Third-party data isn't dead, you're just using it wrong

How to use third-party data to bring focus to who your customers actually are

Understanding your customer is paramount to any successful growth strategy, and data acts as the building blocks for doing so.

Those blocks come in two forms: first-party data and third-party data.

  • First-party data is the data you own. You can generally trust it to be correct because you collected it, but there is usually never enough of it.
  • Third-party data is data that was collected outside of your organization. It can be used to help fill in the gaps in your own data.

I often hear people assert that first-party data is high quality and third-party data is crap. It’s an opinion that is rooted in some historical truth, but there is nothing inherently crappy about third-party data. Instead, it relates to how third-party data has been packaged for consumption.

Ideally, we could eliminate any preconceived notions we had about data so we can better grasp its strengths and weaknesses. But with data being such a nebulous concept, that can be hard to do. So instead, let's talk about data in the context of pixels in an image: If you are trying to build a better picture of your customers, more pixels means a higher resolution image.

First-party data is precise, but rarely sufficient

Let's start with first-party data. First-party data is information you collect directly from your customers, like transactions made with your business, actions made on your app or website, details collected from registrations and surveys, and the like.

We can think of each of these pieces of information as pixels of a different color, like this:

First party data are pieces of information you collect about your audience directly, such as age, gender, location, last website visit, or last tweet.

When assembling all of our first party data we start to get an image of who our target customer is. In a perfect world, a brand would have enough first-party data to build a high-resolution picture of their customers, but that is almost never the case. Instead, what the brand gets looks more akin to an 8-bit character from an 80’s video game.

With your first-party data, you can start to understand who your customers are, but at best it's only 8-bit resolution.

 

Third-party data provides additional information, but often at the expense of precision

Now let's look at third-party data as it has been thought about traditionally. It shares some similarities with first-party data insofar that it encodes information about the user, but the similarities stop there.

Third-party data is often sold as segments, such as business traveler or wine drinker, but can also be things like location data or demographic data.

In our rendering of third-party data, notice that the pixels are much larger than the first-party data pixels. That is because third-party data is typically not nearly as precise as first-party data can be.

You’ll also notice that some third-party data disagrees with itself, which is related to the precision, but also a side effect of the way that third-party data is typically sold. (More on that later.)

When you mix imprecise and often incorrect third-party data with first party data, you don’t create a sharper picture. Instead, you create an image that an impressionist painter might be proud of, but that does nothing in helping you understand your customer.

When you start to combine first- and third-party data, you don't end up with a clearer picture, you end up with a mess.

One might argue that we’re done here. Case closed. Let’s just stop using third party data. In fact we’ve seen this argued before.

I would suggest an alternative. What if, instead of having third-party data look nothing like first-party data, we could bridge the gap and have them take the same form?

Yeah, I’m saying it can happen.

Third-party data is less precise than your own data, but it doesn't have to be

The thing to about third-party data is that, typically, it is non-specific. It is a binary representation of a classification someone has made about a person. Are they a business traveler, yes or no? Do they plan on buying a car, yes or no? Are they passionate about bird watching, yes or no?

The issue is that each of those questions is somewhat subjective. How often does someone have to travel for business to be a business traveler? When do they plan on buying the car? How does one define passion?

Returning to our image analogy, the more specific the information that a given pixel can encode, the sharper the picture becomes.

Pixels representing timestamp, location, latitude and longitude, dwell time, and unique identifier.

As you can see here, we are no longer trying to use third-party data for the purpose of creating a broad classification. Instead, we are using it to define very specific actions at a snapshot in time. Simply put, we’re making the third-party data represent something more precise, something more akin to the first-party data we represented earlier.

Pixels representing a behavior on mobile phone, including timestamp, the mobile app, the action made, and a unique identifier.

One of the nice things about encoding data this way is that you can have it represent completely disparate behaviors. In the previous example, the data represented a location. Here, it represents a behavior on their mobile phone.

Pixels representing a browser-based behavior, including timestamp, URL, action taken on the browser, and unique identifier.

And here, a browser-based behavior.

Now that we’ve created more precise pixels, when assembled we create a higher-resolution picture.

High resolution profiles enable more effective strategies. Customer acquisition no longer has to be serendipitous. It can now be laser-guided.

Here I am, in all of my glory.

A new understanding of third-party data is needed

So why, then, has third-party data gotten such a bad rap? The reason comes down to the way third-party data has traditionally been sold.

Companies like LiveRamp, Oracle Data Cloud, and Lotame, among others, pay publishers and other data owners for access to their first-party data and then aggregate this data into large data sets that they can sell to others based on target audience characteristics.

The incentive for these companies is to collect as much data as possible from as many different sources in order to maximize the scope and reach of their audience segments. With data coming from so many sources and no way to verify the original source of any individual data point, the potential for inaccuracy is high.

Instead of using pre-packaged segments of questionably useful data from unknown sources, brands must be able to access precise data sets from verifiable suppliers. Only then will they be able to build the high-resolution view of their customers needed to generate actionable insights and informed decisions off of third-party data. 

Using the Narrative Acquire platform, brands can utilize highly customizable filtering options to buy only the exact observations needed, and none they don’t. The platform also provides tools for analyzing dataset overlap, match rates, and truth sets to ensure the data being purchased is accurate and precise.

In the end, data is going to be an important component of any brand’s strategy going forward. It’s our hope that they start to look at more precise data sets so they can encode a higher resolution composite view of their customers.

Interested in learning how Narrative Acquire can help you bring focus to who your customers are? Request a demo today.