Metrics

Key performance metrics and KPIs

Term	Definition
Spend	The total amount of money spent on advertising campaigns. Pulled directly from your connected ad platforms.
Revenue	The total revenue generated from conversions attributed to your ads. Calculated based on conversion values tracked by the Adverfly pixel.
ROAS	Return on Ad Spend. The ratio of revenue to ad spend. Calculated as Revenue / Spend. A ROAS of 3.0 means $3 earned for every $1 spent.
ROI	Return on Investment. The percentage return on your advertising investment. Calculated as ((Revenue - Spend) / Spend) × 100.
CPA	Cost per Acquisition. The average cost to acquire one customer or conversion. Calculated as Spend / Conversions.
CAC	Customer Acquisition Cost. The cost of acquiring a new customer. Often used interchangeably with CPA.
CPM	Cost per Mille. The cost per 1,000 impressions. Calculated as (Spend / Impressions) × 1000.
CPC	Cost per Click. The average cost for each click on your ad. Calculated as Spend / Clicks.
CTR	Click-Through Rate. The percentage of impressions that resulted in a click. Calculated as (Clicks / Impressions) × 100.
CVR	Conversion Rate. The percentage of sessions that resulted in a conversion. Calculated as (Conversions / Sessions) × 100.
AOV	Average Order Value. The average value of each order/conversion. Calculated as Revenue / Conversions.
LTV	Lifetime Value. The total revenue expected from a customer over their entire relationship with your business.
Impressions	The number of times your ad was displayed to users.
Clicks	The number of times users clicked on your ad.
Conversions	The number of desired actions completed (purchases, sign-ups, etc.) attributed to your ads.
Sessions	A single visit to your website tracked by the Adverfly pixel. A session groups all events from one visitor within a continuous browsing period. Sessions are the denominator for Conversion Rate (CVR).
Hook Rate	The percentage of viewers who watched the first 3 seconds of a video ad. Calculated as (3-Second Video Views / Impressions) × 100. Measures how well a creative captures attention.
Hold Rate	The percentage of viewers who watched a video ad to the end (ThruPlay). Calculated as (ThruPlays / 3-Second Video Views) × 100. Measures how well a creative retains attention.
ThruPlay	A video metric counting when someone watches a video to the end or for at least 15 seconds.
Net ROAS	Net Return on Ad Spend. ROAS adjusted for costs beyond ad spend (e.g., COGS, shipping). Provides a profitability-focused view of ad performance.
MER	Marketing Efficiency Ratio. Total revenue divided by total marketing spend across all channels. Unlike ROAS which is per-channel, MER measures overall marketing efficiency.
CPO	Cost per Order. The average cost to generate one order. Calculated as Spend / Orders.

Attribution

Attribution models and terminology

Term	Definition
Attribution	The process of assigning credit to marketing touchpoints that contributed to a conversion.
Attribution Window	The time period during which a touchpoint can receive credit for a conversion. E.g., a 7-day click window means clicks within 7 days before conversion get credit.
Click Attribution	Credit given to ad clicks that led to a conversion.
View Attribution	Credit given to ad impressions (views) that may have influenced a conversion, even without a click.
Last Click	An attribution model that gives 100% credit to the last touchpoint before conversion. Useful as a baseline comparison.
First Click	An attribution model that gives 100% credit to the first touchpoint in the customer journey. Useful for understanding awareness drivers.
Linear	An attribution model that distributes credit equally across all touchpoints in the customer journey. Provides a balanced view when every interaction is considered equally important.
U-Shaped	An attribution model that emphasizes the first and last touchpoints (typically 40% each), with less credit distributed among the middle interactions. Also known as position-based attribution.
Total Impact	An attribution model that measures the incremental impact of marketing across channels by evaluating each touchpoint's contribution to the final outcome, combining click and impression signals.
MTA (Multi-Touch Attribution)	A user-level attribution approach that distributes conversion credit across multiple touchpoints in a single customer journey. MTA provides the most granular view of marketing performance and acknowledges that multiple interactions collectively influence purchasing decisions.
MMM	Marketing Mix Modeling. A statistical modeling technique that quantifies the incremental impact of each marketing channel on revenue using aggregated data, saturation curves, and external factors. See the dedicated MMM glossary for detailed terms.
UMM	Unified Marketing Measurement. A holistic approach combining multiple measurement methodologies (MTA, MMM, incrementality testing) into a single framework. Instead of treating each method in isolation, UMM cross-validates insights to reduce conflicting answers and produce defensible budget decisions. See the Measurement glossary for more details.
Incrementality	A measurement approach that isolates the portion of performance that would not have happened without advertising. By comparing exposed and control groups, incrementality reveals the true causal lift of marketing efforts. See the Measurement glossary for detailed terms.

