Scaling zero-party (0P) and first-party data (1P) strategies is becoming a coveted tactic for improving customer experience. But connecting with customers comes with one major challenge – they must be willing to share their personal data with you.
Privacy regulations are putting consumers in control over how they want to communicate with brands, including what information they are willing to share – and consumers are listening. A large share of consumers actively try to withhold personal data from brands. Others opt to obscure their personal information, either by using alternate email addresses or providing false data about themselves entirely.
So, where’s the disconnect? It could be a matter of trust. McKinsey found that only 33 percent of American consumers believe companies are using their personal data responsibly (McKinsey). Addressing this breakdown in trust is critical for brands.
While considering how to use zero-party and first-party data, think about the following ways you can show your customers how sharing their data with you improves their experiences, builds relationships, and strengthens the data value exchange.
Clearly Communicate How You Collect and Manage Personal Data
The first step is to provide full transparency on how customer data is used and managed. Privacy policies on websites and mobile applications are vital to delivering this transparency. These policies outline what data needs to be collected, and why. They also need to explain how the consumer can request the list of data collected so they can see what they have agreed to.
A consent management system allows organizations to manage consumer preferences on data collection, and integrate it into the customer journey. Consent management reinforces a company’s commitment to transparency and helps build consumer trust. It can also ensure that the company future-proofs itself for new and changing privacy regulations.
Build Trust Through Relevancy
A consent-based approach to customer data strategy allows companies to be strategic about their products and services. Access to rich customer data, including past purchases, email engagement, and website activity, enables improved messaging and reduced ad spend. By allowing customers to set their communication preferences, they are telling companies exactly how to engage with them, increasing the brand’s opportunity to reach and convert customers.
For the customer, it means more relevant communications. They are in control of what messages they receive and when. And, they trust a company to only deliver content and offers that match what they want to see.
Make Customers Part of the Process By Asking for Feedback
Direct engagement with consumers allows them to become a part of the process. The best way to engage customers is to ask for feedback. Customers want to know that their opinions matter and that when asked for them, companies actually listen and use them to improve the customer experience.
When you ask for feedback, whether by survey, phone call, or feedback form, you are showing your customers that you value them for more than just their money. Use feedback to improve products and services, marketing campaigns, customer support, and success strategies.
Tap into Customer Service Conversations
A Gartner survey found that 71 percent of B2C and 86 percent of B2B customers expect companies to be well-informed about their personal information during service interactions (Gartner, 2022). What if you could offer this level of understanding during a support call and then listen intently to understand the customer’s challenges?
Customer service is a gold mine of information you can use to improve products and services, and identify improvements or shortfalls in the customer journey. A customer data platform (CDP) equipped with the ability to integrate with customer service tools can help bring this data into a complete customer profile.
Integrate Data Privacy Into Your Brand Values
Like sharing your privacy practices, consumers care about brand values and want to work with brands who share the same values. Consumers are more likely to purchase products and services from a brand that aligns with their values.
If consumers will purchase from a brand that shares their values, they’ll also trust that brand and be more willing to share their personal information. Remember, though: it’s essential to be authentic and live the values you espoused. Saying one thing and doing something else erodes brand trust and drives customers away.
Make Loyalty Programs Count
Many brands have loyalty programs that allow consumers to provide personal data in exchange for greater personalization, rewards, and other incentives. Loyalty programs are a top investment priority for brands that want to collect zero-party data.
There are concerns that customers don’t always provide accurate data when they sign up for loyalty programs. To ensure the data is accurate, the brand must use the data captured to provide real value to the customer. White-glove services are one way a brand can use loyalty program data, where in-store salespeople can access customer data from loyalty programs to help them personalize the shopping experience. Yotpo’s loyalty survey found that loyalty members want early access to sales (60.1 percent), early access to new products (50.8 percent), and offers and recommendations tailored to them (38.9 percent). Customers who sign up for loyalty programs are also looking for early access to sales, early access to new products, and offers and recommendations tailored to them.
When you use the information customers provide in campaigns and programs that meet their expectations, they will want to give you accurate, up-to-date information.
Prove to Customers You Value Their Data
Access to accurate first-party and zero-party data is critical to delivering relevant, personalized experiences. But it can be challenging to get customers to share their data willingly. Trust and transparency are essential to convincing customers that you will keep their data safe and use it appropriately to improve how you deliver products and experiences.
There are many ways you can collect data from your customers. The key is to show them why you are collecting it and how you will use it. Show your customers you value them as true partners and build a customer data strategy that embodies those values.
For research-backed guidance on building a first-party data program, see How to Optimize Your First-Party Data Strategy.
Read More: How to Build a First-Party Data Strategy
Collect less data than you think you can
The fastest way to undermine a transparency program is to hold more data than customers can make sense of. Every field you collect is a promise you have to keep: a reason it exists, a place it is stored, and an explanation you can give the customer who asks about it. Fields that fail that test are not assets. They widen your exposure when a breach happens, they decay into the inaccurate records that make personalization feel careless, and they are exactly what customers suspect you are hiding.
