| Average Daily First-time Visitors |
Average daily first-time visitors is the mean number of new guests a restaurant attracts per day — guests with no prior recorded visit. Tracked through WiFi-based identity resolution, it measures acquisition velocity and, paired with returning-visitor data, shows whether a restaurant is genuinely growing its guest base or simply recycling the same regulars. |
| Average Daily Traffic |
Average daily traffic is the mean number of guests visiting a restaurant per day over a defined period. Captured passively through WiFi rather than estimated from covers, it reveals visit volume trends, peak days, and the split between new and returning guests — a foot-traffic baseline that makes changes in guest behavior visible. |
| Benchmark |
A benchmark is a reference point used to measure performance against a standard. For restaurants, benchmarking compares metrics like retention, review rating, and guest frequency against peer locations or the broader market — turning an isolated number into context. A platform spanning hundreds of restaurant brands can benchmark a location against its true peer set, not a generic average. |
| Best Practices |
Best practices are the proven methods that reliably produce strong results in a given field. In restaurant guest marketing, they include capturing first-party guest data from every touchpoint, segmenting by behavior, automating timely win-back of at-risk guests, responding to every review in brand voice, and attributing campaigns to real revenue — the repeatable habits that compound guest value over time. |
| CDP-Overlaid Feedback |
CDP-Overlaid Feedback is survey and review data segmented by guest value tier rather than reported as a flat average. A single NPS of +42 becomes Super Guests +72, Regulars +56, New Guests +38, and At-Risk −14 — revealing which guests are unhappy and attaching a one-click recovery action. A standalone survey tool cannot produce this because it has no customer data platform underneath. |
| Customer Churn Rate |
Guest churn rate is the percentage of a restaurant's guests who stop visiting over a given period. Because most restaurants never capture guest identity, churn usually happens invisibly — a once-loyal regular simply fades away unnoticed. A customer data platform makes churn measurable by tracking visit frequency, so declining guests can be detected and recovered before they are gone for good. |
| Customer Intelligence (CI) |
Customer intelligence (CI) is the process of gathering and analyzing guest data to drive better marketing and operational decisions. In restaurants, it means unifying behavioral, transactional, and sentiment data into living guest profiles — so a restaurant understands not just what was ordered, but who each guest is, how their loyalty is trending, and what action will keep them coming back. |
| Customer Lifetime |
Customer lifetime is the total length of the relationship between a guest and a restaurant, from first visit to last. The longer the lifetime, the more value a guest delivers — which is why extending it through retention and timely win-back of fading regulars is one of the highest-return moves a restaurant can make. It is the time dimension behind guest lifetime value. |
| Customer Lifetime Value |
Guest lifetime value (often called customer lifetime value or CLV) is the total revenue a restaurant can expect from a single guest across the entire relationship — every visit, order, and referral. It reframes a guest from the price of one meal to the worth of a multi-year relationship, which is why recovering an at-risk regular is far more valuable than acquiring a one-time diner. |
| Data Authority |
Data Authority is the discovery advantage a restaurant earns when its online presence is backed by verified first-party data — real review velocity, accurate menu and transaction signals, and monitored AI citations — rather than unverifiable marketing claims. AI engines and search algorithms preferentially recommend entities they can corroborate, so the restaurant with genuine data authority gets surfaced over competitors with thin marketing copy. |
| Demographics |
Demographics are the statistical traits of a guest population — age, income, household, location. Restaurants use demographics to understand who their guests are, but behavioral data (how often they visit, what they order, when they slow down) is far more predictive of revenue. The strongest guest profiles combine both: who a guest is and how they actually behave. |
| Discovery Flywheel |
The Discovery Flywheel is the self-reinforcing cycle where verified guest data from a restaurant's CDP — real transactions, visits, and sentiment — powers website content that AI engines trust, which surfaces the restaurant to new guests in AI search, traditional search, and voice. Those new guests enter the CDP, strengthening the data, which improves discovery further. Each turn compounds. |
| Dwell Time |
Dwell time is the length of time a guest spends inside a restaurant during a visit, measured passively through WiFi session data. Tracked across visits, dwell time reveals seating and service pace, daypart patterns, and early signals of a declining experience — behavioral intelligence a POS transaction alone cannot capture. |
| Guest Intelligence Flywheel |
The Guest Intelligence Flywheel is the self-improving cycle at the core of a restaurant data platform: unified guest data trains smarter segmentation, smarter segmentation drives better-targeted marketing and reputation actions, and the results of those actions feed back as new data. Every guest interaction makes the next decision sharper, so the platform compounds in value the longer it runs. |
| Key Performance Indicator |
A key performance indicator (KPI) is a measurable value that shows how effectively a goal is being met. Core restaurant guest KPIs include visit frequency, guest lifetime value, at-risk guest count, average ticket, retention rate, and review sentiment — the metrics that reveal whether a guest base is growing healthier or quietly eroding. |
