The Evolution of News

It's not news that digital publishers have had more than their fair share of challenges in the shift from print to digital. In fact, journalism this year has had the most layoffs since the recession - reporters are actually losing jobs at a faster rate than coal miners under Trump. The industry is undergoing dramatic changes, but there may be a light appearing at the of the tunnel.

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To great fanfare, Apple announced Apple News Plus, a $10 / month product that would provide unlimited access to publisher content. Apple would effectively serve as a massive aggregator of subscription revenue, splitting it 50/50 between Apple and all the three hundred publishers on the platform - including WSJ, NY Magazine and Vox (notably not the NY Times and Washington Post), with the allocation determined by content consumption. The publishing industry fretted that this was an unfair revshare and that it would it spur publishers to focus even more on click-bait articles and headlines to increase their allocations. Instead, Apple News Plus has substantially failed to gain traction - performing at around 5% of the projections Apple gave to publishers. With such limited traction and with the significant constraints on their model imposed by Apple News Plus, publishers have started to seriously doubt the platform. In response, Apple appears to be listening to publishers and building a product that more directly serve their needs as well as those of their consumers.


On the other hand, Facebook historically served as a driver of referral traffic to publishers. As part of its Time Well Spent initiative, the company significantly reduced publishers' organic reach. This has had a massively negative impact on the news industry, not to mention the economics of branded content. But now Facebook is getting back into news with rumors of a dedicated News tab. The actual product is unclear, but there are expectations that publishers would keep all of the revenue from subscriptions they sell. While the economics are more enticing, Facebook will have significant trust issues to overcome with both publishers and users. But the News tab would need to actually be successful. Given the limited success of the Watch tab, many are dubious. But Facebook is actively working with publishers with a more partnerships-oriented disposition than it has in the past.


Meanwhile, Digital Content Next, a digital publisher lobbying group, released a study showing the publishers they work with have grown non-advertising revenue at a rate faster than advertising revenue - changing their share from 80/20 to 77/23 in favor of ad revenue. The numbers are similar even if you remove the top three publishers for digital subscriptions (NY Times, WSJ and Financial Times). Even without these three, the majority of the revenue growth comes from subscriptions.


Finally, one could argue that the shift towards privacy benefits publishers. This is a contentious claim, but the idea is that programmatic incentivized a shift towards audience-based buying in which marketers abandoned any concern around the nature of the publishers and simply found their audience as cheaply as possible on the open exchange. Cookies slowly crumble - either technically or through regulation - and buyers slowly pull back from unknown publishers on the open exchange due to fraud concerns, targeting those known for quality and context. Thus the pendulum is slowly swinging back to high quality digital publishers.

As the above anecdotes show, there are several outstanding issues that remain. Yet, with the appropriate rose-colored glasses, even the first three can be viewed as green shoots. Publishers are right-sizing for the digital economy and platforms that previously dismissed the interests of publishers are now consulting them closely. Publishers are working with the platforms to add subscription revenue, and are iterating in a way that enables their continued growth. If nothing else, the current administration has underscored the value of a vibrant and independent journalism industry. Platforms have come to understand their roles in sustaining this industry beyond lip service - and the industry is embracing different ideas and working to be nimble. It is likely we will continue to see stories like the above as platforms, publishers, and consumers continue to rethink their relationship with news.




Inventory Forecasting

We have previously discussed automated guaranteed - a way to buy advertising inventory in a non-RTB, but programmatic fashion. If you are going to slice and dice your inventory and sell it in a guaranteed fashion, it is essential to forecast your inventory correctly, which gets to the question of what is inventory forecasting, why is important, and how does it work?

Website owners want to maximize the revenue they can get by selling advertising. In the most simple case, a site is only a single type of page, with a single type of advertisement, with one visit per user per day. Here, they'd simply use historical data, modeled against day of week, holidays, adjustments for unusual traffic spikes or dips - and other factors deemed relevant - and simply predict how many impressions they'd likely see. When selling campaigns, websites often have fixed obligations to deliver a certain number of impressions. So they wouldn't sell guaranteed campaigns against exactly what they predicted - there would be some room for error. But because they're selling a fairly simple version of their inventory, it could be pretty close. The rest you'd need to sell as non-guaranteed, which generally goes for less. So the more confidently you can forecast, the more money you can make. If you're at risk of under-delivery as a publisher, you might have to a pay a make-good to the advertiser - or you might have to buy traffic from Facebook or Taboola/Outbrain/etc to hit your targets.

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This gets significantly more complicated as you slice and dice your inventory in more different ways. For example, audience segmentation can fetch a premium (e.g. 18-24 males) as well as users that have expressed certain proclivities (e.g. science readers). Further, many sites - like Forbes - have first-impression-of-the-day interstitials, and different ads based on the type of device as well as the type of page (homepage v article page). Finally, many campaigns have frequency caps - such as daily or lifetime. Forecasting among all these different, overlapping groups is a particularly challenging question. 

There are a few major vendors in the inventory forecasting space - generally the publisher adservers. This includes DFP, OpenX, and AppNexus/Yieldex. Each is a black box, meaning it won't tell you exactly what its algorithms are. The general idea, however, is that a publisher wants to know if it can sell a particular campaign - given what it has already committed to over a particular period - or whether it's already sold campaigns are on pace to deliver. Behind the scenes, a forecasting tool will run a simulation based on historical data. DFP states explicitly that it uses the traffic data from the prior 28 days to create a model. It also adjusts automatically for standard factors including day and date - meaning that if you run 28 simulations, one for each of the prior 28 days, or one 28 day simulation, you will get nearly the same totals. The simulation will also model factors including user sessions (how many pages various types of visitors visit, how often it's the homepage, etc), device, and relative prioritization of line items to see which would deliver and which would not. Publisher adservers have access to your data, as well as that of all the publishers on their platform - so they can tell how your traffic tends to deviate, and they can also easily identify global or cyclical changes and apply them in their models. Because none say exactly how they do it, it's hard to say for sure.

Ultimately, forecasting is an incredibly hard challenge. The more a publisher divides its inventory, the less reliable any forecasting is. So there will be a tradeoff - which is largely effected by publishers limiting sellable guaranteed inventory at some fraction of forecasted availability. Forecasting may become more accurate, though the fundamental math here is unlikely to be improved upon too significantly. For individual publishers, this is a genuine challenge. Even platforms like TripleLift struggle with this to some degree - our sales planning and account management teams regularly confer to ensure we can deliver on certain proposals for guaranteed delivery, and we will likely build out better tools to analyze our historical data for exactly this purpose.