Minimization is a quality decision as much as a privacy one. Data that leaves your hands for ad exchanges and other intermediaries the customer never chose is data whose accuracy you no longer control, and every handoff degrades it. A profile built on a small set of declared, current, permissioned attributes outperforms a sprawling one stitched together from sources the customer cannot see. Relevance assembled from invisible inference is what reads as surveillance; relevance built on what a customer deliberately told you is what reads as service.
So audit what you hold the way you audit spend. For each category of data, decide deliberately whether it still earns its place:
| Data you hold | What it powers | Keep collecting when | Stop collecting when |
|---|---|---|---|
| Declared preferences and stated intent | Preference-based messaging, lifecycle offers | Customers can see campaigns change based on what they told you | A field has sat untouched for two quarters and no campaign uses it |
| Behavioral data — purchases, site and email activity | Personalized recommendations, send timing, channel choice | The behavior maps to an experience the customer notices | It feeds only internal reporting no customer will ever feel |
| Inferred attributes — modeled interests, predicted churn risk | Next-best-offer selection, audience suppression | You can explain the inference in one sentence and correct it on request | You cannot show where the attribute came from when a customer challenges it |
| Legacy and backlog records | Historical trending, retention requirements | A written retention rule and a named owner exist | No one on the team can say why the data is still stored |
The failure mode is silent accumulation: data enters through a form, an integration, or a campaign, and no one is ever assigned to remove it. Attach a review date and a named owner to every field when it is created. When a field cannot pass the test above, delete it and mention the deletion in your next privacy communication. Customers read a shrinking footprint as evidence that you mean what your policy says.
Set rules for how AI uses customer data
In 2026 the trust question is no longer only what you collect. It is what your software does with the profile while no one is watching. Agentic personalization puts AI agents in the decisioning seat: they choose which offer, message, or recommendation each customer sees, drawing on first-party data those customers shared for reasons they were told at the time. When an agent acts outside the intent a customer expressed, the damage is worse than an over-collected field, because there is no human decision for the customer to appeal to.
The fix is a written boundary between your agents and your customers’ data. Define which fields an agent may read, which actions it may take without human sign-off, and which customers must never be targeted automatically. Treat suppression as an instruction the agent reads every time it runs, not a list a person reviews monthly. Keep a decision log, because the question every privacy-conscious customer eventually asks is simple: why did I receive this? If no one can answer it, the program is not ready to run autonomously.
Customers are running agents of their own. In agentic commerce, a customer’s assistant reads your policies, weighs your offers, and decides whether you are worth sharing data with — increasingly without a click through to your site. Zero-click search reached 74.6% in 2026 (First Page Sage, 2026), so the moments in which a customer deals with you directly are fewer and carry more weight. Treat each one as a referendum on your data promises: handle the customer’s data exactly the way your privacy notice said you would, at the moment the customer is watching most closely.
Measure trust like a funnel, not a feeling
Most teams assert that customers trust them and never instrument the claim. Trust leaves a data trail, and every signal in it is a number you already own. Watch these five:
- Consent and opt-in rate by channel — the share of customers who say yes when you ask permission. A falling rate means the ask has drifted away from the value you return.
- Preference-center completion — how many customers specify what they want, how often, and on which channels. Empty fields are abstentions, not missing data.
- Accuracy of declared data — the share of loyalty and survey records that survive a campaign without bouncing or contradicting observed behavior.
- Opt-down rate — customers who reduce frequency instead of leaving are telling you the exchange still works but the volume does not.
- Feedback and support volume — customers stop telling you things when they stop believing it changes anything.
The trap is measuring volume alone: rows captured, profiles enriched, records appended. A database that grows while consent and accuracy trend downward is not an asset — it is a liability with good marketing. Read the five signals together, quarterly, and let a worsening trend change what you collect, not just what you send.
FAQ
How do you rebuild customer trust after a data breach?
Rebuild trust after a data breach by disclosing fast, explaining in plain language what happened and what you changed, and treating recovery as a multi-year record of consistent behavior rather than a campaign. Notify affected customers directly instead of letting them learn about it from the news, pair the notice with the specific controls you added, and honor every preference they set afterward — the months following an incident are when customers test whether your promises hold.
Does collecting less data hurt personalization?
No — accurate, permissioned data personalizes better than a large set you cannot keep current. Personalization fails on stale and wrongly inferred attributes, not on missing ones. A profile built on declared preferences and recent behavior produces recommendations customers recognize as relevant, while a hoarded profile produces uncanny ones. Fewer fields make it easier to explain why a recommendation appeared, and the explanation turns personalization from a privacy risk into proof that sharing data was worth it.