> For the complete documentation index, see [llms.txt](https://docs.zigpoll.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zigpoll.com/guides/how-to-build-your-ideal-customer-profile.md). # How to Build Your Ideal Customer Profile Most brands have a customer persona. It was written once, it lives in a question deck, and it was largely invented. Meanwhile the real pattern in who buys — and who buys *the most* — is sitting unexamined in the order data. A profiling survey replaces the invented persona with a measured one. The goal isn't a demographic portrait for its own sake; it's to find the segment worth spending your acquisition budget on. *** ## Building the survey Keep this short. Profiling questions have no immediate payoff for the respondent, so every extra question costs you completions. Four questions, post-purchase, is the sweet spot. ### Question 1 — Who is this for? Add a **Binary Choice** or **Single Choice** question: **"Who are you buying this for?"** * Myself * A gift for someone else This single question routinely reshapes a brand's entire marketing. Gift buying runs far higher than most stores assume, and a gift buyer is a completely different person from the end user — different motivations, different price sensitivity, different creative. One brand found 30% of purchasers were women buying gifts for partners and rebuilt their ad creative around it. ### Question 2 — The job to be done Add a **Single Choice** question: **"What are you mainly going to use this for?"** Not "what do you do for a living" — **what are you hiring this product to accomplish?** The use case predicts behavior far better than demographics do. Two customers with identical age, income, and geography who bought for different reasons will churn differently, spend differently, and respond to different ads. ### Question 3 — The segment marker Add a **Single Choice** question for whichever attribute genuinely distinguishes your customers. For B2B that's usually role or company size; for consumer brands it might be experience level ("first time trying this category" vs. "I've used these for years"). Pick the one attribute that would change how you'd sell to someone. Skip the rest — age and gender are usually available from your ad platforms anyway, and they explain less than people expect. ### Question 4 — Their words Add a **Long Answer** question: **"What made you choose us over the alternatives?"** This is your positioning, dictated by customers. The phrasing that appears repeatedly here belongs on your homepage, verbatim. {% hint style="info" %} Add an **Email Capture** question if you want to connect responses to individual customers in your CRM. On Shopify, you can skip it — pass `shopify_customer_id` as [participant metadata](/embed/participant-metadata.md) and the response is linked automatically. {% endhint %} *** ## Where to run it * **Post-purchase** — The best placement by far. The customer just bought, they're engaged, and every response is attached to a real order with a real value. See [Order Paid Survey](/tutorials/order-paid-survey.md). * **Email to your existing customer list** — Profiles your whole base, not just recent buyers. On Shopify you can [target a specific customer segment](/tutorials/email-shopify-customer-segment.md). * **Layered onto an existing survey** — If you're already running attribution post-purchase, add one profiling question to it rather than launching a second survey. See [Layered Post Purchase Surveys](/tutorials/layered-post-purchase-surveys.md). *** ## Reading the results — the part that matters Everything above is setup. This is where the ICP actually emerges. **Do not read the results as an average.** The average customer is a statistical fiction, and marketing to them is how you end up appealing to nobody. Instead, cut the data by value. Zigpoll automatically correlates order values with each response, so for every segment you can compare: * **Average order value** — Which use case spends the most per order? * **Purchase frequency** — Which segment comes back? * **Acquisition channel** — Where did the high-value segment come from? Cross-reference with your [attribution survey](/tutorials/attribution-surveys.md). Your ICP is the intersection: **the segment with the highest order value that also comes back, and that you can reliably reach.** All three conditions matter. A high-value segment you can't target is trivia, not a strategy. Ask Z-GPT Chat in the **Insights** tab to break responses down by order value, or export from the **Table** tab and pivot it in a spreadsheet. *** ## Turning the profile into action * **Retarget the lookalike.** Feed the high-value segment's traits into your ad platform's audience builder. * **Tag customers in Shopify.** Use the **Add Customer Tag** and **Add Customer Metafield** [Question Logic](/questions/question-logic.md) actions to write the segment onto the customer record, then build Shopify segments and Klaviyo flows on top of it. * **Segment your email.** Push the profile to [Klaviyo](/integrations/klaviyo.md) and stop sending gift buyers the same emails you send to daily users. See [Klaviyo Segmentation](/tutorials/klaviyo-segmentation.md). * **Rewrite the site copy** in the language from Question 4. {% hint style="warning" %} Re-run this yearly. ICPs drift — a brand that started with enthusiasts and grew into the mainstream is now serving a different customer than the one its marketing still addresses, and it's usually the last to notice. {% endhint %} *** ## Related {% content-ref url="/pages/VPrdaap3vre6ReWUX9da" %} [Attribution Surveys](/tutorials/attribution-surveys.md) {% endcontent-ref %} {% content-ref url="/pages/uZzmwXRquTBAxLPBAgeA" %} [How to Test Your Messaging and Positioning](/guides/how-to-test-your-messaging-and-positioning.md) {% endcontent-ref %} {% content-ref url="/pages/Cpctg3qiKtbrlvPfaYLD" %} [How to Measure Product-Market Fit](/guides/how-to-measure-product-market-fit.md) {% endcontent-ref %} --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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