Tag Archive for: attribution

customer information on computer

Zero Party Data: What It Means + Why And How You Should Be Collecting It

customer information on computer

As a growth marketing agency, our ability to access customer data is a major (if not the most important) factor in developing growth marketing strategies and executing them to find traction or scale. That’s why—before we spend a dollar with a new partner—we make sure proper tracking is set up and correctly capturing the user’s data. 

In the last few months, accessing specific types of data about customers has been increasingly more difficult. From fintech to eCommerce to SaaS – all industries have taken a major hit. It’s a topic that’s so monumental to the future of marketing and even business growth, as well as user privacy, that it’s been making headlines across the world. Don’t get us wrong, though, we believe this is a good thing; consumers should be able to control how their data is used in advertising. 

Despite that, brands should know the realities of what the advertising landscape looks like in a world without pixel tracking. 

There are varying levels of customer data that have become more difficult to access due to restrictions put in place by tech giants like Apple & Google that have reshaped the online advertising space (ahem, iOS 14). To understand how these changes have impacted digital advertising, let’s start with zero party data. 

What is Zero Party Data?

Zero party data is any customer data that is shared with you by the customer. The way to collect zero party data is to prompt your end customer with a way to provide you (the brand) with valuable data that will improve their personalized experiences on your website. 

For example, you might position a “how did you hear about us” survey at the end of your customer journey after they convert. You can then tie the conversion amount to the source and then back to your ad spend to determine effectiveness.

Forrester Research first defined the term as follows:

“Zero-party data is that which a customer intentionally and proactively shares with a brand. It can include preference center data, purchase intentions, personal context, and how the individual wants the brand to recognize [them].”

Why is Zero Party Data Important?

20 years ago, the internet embraced digital advertising and, in the process, devalued traditional forms of advertising like print, FM/AM radio, and television. 

The reason? Hyper-specific targeting. 

Before the internet, advertising used broad targeting that was rather inefficient in comparison to what the internet made possible. Reaching your audience was done through the earlier mentioned traditional forms of advertising and their targeting options were fairly general. 

As Brian X. Chen from the New York Times explained in his article “The Battle For Digital Privacy is Shaping the Internet”, the internet made hyper-specific targeting available at scale and for a much lower cost than radio and tv could sell. 

“Brands splashed their ads across websites, with their promotions often tailored to people’s specific interests. Those digital ads powered the growth of Facebook, Google and Twitter, which offered their search and social networking services to people without charge. But in exchange, people were tracked from site to site by technologies such as cookies and their personal data was used to target them with relevant marketing.” 

We’re coming into an age of a cookieless world that embraces more consumer privacy and less tracking without permission. Zero party data is part of this future because it is based on consumer-permissioned data. 

How You Can Benefit from Using Zero-Party Data

The great thing about zero-party data is that it displays intentionality between the user or customer and brand. It’s given by the user as a gesture that says, “I trust you to use this appropriately.” 

That’s something that cookies and tracking pixels could never do – show trust between all parties. 

On top of the established trust it provides between you and your end customers, it’s also a great way to collect data to compensate for the lack of data you receive due to the future of a cookieless world, iOS 14, and other measures that have been put in place to slow down the flow of private data. 

Examples of Zero Party Data

Take StichFix for example, they’ve built the majority of their $2 billion business off of their sign up quiz, which asks users numerous questions about how they feel about shopping and what types of clothes they like to wear to develop a consumer profile for them that enables StitchFix to send their customers customized clothing selections in a subscription service. 

Ultimately, we know that as consumers get savvier and the methods for blocking data trackers like pixels gets more sophisticated, relying on first, second, and third-party data will only get more complex. So, although it takes more time, intentionality, and strategy, we believe zero-party data is the future.

Comparison of Customer Data Types

Zero Party Data

As discussed above, Zero Party Data is any consumer-permissioned data provided to you directly by your end customer. This could be your customer telling you how they found out about you in a post-purchase survey or a customer telling you demographic information, what brand they buy from, or what style of products they need in a pre-purchase quiz that asks for their email address. 

