Zero party data refers to the information that a customer intentionally and proactively shares with a business in hopes of better customer experience. It can include customer preferences, purchase intentions, personal contexts, and data on how an individual wants to be recognised by the brand.
With enforcement becoming stricter on data regulation policies like GDPR, it is crucial to collect this type of personal data directly from your customers to ensure transparency, build trust, and provide personalised experiences tailored to their preferences.
This article explores how Tealium Moments IQ and Adobe Target enable the effective use of zero-party data, facilitating the creation of dynamic, context-aware interactions that resonate with individual customers. This approach not only respects user privacy but also drives significant improvements in engagement and conversion rates.
First-party, Third-party, and Zero-party data
The following table highlights the key differences between first-party, third-party, and zero-party data.
| Feature | First-party data | Third-party data | Zero-party data |
|---|---|---|---|
|
Source |
Collected directly from your own customers through your website, app, CRM, etc. |
Purchased or aggregated from external sources (data brokers, other companies). |
Intentionally and proactively shared by customers directly with your brand. |
|
Collection method |
Implicitly collected through customer interactions (website visits, purchases, app usage, etc.). |
Obtained through cookies, tracking pixels, and other methods used by third-party providers. |
Explicitly provided by customers through surveys, forms, preference centers, etc. |
|
Customer control |
Limited control; customers may not be fully aware of what data is being collected. |
No direct control; customers may not know their data is being used. |
Complete control; customers actively choose what information to share. |
|
Accuracy & Reliability |
Generally accurate for understanding customer behavior on your own properties. |
Can be inaccurate or outdated due to aggregation and inference. |
Highly accurate and reliable as it comes directly from the customer. |
|
Privacy & Compliance |
Requires adherence to privacy regulations (GDPR, CCPA), but easier to manage as you own the data. |
Raises significant privacy concerns due to lack of transparency and consent. |
Aligns with privacy regulations as it’s based on explicit consent. |
|
Purpose |
Personalisation, analytics, marketing optimisation within your own ecosystem. |
Audience targeting, ad retargeting, cross-site tracking. |
Personalisation, building trust, enhancing customer experience based on explicit preferences. |
|
Examples |
Purchase history, website browsing behavior, email engagement, demographics from profile data. |
Demographic data from data brokers, interest categories inferred from browsing across multiple sites, retargeting lists. |
Preferences for product categories, dietary restrictions, communication preferences, personal goals. |
Importance of Zero-party data
Zero-party data is collected directly with the customer’s explicit consent, ensuring that businesses engage with consumers in a personalised yet ethical manner. This fosters stronger relationships, enhances brand credibility, and encourages long-term customer retention.
Zero-party data eliminates guesswork and ensures authenticity, providing businesses with precise insights into customer preferences, needs, and intentions. This leads to more effective personalisation and data-driven decision-making.
By collecting data directly from users—such as preferences, interests, and intent—brands can create more relevant and meaningful engagements.
With evolving privacy regulations, it is important to foster meaningful relationships with customers. Zero-party data empowers brands to deliver value-driven interactions without intrusive tracking.
Zero Party Data in Action
My Jewellery, a clothing and jewelry retailer based in the Netherlands, implemented a product-based “Style Finder” quiz to collect zero-party data from its customers. The quiz displayed items sequentially, allowing customers to express their preferences by clicking either a heart (to like) or an X (to dismiss). Using the data collected, My Jewellery personalised its email campaigns, resulting in a 55% increase in email open rates and, ultimately, higher conversions.
Use Case Motivation
In today’s digital landscape, customers are bombarded with endless choices, creating a sense of overload that complicates decision-making. This can negatively impact customer behavior, leading to frustration, indecision, and even abandoned purchases. This phenomenon, known as the paradox of choice, was initially proposed by American psychologist Barry Schwartz and has become an increasing concern in the modern world. So much so that it can even lead to lower conversion rates, as seen in Procter & Gamble’s findings, which showed a significant 10% revenue uplift after reducing product choices.
