test
Definisjon
En prosedyre som har til hensikt å fastslå kvaliteten, ytelsen eller påliteligheten til noe, spesielt før det tas i bred bruk; å utføre tiltak for å kontrollere kvaliteten, ytelsen eller påliteligheten til noe.
Synonymer6
Antonymer3
Eksempler på bruk1
The students took a math test yesterday; The engineer tested the new software for bugs; This medicine was tested extensively before approval.
Etymologi og opprinnelse
From Old French 'test', meaning 'earthen pot' used for assaying metals, derived from Latin 'testum' meaning 'earthen pot, lid, cover'. The sense evolved in English to mean a trial or examination, reflecting the original use of testing metals for quality.
Relasjonsmatrise
Utforsk forbindelser og sammenhenger
"ABC-Analyse (Strategic Method of Inventory Management)"
is a tool for
a/b-testing
In marketing, business, and digital strategy, 'test' refers broadly to any experiment or evaluation designed to measure the effectiveness of a specific variable or tactic. A/B testing is a structured form of such a test that compares two variants (A and B) to determine which performs better against a defined metric, such as click-through rate or conversion rate. The relationship is practical and procedural: A/B testing operationalizes the concept of a 'test' by providing a controlled, data-driven methodology to isolate the impact of a single change (e.g., headline, call-to-action, layout) on user behavior. This allows marketers and strategists to make evidence-based decisions rather than relying on intuition. Essentially, every A/B test is a test, but not every test is an A/B test. The 'test' is the broader category of experimentation, while A/B testing is a specific, rigorous approach within that category that enhances decision-making precision and reduces risk in campaign optimization and product development. Therefore, the 'test' concept underpins A/B testing, and A/B testing exemplifies how to execute a test effectively in digital marketing and business contexts.
Ad placement
Ad placement and testing are intrinsically linked in digital marketing strategies because the effectiveness of an ad heavily depends on where and how it is positioned across channels. Testing—often through A/B or multivariate methods—allows marketers to systematically evaluate different ad placements (such as website locations, social media feeds, time slots, or programmatic inventory sources) to identify which placements yield the highest engagement, click-through rates, or conversions. By running controlled tests that vary only the ad placement while keeping creative and targeting constant, businesses can isolate the impact of placement on performance. This iterative testing process informs budget allocation and optimization, ensuring that ad spend is concentrated on placements that maximize ROI. Without testing, ad placement decisions rely on assumptions or historical data that may not reflect current audience behavior or platform algorithms. Therefore, testing operationalizes the optimization of ad placement, making it a critical feedback mechanism to refine where ads appear for maximum business impact.
Ad format
In digital marketing, the 'Ad format' directly influences how a 'test' is designed and executed to optimize campaign performance. Different ad formats—such as video, carousel, static image, or interactive ads—have unique engagement patterns, creative constraints, and performance metrics. When marketers conduct tests (e.g., A/B or multivariate testing), they must tailor their test variables and hypotheses around the specific ad format to accurately measure impact. For example, testing a video ad’s length or call-to-action placement differs fundamentally from testing headline text or image composition in a static ad. Moreover, the choice of ad format determines which performance indicators are most relevant and how user interaction data is collected and interpreted during testing. This relationship is critical because without aligning the test design to the ad format’s characteristics, the insights gained may be misleading or non-actionable, leading to suboptimal optimization decisions. Therefore, understanding the nuances of each ad format enables marketers to construct precise, format-specific tests that yield actionable data to improve engagement, conversion rates, and ROI.
ad exchange
An ad exchange is a digital marketplace that facilitates the buying and selling of advertising inventory through real-time auctions, enabling advertisers to reach targeted audiences efficiently. Testing, particularly in the form of campaign or creative testing (such as A/B testing or multivariate testing), is critical within ad exchanges to optimize ad performance and maximize return on investment. Advertisers use testing to experiment with different creatives, bidding strategies, audience segments, and placement options within the ad exchange environment. This iterative testing process allows marketers to identify which variables yield the best engagement, conversion rates, or cost-efficiency when bidding on inventory. Without systematic testing, advertisers risk inefficient spend and suboptimal campaign outcomes despite the sophisticated targeting capabilities of ad exchanges. Therefore, testing acts as a feedback mechanism that informs bidding and creative decisions in the ad exchange, directly impacting campaign success and budget allocation.