Measurement

Unified measurement, incrementality testing, and cross-method validation

Term	Definition
Unified Measurement	A holistic approach that combines attribution (MTA), Marketing Mix Modeling (MMM), and incrementality testing into a single measurement framework. Instead of relying on one method, unified measurement cross-validates insights across all three to reduce conflicting answers, uncover hidden growth opportunities, and produce defensible budget recommendations.
Triangulation	The practice of validating marketing insights by comparing results across multiple measurement methods — such as MMM, MTA, and incrementality. When all three methods agree, confidence in the insight is high. When they disagree, it signals the need for further testing.
Causality	The ability to prove that a marketing action directly caused an outcome, rather than merely being correlated with it. Incrementality testing is the primary method for establishing causality in marketing measurement.
Incrementality	A measurement approach that isolates the portion of performance that would not have happened without advertising. By comparing an exposed group to a control group, incrementality reveals the true lift caused by marketing — as opposed to conversions that would have occurred organically.
Incremental Lift	The difference in outcomes between a treatment group (exposed to ads) and a holdout group (not exposed). Represents the additional conversions or revenue directly caused by advertising efforts.
Lift (%)	The percentage share of outcomes caused by ads during a test window. Calculated as (Treatment − Holdout) / Holdout × 100. A higher lift percentage indicates a greater causal impact of the advertising.
iROAS (Incremental Return on Ad Spend)	The revenue generated specifically because of advertising, divided by the incremental ad spend. Unlike standard ROAS, iROAS excludes organic conversions and measures only the true return from ad investment.
Treatment vs. Holdout	An experimental design where one group (treatment) is exposed to advertising while another group (holdout) is intentionally excluded. Comparing outcomes between the two groups reveals the incremental impact of the ads.
Conversion Lift Test	A user-level holdout test typically managed by ad platforms (Meta, Google, TikTok). The platform splits audiences into exposed and holdout groups using its identity graph, then measures the difference in conversions. Also known as a holdout test.
Geo Lift Test	An incrementality test that measures impact by comparing performance across geographic regions (cities, states, or postal codes) with and without advertising exposure. Instead of user-level data, it uses regional sales trends to assess incremental impact — making it privacy-friendly and scalable.
DMA (Designated Market Area)	A geographic region used as the recommended granularity for geo lift tests. DMAs define distinct media markets, making them ideal for isolating advertising impact by region.
Matched Markets	In geo lift testing, control markets selected because they closely match the treatment markets on historical performance and trends. For example, if Berlin is a treatment market, Hamburg might serve as a matched control.
Synthetic Control	A statistical method used in geo lift tests that creates a weighted blend of multiple control regions to closely approximate the treatment region's pre-test behavior. Provides a more precise comparison than a single control market.
View-Through Conversion (VTC)	A conversion credited to an ad that was viewed (impressioned) but not clicked. VTCs capture the influence of brand awareness and visual advertising that standard click-based attribution misses.
Modeled Conversions	Estimated conversions inferred by ad platforms when direct tracking is unavailable due to privacy restrictions, cookie deprecation, or signal loss. Platforms use machine learning to fill gaps in observable conversion data.
Point of Diminishing Returns (PDR)	The spend level at which additional investment in a channel yields progressively less incremental return. Unified measurement helps identify PDR across channels to prevent over-investment and guide budget reallocation.
Triangulation Weights	The confidence-based weighting used to combine measurement methods into a unified ROI. With geo-test: 50% Experiment + 30% MMM + 20% MTA. Without geo-test: 60% MMM + 40% MTA. Fallback: 100% MTA. Weights reflect each method's causal reliability.
Confidence Score	A numeric score (0–1) indicating how trustworthy a unified measurement result is. Ranges from 0.95 (significant geo-test available) to 0.40 (MTA only). Higher scores mean more causal evidence supports the result.
Ground Truth Feedback	When a geo-test reveals a channel's true causal impact, that measured lift is fed back into the MMM as a Bayesian prior — forcing the model to converge toward experimentally validated truth. This closes the loop between experiments and statistical modeling.
Difference-in-Differences (DID)	A statistical method used in geo-tests that compares the change in outcomes between treatment and control regions before and after an intervention. Uses a frequentist t-test to determine whether the treatment effect is statistically significant.
Halo Effect (Channel)	The indirect revenue and organic traffic uplift caused by paid marketing activity beyond direct tracked conversions. This effect applies to all channels (not just influencers) and is inherently captured by MMM: because the model correlates total spend with total revenue over time, any indirect lift — brand searches, word-of-mouth, organic traffic spikes — is naturally attributed to the channel whose spend drove it. No separate measurement phase or feature is required; the halo effect is a built-in property of the Marketing Mix Model.