Fortunately, several strategies can effectively cut through this complexity and guide customers toward confident decisions.
- Use smart filters: Ease the cognitive load on customers by implementing smart filters. Offer robust filtering options (by category, attributes, etc.) and use AI-driven personalisation based on browsing history to act as a personal shopper, guiding users efficiently to relevant products.
- Provide comparison tools: Help customers evaluate similar options with less effort by providing comparison tools. Displaying product attributes side-by-side reduces the cognitive load of comparing final contenders and supports a more informed choice.
- Feature Bestsellers and Curated Suggestions: Reduce choice paralysis by directing attention to personalised recommendations and popular products. Highlighting bestsellers taps into social proof, while specific suggestions narrow options, making it easier for customers to choose.
Our use case will focus on addressing this issue by capturing shopping intent via zero-party data to provide personalised product recommendations in real time.
The Use Case
Customer journey for the use case configured is shown in the following video.
Use Case Configuration
The use case is configured using Tealium Moments IQ and Adobe Target Recommendations. Tealium Moments IQ is a low/no-code platform for enabling zero-party data collection on your digital properties. It helps you build custom surveys, preference centers, and progressive profiling forms without requiring extensive technical expertise. Adobe Target Recommendations is a powerful tool for delivering personalised content and product suggestions to users, enhancing their experience and driving engagement.
High level overview of the configuration is shown in the diagram below.
The configuration steps involved are listed below:
1. Configure the MIQ tags in Tealium
Tag configuration in Tealium involves 3 steps:
Step-1: Creating a design for the popup: Configure the design for the popup including type, header, question text etc. using the UI provided in Tealium’s tag configuration menu. Use the custom code template to make any additional changes to the styling.
Step-2: Set up load rules and events as triggers: Set up triggers for when the popup should be shown to the users. For ex. – the first popup could be shown for “scroll depth >40%” and the second popup could be shown when “miq_answer1 is submitted”
Step-3: Adding extensions for advanced data handling: Use tag extensions to store the popup answer data as a cookie value and also run Javascript code containing the adobe.target.getOffer() function.
2. Pass MIQ data to Adobe Target
We use the adobe.target.getOffer function to send the intent data to adobe target.
var targetParams = {
"mbox": "target-global-mbox",
"params": {
"profile.miq1_ans": b['utag_main_moments', 'momentsiq_answer1'],
"at_property": "your property id"
}
};
console.log('triggered');
// Fire Adobe Target request
adobe.target.getOffer({
mbox: targetParams.mbox,
params: targetParams.params,
success: function (offers) {
adobe.target.applyOffer({
mbox: targetParams.mbox,
offer: offers
});
console.log("Adobe Target call successful", offers);
},
error: function (status, error) {
console.error("Adobe Target call failed", status, error);
}
});
3. Set up Recommendations as an XT activity in Adobe Target
Following steps are required to set up recommendations as XT activity in Adobe target
Note – Create separate audiences and collections for each product category.
The final activity summary should look something like this.
Make the activity status live and perform QA checks.
Best Practices:
Set up a redundancy check in the target recommendations design template to avoid multiple recommendations div.
Ensure that you have ‘backup recommendations’ enabled in case of insufficient algorithm data.
Perform rigorous QA checks at every step to ensure the sanity of the data and experiences.
Conclusion:
Leveraging zero-party data through solutions like Tealium Moments IQ and Adobe Target enables businesses to deliver highly personalised experiences while building customer trust. By capturing and utilising intentional customer inputs, companies can simplify decision-making and overcome the overwhelming effects of excessive choice. This approach not only ensures data compliance but also enhances user engagement and satisfaction through tailored product recommendations.
Want to learn more about the use case
Check out our Zero Party Data module in Dexata Academy for a more detailed explanation of the use case configuration.
About The Authors
Utkarsh Balooni
What do you think?
This article provides great insights on how zero-party data is transforming hyper-personalisation strategies. The integration of Tealium Moments IQ with Adobe Target sounds very powerful!