modellering
Definisjon
Prosessen med å lage en representasjon eller simulering av et system, konsept eller objekt, ofte for analyse, studie eller designformål.
Synonymer4
Antonymer3
Eksempler på bruk1
The scientist used computer modelling to predict climate change; Architectural modelling helps visualize building designs before construction; 3D modelling is essential in modern animation and game development.
Etymologi og opprinnelse
Derived from the verb 'to model,' which originates from the Old French 'modeler,' itself from Italian 'modello,' meaning 'a measure, standard, or example,' ultimately from Latin 'modulus,' a diminutive of 'modus' meaning 'measure' or 'manner.' The suffix '-ing' forms a noun indicating the action or process.
Relasjonsmatrise
Utforsk forbindelser og sammenhenger
ad exchange
An ad exchange is a digital marketplace that facilitates the real-time buying and selling of advertising inventory, enabling advertisers to bid on impressions programmatically. Modellering (modeling), particularly in marketing and digital strategy, involves creating predictive or prescriptive models—such as audience segmentation, attribution models, or bidding optimization algorithms—that analyze data to forecast user behavior, optimize targeting, and improve campaign performance. The relationship between ad exchanges and modellering is practical and integral: modeling techniques are applied to the data streams and auction dynamics within ad exchanges to inform bidding strategies, optimize budget allocation, and enhance targeting precision. For example, predictive models can estimate the likelihood of conversion for a given impression, allowing demand-side platforms (DSPs) connected to ad exchanges to adjust bids in real time to maximize return on ad spend. Additionally, modeling helps interpret the vast, complex data generated by ad exchanges to refine audience segments and improve campaign outcomes. Without sophisticated modeling, the efficiency and effectiveness of programmatic buying via ad exchanges would be significantly diminished, as manual or heuristic approaches cannot scale or adapt to the rapid, data-driven environment of these platforms.
"ABC-Analyse (Strategic Method of Inventory Management)"
is used for
Ad copy
In marketing and digital strategy, "Ad copy" refers to the crafted textual content designed to persuade or engage a target audience, while "modellering" (modeling) involves creating analytical or predictive frameworks, often using data-driven techniques, to optimize marketing outcomes. The relationship between the two is practical and iterative: modeling techniques analyze historical campaign data, audience behavior, and conversion metrics to identify which elements of ad copy—such as tone, length, keywords, or calls to action—drive the best performance. By applying these insights, marketers can systematically refine and personalize ad copy to maximize engagement, click-through rates, and conversions. Conversely, the effectiveness of different ad copy variants feeds back into the modeling process, improving the accuracy of predictive models. This creates a feedback loop where modeling informs the strategic creation and testing of ad copy, and ad copy performance data enhances the modeling itself. Thus, modeling transforms ad copy from a creative guesswork exercise into a data-optimized asset within digital marketing strategies.
a/b-test
A/B testing and modellering (modeling) intersect in marketing, business, and digital strategy by enabling data-driven decision-making through iterative experimentation and predictive analytics. Specifically, A/B testing provides empirical evidence on the performance of different variants (e.g., webpage designs, messaging, pricing) by isolating variables and measuring their impact on key metrics. Modellering complements this by building statistical or machine learning models that generalize insights from A/B test results to broader contexts, predict outcomes under different scenarios, and optimize strategies at scale. For example, after running multiple A/B tests, a marketing team can use modeling techniques to identify underlying patterns in user behavior, segment customers more effectively, or forecast the long-term impact of a tested change beyond the immediate test window. This integration allows businesses to move from reactive experimentation to proactive strategy optimization, ensuring that A/B tests inform robust models that guide resource allocation, personalization, and growth tactics. In essence, A/B testing generates high-quality experimental data that feed into modellering processes, while modellering amplifies the value of A/B testing by extending insights and enabling scenario planning and optimization beyond isolated tests.
Account based marketing (ABM)
Account Based Marketing (ABM) focuses on targeting and engaging specific high-value accounts through highly personalized campaigns. Modellering (modeling) in this context refers to the use of data-driven predictive and prescriptive models to identify, prioritize, and understand these target accounts more effectively. Specifically, modeling techniques such as predictive lead scoring, propensity modeling, and customer lifetime value estimation enable marketers to pinpoint which accounts are most likely to convert or generate high ROI. This allows ABM strategies to allocate resources efficiently and tailor messaging based on modeled insights about account behavior, firmographics, and engagement patterns. Additionally, modeling supports continuous optimization of ABM campaigns by analyzing historical data to refine target account lists and personalize content dynamically, thus enhancing campaign precision and effectiveness. Therefore, modeling acts as a foundational analytical layer that informs and sharpens ABM execution, making the relationship essential for data-driven, scalable ABM programs.
Account executive
In marketing, business, and digital strategy, an Account Executive (AE) acts as the primary liaison between the client and the agency or company, responsible for managing client relationships, understanding their business goals, and translating those into actionable marketing strategies. 'Modellering' (modeling) refers to the creation of data-driven frameworks or simulations—such as customer segmentation models, predictive sales models, or marketing attribution models—that help forecast outcomes, optimize campaigns, and allocate resources effectively. The AE leverages these modeling outputs to craft tailored proposals, justify budget allocations, and demonstrate potential ROI to clients. Specifically, the AE uses insights from modeling to identify high-value customer segments, predict campaign performance, and recommend strategic adjustments, thereby enhancing client trust and campaign effectiveness. This relationship is practical and iterative: the AE provides client context and feedback that can refine modeling assumptions, while modeling delivers quantitative evidence that empowers the AE to make data-backed decisions and communicate value clearly to stakeholders.
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