Written by: Fenna Bullen
July 7, 2022

The limiting factors of 3rd-party data in market research

Gathering 3rd-party data has always been important to insight professionals. If only to validate your own research or to enrich a data set you gathered. But there are some problems around it that even affect 2nd-party and 1st-party data. Let's dive into them and how you can do better.

Why create a brand community: 5 blogs about why you need to pursue this right now

First, let's take a few steps back and explore brand communities. Creating a brand community allows you to gather zero-party data and increase engagement with your ideal customers. A welcoming trend for those worrying about the ramifications of stricter privacy laws and the sunsetting of the 3rd-party cookie. Today we start with blog 3/5.

Why do brands need to create communities in the first place?

Getting market research right is a very competitive advantage nowadays. But there's a lot more going on than just that. Brands have been building communities on social media platforms that are now pushing them to pay up (for example, by buying ads) if they want to keep their engagement and visibility high enough.

All the while, privacy laws are getting stricter and fewer data can be shared by the likes of Google, Meta or your average 3rd-party data broker.

And to be honest, that 3rd-party or even 2nd- and 1st-party data may not be as reliable as you think anymore.

Meaning, everything is converging towards a scenario where you need to know more to be able to compete but must do with less available data. On top of that, the communities you've built on social media platforms are not yours. And you're finding out right now by literally paying a hefty price.

That's why creating a community on your terms around your brand or product is a no-brainer. These people aren't just your customers. They're your ideal customers - your fans! The ones that spread the word about it in their inner circle and love to share their opinions on how to make it even better.

In this series, we'll dive into the reasoning behind creating such a community. Because the chances are that you're reading this because you see some of these trends too.

And we're going to arm you with all knowledge you need to get cracking with setting up that brand community of fans you need so badly.

Reason 3: 1st, 2nd and 3rd-party data is in flux

So, the net effect of Google's decision is that the 3rd-party cookie data will be phased out very soon. But 3rd-party cookies aren't equal to all 3rd-party data in general you can buy from data brokers and markets. Legitimate brokers can still provide meaningful data in plenty of ways if their databases are good enough.

But what about the 1st- and 2nd-party data then?

2nd-party data is safer than 3rd-party data because it's from a trusted partner who's selling their 1st-party data. There are scenarios where 1st- 2nd- and 3rd-party data is more beneficial than zero-party data and the other way around.

Quantitative research would be an example where 1st- 2nd- and 3rd-party data can shine. As for zero-party data? Say, for example, you have an online tool with paid add-ons. Now let's say many users may do a brand search, click through to the homepage and then immediately click on your login button.

One way of looking at this may be that the homepage is boring and isn't generating upsell from frequent users. Another way of looking at it is that the users don't bother with bookmarks and use these steps to log in to their tool.

Sure, you can enrich the data, but then you're still not 100% sure about the user's original intent. In that case, you don't have a solid base to build your analyses on, now do you?

In a zero-party data situation, a user would have told you they want an easier way to login to the tool instead of you and your data scientists, researchers and marketers scratching your head on why the user did something.

So what can you do right now?

Data quality is a continuous challenge for insight professionals and data creatives everywhere. All this doesn't mean you should completely discard anything that isn't zero-party data.

You should be aware that this data is less reliable than before and shouldn't be taken at face value. For example, it should be weighted less heavily in data models. It also means you should be careful when you enrich your zero-party or 1st-party data using 3rd-party data.

Looking forward to the other blogs?

We've got you covered! We based these blogs on our latest white paper. Download the white paper below:

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