Dating industry glossary.

Plain-English definitions of the terms operators use every day. Linked from across the site.

3

3-D Secure: An authentication step that asks the cardholder to verify a payment with their bank, shifting some fraud liability away from the merchant. Used well it cuts disputes, but heavy-handed prompts can add friction that lowers conversion, so it is tuned, not switched on blindly.

A

A la carte: Selling discrete items such as boosts, super likes, or message packs instead of, or alongside, a subscription. Also called the credits or coins model, it converts intent into revenue in the moment and is far less exposed to monthly cancellation, but it is lumpier and depends on frequent high-intent sessions.
Age assurance: The set of methods used to confirm a user is old enough to use the service, ranging from document checks to facial age estimation. Regulators in the UK and EU now require highly effective age assurance for dating apps, and self-declaration alone no longer counts.
ARPDAU: Average revenue per daily active user, a single day's revenue divided by that day's active users. It is the metric live-and credit-based dating products watch most closely, because it captures both how many people show up and how much they spend per visit.
ARPPU: Average revenue per paying user. This is the number that actually drives dating unit economics, because the business runs on the small slice of users who subscribe or buy boosts.
ARPU: Average revenue per user, calculated across every user including the unpaid majority. In dating, ARPU is small because most users never pay, so it is most useful for company-level forecasting, not for unit economics.
Attribution: The practice of assigning installs or paying users to the channels that drove them. Honest attribution is essential to real CAC, because dropped or misassigned attribution is one of the most common ways CAC gets understated.

B

Billing descriptor: The text a customer sees on their card statement for your charge. A clear, recognizable descriptor is one of the cheapest ways to cut disputes, because many chargebacks start with a customer not recognizing the line item.
Blended CAC: The average cost to acquire a paying user across all channels, paid and organic combined. It is convenient but dangerous in dating, because it hides which side of the marketplace and which geography you actually bought.
Boost: A paid product that raises a user's visibility in others' queues for a set window. Boosts monetize the desire to be seen and are a staple of the a la carte model, but they only feel worth it when there is enough liquidity to convert the extra views.
Buy-side: Advisory or principal work that represents the acquirer, sourcing and evaluating targets. In a market where the same party may both invest and advise, the buy-side and sell-side distinction is a disclosure issue founders should insist on.

C

CAC: Customer acquisition cost. The blended cost to acquire one paying subscriber, including paid media, attribution loss, and any incentives. In dating, CAC is especially sensitive to male-female balance and geography.
CAC Payback: The number of months it takes for a paying user to return their CAC in gross revenue. Operators in dating target under three months because cash recovery speed determines how fast they can scale spend.
Catfishing: Using a fake or stolen identity to misrepresent who you are to other users. It erodes trust and reply rates, and fighting it with verification and detection is part of keeping a marketplace worth showing up to.
Chargeback: A forced reversal of a card payment initiated by the cardholder's bank rather than the merchant. In dating, chargebacks are both a direct loss and a compliance risk, because card networks treat the category as high risk and watch dispute levels closely.
Churn: The percentage of paying users who cancel in a given period, usually a month. Dating apps run hot on churn because the product works against itself: a successful match removes the reason to subscribe.
Cohort analysis: Grouping users by the period they joined and following each group's retention and revenue over time. Cohorts reveal whether a pricing or product change actually held, something a blended company-wide average will hide for months.
Cold start: The chicken-and-egg problem every new dating product faces: without users there are no matches, and without matches there is no reason to stay, so there are no users. Solving it means launching with enough of the right liquidity that early users find real matches on day one, not an empty app.
Contribution margin: What one more paying user contributes after the variable costs tied to serving and acquiring them. It is the honest test of whether growth funds itself, because it strips out fixed overhead and shows the real per-user profit you can reinvest.
Conversation rate: The share of matches that become a back-and-forth exchange rather than a dead end. It is a better health signal than raw match counts, because conversations, not matches, are what lead to dates and to retention.

