The diversity of channels and devices available means that measuring the effectiveness of ads in the streaming age is incredibly difficult.
There is no doubt that the Covid-19 pandemic has accelerated consumers’ love of streaming, as people are spending more time at home. We deliver content in so many different ways and the data is everywhere. Yet the irony is that it’s harder than ever to gain valuable insight into brand awareness, consumer perception and intentions.
In an ideal world, marketers would be able to measure all consumer behaviors across different devices and media channels. Unfortunately, in today’s ecosystem, this is incredibly complex, if not impossible, as people consume media using mobile systems, tablets, PCs, TVs, and games.
Universal measurement is probably an unrealistic dream for short-term marketers. On the one hand, it would require publishers and measurement companies to agree on a unique, privacy-centric and universally used advertising identification system, and despite several ideas being submitted to the industry for potential approval, to this stage, neither seems likely to be widely adopted. .
Add to that the fact that identifiers like third-party cookies are outdated, and it becomes even more difficult to understand who is viewing and interacting with your ad. In addition to the challenges of universal adoption of a new identifier, some of the cookie-less measurement solutions under development seem doomed to fail from the outset.
The fundamentals of ad measurement have not changed over the years. The more involved a person is in the content, the better the ads.
A number of measurement companies are looking to fill the void left by deprecating cookies by obtaining data on ad exposure through direct correspondence with publishers using hashed email addresses. For publishers that have substantial reach and have done a good job of getting their users to sign up with their email address, it should be possible to do a good job with this publisher to measure ad serving.
While this method provides some of the same data that we receive from tags today, it dramatically reduces the number of ad impressions seen for this publisher in proportion to the email match scale and the number of visitors to the site. site that actually provided an email address. . This approach is further complicated by the challenge of potentially having hundreds of such integrations, which will make it virtually impossible to perform single-source cross-publisher studies through a direct publisher integration approach.
Without a clear view, it’s hard to create ads with the right message, deliver them to the right people, or buy and optimize the most effective ad inventory.
Have to adapt
In this new world of advertising effectiveness measurement, stakeholders on both the buy and sell side must adapt.
Advertisers and publishers have difficult conversations ahead about the audiences they reach, given the challenges of identifying and understanding the various ways people now view streaming content.
On the buyer’s side, marketers and agencies need to answer some important questions. How to take an advertising campaign and create synergies based on similar creations on several platforms? How do you create different versions of a creative that deliver a compelling and engaging message and viewing experience for a 60 second TV ad, 15 second YouTube ad, and a short Facebook video?
Measurement becomes more difficult in large part because the behaviors that govern how people view content are just as diverse 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 watch streaming services. And most use both, following specific content or programs across multiple platforms. Today’s complexity means brands need to think differently about how they target.
For example, traditionally, if a brand wanted to target women between the ages of 25 and 55, it would buy for particular TV shows. This strategy does not work so well today. You have to consider smart TV, delayed streaming to a variety of devices, and how people are going to grab 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 involved a person is in the content, the better the ads.
Consumers can certainly be dissatisfied with the advertising they see when they show content, whether it’s because it’s poorly targeted or too repetitive, annoying viewers by showing the same creative content too frequently.
Technology itself can also create difficulties for brands. Viewers will abandon internet streaming content if it stalls or takes too long to load, for example.
A big bright spot in this space has been the success of engaging content on mobile devices. Initially, many in the industry believed that watching ads on a mobile would be less appealing than watching content on a larger screen such as a TV.
When it comes to television measurement, many advertisers have invested in behavior measurement using Automatic Content Recognition (ACR) data generated by the television itself or by an accompanying device. Decoder data (return path) can also be used for this purpose. These approaches are not without limits. A brand can certainly register whether an ad served a particular household, but marketers cannot tie engagement or awareness to a specific person in the house.
A blended approach works best when behavioral data from a household is combined with an individual’s self-reported audience data, to create a more complete picture of exposure to an advertisement.
The chain challenge
Engaging content is essential to increase reach, and the demand from brands to achieve robust reach is putting pressure on the measurement industry.
Suppliers need to determine the overlap between different channels and take into account the various limitations of 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 compete 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 datasets by maximizing first party data and combining it with first and third party sources. One of Dynata’s main strengths when it comes to first-party data is its 62 million people panel – comprising hard-to-reach audiences, such as B2B, with thousands of attributes on each – which is backed by patented connected data capabilities. This way, you can be sure to find the right audience, consisting of real people with real opinions.
It is clear that many marketing teams will need to improve their skills or bring in specialized knowledge.
In the future, marketers may need to prioritize where they’re measuring and assume that any channel they aren’t able to measure too closely has worked because the data tells them the advertising was successful on other channels. It makes sense if budgets are tight. Marketers will need to be pragmatic and measure only the channels they know that will provide the most valuable and trusted information.
Given all that we know about measuring the effectiveness of ads around streaming, it’s clear that many marketing teams will need to upgrade their skills or bring in specialized knowledge.
We expect to see more internalization around data science, with increased reliance on agencies to provide some of this expertise.
Brands will also need to become more savvy about the combination of the many datasets they have and ensure they respect first-party data privacy to help them when identifiers go missing.
We will almost certainly see a return to some of the measurement techniques that were more popular 20 years ago, before the era of digital personal identification, so knowledge of these will be essential. We might see an additional investment in copy testing, for example, to ensure brand messages are strong and relevant. Brands can also rely more on market mix modeling which overlay aggregate data such as competitor spend by channel, economic conditions in different geographies and seasonality, to assess the performance of each of the company’s channels. A brand.
And, at the end of the day, any metrics around streaming should tie into the brand’s KPIs.
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