Marketing Mix Modeling

MMM concepts, saturation modeling, and budget optimization terms

Term	Definition
Marketing Mix Modeling (MMM)	A statistical modeling technique that quantifies the incremental impact of each marketing channel on revenue. Unlike attribution, MMM works with aggregated data and can account for external factors like seasonality and weather.
Base Revenue	The revenue a business would generate without any marketing spend — driven by brand equity, organic demand, and repeat customers. Also called baseline or organic revenue.
Incremental Revenue	The additional revenue directly attributable to marketing activities. Calculated as Total Revenue minus Base Revenue.
Seasonality Effect	The cyclical variation in revenue caused by recurring patterns like holidays, weather, or industry cycles. MMM isolates this so it doesn't get falsely attributed to marketing channels.
Revenue Decomposition	The process of breaking total revenue into its component parts: base sales, seasonality, and each channel's contribution. Typically visualized as a waterfall chart.
Hill Function	A mathematical function used to model saturation in marketing response curves. Formula: response = L * (spend^S) / (K^S + spend^S), where L is the maximum response, K is the half-saturation point, and S is the shape parameter.
Saturation Curve	An S-shaped curve showing the relationship between spend and response for a channel. As spend increases, each additional euro generates less return — this is the diminishing returns effect.
Diminishing Returns	The economic principle that each additional unit of spend in a channel generates progressively less revenue. Visualized as the flattening portion of the saturation curve.
Half-Saturation Point (K)	The spend level at which a channel reaches 50% of its maximum possible response. A key parameter in the Hill function — channels with higher K values need more spend to reach their potential.
Maximum Response (L)	The theoretical upper limit of revenue a channel can generate regardless of how much is spent. The asymptote of the saturation curve.
Shape Parameter (S)	Controls the steepness of the saturation curve. Higher values mean the curve transitions more sharply from growth to saturation. Also called the Hill coefficient. In Adverfly's implementation (LogisticSaturation), S is fixed at 1.0 — the saturation behavior is controlled entirely by the half-saturation point K.
Operating Point	The current position on a channel's saturation curve based on actual spend. Shows how close a channel is to saturation and how much room exists for scaling.
Historical ROI	The average return on investment over a period: total revenue attributed to a channel divided by total spend. Reflects overall efficiency but not marginal efficiency.
Marginal ROI (mROI)	The return generated by the next euro of spend in a channel. Calculated as the derivative of the Hill function. A channel can have a high historical ROI but a low mROI if it's near saturation.
Marketing Efficiency Ratio (MER)	Total revenue divided by total marketing spend across all channels. A blended efficiency metric that shows the overall return on marketing investment.
Budget Optimization	The process of reallocating budget across channels to maximize total revenue. Uses saturation curves to find the optimal spend level for each channel where the combined marginal returns are maximized.
R² (R-Squared)	The coefficient of determination — measures how well the MMM model explains the variance in actual revenue. Values range from 0 to 1, where 1.0 is a perfect fit. Values above 0.85 are considered strong for MMM.
MAPE	Mean Absolute Percentage Error — the average percentage deviation between actual and predicted values. A MAPE below 10% is considered excellent, below 20% is good.
Adstock	The carryover effect of advertising — the idea that a marketing message continues to influence consumers after it was shown. Modeled as a decay function over time.