| Marketing Analytics |
Marketing analytics is the measurement of marketing performance to understand what drives results. In restaurants, true marketing analytics goes beyond opens and clicks to closed-loop attribution — connecting a campaign to the guest who received it, the visit it produced, and the revenue rung up at the POS, so every marketing dollar is tied to an actual outcome. |
| Marketing Automation |
Marketing automation is the use of software to trigger and deliver marketing messages based on behavior rather than manual effort. For restaurants, it means email and SMS campaigns that fire automatically from guest behavior — a welcome series for new guests, a win-back offer when a regular's visits slow — with results attributed back to actual visits and transactions at the POS. |
| Predictive Analytics |
Predictive analytics is the practice of analyzing historical data to forecast future events. In a restaurant, it turns guest history — visit frequency, spend, recency, and sentiment — into forward-looking predictions: who is likely to return, who is at risk of churning, and what each guest is worth over time, so operators can act before revenue is lost rather than after. |
| Predictive Guest Scoring |
Predictive guest scoring is the use of AI and historical guest data — visit frequency, spend, recency, and sentiment — to forecast each guest's likelihood to return, risk of churning, and future lifetime value. Restaurants use these scores to act on the right guests before revenue is lost, not after. Bloom Intelligence assigns them automatically across every guest profile. |
| Psychographics |
Psychographics describe the attitudes, values, interests, and lifestyle of a guest — the why behind their choices, as opposed to demographics' who. For restaurants, psychographic signals (a preference for healthy dishes, a taste for special-occasion dining) sharpen segmentation and messaging, letting campaigns speak to motivation rather than just age or zip code. |
| Restaurant Customer Data Platform (CDP) |
A restaurant customer data platform (CDP) unifies guest data from WiFi, POS, online ordering, reservations, reviews, and surveys into a single identity-resolved profile for each guest. Unlike a CRM built on manual entry, a restaurant CDP updates continuously from real behavior and segments guests automatically — turning scattered data into marketing, reputation, and operational action. |
| Retail Analytics |
Retail analytics is the analysis of customer, sales, and traffic data to improve retail performance. Applied to restaurants and food retail, it spans foot-traffic patterns, basket and ticket analysis, and guest behavior — turning point-of-sale and visit data into decisions about staffing, menu, and marketing that protect margin in a thin-margin business. |
| Revenue Flywheel |
The Revenue Flywheel is Bloom Intelligence's organizing architecture — four reinforcing loops (Marketing, Sentiment, Operations, and Discovery) running on one unified customer data platform, where every guest interaction makes every other loop smarter. Unlike a marketing funnel, which is linear and ends at conversion, a flywheel compounds: the longer it runs, the more guest intelligence accumulates and the wider the advantage grows. |
| RevPASH (Revenue per Available Seat Hour) |
RevPASH (revenue per available seat hour) measures how much revenue a restaurant generates for each seat during each hour it is open — total revenue divided by available seat hours. It exposes the true efficiency of a dining room by combining table turnover with spend, helping operators spot underperforming dayparts that raw sales totals hide. |
| Segmentation |
Segmentation is the practice of dividing guests into groups so marketing can target each with relevance. In restaurants, the most actionable segmentation is behavioral — grouping guests by visit recency, frequency, and value into dynamic audiences like super guests, regulars, new guests, cooling-off guests, and at-risk guests — so the right message reaches the right guest automatically as their behavior changes. |
| The Broken Loop |
The Broken Loop is the default state of most restaurants: guest visits generate data — who came, what they ordered, how they felt — that never feeds back into how the next guest discovers the restaurant. The data dies in a silo instead of compounding. The Discovery Flywheel is the repair, turning every visit into fuel for the next guest's discovery. |
| Triple Crown Framework |
The Triple Crown Framework is the practice of optimizing a restaurant's online presence simultaneously for the three entities that now decide discovery: traditional search engines (SEO), AI answer engines like ChatGPT and Perplexity (AEO), and voice assistants. Each uses a different algorithm to recommend restaurants, so optimizing for only one forfeits the others. Bloom Intelligence optimizes for all three at once. |
| True Attribution |
True Attribution is closed-loop measurement that connects a marketing campaign to the guest who received it, the return visit it produced, and the revenue rung up at the POS — verified, not modeled. Most restaurant marketing tools stop at opens and clicks; true attribution follows the dollar all the way to the table, which is how Bloom Intelligence ties campaigns to real recovered revenue. |
| Voice of the Guest |
Voice of the Guest is the actual language guests use across reviews and surveys — the words they choose to praise a dish, describe service, or express frustration. Bloom Intelligence's AI continuously extracts this language and layers it with each brand's configured voice and rules, so AI-generated review responses and campaigns sound authentically like the restaurant rather than a generic template. |
