The diversity of available channels and devices means measuring ad effectiveness in the age of streaming is incredibly difficult.
There is no doubt the Covid-19 pandemic accelerated consumers’ love of streaming as people spent more time at home. We are streaming content in so many different ways and data is everywhere. Yet the irony is it is harder than ever to gain valuable information about brand awareness, consumer perception and intentions.
In an ideal world, marketers would be able to measure all consumer behaviours across different devices and media channels. Unfortunately, in today’s ecosystem it is incredibly complex, if not simply impossible, as people consume media using mobile, tablets, PC, TV and gaming systems.
Universal measurement is probably an unrealistic dream for marketers in the near term. For one thing, it would require publishers and measurement firms to agree upon a single, privacy-centric, universally used ad ID system, and despite several ideas being brought to the industry for potential approval, at this point none look likely to be broadly adopted.
Add in the fact that identifiers such as third-party cookies are being deprecated, and it becomes even harder to understand who is watching and engaging with your advertising. In addition to the challenges of universal adoption of a new identifier, some of the cookieless measurement solutions being developed seem doomed to fail from the start.
The fundamentals of ad measurement haven’t changed over the years. The more engaged someone is with the content, the better the ads tend to do.
A number of measurement firms are looking to bridge the gap left by cookie deprecation by obtaining ad exposure data through direct publisher matches using hashed email addresses. For publishers that have substantial reach, and which have done a good job at getting their users to sign up with their email address, it should be possible to do a good job with that one publisher to measure ad delivery.
While this method provides some of the same data we receive today from tags, it greatly reduces the number of ad impressions seen for that publisher in proportion to the scale of the email match and to the number of site visitors that have actually provided an email address. Further complicating this approach is the challenge of having possibly hundreds of these integrations, which will make single source cross-publisher studies virtually impossible to conduct through a direct publisher integration approach.
Without a clear view, it is difficult to create ads with the right messaging, to deliver them to the right people, or to buy and optimise the most effective ad inventory.
Having to adapt
In this new world of measuring ad effectiveness, stakeholders on both the buy side and the sell sides are having to adapt.
Advertisers and publishers have difficult conversations ahead about the audiences they are reaching, given the challenges of identification, and of understanding the diverse ways in which people are now viewing streaming content.
On the buy side, marketers and agencies must answer important questions. How do you take an ad campaign and create synergies based on similar creative across multiple platforms? How do you build different versions of a creative that deliver a compelling, engaging message and viewing experience for a 60-second TV ad, a 15-second YouTube ad and a short video on Facebook?
Measurement is becoming more difficult largely because the behaviours that govern how people are viewing content are just as varied as the devices they use and the services they subscribe to. Some of us watch TV, but only TV, on different devices, while others only ever watch streaming services. And most use both, following specific content or programs across multiple platforms. The current complexity means brands need to think differently about how they target.
For example, traditionally, if a brand wanted to target 25- to 55-year-old women it would buy against particular TV shows. That strategy does not work so well today. You have to take into account connected TV, delayed streaming on a variety of devices, and how people are going to pick up snippets of content online.
Another challenge for advertisers is how to bridge the gap between engagement and ad effectiveness.
Ultimately the fundamentals of ad measurement haven’t changed over the years. The more engaged someone is with the content, the better the ads tend to do.
Consumers can certainly be dissatisfied with the advertising they see when streaming content, whether because it is poorly targeted or too repetitive, annoying viewers by serving the same creative content too frequently.
The technology itself can also create difficulties for brands. Viewers will abandon content delivered over the internet if it stalls or takes too long to load, for example.
One big positive in this space has been the engagement success of content on mobile devices. Initially, many in the industry felt that watching ads on a mobile would be less engaging than when viewing content on a larger screen such as a TV.
When it comes to TV measurement, many advertisers have invested in behavioural measurement using automatic content recognition (ACR) data generated by the television itself or some accompanying device. Set-top box (return path) data can also be used for this purpose. These approaches are not without their limitations. A brand can certainly record whether an advert has been served to a particular household, but marketers cannot tie engagement or awareness back to a specific person in the home.
A blended approach works best where the behavioural data from a household is combined with an individual’s self-reported viewership data, to create a fuller picture of exposure to an ad.
The channel challenge
Engaging content is a must to boost reach, and the demand from brands to achieve robust reach is putting pressure on the measurement industry.
Providers have to work out the overlap between different channels and take into account various limitations to what they are able to do. For example, there are only two companies approved by Google to measure brand lift on YouTube, where the reach can be huge. Dynata is one of the Google Ads Data Hub partners helping brands to measure in this new world.
The impact on data collection is also significant. In this world of streaming, it is important to invest in connecting multiple data sets by maximising first-party data and combining it with first- and third-party sources. One of Dynata’s core first-party data strengths is its 62 million-strong panel – including hard-to-reach audiences, such as B2B, with thousands of attributes on each – which is backed by patented connected data capabilities. That way, you can be sure you can find the right audience, made up of real people with real opinions.
It is clear many marketing teams will need to upskill or bring in specialist knowledge.
In future, marketers may have to prioritise where they measure, and assume any channel they are unable to measure too closely has worked because the data tells them that the advertising was successful in other channels. This makes sense if budgets are limited. Marketers will have to be practical and only measure the channels they know will provide the most valuable and reliable information.
Given everything that we know about measuring ad effectiveness around streaming, it is clear many marketing teams will need to upskill or bring in specialist knowledge.
We expect to see more insourcing around data science, with an increased reliance on agencies to provide some of that expertise.
Brands will also need to become savvier about combining the many datasets they have, and ensure they are privacy-compliant around first-party data to help them when identifiers disappear.
We will almost certainly see a move back to some of the measurement techniques that were more popular 20 years ago, prior to the age of digital personal identification, so knowledge of those will be a must. We could see added investment in copy testing, for example, to ensure brand messages are strong and relevant. Brands may also rely more heavily again on market mix modelling that layers in aggregate data such as competitor spend per channel, economic conditions in different geographical areas, and seasonality, to evaluate how each of a brand’s channels is performing.
And, ultimately, any measurement around streaming needs to relate to the brand’s key performance indicators.