Saturation Threshold	The spend level beyond which marginal returns drop significantly (typically below 20% of the initial mROI). Spending beyond this point yields rapidly diminishing returns.
Point of Diminishing Returns (PDR)	The spend level at which additional investment in a channel generates progressively less incremental revenue. MMM applies guardrails to prevent investment beyond the PDR, ensuring efficient budget allocation.
Data-Driven Budget Allocation	The process of using MMM outputs — saturation curves, marginal ROI, and revenue decomposition — to provide intelligent budget recommendations across every marketing channel, down to the campaign level.
Predictive Capabilities	The ability of MMM to forecast future marketing performance by understanding historical patterns, saturation dynamics, and seasonal trends. Enables scenario planning before committing budget.
Competitive Insights	MMM can incorporate competitor activities and market dynamics into its models, helping you understand how external competitive pressure affects your marketing performance.
Model Calibration	The process of refining MMM predictions using incrementality test results. When a geo lift or conversion lift test reveals a channel's true causal impact, those results recalibrate the model's channel coefficients, improving accuracy over time.
Feedback Loop	A continuous improvement cycle where incrementality results calibrate MMM, MMM identifies high-value tests, and MTA provides granular optimization signals. This iterative process makes the measurement system smarter with every decision.
Endogeneity	When a variable is determined inside the system being measured rather than outside it. Endogenous variables (CTR, CPC, clicks, impressions, ROAS, CPA) are results of the marketing process — including them in MMM creates circular reasoning. Only exogenous inputs (spend) and control variables (weather, holidays) may enter the model.
Bayesian Updating	The process of using yesterday's model posteriors as today's priors, creating a daily learning chain. This stabilizes the model (no wild daily swings), enables rapid adaptation to genuine changes, and supports anomaly detection when results diverge beyond 3 standard deviations.
PyMC Marketing	An open-source Bayesian MMM engine (Apache 2.0, maintained by PyMC Labs) built on PyMC. Supports hierarchical modeling across multiple content dimensions, geometric adstock, logistic saturation (Hill function), and full prior control for geo-test calibration.
Hierarchical Priors	A Bayesian modeling technique where channels sharing common attributes (e.g., same angle or format) pool information through shared prior distributions. This improves estimates for channels with limited data by borrowing strength from related channels.
Posterior Distribution	The updated probability distribution of a model parameter after observing data. In daily Bayesian updating, today's posteriors become tomorrow's priors — creating a chain that converges toward the true parameter values over time.
Control Variable	An external factor included in MMM to prevent false attribution. Weather, holidays, seasonality, and news sentiment are control variables — they affect revenue but are not marketing levers. Without them, the model would incorrectly credit weather-driven sales to marketing.
Geometric Decay (Adstock)	A specific adstock model where the advertising effect decays by a fixed proportion each day: effect(t) = spend(t) + decay × effect(t-1). Decay values range from 0.1 (search, short memory) to 0.9 (brand/YouTube, long memory).
ArviZ Diagnostics	A suite of Bayesian model diagnostic checks including R-hat convergence (must be < 1.05), divergent transitions (must be 0), and Effective Sample Size (must be > 400). These ensure the MCMC sampling has converged and the model results are reliable.

