December 26, 2017 - By Ginna Hall, Senior Writer, Visual IQ
Simply put, marketing intelligence is the information you need to understand and optimize marketing and advertising performance in the context of key audiences. This information becomes possible when you consolidate audience data with multi-touch attribution in a platform (such as Visual IQ’s).
Marketing intelligence is an advanced discipline. There are many terms you need to know to be effective—with more new ones every day. We’ve defined some of the most important concepts to help you boost your marketing and advertising expertise.
Marketing intelligence helps you understand consumer attributes and how different audiences interact with your brand across digital, mobile and physical environments, so you can orchestrate better experiences and optimize budgets across all consumer touchpoints.
This post includes definitions of common terms associated with marketing intelligence -- thirty-one terms specifically related to attribution. We want to demystify words you’ve heard but haven’t had a chance to clarify. Check out 32 Marketing Intelligence Terms You Need to Know (Part 1) for terms related to marketing and media.
Algorithmic Attribution: A multi-touch attribution methodology that uses machine-learning to calculate and fractionally assign credit for a given success metric to the influential marketing touchpoints and dimensions (campaign, placement, publisher, creative, offer, etc.) along the consumer journey, as well as to predict the outcome of future marketing spend allocations.
Container Tag: A delivery mechanism for pixels that eliminates the need to place multiple data tracking codes on a website.
Cookie: A unique piece of code that is placed on a user’s browser by a tracking technology the first time that user accesses a given digital asset. Each piece of information a cookie tracks is called an “event,” and events are transmitted to ad servers or data collectors via codes called tags. For example, each time a user searches for an item online, it is recorded in their browser and later retrieved by ad servers to remember their behavior and better customize their search queries.
Cross-Device Mapping: The process of matching a single user to two or more connected device ID’s associated with that user. This process may also be referred to as “cross-device matching,” “cross-device pairing,” or “cross-device bridging.” There are three different approaches to this process that can be used individually or together to maximize accuracy and scale: first-party deterministic, third-party deterministic, or third-party probabilistic.
Device ID: A unique identifier that can be used to identify a mobile device.
Dimension: A characteristic or feature associated with a marketing or media touchpoint. Each touchpoint may include over a dozen dimensions. For example, an online display ad may include the campaign, placement, publisher, ad size, creative, line of business, etc.
Even Weighted (aka Linear): A rules-based attribution model that distributes equal credit for a given success metric to each touchpoint experienced by a user.
First Click/First Touch: A rules-based attribution model that gives 100% of the credit for a given success metric to the first touchpoint experienced by a user.
First-Party Deterministic: One of three cross-device mapping approaches. It may also be referred to as “brand authentication” since this approach uses the brand’s own first-party, authenticated user data. User authentication may include a website login, app interaction, email interaction, etc.
Halo (aka Halo Effect): A value that quantifies the degree to which different channels lift, or in some cases, cannibalize each other. For example, as the result of a halo effect, a marketer may see an increase in branded search queries or conversions while running an online display campaign.
Last Click/Last Touch: A rules-based attribution model that gives 100% of the credit for a given success metric to the last marketing touchpoint experienced by a user.
Lookback Window: The defined period of time for which an ad can be expected to influence a user to convert or engage with a brand after exposure.
Marketing Mix Modeling: A statistical modeling attribution approach that uses summary-level data from both non-addressable channels and addressable channels to infer the relationships between different channels and tactics and deliver recommendations for optimization. The summary-level data that feeds a marketing mix 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 a marketing message was viewed; as well as exogenous factors, such as economic, seasonal and competitive data that have an impact on performance, such as interest rates, the weather, new product launches, etc.
Match Rate: The number of user ID’s associated with a marketer’s touchpoint data that also match a set of user ID’s associated with another data provider (such as a cross-device data provider), divided by the total number of user ID’s associated with the marketer’s touchpoint data. Match rate is expressed as a percentage.
Model Validation: The process of verifying the accuracy of attribution measurement and the optimization recommendations it delivers by comparing the predicted results with the actual results.
Movement Data: Location-based data that represents the anonymous offline movement and visitation patterns of consumers.
Multi-Touch Attribution: A rules-based or algorithmic attribution methodology that leverages individual, user-level data from addressable channels like online display and paid search to calculate and assign fractional credit of a success metric to the influential marketing touchpoints and dimensions (campaign, placement, publisher, creative, offer, etc.) along the consumer journey to measure past marketing performance.
People-based Marketing: The process of combining various, disparate IDs associated with a single consumer (e.g. cookie ID, device ID and offline ID) into a single, anonymous unique identifier for the purpose of understanding consumer attributes and behaviors within the context of marketing performance measurement.
Personally Identifiable Information (PII): Any data that can be used to identify a specific individual. Most marketers prefer to use non-PII data in order to protect consumer privacy.
Pixel: A transparent gif file or clear image placed within websites, ads and emails to track digital activity (e.g. impressions, website visits, etc.) at a user level. A pixel may also be referred to as a tag.
Point of Diminishing Returns: The point at which increased investment in a particular piece of marketing or media 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.
Rules-Based Attribution: A subjective multi-touch attribution methodology that distributes credit for a success metrics across one or more marketing touchpoints using a prior defined or arbitrarily assigned weight. Examples of rules-based attribution models include first click, last click, u-shaped and even weighted methods.
Scenario Planning: The use of predictive analytics to forecast future performance and produce customized marketing and media plans containing the optimal mix of channels and tactics needed to maximize marketing return while also accounting for constraints.
Tag: A piece of code placed on a website or an ad by which user-level data is collected. A tag may also be referred to as a pixel.
Taxonomy: Predefined, hierarchical classifications and naming conventions of touchpoint dimensions, KPIs and other brand-specific categorizations. Taxonomies differ across marketing organizations and ensure that the analyses, insights and recommendations map directly to a brand’s unique jargon and business goals.
Third-Party Deterministic: One of three cross-device mapping approaches. It may also be referred to as “third-party authentication” because this approach uses a third-party common login across devices (e.g. Google, Facebook, Twitter, etc.).
Third-Party Probabilistic: One of three cross-device mapping approaches. It may also be referred to as “third-party inferred match” because this approach requires partnering with a cross-device technology vendor to make an inferred match across multiple devices to a single user. The connection is made using consumer behavior, relationship-based patterns, Wi-Fi linking, geo-clustering, as well as other variables.
Time Decay: A rules-based attribution model in which the percentage of success metric credit gradually builds while leading up to the last touchpoint in the consumer journey.
U-Shaped (aka Position based: A rules-based attribution model in which the majority of success metric credit is assigned to the first and last touchpoint experienced by a user, and the remaining credit is distributed evenly to the touchpoints in between.
Unique Identifier/Unique ID: The mapping of multiple, disparate, anonymous user IDs associated with a unique user to a single, anonymous user ID that identifies the unique user across platforms, channels and devices.
Yield Curve: A line that plots the maximum number of theoretical “what-if?” marketing and media spend scenarios available to a marketer based on historical marketing performance data for purposes of forecasting marketing performance.
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