Tag Archives: basics and background

The Basics: Audience Buying

Last week we looked at how publishers can take control of their own first party data in order to keep control of the sales process, extend their audiences, and offer additional value to marketers..

This week, let’s take a step back and revisit the path from buying media as a proxy for an audience, to being able to buy that audience itself.

Audience by proxy

RocketFuel's Audience Extension
Graphic from RocketFuel’s Audience Extension report.

Traditionally, advertisers were only able to target their audience indirectly, by trying to estimate who would be interested in a particular product or service based on a generalised demographic. This has supposedly helped advertisers to determine when and where to advertise—which publications, which venues, and what kinds of ads.

For example, want to advertise house cleaning products? Advertise on daytime TV or in women’s lifestyle magazines. Want to sell a Rolex? Target a wealthy and ‘masculine’ demographic in publications such as Forbes, or via outdoor on Wall Street and in the City of London. These may be somewhat dated examples but what they exemplify, apart from the reinforcement of age-old stereotypes, is a form of advertising based on certain assumptions about different kinds of people.

The idea is that, since you can’t directly buy the attention of a particular individual, you buy ad space where that audience is statistically more likely to be. And in many people’s minds, that is Advertising 101.

The rise of audience buying

The promise of digital for marketers has always been the increased ability to focus advertising messages to reach ‘the right person at the right time’, and over time this has indeed become more precise. But for a long time, digital marketing never fully managed to live up to this promise. Advertisers have still had to rely on publishers to reach audience segments that they deem statistically more likely to buy.

In recent years, though, things have started to change. We’ve seen the rise of ‘audience buying’: purchasing access to these audience segments directly rather than buying proxies for audiences. Thanks to the shift to digital and the increasingly sophisticated ability to track and store user behaviour, we have the ability to detect users’ activities, and to predict their interests based on patterns of behaviour.

In theory, this fulfils the promise of right time, right place, right person, right everything. The hyper-specific targeting of audience buying is more precise and more cost-effective, ensuring that consumers see ads that are far more relevant to them, and reducing wastage on ad spend for marketers. It also allows for a far more accurate attribution of ad spend, by making it clear which ad placements are truly driving engagement. In other words, it gives marketers full transparency, letting them see where their spend is going and with whom their message is resonating.

Fragmented industry

Audience TargetsThis shift has turned a hundred-year-old advertising industry on its head. It means that advertisers aren’t necessarily reliant on measurement and ratings companies to present them with their approximate audience segments, because they can create their own, collecting data from multiple sources and advertising anywhere that users can be detected and tracked. As we explored with the advent of publisher trading desks, publishers are also striving to reframe their own worth in this new context by exploiting their own rich user data, which they can sell within the audience buying model.

In some ways, though, this has led to such a fractured advertising landscape that we might wish for simpler times. The biggest problem is that the new and old models aren’t necessarily compatible, and yet many advertisers still try to use the old one and think audience buying can simply be tacked on.

For example, traditional TV advertising still mainly follows the old model while digital TV allows for much more direct audience targeting based on metrics like impressions and time spent with ads.

“The cost of using such different methodologies of buying selling and measuring TV and video is catching up to media companies that straddle both worlds,” says Lorne Brown, CEO of Operative. Brown continues:

Buyers and sellers often have separate media planning teams, technology stacks, sales teams, contracts and billing processes for the two worlds. As digital grows, it will not be sustainable to have two parallel processes. It also won’t be effective for advertisers, which will increase their expectations of measurable and effective cross-screen campaigns.

A cohesive model

At the same time, it might not be wise to ditch contextual targeting altogether because it can serve a different purpose. While audience buying is focused on the pre-existing behaviours of users whose data and habits have been tracked, marketers may still want to throw a wider net for growth on a larger scale.

Malcolm Coles of The Telegraph (UK) puts this in terms of ‘driving scale or becoming niche’:

a publisher has to choose between wanting to drive general engagement with their site and their brand, seeking growth over time, versus attending to the pre-existing audiences they have for, say, Telegraph Sport, or even a dedicated Arsenal FC page.