Account executive
In marketing, business, and digital strategy, an Account Executive (AE) plays a critical role in managing client relationships and ensuring campaign objectives are met. The AE often acts as the liaison between the client and internal teams, including those responsible for executing tests such as A/B tests, usability tests, or market tests. The AE must understand the purpose and design of these tests to set realistic client expectations, interpret preliminary results, and communicate actionable insights back to the client. This involves coordinating test timelines, ensuring test parameters align with client goals, and using test outcomes to refine campaign strategies or business proposals. For example, when a digital marketing campaign includes A/B testing different ad creatives, the AE ensures the client understands the rationale behind the test, monitors progress, and leverages the test data to recommend optimizations or justify budget reallocations. Thus, the AE’s involvement in the testing process is essential for translating test results into strategic decisions that drive client satisfaction and campaign effectiveness.
Ad copy
In marketing and digital strategy, "ad copy" refers to the text crafted to persuade or engage the target audience, while "test" typically involves systematically experimenting with different versions of that ad copy to identify which performs best. The relationship is rooted in optimization: by running controlled tests—such as A/B tests or multivariate tests—marketers can measure how variations in headlines, calls-to-action, tone, or value propositions affect key performance indicators like click-through rates, conversions, or engagement. This iterative testing process enables data-driven refinement of ad copy, ensuring messaging resonates more effectively with the audience and maximizes return on ad spend. Without testing, ad copy remains static and unvalidated, potentially missing opportunities for improved impact. Thus, testing is an essential mechanism to validate and enhance ad copy effectiveness within campaigns.
Account based marketing (ABM)
Account Based Marketing (ABM) focuses on targeting high-value accounts with highly personalized campaigns, requiring precise messaging and channel strategies tailored to each account. Testing within ABM is critical to validate assumptions about account needs, messaging effectiveness, channel preferences, and timing. For example, marketers run controlled experiments (such as A/B tests on email subject lines, content offers, or outreach sequences) within specific accounts or account segments to identify which approaches yield higher engagement or conversion rates. This iterative testing enables continuous optimization of ABM tactics, ensuring resources are allocated efficiently toward strategies that resonate with target accounts. Without systematic testing, ABM campaigns risk relying on unvalidated hypotheses, leading to wasted spend and missed opportunities. Thus, testing acts as a feedback mechanism that refines ABM execution by revealing what works best at the account level, directly improving campaign precision and ROI.
a/b-test
In marketing, business, and digital strategy, 'test' refers broadly to any experiment or evaluation conducted to validate hypotheses, optimize processes, or improve outcomes. An 'A/B test' is a specific, structured form of testing where two variants (A and B) are compared simultaneously to determine which performs better against a defined metric. The relationship is that A/B testing operationalizes the concept of testing by providing a rigorous, data-driven method to isolate variables and measure impact in real time. This allows marketers and strategists to move beyond assumptions and qualitative insights to make decisions grounded in empirical evidence. For example, when optimizing a landing page, a general 'test' might involve trying different headlines or layouts sequentially without control, whereas an A/B test splits traffic between two versions concurrently, controlling for external factors and enabling statistically valid conclusions. Thus, A/B testing is a practical implementation of the broader concept of testing that enhances decision-making precision and accelerates iterative improvement cycles in digital campaigns and business strategies.
Ad monitoring software
Ad monitoring software enables marketers to systematically track and analyze the performance and placement of advertisements across various channels in real time. When conducting tests—such as A/B tests or multivariate tests—on ad creatives, targeting parameters, or bidding strategies, ad monitoring software provides the critical data needed to evaluate which variations perform better. Specifically, it captures metrics like impressions, click-through rates, conversion rates, and competitor ad activity, allowing marketers to validate hypotheses during testing phases. This feedback loop ensures that tests are data-driven and that decisions to scale or pivot campaigns are based on accurate, timely insights rather than assumptions. Without ad monitoring software, test results would lack granularity and context, making it difficult to optimize ad spend and improve campaign effectiveness. Thus, ad monitoring software acts as the measurement backbone that transforms marketing tests from guesswork into precise, actionable experiments.
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