E-commerce
June 26, 2026
Zero-party data refers to information that a customer chooses to share with your brand: preference, size, budget, goal, constraint, preferred channel, purchase occasion. It is valuable because it is not guessed: the customer tells you what they need.
The problem is that many stores collect it poorly. They ask too many questions, too early, and then do not use the answers. The customer feels like they are filling out a form, not receiving better advice.
This article #15 approaches the subject from a practical angle, different from standard content on customer feedback: how an e-commerce chatbot can collect useful declarative data, activate it immediately, and then link it to personalization without losing trust.
Summary
What is zero-party data?
Zero-party data is data voluntarily shared by the customer. Shopify defines it as information explicitly shared with a brand: preferences, purchase intentions, personal characteristics, or how they want to be interacted with (Shopify, Customer Data 2025).
E-commerce examples
Fashion: size, preferred fit, style, occasion
Beauty: skin type, sensitivity, goal
Sports: level, terrain, frequency of use
Home: room, dimensions, budget, ambiance
Gifts: recipient, date, budget, relationship
It is not a post-purchase review or a simple observed click. It is a preference that the customer entrusts to you to obtain a more tailored experience.
Why is a chatbot a good channel?
The chatbot collects data at the moment when it makes sense. A customer asking "which cream should I choose?" understands why you are asking for their skin type. A customer looking for a gift understands why you are asking for the recipient and the budget.
The golden rule
A question must purchase immediate value: a better recommendation, a clearer comparison, a more reliable variant, or a more relevant follow-up.
Klaviyo cites quizzes, surveys, and conversational SMS among the best ways to collect this type of preference to then personalize journeys (Klaviyo, ecommerce personalisation).
Which data should be requested first?
Do not start with the data that your marketing team would like to have. Start with the data that actually changes the chatbot's response.
Need: for which use, which problem, which occasion
Constraint: size, compatibility, allergen, material, budget
Temporality: delivery date, season, urgency, event
Preference: style, color, channel, contact frequency
Level: beginner, intermediate, expert, occasional or intensive use
If the answer changes neither the recommendation nor the future segmentation, do not ask for it yet.
Where should the question be asked in the user journey?
The right placement depends on the intent.
On a category page: ask about usage or budget to narrow down the choices
On a product page: ask about size, compatibility, or constraints
After a search with no clicks: ask what the customer was actually trying to find
In the cart: check for an objection before abandonment
After purchase: enrich the profile for the next recommendation, sparingly
Example: after a search for "Mother's Day gift", the chatbot can ask "For what budget and style?" rather than pushing a generic product grid.
How can you phrase this without creating friction?
Prefer quick choices
Buttons, sliders, yes/no, multiple choices. On mobile, a long free-text field quickly becomes tiring.
Explain the benefit
"To help you avoid incompatible models, which phone do you use?" is better than "indicate your device".
Let them pass
The customer must be able to skip the question. Forced data quickly becomes a source of friction.
Reuse immediately
If the customer answers "sensitive skin", the following recommendation must show it clearly: "I am leaving out the scented formulas and keeping two gentle options."
How can data be stored properly?
Useful collection requires proper naming. Avoid improvised fields in each tool.
Field: skin_type, preferred_size, gift_budget, usage_goal
Values: closed lists whenever possible
Source: chatbot, quiz, customer account, post-purchase
Date: useful because a preference can change
Scope: session, customer profile, marketing segment
Shopify emphasizes the importance of unifying customer data and preferences into an actionable model across e-commerce, marketing, and other channels (Shopify, zero-party vs first-party data).
Mini Data Map
For each field, write down in black and white: question asked, possible values, storage location, validity duration, session usage, marketing usage, internal owner. This map prevents forgotten preferences in a tool that no one consults.
How do I activate it right away?
Zero-party data becomes interesting when it is used within the minute.
Recommendation: filter by budget, size, or usage
Comparison: explain why A rather than B
Content: highlight the relevant material, delivery, or warranty
Cart: suggest a truly compatible add-on
Follow-up: adapt the email according to the declared intention
Klaviyo points out that profiles, preferences, and browsing data can power real-time personalization in conversational agents and marketing flows (Klaviyo AI Shopping Assistant).
Activation Example
A customer states "baby shower gift, budget €50, delivery before Friday". The chatbot recommends three available gift sets. If the customer accepts the opt-in, the CRM can then segment them as a baby shower gift buyer, not as a parent, which avoids awkward personalization.
Which script examples should be used?
Beauty
“To avoid a recommendation that is too general, what is your skin type: dry, combination, oily, or sensitive?”
Fashion
“Are you looking for a slim, straight, or comfortable fit? I will suggest the lowest-risk sizes.”
Sport
“Do you mainly practice in the city, on paths, or in the mountains? I filter the models according to actual use.”
Gift
“Who is the structures gift for and what is the budget range? I will offer you a maximum of three ideas.”
The tone must remain helpful, not intrusive. The conversation is not an interrogation.
Which consent rules should be applied?
Voluntary data does not exempt from transparency. The key question: what do you do with the response after the conversation?
Session: use the response to recommend immediately
Profile: save the preference in the account or CRM
Marketing: re-use for email, SMS, or targeted audience
Deletion: allow the customer to correct or remove the data
Minimization: do not ask for what you do not use
Simple formula: "I can save this preference for your next recommendations. You can modify it at any time."
What not to do
Do not turn a response given for an immediate recommendation into permanent marketing targeting without explaining it. That is the best way to break trust, whereas zero-party data relies precisely on clear exchange.
Which KPIs should be monitored?
Do not just measure the number of fields completed. Measure the value created.
Response rate: share of customers who agree to share
Skip rate: ignored or abandoned questions
Recommendation click: product suggested then opened
Assisted cart addition: action after declared preference
Assisted conversion: order after conversation
Average cart: personalized vs non-personalized sessions
Repurchase: enriched vs classic segments
Go deeper with e-commerce chatbot KPIs and e-commerce personalization.
How does Qstomy use it in practice?
Qstomy can ask one or two contextual questions within the conversation, use the answers to make recommendations, and then turn these preferences into useful signals for your marketing and merchandising.
Beauty DTC Scenario
Shopify store, 280 SKUs. Qstomy asks for skin type and goal only on skincare pages. Out of 1,200 monthly conversations, 54% answer at least one question, 310 recommendations are clicked, 96 cart additions are assisted, and the "sensitive skin" and "anti-imperfection" segments then feed into two dedicated email flows.
The goal is not to collect more data. It is to advise better, and then properly reuse the preferences that have already proven their utility.
The same logic applies for: preferred size and fit on pants pages, followed by recommendations of available variants. In sports: level and terrain, followed by a selection of suitable models and targeted post-purchase maintenance content.
See AI sales assistant, Qstomy data & analytics, Shopify integration and request a demo.
Which playbooks should be launched this week?
Playbook 1: one question, one recommendation
Choose a category. Add a single question that genuinely changes the product choice. Measure clicks and cart additions.
Playbook 2: gift preferences
On gift pages, ask for the recipient, budget, and date. Suggest a maximum of three products to avoid the catalogue effect.
Playbook 3: reusable profile
If the customer agrees, save their size, skin type, or goal. Reuse only in coherent messaging.
Playbook 4: monthly cleanup
Delete rarely answered questions, merge similar values, and verify that each field fuels at least one real use case. Also, keep an example of a successful conversation to train the team.
Useful Links
Discovery: discovery chatbot vs internal search
Product questions: automated product FAQ
Shopify data: training a chatbot with Shopify
Recommendation: AI product recommendations
Enzo
June 26, 2026
