Klaviyo + WiFi Guest Data: E-Commerce Retargeting

Key Takeaways: Klaviyo is the dominant email/SMS platform for e-commerce — 143,000+ brands, $30B+ in attributable revenue in 2025 (Klaviyo Q4 2025 Earnings). WiFi guest data pushes physical store visit behavior into Klaviyo profiles, enabling retargeting flows that combine online browsing with in-store engagement. Retailers using unified online/offline Klaviyo profiles see 42% higher email revenue per recipient than those with online-only data. Integration via Klaviyo's API (Identify + Track calls) or Zapier.

Klaviyo's competitive advantage is its customer data model. Every Klaviyo profile is a living record of everything that person has done — emails opened, products viewed, carts abandoned, purchases made, SMS received. The platform's predictive analytics layer runs on top of this data, forecasting next purchase date, expected lifetime value, and churn risk.

But the model has a blind spot: physical stores. Klaviyo sees the online world perfectly and the offline world not at all.

WiFi guest data fills that blind spot. When a customer connects to in-store WiFi, their email enters Klaviyo via API, carrying visit frequency, dwell time, and location data. Now Klaviyo's predictive engine has physical engagement signals alongside digital ones. A customer who browses online AND visits the store is a different (more valuable) profile than someone who only does one.

Integration Architecture

Klaviyo's API is built for this. Two key endpoints:

Identify (Profile Creation/Update)

POST https://a.klaviyo.com/api/identify

Sent when a WiFi guest completes the portal. Creates or updates the Klaviyo profile:

{
  "data": {
    "type": "profile",
    "attributes": {
      "email": "guest@example.com",
      "first_name": "Sarah",
      "last_name": "Chen",
      "phone_number": "+14155551234",
      "properties": {
        "wifi_location": "Downtown Flagship",
        "wifi_visit_count": 3,
        "wifi_first_visit": "2026-01-15",
        "wifi_last_visit": "2026-03-26",
        "wifi_avg_dwell_minutes": 28,
        "wifi_login_method": "email",
        "wifi_device": "iPhone",
        "source": "in-store-wifi"
      }
    }
  }
}

Track (Event Logging)

POST https://a.klaviyo.com/api/events

Sent on each WiFi event (connect, disconnect, milestone). Creates a timestamped event on the profile:

{
  "data": {
    "type": "event",
    "attributes": {
      "metric": { "data": { "type": "metric", "attributes": { "name": "Store Visit" } } },
      "profile": { "data": { "type": "profile", "attributes": { "email": "guest@example.com" } } },
      "properties": {
        "location": "Downtown Flagship",
        "visit_number": 3,
        "dwell_time_minutes": 42
      },
      "time": "2026-03-26T14:30:00Z"
    }
  }
}

Custom events appear in the profile timeline alongside Klaviyo's standard events (Placed Order, Added to Cart, Opened Email). The profile becomes a complete record of both online and offline behavior.

Setup Options

Option 1: Zapier (No Code)

• •Trigger: New WiFi Guest / Guest Update (MyWiFi webhook)
• •Action: Identify Profile (Klaviyo) + Track Event (Klaviyo)

Zapier handles the API formatting. Setup time: 20 minutes.

Option 2: Direct API (Best Performance)

• •Configure MyWiFi webhook to fire to a middleware endpoint
• •Middleware formats the payload for Klaviyo's API
• •Direct API calls with proper authentication

Better for high-volume deployments (1,000+ daily WiFi events). No Zapier task limits to worry about.

Option 3: Klaviyo's Segment Integration

If the client uses Shopify + Klaviyo together (very common), WiFi data synced to Shopify customer profiles automatically flows to Klaviyo via Klaviyo's native Shopify integration. This means: WiFi → Shopify → Klaviyo, with no separate Klaviyo integration needed.