Tracking

Tracking and data collection terms

Term	Definition
Pixel	A small piece of JavaScript code installed on your website that tracks user behavior and conversions.
Event	A specific user action tracked by the pixel, such as a page view, add to cart, or button click.
Conversion	A completed desired action, typically a purchase or sign-up, that represents a successful outcome.
Pageview	An event fired when a user loads a page on your website.
Add to Cart	An event fired when a user adds a product to their shopping cart.
Initiated Checkout	An event fired when a user begins the checkout process.
Purchase	A conversion event fired when a user completes a transaction.
UTM Parameters	URL parameters used to track traffic source, medium, campaign, term, and content. Includes utm_source, utm_medium, utm_campaign, utm_term, utm_content.
First-Party Data	Data collected directly from your own website or app, owned and controlled by you.
Third-Party Data	Data collected by external parties and shared or sold to advertisers.
Cookie	A small file stored in a user's browser used to identify and track users across sessions.
Server-Side Tracking	Tracking that sends data from your server to analytics platforms, rather than from the user's browser.
CAPI	Conversions API. Server-side APIs provided by ad platforms (Meta, Google, etc.) to send conversion data directly from your server.
Data Bridge	A feature that syncs first-party pixel data back to ad platforms (Meta, Google, TikTok, Microsoft Ads) with one click. Improves platform optimization and match rates by sending enriched conversion signals server-side.
Adverfly Layer	The global window.adverfly object used to configure the pixel and pass customer context. Properties include store_id, store_currency, store_timezone, customer_id, and transaction_id.
Visitor Identification	The process of associating anonymous pixel events with a consistent visitor ID across sessions. Uses first-party cookies and server-side matching to maintain identity continuity.

Customers & Segments

Customer data, segmentation, lifetime value, and loyalty

Term	Definition
Customer Segment	A group of customers defined by shared characteristics — purchase behavior, geographic location, acquisition channel, or custom rules. Segments are created using a rule builder and automatically updated as new customers match the criteria.
Segment Rule	A condition that determines segment membership. Rules consist of a dimension (e.g., country, first purchase date, acquisition channel), an operator (equals, greater than, contains), and a value. Multiple rules can be combined for precise targeting.
Segment Member	A customer who matches all rules defined for a segment. Membership is dynamic — as customers make new purchases or their attributes change, they may join or leave segments automatically.
Customer Lifetime Value (LTV)	The total revenue a customer is expected to generate over their entire relationship with your business. In Adverfly, LTV is calculated from pixel conversion data and can be segmented by acquisition channel, campaign, or customer segment.
RFM Analysis	A customer segmentation framework based on Recency (how recently a customer purchased), Frequency (how often they purchase), and Monetary value (how much they spend). In Adverfly, RFM dimensions are available as individual segment rules — you can build segments using recency, frequency, or monetary filters — but there is no standalone RFM scoring feature.
Cohort Analysis	Grouping customers by their acquisition date (or another shared event) and tracking their behavior over time. Reveals whether customer quality is improving or declining and how different acquisition channels produce different retention patterns.
Loyalty Program	A rewards system that incentivizes repeat purchases through points, tiers, and exclusive perks. Customers earn points on purchases, reviews, and referrals, and advance through membership tiers (Bronze, Silver, Gold, Platinum) to unlock increasing benefits.
Loyalty Tier	A membership level within a loyalty program. Higher tiers unlock better rewards (exclusive discounts, early access, free shipping). Tiers create aspirational goals that drive repeat purchases and increase customer lifetime value.
Redemption Rate	The percentage of earned loyalty points that customers actually redeem. A healthy redemption rate (40-60%) indicates the rewards are valuable enough to drive engagement without being too easy to earn.
Self-Reported Attribution	Qualitative data collected through post-purchase surveys asking customers how they discovered your brand. Captures channels that digital tracking misses — word-of-mouth, podcasts, TV, influencer mentions — providing a complementary signal to quantitative attribution methods.
Post-Purchase Survey	A short questionnaire shown to customers after checkout, typically asking 'How did you hear about us?' The most common method of self-reported attribution. In Adverfly, surveys are configured in the Surveys app and tracked via pixel events.
New vs. Returning Customer	Classification based on purchase history. A new customer has no previous transactions; a returning customer has purchased before. Tracked via the pixel's is_new_customer flag on conversion events. Critical for understanding whether campaigns acquire new customers or re-engage existing ones.