First-Party Data

First-party data is any data collected by a company about their customers. This information is compiled through a brand’s website and used to develop various marketing strategies that cater to an individual or group (ex: target audience). For example, we use first-party data at Tuff when we look at a brand’s users’ website behavior, listen to inbound sales call recordings, or analyze purchase history to learn more about a company’s existing users and customers. 

Second-party data: Provided by a Partner

Now that you understand first-party data, second-party data will be easy to pick up. Second party data is first-party data that is provided by a known partner. Say you run an online outdoor publication and you know that your customer list would be good for a specific brand that sells backpacking tents. The brand that receives that audience to use in their targeting would be receiving second-party data.

Third-party data: Provided by an Outside Source 

Unlike first-party data, third-party data usually comes from an outside source (third party) that has collected the data about its customers. I don’t want to name names but so let’s make up a third-party platform that might collect vast amounts of data about its users to be used for advertising. We’ll call it Facebook. Hypothetically speaking, let’s say Facebook has 2.89 Billion active users on its platform and due to how it tracks users on its platform and used to be able to across the web, then Facebook has copious amounts of data on a large chunk of the world’s population. Facebook then shares that data with advertisers to help them target specific audiences.

That’s third party data! 

Data Privacy and Restrictions on Data

In April 2021, Apple released an update to their iPhone software called iOS 14 which contained a feature that enabled iPhone users to block their personal information from being shared. The feature has disrupted the advertising industry because it has made it fairly difficult for advertisers to retarget to users as well as measure how their ads are impacting conversion. 

Once a user on an iPhone clicks an ad on Facebook and leaves a Facebook-owned property,  without pixel tracking, we lose the data that tells us where that person came from when they get to the external website and begin their customer journey.

One way to get around the decrease in retargeting volume is to use You can circumvent proprietary algorithms like Black Crow that enable you to create Look-a-like audiences of your retargeting audiences. Learn how we used Black Crow Audiences to circumvent smaller sized retargeting audiences on Facebook using $1.1M in Ad Spend. 

iOS14 makes it incredibly difficult for us to analyze data in such a way that says definitively for every $1 you put into this paid acquisition channel you will receive $5 from a paying customer or with this channel you see an average cost per sale of $10. The reason? We have no idea of the channel source. 

To be clear though – attribution was never perfect, but it’s harder than ever now.

Do you have any strategies in place for collecting zero-party data? Whether your answer is yes or no, we’d love to talk!

pulling reports on a computer from google analytics

The Best Attribution Model for B2C and eCommerce Brands

pulling reports on a computer from google analytics

Attribution is a big topic in the growth marketing world and for good reason. Knowing which channels, campaigns, audiences, and ads hold the most value for your brand’s performance is incredibly important for growth. 

The challenge for B2C and eCommerce businesses though is that running multi-channel growth marketing strategies with perfect attribution is incredibly difficult. And even when you get strategic with Google Analytics attribution models, it’s still not always easy to know exactly how each channel is impacting your bottom line. 

So what should you do to figure out how to scale? 

In this article, I’ll lay out a few different attribution models and look at their pros anc cons, then show you how to analyze conversion paths and use them in tandem with attribution models to (hopefully) give you some deeper insights as to how your channels are doing and where to scale spend. 

Channel Platform Attribution 

As a growth marketing agency, when we hear about astronomical Return On Ad Spends (ROAS) above 5x and getting up into the 20s, it usually has something to do with Channel Platform Attribution. 

Channel Platform Attribution for the uninformed is when you pull performance data from dashboards within the ad platforms you are advertising on. For example, let’s say you’re running Facebook (Meta?) Ads. Within the Facebook Ads Manager, you can see performance metrics that help you understand how your campaigns, ad sets, and ads are performing. 

The logic for using this has generally been that it will be the most accurate because it’s the platform you’re using for the actual ads. 