D

DAU: Daily active users, the count of unique people who use the app in a day. It is the heartbeat metric for engagement and the denominator behind ARPDAU.
Deep link: A link that opens a specific place inside an app rather than its home screen, used in marketing, referrals, and re-engagement. Good deep linking reduces drop-off between an ad or invite and the in-app moment it promises.
Digital Services Act: The EU framework setting platform-safety and transparency obligations, alongside the bloc's rollout of privacy-preserving age verification. Dating apps serving EU users fall within its expectations around safety and effective, data-minimizing age checks.
Dispute rate: The share of card transactions that customers dispute, usually measured monthly. Sustained rates above roughly one percent can put a merchant account at risk. Well-run dating books keep this low through clear billing descriptors, honest paywalls, fast cancellation, and fraud screening.
Double opt-in: The rule that two users must both express interest before they can message, popularized by swipe apps. It reduces unwanted contact and raises reply rates, but it also caps liquidity, so the matching algorithm has to work harder to create overlap.
Drop-off: The point in a funnel, such as onboarding or the paywall, where users abandon. Mapping drop-off shows where to fix the experience, and in dating the most damaging drop-off is a new user leaving before a first quality match.

E

Earnout: A deal structure where part of the price is paid later, contingent on the business hitting agreed targets. Earnouts bridge a gap on valuation but tie founders to outcomes after the sale, so the targets and the definitions behind them deserve close attention.
EBITDA multiple: A valuation expressed as a multiple of earnings before interest, taxes, depreciation, and amortization, used for profitable, cash-generative businesses such as mature matchmaking or established apps. It rewards real margin, which puts the focus back on net unit economics.
eCPM: Effective cost per thousand impressions, used to compare the price of advertising inventory across channels and to value ad revenue if you run ads. In acquisition it is a building block of CAC, because rising eCPMs raise the cost of every install.
eIDAS: The EU framework for digital identity, including the move toward digital identity wallets, which is increasingly relevant to privacy-preserving age verification. It points toward verifying a fact like age without a platform hoarding identity documents.

F

Fake profile: An account that does not represent a real, available person, whether a bot, a scammer, or a duplicate. Fake profiles destroy liquidity quality and reply rates, so detection and removal are central to both safety and unit economics.
Financial buyer: An acquirer, typically private equity, that buys to generate a financial return through growth and eventual resale. Financial buyers price on cash flow and multiples, so clean, honest unit economics matter more to them than narrative.
First-date rate: The share of users or matches that result in an actual in-person meeting. It is the truest measure of whether a dating product delivers its core promise, and the metric intentional and matchmaking products optimize for over time on app.
Free-to-paid conversion: The share of free users or installs that ever become paying customers. In dating this is commonly low single digits, so small improvements in conversion move revenue as much as large increases in installs.
Freemium: A model where the core product is free and revenue comes from upgrades that unlock features or visibility. It maximizes the top of the funnel but puts all the weight on conversion and on a paywall that gates the right moment.
Friendly fraud: A dispute filed by a genuine customer who did make the purchase, often to hide a subscription or avoid an awkward conversation. It is common in dating and is fought with clear descriptors, easy cancellation, and good transaction records for representment.

G

Ghosting: Ending contact abruptly without explanation, a common frustration in low-intent dating. It matters commercially because the fatigue it creates is part of the demand for intentional products that respect the user's goal.
Gross margin: Revenue left after the direct costs of delivering the service, such as app store commissions, payment processing, and hosting. In dating, store and processing costs make gross margin meaningfully lower than founders expect, which is why net unit economics matter more than headline revenue.

H

Hard paywall: A gate that blocks core functionality until the user pays, common in some serious-dating and niche products. It lifts ARPPU and filters for intent, but it shrinks the top of the funnel and demands real perceived value up front.
High-risk merchant account: A payment account classified as elevated risk by processors, which dating usually is, carrying higher fees and stricter dispute thresholds. Losing it can halt revenue overnight, so protecting the account is an operational priority, not a back-office detail.

I

Incognito mode: A paid feature that lets a user browse without appearing to others until they choose to engage. It monetizes privacy and control, and is common in serious-dating products where discretion matters.
Intentional dating: A shift toward serious, values-led dating focused on helping people meet people rather than maximizing time in the app. It is the thesis that a product which gets users off the app faster, but happier, can win on trust, word of mouth, and durable monetization.

K

KYC: Know your customer, the practice of verifying who a user is. In dating it supports fraud prevention, age checks, and trust, and it increasingly overlaps with the age-assurance rules now required in several markets.