Advertising

Advertising platforms and campaign terms

Term	Definition
Campaign	A set of ad groups organized around a specific marketing objective or theme.
Ad Set / Ad Group	A collection of ads within a campaign that share targeting, budget, and schedule settings.
Ad / Creative	The actual advertisement shown to users, including images, videos, copy, and call-to-action.
Audience	A defined group of users targeted by your ads based on demographics, interests, behaviors, or custom data.
Lookalike Audience	An audience of users who share similar characteristics with your existing customers or website visitors.
Retargeting	Showing ads to users who have previously interacted with your website or app. Also called Remarketing.
Prospecting	Targeting new potential customers who haven't interacted with your brand before.
Frequency	The average number of times each user has seen your ad.
Reach	The total number of unique users who saw your ad.
Ad Fatigue	A decline in ad performance that occurs when users see the same ad too many times.
Creative Fatigue	Declining performance of a particular creative asset due to overexposure.
A/B Testing	Comparing two versions of an ad or landing page to determine which performs better.
Bid	The maximum amount you're willing to pay for a specific action (click, impression, conversion).
Budget	The total amount allocated to spend on a campaign or ad set over a specific time period.
Optimization	The process of improving campaign performance through adjustments to targeting, bidding, creatives, or other settings.
Influencer	A person with a social media following who promotes products or services to their audience. In Adverfly, an influencer record tracks a unique affiliate code/link.
Creator	A content creator or influencer who has access to the Creator Dashboard to view their assigned affiliate codes and track performance.
Affiliate Code	A unique URL parameter or code assigned to an influencer/creator to track traffic and conversions from their promotions.
Commission	The payment an influencer/creator earns for each conversion or sale generated through their affiliate code. Can be percentage-based or a fixed amount.
Creator Dashboard	A dedicated portal in Adverfly where creators can view all their assigned affiliate codes, track performance, and see commission information across multiple workspaces.
Responsive Search Ad (RSA)	A Google Ads format where you provide up to 15 headlines and 4 descriptions. Google automatically tests combinations and serves the best-performing mix. In Adverfly, RSA assets are stored as text-only creatives (no image URL) with all headline and description variants in the properties field.
Performance Max (PMax)	A Google Ads campaign type that serves ads across all Google channels (Search, Display, YouTube, Gmail, Maps) from a single campaign. Assets are organized in Asset Groups rather than individual ads. In Adverfly, the campaign_id acts as the ad_id unit for spend attribution.
Asset Group	The creative container within a Performance Max campaign. Each asset group contains a mix of headlines, descriptions, images, logos, and videos. Google automatically assembles these into ads. Adverfly collects all unique assets from all asset groups within a campaign.
Responsive Display Ad (RDA)	A Google Display Network format where you provide multiple images, headlines, and descriptions. Google assembles and optimizes the combinations. In Adverfly, marketing images and YouTube video thumbnails are extracted and stored as individual assets.

