The graphs below show the frequency of bad ads (y-axis) by $0.10 bid buckets ranging from $0 to $50+ CPMs (x-axis).

What this data tells us is that while setting price floors at sub $1.00 price buckets can weed out some bad ads, this strategy is largely ineffective in stopping ad fraud from getting through to publisher’s sites. As seen above, ad fraud is perpetrated at all different bid levels with strong variances from bidder to bidder. Not only that, publishers are potentially decreasing their fill rates, and ultimately their net profit,  by utilizing hard price floors at lower CPMs. Not all bids at low CPMs are fraudulent, and by flooring, publishers lose out on those impressions ever being filled.

As the programmatic landscape continues to evolve, so to should the technologies and methods we use.

At Freestar, we’re utilizing data science to block bids at specific bid buckets based on the bad ad frequency by the individual bidder. This method results in no negative effect on fill rates, while allowing us to decrease the frequency of bad ads that ever reach our partner sites. Here’s how this works:

Site A makes a call to the ad server and Bidders 1, 3, 5 and 8 return the below bids:

Taking into account just the bid data it appears Bidder 8 would win with a CPM of $3.07.  However, utilizing the sample of bad ad data above it’s clear in the bid bucket of $3.00-$3.10 Bidder 8 is much more likely to deliver a bad ad than the other bidders. In this scenario, Freestar’s machine learning would have kicked in stopping only Bidder 8 from returning a bid at this price due to its high frequency of ad fraud at this level, while still allowing other bidders to step in to fill the impression.

Site A would still receive a $2.87 CPM from Bidder 1 in this auction, only $0.20 less than the revenue they would have received if Bidder 8 won, but with a far less likely chance a bad ad will have been delivered to their site. To verify our findings are correct, our algorithms continuously sample each bucket to verify which bids to block for each bidder decreasing overall frequency of bad ads by 50x across our partner sites. 

As the programmatic landscape continues to evolve, so to should the technologies and methods we use. Changing price floors to not only be dynamic but fluid by bidder and by price bucket is the next step in improving the digital advertising ecosystem for publishers.

The post Analyzing Bad Ads by Bid Bucket first appeared on Freestar.

It’s no secret that for the past several years, Facebook and Google have been getting infinitely more powerful thanks to the countless publishers who both resent them and desperately need them. So, when big budget brands like P&G, Verizon and AT&T decided to take a stand and demand that higher standards be put in place, one of my first thoughts (and probably yours, too) was: those ad dollars have to go somewhere.

Well, if brands are loudly stating they want higher quality content, more control and transparency, and prioritization of their needs as paying customers, I know who can deliver: premium publishers. Many of these websites have made these exact selling points the cornerstones of their businesses for decades. Some have been doing it since the days of print.

Now is their time to step in and leverage this disruption or miss an enormous opportunity. By banding together (which some publishers are already doing), they have enormous scale to offer, among other obvious assets, and since nothing breeds innovation like disruption, now’s the time to get creative with new solutions that prove publishers’ value.

It’s likely Google will turn this situation around quicker than we think (they’re already fast at work creating new brand safety tools), but regardless of how long this window of opportunity is open, the point is that it is open, and premium publishers should see this as an unexpected moment to remind brands of their value, strength and relevance.

The post The YouTube Revolt: A Pivotal Moment for Premium Publishers first appeared on Freestar.