Volume 5, Issue 5 - May, 2015
Shilpi Desai, Sr. Account Manager, Visual IQ
Every industry has its own language, or jargon, that describes ideas and concepts that are unique to the field. The marketing attribution space is no different, and when you’re unfamiliar with the lingo, it’s easy to get confused. Here are 30 commonly used attribution terms to help your business navigate through the jargon.
Addressable Channels: Includes channels where individual, user-level data (such as cookie data) is available to inform media spending and allocation decisions.
Advanced Attribution: Leverages a multi-dimensional, algorithmic modeling approach to scientifically calculate and then fractionally assign conversion credit to every touchpoint and touchpoint attribute (ad size, placement, publisher, creative, offer, etc.) experienced by every converter and non-converter across all channels to deliver a truly holistic view of marketing performance. It consists of three core capabilities: 1) holistic performance measurement, 2) cross channel marketing planning, and 3) deployment of insights into action.
Algorithm: A mathematical formula that an attribution solution uses to assign conversion credit across the marketing channels and tactics to which a prospect was exposed prior to converting, as well as predict the outcome of future marketing spend allocations.
Audience Attribution: Overlays demographic and behavioral data with media, response and customer data to uncover the marketing strategies and tactics that produce the greatest performance by specific audience segments. The outputs of audience attribution include clear breakdowns of the customers with the highest propensity to convert, as well as the optimal combination of media and tactics needed to produce the best return for each audience segment. Audience segments may be defined by a marketer’s own first-party data, a third-party audience data aggregator, or a combination of the two.
Attribute (aka Dimension): A characteristic or feature associated with a marketing touchpoint. Each touchpoint may include dozens of attributes. For example, attributes of an online display ad may include the ad size, placement, publisher, timing, creative, offer, etc.
Baseline (aka Brand Equity): The intrinsic value and level of performance against a set of key performance indicators (KPIs) derived solely from a brand’s recognition in the marketplace. It provides an initial value from which to evaluate the impact of incremental marketing investments.
Bottom-Up Modeling: Leverages individual, user-level data from addressable channels like online display and paid search to measure past marketing performance and predict future performance at the most granular level of data, such as creative, offer, keyword and more.
Constraints: A range of values in which media spend can vary to ensure the recommendations produced by an attribution solution can realistically be put in market.
Conversion Window: The period of time during which conversions are being tracked.
Cross-Device Attribution: The process of applying algorithmic attribution to understand media impact across different devices beyond desktop computers, so that marketing spend can be optimized across smartphones, desktop, tablets and other devices.
Engagement Stack: A chronologically ordered list of all the marketing touchpoints experienced by an individual user.
First Click/First Touch: An attribution model that gives 100% of the conversion credit to the first marketing touchpoint experienced by a prospect prior to becoming a customer.
Halo (aka Halo Effect): Quantifies the degree to which different channels lift, or in some cases, cannibalize each other. For example, as the result of a halo effect, an advertiser may see an increase in branded search queries or conversions while running an online display campaign.
Inter-Channel: The affinity between tactics used within one channel and those used within another, such as which online display ads drive searches on which keywords.
Intra-Channel: The affinity between different tactics used within the same channel, such as which non-branded keywords drive searches on which branded keywords.
Last Click/Last Touch: An attribution model that gives 100% of the conversion credit to the last marketing touchpoint experienced by a prospect prior to becoming a customer.
Look-Back Window: The time period within which an advertisement will have an effect on a user.
Match Rate: The percentage of events or interactions that can be attributed to a single, individual user, such as clicks and opens across all digital channels, as well as mobile events, in-store and call center transactions, direct mail, and more.
Model Validation: The process of verifying the accuracy of attribution measurement and the media spend recommendations it delivers by comparing the predicted results with the actual results.
Non-Addressable Channels: Includes channels like broadcast TV, radio, print, out-of-home, in-store displays, etc., where marketing messages are delivered to individuals who cannot be identified at a user-level.
Point of Diminishing Returns: The point at which increased investment in a particular area results in a decrease in the overall return (assuming all variables remain fixed). For example, the point of diminishing returns for a PPC campaign occurs when increasing the budget results in a decline in conversion rates and an increase in cost per acquisition.
Refresh: The process of applying algorithmic attribution within a defined conversion window and look-back period.
Response Channels: Any channel that enables a customer or prospect to initiate a desired action in response to exposure to a marketing stimulation created by a stimulation channel. Some advertisers call these “sales channels” or “revenue channels.” Examples include an eCommerce website, mobile website, traditional retail store, call center and more.
Rules-Based: An attribution model that distributes conversion credit across more than one marketing touchpoint using a manually or arbitrarily assigned weight. Examples of rules-based attribution models include even weighting, time decay, opener/advancer/closer, and linear models.
Scenario Planning: The use of predictive analytics to forecast future performance and produce customized media plans containing the optimal mix of channels and tactics needed to maximize marketing return on investment.
Segment: An identifiable group of individuals who share similar characteristics or needs and generally respond in a predictable matter to a marketing stimulation.
Stimulation Channels: Any channel that produces a marketing impression with a customer or prospect. Some marketers call these “impression channels,” “communication channels,” or “marketing channels.” Examples include paid search, online display, TV, radio, OOH, e-mail, direct mail and more.
Taxonomy: Predefined classifications and naming conventions of attributes. Taxonomies differ across organizations and ensure that the analyses, insights and recommendations derived from attribution map directly to a company’s unique jargon and business goals.
Top-Down Modeling: Leverages summary-level data from non-addressable channels that do not collect individual user-level data (such as broadcast TV, radio, print, PR, out-of-home, etc.) to infer the relationships between different channels and tactics and deliver recommendations for optimization. The summary-level data that feeds a top-down model may include counts of individuals who were exposed to and/or took action upon various marketing initiatives; the particular date, time or location from which an advertisement was viewed; as well as econometric, seasonal and competitive data that have an impact on performance, such as interest rates, the weather, new product launches, etc.
Touchpoints: Any media interaction to which a customer or prospect is exposed. Touchpoints can include a wide range of interactions, from seeing a television commercial to conducting online price comparisons on a comparison shopping engine site.
Learn more about our cross channel marketing suite of products.
Click here to subscribe to The Visual IQ monthly e-newsletter.