According to a white paper from Xaxis titled Harnessing Big Data for Audience Buying, a cohesive advertising model should have several ‘audience tiers’, ranging from ‘demonstrated affinity’ at one end (‘the consumers who have demonstrated through action a predisposition for the brand’), to ‘inferred affinity’ at the other end (‘the consumers who have not yet expressed affinity through direct action, but may gravitate towards the brand based on broader data such as demographics’).

The stronger the affinity with a brand, the smaller the size of a group, which means that targeting only users with proven affinity—in other words, only direct audience buying—may be insufficient for growth on its own. A good marketing plan, therefore, needs a cohesive model that encompasses both a hyper-targeted audience and a broader contextual reach.

The Basics: Publisher Trading Desks

In the traditional advertising model advertisers and their agencies buy advertising space from media owners and networks. They do this in order to reach an audience they think will be interested in buying their products and services. Simple. Increasingly, however, these relationships are becoming increasingly murky as advertisers morph into media owners and media owners become buyers. One way the latter is taking shape is through the formation of so-called Publisher Trading Desks.

As the advertising and marketing market shifts from trading on media properties as a proxy for audience and toward a more audience-centric trading paradigm, publishers are finding new ways to leverage first party data. By doing so publishers can capitalise on their own audiences, to give them the competitive edge. The premise is simple: publishers can leverage the rich data from their own pre-existing audiences to advertise more effectively, and not just on their own sites, but on third-party sites too.

As such, they can charge a premium for the use of their proprietary data, something to which most advertising companies simply don’t have access. So clients come directly to the publisher and pay the premium to have their product or service advertised across the publisher’s trusted affiliate network.

So how does it work?

PTD Simplified Model
On the left is the traditional buying model. On the right, the publisher moves up the chain, closer to the buyer, to trade the publisher’s own audience across both their own sites as well as on the open exchanges.

A publisher sets up a trading desk using what was traditionaly buy-side technology like MediaMath, DataXu, AppNexus or one of the other Demand Side Platforms (DSPs). This tech allows them to better manage and monetise their audience data. Previously, this would have required advertising middlemen, who would have taken a chunk of the revenue, but the publisher having their own trading desk makes this unnecessary. The actual purchasing of inventory is still done through real-time bidding, but the publisher leverages their proprietary data to target their ads.

This works particularly well for vertical or trade publications. For example, a publisher such as The Guardian, which has its own trading desk, will have rich data on its own readership, segmented into different interest groups. They will be able to look at the data for their travel section or their business section, and can analyse user behaviour to determine which campaigns their readership has been attracted to, which devices they tend to use and other demographic data.

This data then informs the publisher’s advertising on third-party sites—determining the nature of the ads, who to target, and on which of these third-party sites it would be most appropriate to advertise. It can also help to determine which devices to optimise for, such as smartphones and tablets.

But it doesn’t end there. When the publisher buys inventory on these third-party sites, these advertisements then collect their own data through cookies, which in aggregate creates an even richer pool of data.

A better proposition

This audience extension creates a much better advertising proposition overall, by not limiting publishers to the ad space of their own websites, and by allowing them to build on their already rich datasets. It also allows publishers to build mobile advertising even if they have no mobile inventory of their own, or video advertising if they have no video inventory.

For advertisers, there’s an element of brand safety guaranteed, as publishers can bank on their own reputations and those of trusted affiliates. Publishers themselves have the opportunity to work with their internal marketing teams to promote to their own audience beyond their website and across their network, increasing the reach of campaigns and promotional events.

However, a publisher trading desk is not without its challenges. The publisher will first have to prove their value to advertisers and other buyers. Publishers will be required to take on all the usual responsibilities of advertisers, which means that they will need to be savvy with their data, build in-house trading expertise where there is none, and source and train on new platforms where they have limited experience.

In other words, while there are many apparent advantages to publishers setting up their own trading desks in order to regain control of their own advertising and boost their own revenue, publishers will still require a thorough, fully-baked data strategy for it to work effectively.