# Segments Segments group your guests into meaningful categories so you can understand your customer base at a glance and target the right audience with your marketing. You'll find them in Merchant Admin under **Understand > Segments**. The Segments page has two tabs: **Lifecycle Map** and **Saved Segments**. *** ## Lifecycle Map The Lifecycle Map is a visual matrix that plots your entire customer base across two dimensions: **how recently they transacted** (x-axis, from old to recent) and **how many transactions they've made** (y-axis, from low to high). Each cell represents a lifecycle stage, with the percentage and count of your guests who fall into it: **Best Customers** — High transaction frequency, recent activity. These are your most valuable regulars. They visit often and recently. **Promising** — Moderate frequency, recent activity. These guests are on their way to becoming your best customers — worth nurturing with the right engagement. **New Customers** — Low frequency, recent activity. They've just started visiting. The goal is to turn their first visit into a second one. **At Risk** — High or moderate frequency, but activity is getting older. These were good customers who are starting to slip away. A timely nudge or offer can bring them back before they lapse entirely. **Lapsed** — Previously active, but haven't transacted in a long time. Re-engagement campaigns with a compelling reason to return are the key here. **Window Shoppers** — Low frequency, old activity. These guests may have interacted once or signed up but never became regulars. You can use the **All Customers** dropdown to filter the Lifecycle Map by a specific saved segment, so you can see the lifecycle breakdown within a particular group. > **Tip:** The Lifecycle Map is a great starting point for understanding where to focus your marketing efforts. If your "Lapsed" segment is large, that's a clear signal to invest in re-engagement automations and campaigns. *** ## Saved Segments The Saved Segments tab shows all your segments in one list — both the pre-built ones from Oddle and any custom segments you've created. ### Pre-Built Lifecycle Segments These are created automatically based on your transaction data. They correspond to the stages in the Lifecycle Map: | Segment | What It Means | | --------------- | -------------------------------------------------------- | | All Customers | Your complete customer database | | Best Customers | High frequency, recent transactions — your top regulars | | Promising | Moderate frequency, recent — on track to become regulars | | New Customers | Recently acquired, low transaction count | | At Risk | Previously active, starting to slip | | Lapsed | Haven't transacted in a long time | | Window Shoppers | Low engagement, old or minimal activity | ### Pre-Built Reservation Segments These are specific to your reservation data: | Segment | What It Means | | ------------------ | ----------------------------------------- | | First Time Visitor | Guests who have booked for the first time | | Repeat Visitor | Guests who have booked more than once | | Regular Customers | Frequent reservation guests | | Reservation Lapsed | Previously booked but haven't returned | ### Pre-Built Proximity Segments These are automatically created based on the physical distance between your guests and your stores: | Segment | What It Means | | --------------------------- | ----------------------------------------------------------------------------------------------------------------- | | Customers Near {Store Name} | Guests who live or work within 3 km of that store — based on their address or what they told you during enrolment | One segment is created per store. These combine two signals: address-based distance (calculated from the guest's address) and self-reported proximity from Enrolment insight questions (guests who said they live or work nearby). Proximity segments are especially useful for location-aware marketing: * **Weekday lunch specials** to guests who work nearby — build a corporate weekday segment and send them your Monday-to-Friday offers * **Monthly limited-time offers** to guests who live nearby — a "this weekend only" or seasonal menu offer sent to local guests converts far better than a broadcast to your entire database * **Happy hour and after-work promotions** to the work-nearby segment — time your sends for late afternoon when they're deciding where to go > **Tip:** Proximity data gets richer as more guests enrol through your check-in programme. The insight questions ask guests whether they live or work nearby, which directly feeds these segments. See **Oddle Enrolments** for details. ### Custom Segments Below the pre-built segments, you'll see any segments you've created yourself by saving filters from the **Customers** page. These are dynamic — they update automatically as guests match or stop matching your filter criteria. Custom segments are useful for targeting specific audiences that matter to your business. For example, you might create segments like "guests within 5 km who haven't ordered in 90 days" or "guests who've only dined in but never ordered online." *** ## Using Segments in Marketing Segments connect directly to your marketing tools: **Broadcasts** — When creating an email campaign, you choose which segment to send it to. You can target a pre-built segment like "Lapsed" or a custom one you've created. **Automations** — Always-on flows like lapsed guest nudges work based on the same underlying customer data and lifecycle stages. **Redeemables** — Distribute vouchers and rewards to specific segments for more targeted promotions. The more complete your customer data, the more accurate and useful your segments become. Every order, reservation, payment, and enrolment enriches the profiles that power your segments. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://www.oddle.me/docs/known-guests/customer-intelligence/segments.md?ask= ``` The question should be specific, self-contained, and written in natural language. 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