![\"Customer](\"https:\/\/www.bloomreach.com\/wp-content\/uploads\/2026\/09\/Walk-in-Locations_person-in-store-1024x630.png\")
<\/figure>\n\n\n\nThat means the locations that generate most of the revenue are the ones where CRM contribution reads close to zero. The usual workaround is guest Wi-Fi sign-in, which almost nobody uses, followed by applying the day’s average transaction value to the email channel. That produces a number, but it\u2019s still just a very rough estimate.<\/p>\n\n\n\n
This is a solvable issue, though. The platforms that credit email for a walk-in visit \u2014 like Loomi<\/a> \u2014 all do the same thing: put the customer’s identity into the email as a scannable code and read it back at the register. The email carries a profile-unique barcode or QR code, and the in-store scan is written back to that customer’s profile as a redemption event. Let\u2019s learn more about how to put this into practice. <\/p>\n\n\n\n Offline attribution is the practice of crediting a digital campaign with an outcome that happens in a physical location. However, most platforms that claim to do offline attribution do it in a couple of fixed ways that don\u2019t address the unique challenges of walk-in businesses. <\/p>\n\n\n\n Both of these approaches take identity for granted, but in a walk-in venue, identity is often the missing piece.<\/p>\n\n\n\n You don\u2019t need a better model of who probably visited. You need the guest to hand over their identity at the register, voluntarily, in a way that takes them two seconds.<\/p>\n\n\n\n Any platform that can do this has to do three things:<\/p>\n\n\n\n Below is an example of our own implementation. Any platform serious about this will have its own version of the same three components, and the detail below is a fair checklist to hold them to.<\/p>\n\n\n\n In Loomi, we have an online-offline customer journey use case<\/a>, with two code variants so you can pick the one your registers can read. The barcode variant renders a Code 128B image in Jinja inside the email itself, with no external service in the loop. The QR variant is generated through api.qrserver.com and can carry tracking parameters rather than a coupon. Either way, the scan at the counter writes the redemption back to the customer profile, and the visit becomes a first-party event on a named person, in the same timeline as the open and the click.<\/p>\n\n\n\n Routing matters as much as the code. The use case assigns each customer a store two ways: nearest_store_id, calculated by sending addresses to the Google Maps Routes Matrix API and saving distance and drive time to the profile, or favorite_store_id, which the customer picks themselves through a weblayer. The second route needs no address data or API spend, and for a group whose guests are loyal to one local store it is usually the better one. From there, a distance threshold (25 miles by default) decides who gets the in-store code and who gets an online offer instead.<\/p>\n\n\n\n The common objection to code-based tracking is that it only works when you attach an offer, and no operator wants to discount every campaign for the sake of clean reporting.<\/p>\n\n\n\n Fortunately, the discount and the code are separable. The code is the identification mechanism; the offer is optional packaging around it. There are three ways to keep one without the other:<\/p>\n\n\n\n Identification only pays off if the credit carries over into reporting that nobody has to rebuild by hand every Monday.<\/p>\n\n\n\n Revenue attribution in Loomi<\/a> works on a last-touch model, where a touchpoint is a combination of three things: the channel, the status or action (delivered, opened, clicked, viewed), and the attribution window. That window is set in hours, days, weeks, or months per touchpoint, so a 24-hour default that never matches how people plan a Saturday night is a setting, not a constraint. Data filters exclude transactional sends from the calculation, and data cleaning strips bot clicks and Apple Mail Privacy Protection opens out of the dashboard figures.<\/p>\n\n\n\n The use case ships its own evaluation dashboard tracking store visit rate, offer redemption rate, cross-channel conversion rate, and email engagement as standing metrics. The redemption event is usable in the flow as well as the report: drop offer_redeemed in as an exit condition on a follow-up wait, and the reminder sequence stops the moment somebody actually walks in.<\/p>\n\n\n\n To implement this, you\u2019ll need scanning capability in the venue. A POS scanner, an iPad, or a staff mobile device all work, as long as your locations can read a code at the counter.<\/p>\n\n\n\n If you\u2019re scoping this on Loomi specifically, you\u2019ll want to look at these three capabilities: email marketing (with SMS as an optional add-on), web personalization, and the customer data engine<\/a> (with audience prediction as an optional add-on). You should also establish what your own contract covers before the business case goes anywhere, no matter which platform you\u2019re on.<\/p>\n\n\n\n To illustrate what connecting the online-offline experience looks like, let\u2019s turn to Popeyes UK<\/a>. For the fast food chain, roughly three-quarters of orders go through in-person kiosks, with no insights into the person ordering. Rather than putting a code in an email and reading it at the register, the brand put a QR code on the receipt, leading to a prize-wheel experience on its mobile site. This gave the guest a quick reason to identify themselves while they were standing in the restaurant that Popeyes could then attach to a profile.<\/p>\n\n\n\n The result was 20% of kiosk and counter orders matched to loyalty profiles<\/strong> (up from zero). Additionally, more than 300,000 rewards were issued, resulting in 8% higher revenue<\/strong> since launch. Guests who engaged with the campaigns were 3x more likely to return<\/strong>, measured on a 30-day window against a non-engaged group.<\/p>\n\n\n\n These numbers prove that identifying walk-in customers and tying them to marketing efforts can lead to real results. It\u2019s important to keep in mind, though, that some guests will never scan anything, and that\u2019s okay. Being able to attribute your campaign to even a fifth of your walk-in customers leads to a much larger audience for you to segment, suppress, or follow up on. That\u2019s where the real value lies. <\/p>\n\n\n\n When you\u2019re searching for the right platform to tie email attribution to walk-in locations, you\u2019ll want to answer these five questions: <\/p>\n\n\n\n Loomi answers all five. Other platforms answer some of them well via different routes. The modeling vendors will answer question two by telling you no scan is needed \u2014 while this is true, this is also how you end up with an aggregate that you can\u2019t actually act on. What matters is whether the combined answers give you a number attached to named people, or a number attached to nobody.<\/p>\n\n\n\n See how Loomi turns an in-store scan into a named event on the customer profile, then into store visit rate on a live dashboard. Request a demo<\/a> today.<\/p>\n\n\n Can you attribute email revenue to a store visit if the customer never books online?<\/p>\n Why Traditional Offline Attribution Doesn\u2019t Fit Walk-in Venues\u00a0<\/h2>\n\n\n\n
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The Fix Is Identification, Not Measurement<\/h2>\n\n\n\n
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![\"Customer](\"https:\/\/www.bloomreach.com\/wp-content\/uploads\/2026\/09\/Walk-in-Locations_in-person-code-1024x630.png\")
<\/figure>\n\n\n\nYou Don\u2019t Have To Discount To Get the Data<\/h2>\n\n\n\n
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What Shows Up in the Weekly Report<\/h2>\n\n\n\n
What It Takes To Run<\/h2>\n\n\n\n
What Success Looks Like<\/h2>\n\n\n\n
![\"How](\"https:\/\/www.bloomreach.com\/wp-content\/uploads\/2026\/09\/Popeyes-case-study-1024x630.png\")
<\/a><\/figure>\n\n\n\nHow To Find the Right Platform<\/h2>\n\n\n\n
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Frequently Asked Questions<\/h3>\n\n