The thing to remember is that ad platforms like Facebook make the majority of their revenue through their advertising platforms. Therefore, it’s in their best interest to have the most liberal attribution models, because the more that can be attributed to their platform, the more you’ll spend in that platform, the more that platform will make from you. 

Last Click Attribution 

The most conservative of all the attribution models, Last Click is the default attribution model within Google Analytics. 

As the name suggests, Last Click attributes conversions to the ad / channel that was last clicked prior to the conversion. 

Sample Paths

  • Direct > Organic > Google Search / Branded > Conversion 
  • Facebook > Organic > Facebook > Conversion 
  • Facebook > Conversion

In the sample paths above, which channels will be attributed with conversion in a last-click model? 

If you guessed, Google and Facebook for each path, then you are correct. 

The problem with this attribution model is that it was developed prior to multi-channel marketing being as dominant as it is in today’s advertising landscape. 

Once you get past a 1 touchpoint conversion path, attributing the channel / campaign / ad that was last clicked before a conversion is a bit of a stretch. 

Was it the Facebook Ad that was last clicked or was it initially the organic search that led to the session over 5 minutes long where the user dove in on the product and in some ways made up their mind. 

Linear Attribution 

With last click, it’s hard to tell which touch point is driving the conversion. In a linear attribution model, the entire conversion path is given equal weight showing that the whole path contributed to the conversion. 

This inherently reveals that more than one channel contributed to the conversion. 

What it fails to get at is which one. 

The Perfect Attribution Model (Hint: There’s not one)

There is no one perfect attribution model and it’s unlikely that there will be one for the foreseeable future. 

What’s more important than searching for the magic crystal ball that will tell you exactly what platform to put your money into is being able to understand each attribution model and what they’re saying about your strategy. Examining multiple attribution models can give you a better understanding of how your channels are doing than just one individual model. 

In addition, it is critical that you understand how each channel / campaign / ad is driving influence for your conversion. 

For instance, if you see that on average your path lengths are 2x touchpoints long and a high percentage of them that end in conversion are: organic / search > branded / search > purchase

  • What is driving people to search my brand organically? 
  • Is my website ranking for high intent Keywords important to my business? 
  • Am I running social campaigns like Youtube or Facebook Ads that are targeting the right audience but resulting in organic searches rather than direct clicks? 

Using attribution models to tell you which channel to increase spend in that path length example of organic > branded > purchase will not yield an increase in conversion.

Only when you understand the conversion path will you be able to understand where to scale (you know like Yoda would say). 

Analyzing Conversion Paths > Using a Single Attribution Model

One of my favorite tools to use for them is the report in Universal Analytics called “Top Conversion Paths” located in Conversions > Multi-Channel Funnels > Top Conversion Paths. 

multi channel funnels report from Google Analytics

This report defaults to showing path length at the Channel Grouping level, but you can go down to the ad set level. 

It’s a great way to see how many touchpoints on average it’s taking for users to convert and which channels are involved in the conversion. 

It’s even better when you use insights from the report in tandem with multiple attribution models. 

The best way to do this is to manually track your results in a spreadsheet in a Week over week (WoW) cadence. 

For each channel that you’re tracking, you’ll want to include multiple attribution models. 

For example, your tracking spreadsheet might look like this: 

paid media reporting spreadsheet

The above is pulling in the marketing spend for all channels, followed by web traffic, then we segment by Last Click + Assisted and Ad Channel Platforms. 

google analytics tracking spreadsheet

By showing multiple types of attribution models we’re able to drill down on what’s working and what’s not then run comparisons of what’s working against other attribution models. 

The result is not a crystal ball look into what’s driving growth, rather it’s an understanding of what channels are contributing most significantly to your overall growth. 

When you pair these insights with the Top Conversion Path report in Google Analytics, your insights will be far deeper than with any one attribution model. 

pulling a report from google analytics

How to Drive Better Strategic Decisions Using Google Analytics’ Attribution Models

pulling a report from google analytics

The way the majority of marketers use Google Analytics’ attribution models to make strategic decisions is broken. I’m talking, “holy cow, this has major implications on our bottom line”, broken.