Klaviyo Flows Powered by WiFi Data

Flow 1: In-Store Welcome Series

Trigger: "Store Visit" event where visit_number = 1 Timing: Start 2 hours after visit

Email 1 (2 hours): "Thanks for visiting [Store Name]!"
  → Online store link + new arrivals + welcome discount code
Wait 3 days
Email 2: "Our top picks based on what's popular in [Location]"
  → Best-sellers by store location
Wait 5 days
SMS: "Quick — your 10% welcome code expires tomorrow."
  → One-time use code with urgency

Expected performance: 35% open rate on Email 1, 15% click-through, 4% conversion to online purchase within 14 days.

Flow 2: Abandoned Store Visit

Trigger: "Store Visit" event + no "Placed Order" event within 48 hours Who: Customers who visited in-store but didn't buy (WiFi confirms the visit; absence of POS data confirms no purchase)

Email 1 (48 hours): "Still thinking about it?"
  → Browse our collection online + free shipping offer
Wait 5 days
Email 2: "Picked these for you"
  → Personalized product recommendations based on online browsing history (Klaviyo's recommendation engine)

This is the physical-store equivalent of abandoned cart recovery. Traditional abandoned cart emails (triggered by online cart abandonment) achieve 41% open rates and 9.5% click rates (Klaviyo 2025 Benchmark). Abandoned store visit emails perform at ~28% open and 3.5% click — lower but still highly profitable.

Flow 3: VIP Store Visitor Recognition

Trigger: "Store Visit" event where visit_number >= 10 Who: Loyal in-store customers who've visited 10+ times

Email: "You're one of our most valued visitors"
  → Exclusive offer (early access, members-only sale, personal styling session)
  → Survey: "What would make your experience even better?"

Flow 4: Lapsed Store Visitor Win-Back

Trigger: Profile property wifi_last_visit more than 30 days ago + wifi_visit_count >= 3 Who: Regular in-store customers who stopped coming

Email 1 (Day 0): "We haven't seen you in a while"
  → Incentive + what's new in-store
Wait 7 days
SMS: "Miss you! Here's 15% off your next in-store visit."
Wait 14 days
Email 2: "Last chance: your personal offer expires Friday"

Flow 5: Cross-Channel Re-Engagement

Trigger: "Store Visit" event for a profile with Placed Order online in the last 90 days Who: Online buyers who just visited the physical store

Email: "Great to see you at [Store]! Did you know about these?"
  → Products related to their last online purchase
  → "Buy online, pick up in-store" promotion

This flow uses Klaviyo's complete profile — combining online purchase history with the new in-store visit signal.

Predictive Analytics Enhancement

Klaviyo's predictive analytics engine calculates three key scores per profile:

1. •Predicted Next Order Date — when this customer is likely to buy again
2. •Predicted Customer Lifetime Value — expected total revenue from this customer
3. •Churn Risk — probability the customer won't buy again

Without WiFi data, these predictions are based solely on online purchase history. With WiFi data, the model gains additional signals:

• •Visit frequency correlates with purchase intent (someone visiting weekly is more likely to buy than someone visiting monthly)
• •Dwell time correlates with engagement (45-minute visits signal higher interest than 10-minute visits)
• •Visit recency indicates active vs. lapsed status

Retailers who feed WiFi data into Klaviyo report that predictive scores become 15–25% more accurate for customers who have both online and in-store data points. That accuracy improvement translates directly to better-timed campaigns and higher conversion rates.

Segmentation Strategies

Build these segments in Klaviyo using WiFi properties:

Segment: In-Store Only Customers

Definition: Has "Store Visit" event. Does NOT have "Placed Order" event. Size: Typically 60–70% of WiFi-captured profiles (many store visitors don't shop online) Strategy: Drive to online store. "Shop from home" campaigns. First-purchase discount.

Segment: Omnichannel Customers

Definition: Has BOTH "Store Visit" event AND "Placed Order" event. Size: 15–25% of WiFi profiles Strategy: Premium segment. Highest LTV. Cross-channel promotions. BOPIS (Buy Online Pick Up In Store). Exclusive access.