L

Liquidity: The presence of enough active, relevant people that a user can reliably find matches and conversations. In dating, liquidity is local and segmented by who each user wants to meet, so a healthy total user count can still mean an empty experience in a given city or age band.
Liveness check: A verification step that confirms a real, present person is behind an account, often via a selfie or short video prompt. It deters bots and stolen-identity fraud and is a building block of both verification badges and age assurance.
Lookalike audience: An advertising audience built to resemble your existing high-value users. Useful for scaling acquisition, but in dating it must be read through the marketplace lens, since efficiently acquiring the abundant side can worsen balance.
LTV: Customer lifetime value. The total gross revenue you expect from a paying user before they cancel. In dating, LTV is dominated by monthly churn and is often shorter than founders assume.
LTV to CAC ratio: Lifetime value divided by customer acquisition cost, the multiple of value created for every dollar spent acquiring a paying user. In paid dating the working floor is around three to one net, after refunds, fees, and disputes. Far above five to one usually means you are underspending on growth.

M

Marketplace balance: The ratio between the sides of the market a product serves, most often men and women in a given area and age band. Balance drives match rates and retention, and acquiring the scarce side cheaply is usually worth more than another wave of the abundant side.
Match rate: The share of likes, swipes, or introductions that turn into a mutual match. It is an early signal of marketplace balance and recommendation quality, and a low match rate is usually a liquidity or targeting problem, not a UI one.
Matchmaking: A high-touch, often offline service where a person curates introductions for paying clients. It commands far higher prices per client than apps and is consolidating as operators roll up regional services, making it a distinct and resilient part of the industry.
MAU: Monthly active users, the count of unique people who use the app in a month. It is the standard scale metric for a dating product and the basis for comparing reach between apps.
Moderation: The combination of automated systems and human review that screens profiles, messages, and behavior for fraud, abuse, and policy violations. In dating it is a continuous operational function, not a one-time filter, and its quality shows up directly in trust and retention.

N

NCII: Non-consensual intimate imagery, a serious category of illegal and harmful content that dating platforms must detect, remove, and report. Handling it well is both a user-safety duty and a regulatory requirement in major markets.
Network effects: The property that each additional user makes the product more valuable to others, because there are more potential matches. Network effects are the prize and the trap of dating: strong once you have liquidity, brutal to start from zero.

O

Onboarding funnel: The sequence a new user passes through from install to an active, set-up profile. A smooth onboarding funnel that gets users to a first quality match quickly is one of the highest-leverage retention investments in dating.
Online Safety Act: The UK law imposing duties on user-to-user services, including dating apps, to use highly effective age assurance and to assess and mitigate illegal content, with significant penalties for failure. Self-declaration of age does not satisfy it.
Online-to-offline: Moving users from the app to real-world meetings and events, abbreviated O2O. As intent rises, online-to-offline shifts from a feature to the product itself, and it is central to events and matchmaking models.
Organic install: An install that arrives without paid acquisition, through search, word of mouth, or referral. Organic installs lower blended CAC and compound over time, which is why a product that earns referral has a durable cost advantage.

P

Paywall placement: Where in the experience you ask the user to pay. Gating the moment of highest intent, such as seeing who liked you or sending the message that matters, converts far better than a generic upsell, and placement usually moves conversion more than price does.
Pricing ladder: A clear good, better, best set of plans that lets users self-select by willingness to pay. A single confusing plan converts worse than a simple ladder, and the ladder also raises ARPPU by giving high-intent users a premium tier to choose.

R

Read receipt: A paid feature that shows whether a sent message has been read. It monetizes the desire for certainty in a conversation and is a common a la carte upsell.
Refund rate: The share of revenue returned to customers as refunds. It eats directly into net LTV and, like disputes, signals whether your billing and value promise are honest. Track it next to the dispute rate, not buried in finance.
Reply rate: The share of first messages that receive a response. It measures whether matches turn into real conversations, and it falls fast when the marketplace is unbalanced or full of low-intent or fake profiles.
Representment: The process of contesting a chargeback by submitting evidence that the charge was valid. Strong representment depends on clean records of consent, usage, and billing terms, which is why honest paywalls and logs pay off when disputes arrive.
Retargeting: Advertising aimed at people who have already interacted with your product, such as installers who never converted. It is usually cheaper than cold acquisition but should still be judged on cost per paying user, not installs.
Retention curve: A chart of the share of a cohort still active or still paying as time passes. A healthy curve flattens into a stable base of long-term users. In dating the early drop is steep, so the shape of the flattening matters more than the first-week number.
Revenue multiple: A valuation expressed as a multiple of revenue, common for growth-stage dating businesses without stable profits. The multiple swings hard with growth rate, retention quality, and how defensible the liquidity is.
Rewind: A paid feature that lets a user undo their last swipe or pass. It is a small consumable that monetizes regret in the moment and is a staple of the a la carte model in swipe products.
Romance scam: A fraud where a bad actor builds a fake relationship to extract money from a victim. It is the most damaging trust problem in dating, a regulatory and reputational risk as much as a user-safety one, and a core reason moderation and detection are non-negotiable.