If you’re making major decisions regarding the allocation of ad spend, or trying to measure the success of a campaign and are using Google Analytics’ default reporting, you’re going to want to read this. 

What is Google Analytics’ Default Attribution Model? 

Google Analytics defaults to a Last Click attribution model for most of its reports (and the key word here is most). Last click attribution gives 100% of the credit to the last source, or campaign a user came from prior to converting. 

Some reports, such as Google Ads breakdown under the “Acquisition” report defaults to a Last Non-Direct Click attribution model. What this means is that Google Analytics will ignore all direct traffic and give 100% of the credit to the last channel a customer clicked through prior to returning via direct traffic. Last Non-Direct click is a preferred attribution model over Last Click for many marketers because most direct traffic has had some sort of interaction with your brand prior to coming to your site. 

What are the problems with using Last Click attribution modeling? Don’t I want to know what made my target audience convert?

It’s true, Last Click, or Last Non-Direct Click can be insightful for measuring the final tactic that caused a user to convert. However, this is an incomplete measurement of the full aspect of the marketing funnel. Chances are, your target audience didn’t convert out of the blue. 

Here are some of the limitations of using Last Click or Last-Non Direct Click attribution modelling: 

  • Can give a distorted view of what is actually driving your target audience to your brand in the first place
  • Doesn’t account for multiple touchpoints in an advertising funnel
  • Silos data and gives 100% of credit to one channel or tactic
  • Doesn’t show how multiple channels and tactics interact with one another
  • Shows an incomplete customer journey

Take this real-client example: if you see that email marketing is accounting for 30% of your e-commerce purchases, and paid social is driving very few last-click purchases (but a lot of email signups!), you wouldn’t want to stop running the social ads that are leading to email signups. In doing so, you’d be shutting down two acquisition channels at once. 

The last-click attribution model is flawed, and doesn’t take into consideration that a customer today has to nurtured to make a conversion. 

Alternatives to Last Click Attribution Modelling

Luckily, Google Analytics offers several attribution models that marketers can use to get a more complete picture of their conversion efforts. There are several ways to do this. 

First, under the Multi-Channel Funnels report, you can select “Assisted Conversions” report and view how many of your conversions were multi-touch. Selecting the “Source / Medium” breakdown allows you to view how different channels function: either as more of an assisted tactic, or a direct tactic. 

assisted conversions in google analytics

A value closer to 0 in the final column means it’s a primarily final conversion tactic. If the value is close to 1, the channel operated equally as a direct and assist tactic.  If the value is over 1, it means the platform assisted in more of an “assist” role. 

Another favorite tool to compare Google Analytics’ Attribution Models is the Model Comparison Tool. At Tuff, we often use the “Last Interaction” vs, “First Interaction” report to identify demand generating campaigns and platforms. 

Google Analytics’ Attribution Models

In this report, we see that Facebook and Google ads have a 20% and 47% increase in attributed conversions when using the first-click attribution model. Google Analytics’ first interaction attribution model gives 100% of the credit to the first source a user interacts with before converting. 

A savvy marketer can also apply a secondary dimension to view campaign filters applied to dig into which ad campaigns on specific platforms are driving the majority of the initial interest in their brand and product. 

Using Google Analytics’ Attribution Models to Make Strategic Decisions

As you’ve probably guessed by now: the answer for how to use Google Analytics’ many different Attribution Models (we haven’t even touched on Time Decay, Linear, or Position Based models, or how to measure via Zero Party data) is not a “one size fits all” solution. 

Instead, we recommend comparing the different models, identifying what campaigns and channels are generating demand and interest for your products, and leveraging different optimization tactics to drive revenue for your brand. It takes more time to compare models, but the savings can be immense. Ready to see how Google Analytics’ first-click attribution modeling can unlock major demand generation wins at the top of funnel for your brand? Drop us a note.