Segment: High Dwell / No Purchase

Definition: "Store Visit" event with dwell_time_minutes >= 30. No "Placed Order" within 7 days. Size: ~20–30% of store visitors Strategy: Consideration-stage content. Product comparisons. Style guides. "Need help deciding?" email.

Segment: Location-Based

Definition: "Store Visit" events filtered by location property Size: Per-location Strategy: Location-specific promotions, local events, inventory highlights per store.

Revenue Attribution

Klaviyo's attribution model assigns revenue to email and SMS campaigns. With WiFi data, extend attribution to include offline revenue:

Standard Klaviyo attribution: Email sent → customer clicks → customer buys online → revenue attributed to campaign.

WiFi-enhanced attribution: Email sent → customer visits store (WiFi confirms) → customer buys in-store (POS confirms) → revenue attributed to campaign.

The second flow requires POS integration (Shopify POS or manual order creation), but it doubles the attributable revenue for campaigns that drive in-store visits.

According to Klaviyo's 2025 E-commerce Benchmark Report, email campaigns that drive in-store visits generate 2.3x the total revenue compared to their online-only attribution — meaning campaigns are significantly under-counted when offline conversions aren't tracked.

Klaviyo Pricing Impact

Source: Klaviyo pricing page (March 2026).

WiFi capture adds 200–500 new profiles per month for a typical retail location. Within 6 months, the profile count grows by 1,200–3,000. Plan for tier upgrades. Factor Klaviyo's per-profile pricing into the client's total WiFi marketing cost.

Technical Best Practices

Deduplicate on ingest. Klaviyo identifies profiles by email. Sending an Identify call with an email that already exists updates the profile rather than creating a duplicate. This is the correct behavior — WiFi data enriches the existing profile.

Use custom metrics (not just properties). Properties are static (last visit date). Events/metrics are timestamped records (visited on 2026-03-26 at 2:30 PM). Use Track calls for each visit so Klaviyo's timeline shows the complete visit history.

Don't over-event. Sending a "Store Visit" event every time a guest's device auto-connects (Welcome Back) can create noise. Filter: only log events for sessions with dwell time > 10 minutes.

Tag WiFi profiles. Apply a Klaviyo tag (e.g., "wifi-captured") so you can always distinguish WiFi-sourced profiles from website-sourced profiles. This enables clean A/B comparisons of channel performance.

FAQ

Is Klaviyo better than Mailchimp for WiFi + retail?

For pure e-commerce retailers with Shopify, yes. Klaviyo's Shopify integration, product recommendation engine, and predictive analytics are purpose-built for retail. Mailchimp is better for non-retail businesses (restaurants, services, hospitality) where e-commerce features aren't needed.

How do I handle customers who use different emails online vs. in-store WiFi?

Klaviyo supports profile merging. If you capture a phone number on both channels, you can merge profiles with the same phone but different emails. The API also supports external_id as a secondary identifier — use a loyalty card number or POS customer ID if available.

Can I use Klaviyo's AI subject line generator with WiFi data?

Yes. Klaviyo's AI features work on any flow or campaign, including WiFi-triggered ones. The AI uses your historical send data to optimize subject lines, send times, and content — the more data (including WiFi engagement data), the better the optimization.

What's the minimum store traffic for this integration to be worthwhile?

If the store captures at least 100 WiFi emails per month, the integration is worth running. Below that, the segment sizes are too small for meaningful campaigns. Above 300/month, you have enough data for predictive analytics and Klaviyo's AI features to work well.

Can I create Klaviyo segments based on both online and WiFi behavior?

Yes. That's the entire point. Example segment: "Viewed product X online AND visited the store in the last 7 days." This cross-channel segment is 3–5x more likely to convert than either signal alone.

How does this compare to using Facebook retargeting from WiFi data?

They're complementary. Klaviyo handles owned channels (email + SMS). Facebook handles paid channels (ads). Use both: Klaviyo for direct engagement with known contacts, Facebook for reaching people at scale. The WiFi data feeds both platforms from a single capture event.