S

Sell-side: Advisory work that represents a company being sold, running the process and finding buyers. Knowing whether your advisor sits on the sell-side or buy-side, and whether they hold both roles, is essential to reading whose interest they serve.
Singles economy: The broad set of spending and behavior tied to single people, well beyond dating apps, spanning events, travel, services, and self-directed leisure. It frames dating as one part of a much larger market that operators can serve.
Situationship: A relationship without defined commitment or direction, a term that captured a wave of frustration with low-intent dating. It matters commercially because the backlash against it is part of what drives demand for intentional products.
Soft paywall: A gate that limits rather than blocks, for example capping likes or hiding who liked you until the user upgrades. It keeps the funnel wide while still creating frequent, well-timed reasons to pay.
Stickiness: The ratio of daily to monthly active users, a quick read on how habitual the product is. Dating stickiness is naturally lower than social apps because successful users leave, so judge it against dating peers, not against messaging or feed apps.
Strategic buyer: An acquirer that buys for operational fit, such as users, technology, or a market position, rather than purely financial return. Strategics often pay more for the right asset because they can extract synergies a financial buyer cannot.
Subscription: Recurring billing on a weekly, monthly, quarterly, or annual cycle in exchange for premium features. It gives predictable revenue but hands the user a recurring decision to cancel, which is where dating's high churn does its damage.
Super boost: A higher-tier boost that raises a user's visibility more aggressively or for longer than a standard boost. Like boosts, it only feels worth buying when there is enough liquidity to convert the extra views.
Super like: A paid or limited signal that tells another user they were singled out, raising the odds of a match. It is a small consumable that monetizes intent and stands in for the credits economy in swipe products.
Swipe economy: The dominant model of the last decade, where users rapidly judge profiles with a swipe and the product monetizes volume and visibility. It is efficient at scale but is increasingly blamed for low intent and burnout, which is the opening intentional products are built into.
Swipe fatigue: The burnout users feel from endless, low-intent swiping. It is a key driver of the shift toward intentional dating, and an opening for products that get users to real outcomes rather than maximizing time on app.

T

Take rate: The share of a transaction a platform keeps, relevant where a dating business intermediates payments or runs a marketplace with third-party providers. It shapes net revenue and should be read net of the fees and disputes that sit beneath it.
TAM, SAM, SOM: Total addressable market, serviceable addressable market, and serviceable obtainable market, the nested way investors size an opportunity. In dating, credible founders define these by segment and geography rather than citing a single global figure.
Time to first match: How long it takes a new user to get their first real match after signing up. It is a leading indicator of early retention, because a user who matches quickly comes back and a user who sees an empty app does not.
Trial conversion: The share of free-trial users who convert to a paid plan when the trial ends. In dating it is a critical sub-step of overall conversion, and an honest trial that sets clear expectations converts better and disputes less than one that surprises the user at renewal.
Two-sided marketplace: A product whose value depends on bringing together two distinct groups who need each other. Dating is the classic example, which is why acquisition is really about buying a balanced ratio, not buying users one at a time.

V

Verification badge: A marker showing that a profile has passed identity or liveness checks. Badges deter fakes and raise reply rates by signaling that profiles are real, and they support both trust and the marketplace quality liquidity depends on.

W

White-label dating platform: Infrastructure that lets a business launch and run its own branded dating product without building the technology, payments, moderation, and apps from scratch. The strongest versions also solve the cold start by launching new brands into an existing active network.
Win-back: A flow that brings a lapsed paying user back, often with a relevant new match, a notable change in their area, or a thoughtful offer. Win-back is far cheaper than new acquisition and matters in dating because lapses are frequent and not always permanent.