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Kevin Krim March 27, 2023 4 min read

Signal Loss From Privacy Changes Have Made Upfront Planning Very Fuzzy

Here’s how Modern Marketers are getting a clearer picture.

The Upfronts are right around the corner now, and if you’re not yet planning, you’re too late. Even worse, if you’re planning with last year’s data, you’re still too late. And while Multi-touch Attribution (MTA) is a powerful tool, it’s reliant on old data that is very vulnerable to challenges that make it unusable for planning. That means you’re running the risk of leaving missed customers and dollars on the table.

Here’s why.

MTA was once the gold standard. Marketers tracked user-level activity data to determine which media touches had the most impact in convincing customers to buy something. 

Ah, the good old days of 2010. 

But as consumers and companies lock down on privacy data, companies face major challenges in identifying and tracking their target audience.

Apple recently made privacy changes that make it nearly impossible to track 55% of the US mobile phone market. As browsers default increasingly to “Do not track” settings, cookies are less valuable. And the proliferation of devices used to stream TV and other video leave marketers guessing which device IDs represent a person or even the same household.

The challenge of understanding advertising efficacy is nothing new. When commercial TV broadcasts started about 100 years ago, marketers had to figure out how to track the success of their ads. 

Measuring consumer intent: Is your advertising working?

We know what you’re up against planning for the Upfronts this year, and we know how different you and your competitors are expecting it to be too. 

It’s getting harder, not easier, to connect a touch to a purchase. With traditional MTA, it’s almost impossible to determine which touch deserves the most weight. There’s first touch, last, equal and time decay to consider. 

A pioneer in MTA

EDO built the first AI-driven MTA modeling solution based on big data. In research data parlance, EDO relies on syndicated data – that means we collect ourselves or via partners ad occurrence and behavioral outcome that covers all of the advertising categories on TV including years of historical data, without relying on increasingly challenged tracking techniques like cookies. As we are all learning from ChatGPT and other AI software, more data is strictly better for training AI models. 

Now, being the first doesn’t necessarily make anyone the best at anything, but it does show how willing we are to approach tried-and-true thinking with new angles. Prior to our company starting its work in 2015, MTA was ploddingly slow. It was limited to one client at a time. And it worked only if there was enough data available to track.

But that was then, and this is now.

EDO has minute-by-minute Google search data for every major TV advertiser across all categories dating back to the start of 2015 (and we have since added huge web site visitation data too). That trove of data gives us insight you can’t find anywhere else. And it’s actionable information, which is getting harder and harder to find in the marketing industry. 

Why is EDO’s data better? 

We know what you’re thinking. You already have a lot of measurement partners telling you what’s what for the Upfronts. So why rock the boat? Simple – you need signals that move at the speed of business – like predictive outcomes data. Which is where EDO comes in. EDO tracks every Convergent TV ad’s performance. We see how every airing performs. We know which ad performs best on which network. We know which ad performs best at which time of day. Every ad, everywhere, all at once 

Think about that for a second. 

Looking specifically at your creatives, EDO can tell you which one is working the best. That might mean airing one advertisement on one linear network and a different ad targeted at a streaming audience segment. EDO can tell you when it’s time to sunset a formerly effective ad out of rotation, but we can also tell you when you should spike an underperforming creative. 

That’s all useful, but there’s more. 

We also see what’s working for your competitors – and also what they’re doing wrong. If you’re in the pharmaceutical business, we can tell you how your competitors are advertising and suggest paths forward that will help you compete and win. Our AI models and big data even enable you to extrapolate what will happen if you want to try a different strategy altogether. 

More privacy, better data for advertisers

Obviously, MTA still has a place in a marketer’s toolbox. You should track the moments driving consumers to buy what you are selling. But tracking touches to sales accurately isn’t always possible, and that situation is becoming more opaque, not clearer. 

Consumers will continue to make buying decisions because of an ad they saw. And at some point in the buying process, they give off a signal to companies that they intend to buy something. Don’t let signal loss sabotage your Upfront planning. 

As you head into Upfront season, you need to know what works. And, when it comes to MTA, you need investment grade signals that you can use.