The complex world of marketing measurement invokes buzzwords galore and confuses a layman. Navigating them can be tricky. This quick reference introduces you to some commonly used Marketing Measurement Terminology. We define them all in an easy-to-understand way for you.
Common Marketing Measurement Terminology
A process of launching two different versions of an ad: version A and version B, to an equal number of people within specific target audience and tracking behavior changes and overall results based on the version viewed. Version A is usually an existing control and B is the challenger, typically with a change in one variable, such as a copy or design element.
A/B testing, at its simplest, is randomly showing a respondent one version of a design or page — Version A or Version B — and tracking the changes in behavior based on which version they saw. Version A is normally your existing design control” in statistics lingo); and Version B is the “test,” with one copy or design element changed.
In a “50/50 A/B split test,” you are randomly selecting which version of a design to show. A classic example would be comparing conversions resulting from serving either version (A) or (B), where the versions display different headlines.
A/B tests are commonly applied to many forms of copy testing (including digital tests for clicked-on ad copy and landing page copy) to determine which version drives the more desired result.
Abandonment is the discontinuance of a marketed product. It is also called product deletion or product elimination.
Abandonment may occur at any time from shortly after launch (a new product failure) to many years later.
This term is also used to describe when customers shop online, but fail to complete a purchase.
Abandonment Rate (Shopping Cart Abandonment Rate)
Abandonment rate is a term associated with the use of virtual shopping carts. Although shoppers in brick-and-mortar stores rarely abandon their carts, abandonment of virtual shopping carts is quite common. Marketers can count how many of the shopping carts used in a specified time period result in completed sales versus how many are abandoned.
The abandonment rate is the ratio of the number of abandoned shopping carts to the number of initiated transactions.
E-commerce shopping cart abandonment rates are tracked regularly by the Baymard Institute.
Abandonment rate helps marketers understand website user behavior. Specifically, abandonment rate is defined as “the percentage of shopping carts that are abandoned” prior to the completion of the purchase.
As an example, an online comics retailer found that of the 25,000 customers who loaded items into their electronic baskets, only 5,000 actually purchased:
Purchases not completed = purchases initiated less purchases completed = 25,000 – 5,000 = 20,000.
Abandonment rate = Not completed / Customer initiation = 20,000 / 25,000 = 80% abandonment rate.
Short for advertising technology, specifically software and digital tools that help agencies and brands target, deliver and analyse digital advertising.
A sequence of computer-generated rules that produce a predetermined outcome from a set of inputs. Marketing algorithms are used to automate ad buys at scale and inform strategic decisions, to reduce wasted spend and generate the most value and ROI.
Amazon Web Services (AWS)
A subsidiary of Amazon that provides on-demand cloud computing platforms to individuals, companies and governments, on a paid subscription basis.
A software foundation that is engineered to generate insights from data to drive business decisions.
Application Program Interface (API)
A software intermediary that allows two applications to talk to each other. Each time you use an application like Facebook, send an instant message, or check the weather on your phone, you’re using an API.
Artificial Intelligence (AI)
The ability of a computer or digitally-controlled robot to perform tasks or solve problems commonly associated with human intelligence. The goal of AI is to help companies grow, improve customer experience and optimize both speed and quality.
The practice of evaluating marketing touch-points and assigning credit to specific channels that played a role in conversion. The goal of attribution is to pinpoint channels, touch-points and messages that have the greatest impact on the decision to convert or take the desired next step.
A scientific process that organises marketing data to determine how much credit is given to each channel or touchpoint that contributes to conversion. The insights provided by attribution models into how, where, and when a consumer engages with a brand enables marketers to refine tactics or campaigns to meet consumer requirements and improve ROI.
Big data that includes demographics, consumer intent, digital behaviours, location, such as store latitude and longitude, proximity to store, weather triggers and more.
Automated Data Validation (ADV)
A data governance process of ensuring data quality and accuracy by structuring and implementing an automated set of rules, constraints and routines that check for correctness, meaningfulness and security of data.
A Awareness, Attitudes and Usage (AAU)
Studies of awareness, attitudes and usage (AAU) enable marketers to quantify levels and trends in consumer knowledge, perceptions, beliefs, intentions, and behaviours. In some companies, the results of these studies are called “tracking” data because they are used to track long-term changes in customer awareness, attitudes, and behaviours. AAU studies are most useful when their results are set against a clear comparator. This benchmark may comprise the data from prior periods, different markets, or competitors.
AAU metrics are used to track trends in customer attitudes and behaviours. They relate closely to what has been called the Hierarchy of Effects, an assumption that customers progress through sequential stages from lack of awareness, to awareness, to development of attitudes and beliefs about the product, through initial purchase, to brand loyalty. Information about attitudes and beliefs offer insight into the question of why specific users do, or do not, favour certain brands. Typically marketers conduct surveys of large samples of households or business customers to gather these data.
AAU studies feature a range of questions that aim to shed light on customers’ relationships with a product or brand (i.e., “Who are the acceptors and rejecters of the product?” and “How do customers respond to replay of advertising content?”)
Awareness-trial-repeat (ATR) is a paradigm consisting of three key steps by the intended user. The steps take the person or firm from a state of ignorance about a new product to the point of product adoption.
Awareness (cognition) may be of the product generally, its brand, and one or more of its attributes.
Trial means some form of test purchase or use, following upon favorable affect stemming from knowledge regarding the attributes.
Repeat means the trial was sufficiently successful to warrant one or more repeat purchases.
There are other, similar, paradigms (for example attention, interest, desire, action) but these are not new-product specific and do not cover the entire product adoption process.