Glossary: Funnel Measure terms

This glossary defines the key terms used in Funnel Measure. Use it as a quick reference when working with attribution models, measurement data and insights.

A consultative Funnel Measure plan that supports modeling on both digital and offline data. In addition to digital inputs, it enables the ingestion of offline media (like TV, print and OOH), offline conversions (like store visits) and custom contextual variables. This plan includes access to saturation curves, media planner, budget optimization, forecasting for different scenarios and incrementality testing.

A set of rules or algorithms used to assign credit to different marketing touchpoints along a customer's journey. In Funnel, this category includes both rule-based models (like Last-click attribution or Linear attribution) and probabilistic models (like Funnel Data-Driven MTA).

The estimated volume of conversions or sales that would have occurred without any paid marketing activity. It represents the underlying demand driven by brand equity, returning customers and standard contextual variables like public holidays. A high baseline indicates strong brand health or organic demand.

A data input used in Funnel Digital MMM and Funnel MMM to account for external factors that influence business outcomes independent of marketing spend. Examples include weather, holidays, competitor pricing and inflation. In the Digital Measurement plan, the model automatically uses standard variables like public holidays and major shopping events. Custom contextual variables (like competitor pricing) require the Advanced Measurement plan.

A specific action completed by a customer, such as a purchase, used as the primary outcome for the attribution model. To account for data lost to cookie consent, tracked conversions from GA4 are scaled using verified conversions from a system of record like Shopify or a CRM.

A streamlined Funnel Measure plan designed for digital-first marketers. It offers a self-serve experience that combines Funnel Digital MMM and Funnel Data-Driven MTA to measure performance. This plan supports digital media, digital conversions tracked online and standard contextual variables. It doesn't support offline media, offline conversions or custom contextual variables.

A rule-based attribution model that assigns all credit to the first touchpoint in the conversion path. It's useful for understanding which channels drive initial awareness and new traffic.

A proprietary attribution model that assigns credit across tracked touchpoints using Funnel's own machine learning algorithms. It provides granular, campaign-level insights and allows direct comparison against rule-based models and triangulation of MMM models.

A top-down statistical model that assigns credit across digital marketing activities using Funnel's marketing mix modeling. It measures the impact of digital media and contextual variables to establish a performance baseline. It requires a minimum of 365 days of daily data. In the Digital Measurement plan, only standard contextual variables are included.

An attribution model that assigns credit based on the tracking logic and attribution settings configured in your connected GA4 property. In the Funnel interface, this model may appear as Google Analytics in dropdown menus and column headers.

The additional conversions generated specifically as a result of marketing activities, over and above the baseline. In Funnel Digital MMM, this represents the distinct uplift attributed to digital media spend. Calculated as: total conversions minus baseline equals incremental conversions.

A performance evaluation feature in Funnel Measure. The Insights dashboard shows ROAS, CPA and other key marketing metrics with the option to break results down by channel, source and campaign and view trends over time.
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A rule-based attribution model that assigns 100% of the credit to the final touchpoint before the conversion occurred. It's useful for identifying which marketing activities closed the sale.

A rule-based attribution model that assigns equal credit to every touchpoint in the customer journey. It's useful when every interaction is considered equally important to the conversion.

A dimension representing the specific store or business location (for example, a UK website), regardless of the customer's physical location. Don't use country or region as a substitute for market.

A statistical analysis technique that uses historical data to quantify the impact of various marketing tactics and external factors on sales or conversions. It calculates a baseline and the incremental lift generated by media spend. Funnel offers two implementations: Funnel Digital MMM as part of the Digital Measurement plan and Funnel MMM as part of the Advanced Measurement plan.

The practice of quantifying the effect of marketing activities on business goals. It encompasses data collection, attribution and modeling to determine ROI, understand customer behavior and optimize future spend. At Funnel, this is delivered through Funnel Measure, which uses a triangulation methodology.

The collective term for all advertising activity data ingested into Funnel. It encompasses both digital media (like platform-tracked channels) and offline media (like file-based channels). It typically consists of data containing cost, impressions and clicks where applicable.

A method of attribution that assigns credit to multiple touchpoints along a customer's journey, rather than just a single interaction. In Funnel, multi-touch attribution models range from rule-based options (like Linear attribution) to Funnel Data-Driven MTA, which uses machine learning.

A data input consisting of daily spend and reach metrics from non-digital advertising channels like linear TV, radio, print or OOH. This data is usually ingested through CSV upload or custom connector. The Advanced Measurement plan is required to include offline media.

An attribution model that assigns credit exactly as reported by each individual advertising platform. Because platforms operate in silos and don't deduplicate, total conversions often exceed actual sales.

A rule-based attribution model that assigns 40% of credit to the first touchpoint, 40% to the last and spreads the remaining 20% across the middle interactions. It's useful when the introduction and the close are considered the most important moments.

A rule-based attribution model that assigns more credit to touchpoints that occurred closer to the conversion and less to earlier ones. It's useful for longer sales cycles where recent activities are more influential.

A dataset representing the total actual volume of online transactions, typically exported from a system of record like Shopify, Magento or a CRM. It's used to calculate a scaling factor that corrects digital conversions for tracking loss caused by cookie consent or ad blockers. Don't confuse this with offline conversions, which refer to in-store or non-digital transactions.