AI & Automation

AI-powered features, automation, and intelligent optimization

Term	Definition
AI Visibility	A feature that tracks how your brand appears in AI-powered search engines and assistants like ChatGPT, Claude, Gemini, and Perplexity. Daily automated queries measure your Visibility Score, Mention Rate, Citation Rate, and Sentiment — giving you a quantitative view of your brand's presence in the AI-driven discovery layer.
Visibility Score	A score from 0 to 100 that measures how prominently your brand appears in AI system responses. Calculated based on whether the brand is mentioned, how early in the response, and whether it's recommended or just listed. Tracked daily to show trends over time.
Mention Rate	The percentage of AI queries that mention your brand in the response. For example, if you track 20 prompts and your brand appears in 12 responses, your Mention Rate is 60%.
Citation Rate	The percentage of AI mentions that include an explicit link to your website. A high Citation Rate means AI systems are not only mentioning your brand but actively driving traffic to you.
AI Suggestion	An AI-generated recommendation for a specific app. Suggestions are context-aware and can be accepted or regenerated for fresh ideas.
Recommendation	An AI-generated optimization suggestion based on your workspace performance data. Recommendations can be accepted (marking them for implementation), dismissed (acknowledging but not acting), or left pending for review. Accepted recommendations are tracked for outcome measurement.
Recommendation Digest	A periodic email summary of new and pending AI recommendations for your workspace. Ensures optimization opportunities are not missed even when the dashboard is not actively monitored.
Outcome Tracking	The measurement of accepted recommendation performance over time. After accepting an AI recommendation, outcome tracking monitors whether the suggested change improved the target metric.
Automation Rule	A configurable AI-driven action that triggers automatically when certain conditions are met. Rules can adjust budgets, pause underperforming campaigns, or send alerts based on thresholds defined in Workspace Settings.
AI Automations Settings	A section in Workspace Settings where you configure AI-driven automation rules — set thresholds, enable or disable specific automations, and control how aggressively the AI optimizes on your behalf.
AI Chat	A conversational interface for querying your marketing data using natural language. Ask questions like 'What was my best-performing campaign last month?' and receive answers backed by your actual data across all measurement methods.
AI Forecasting	A feature that uses historical data and MMM model outputs to predict future revenue, conversions, and spend performance. Forecasts include credible interval bands showing the range of likely outcomes.
AI Labeling	Automated classification of creative assets using AI vision models. Assets are tagged with labels describing their visual content, marketing angle, funnel stage, and format — enabling performance analysis by creative attributes.
Execution Queue	An approval queue where AI-proposed actions (budget changes, campaign pauses, scaling decisions) await human review before execution. Actions are classified by risk tier (low/medium/high) and can be approved or rejected.
Marketing Angle	A strategic messaging approach used in creative assets — such as Social Proof, Urgency, Fear of Missing Out, or Testimonial. Adverfly automatically labels creatives by angle and tracks which angles perform best per channel and audience.
Geo-Lift Test	An incrementality test that compares performance across geographic regions with and without advertising. Measures the true causal effect of ads by analyzing conversion differences between treatment and control cities.
Candidate Finder	An analysis tool that scans your pixel conversion data to identify cities suitable for geo-lift testing. Evaluates stability (CV), trend, and spike ratio to find regions with clean, predictable baselines.

Analytics

Analytics and reporting terminology

Term	Definition
Breakdown	Segmenting data by a specific dimension such as campaign, ad set, creative, date, or device.
Dimension	A categorical attribute used to segment and analyze data (e.g., campaign name, country, device type).
Metric	A quantitative measurement of performance (e.g., spend, revenue, conversions, ROAS).
Filter	A condition applied to data to show only records matching specific criteria.
Date Range	The time period for which data is displayed or analyzed.
Comparison Period	A secondary date range used to compare performance against the primary date range.
Trend	The direction of change in a metric over time (increasing, decreasing, or stable).
Cohort	A group of users who share a common characteristic, often used for analyzing behavior over time.
Funnel	A visualization of the stages users go through from initial interaction to conversion.
Customer Journey	The complete path a customer takes from first touchpoint to conversion and beyond.
Touchpoint	Any interaction between a customer and your brand, including ad views, clicks, website visits, and purchases.
Channel	A marketing platform or medium used to reach customers (e.g., Meta, Google, TikTok, Email).
Source	The origin of traffic or conversions, often referring to the specific platform or campaign.
Medium	The general category of marketing channel (e.g., paid, organic, referral, email).
Dashboard	A visual interface displaying key metrics and data for monitoring performance.
Report	A structured presentation of data and insights, often exported or scheduled for regular delivery.
Custom Report	A user-built report with custom metric selections, formula calculations, and visualization types. Custom reports can be saved, reused, and shared. The report builder supports AI-assisted natural language queries for faster report creation.
Report Chat	An AI-powered conversational interface within Custom Reports that lets you describe what you want to see in natural language. The system generates the appropriate query, metrics, and visualization automatically.
Dynamic Renderer	The engine that interprets a custom report's schema and renders the appropriate visualization — tables, charts, comparisons, or combined views — based on the selected metrics and format.
Workspace Usage	Metrics tracking how much of the platform's resources a workspace consumes — API calls, data volume, model runs, and feature usage. Used for monitoring and billing purposes.
Logbook	An audit trail that records all significant platform actions — model runs, budget changes, automation executions, recommendation decisions, and configuration changes. Provides accountability and historical context for every decision.