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.
Ad monitoring software
Ad monitoring software collects and analyzes real-time data on advertising performance across multiple channels, providing granular insights into metrics such as impressions, click-through rates, conversions, and audience engagement. 'Modellering' (modeling) in marketing and digital strategy involves creating predictive or explanatory models—such as attribution models, customer lifetime value models, or media mix models—that use this data to simulate outcomes, optimize budget allocation, and forecast campaign effectiveness. The relationship is practical and iterative: ad monitoring software supplies the high-quality, time-stamped data necessary for accurate modeling, while modeling techniques interpret and transform this data into actionable strategies. For example, by feeding monitored ad performance data into a media mix model, marketers can identify which channels or creatives drive the most incremental value, enabling more efficient spend and improved ROI. Conversely, modeling can inform which metrics or data points ad monitoring software should prioritize or track more closely, creating a feedback loop that enhances both data collection and strategic decision-making. This synergy is critical for data-driven marketing, where continuous measurement and predictive analytics guide dynamic campaign adjustments and long-term digital strategy planning.
Ad creative testing
Ad creative testing involves systematically experimenting with different versions of ad elements (such as visuals, copy, calls-to-action) to identify which combinations yield the best performance metrics (click-through rates, conversions, engagement). Modellering (modeling), in the context of marketing and digital strategy, refers to building predictive or explanatory models—often using statistical or machine learning techniques—that analyze data to forecast outcomes, optimize resource allocation, or understand causal relationships. The relationship between ad creative testing and modellering is that modeling provides a rigorous, data-driven framework to interpret the results of creative tests beyond simple A/B comparisons. By applying modeling techniques to the data generated from ad creative tests, marketers can quantify the incremental impact of specific creative elements, control for confounding variables (like audience segments or time of day), and predict how new or untested creative variants might perform. This enables more efficient experimentation by prioritizing tests with the highest expected value and supports scaling successful creatives across channels with confidence. In essence, modellering transforms raw test results into actionable insights and strategic forecasts, making creative testing more precise, scalable, and integrated into broader digital marketing optimization efforts.
Ad format
In digital marketing, the choice of ad format (e.g., video, carousel, static image, interactive ads) directly influences the effectiveness and accuracy of modeling (modellering) techniques such as attribution modeling, predictive analytics, and conversion modeling. Specifically, different ad formats generate varying types and volumes of user engagement data—click-through rates, view times, interaction depth—which serve as critical inputs for modeling algorithms to assess campaign performance and forecast outcomes. For example, video ads may provide richer engagement signals (like watch duration) that enable more granular modeling of user intent and conversion likelihood, whereas static image ads might yield simpler click data, limiting modeling complexity. Consequently, marketers must align their ad format selection with the modeling approaches they intend to use to optimize budget allocation and targeting strategies. This alignment ensures that the data collected from chosen ad formats is compatible with and enhances the predictive power and accuracy of the modeling processes, enabling more precise optimization of digital campaigns and better ROI forecasting.
a/b-testing
A/B testing and modellering (modeling) are tightly interwoven in marketing, business, and digital strategy through their complementary roles in optimizing decision-making and resource allocation. Specifically, modellering involves building predictive or explanatory models—such as customer lifetime value models, propensity models, or attribution models—that quantify relationships between variables and forecast outcomes based on historical data. These models generate hypotheses or identify key variables and segments that can be tested in real-world scenarios. A/B testing then operationalizes these hypotheses by experimentally validating the causal impact of changes (e.g., messaging, design, pricing) on user behavior or business metrics. The iterative cycle works as follows: modellering informs which variables or customer segments to target for A/B tests, thereby increasing the efficiency and focus of experimentation; conversely, results from A/B tests provide new data points that refine and recalibrate the models, improving their predictive accuracy and business relevance. This synergy enables marketers and strategists to move beyond intuition-driven decisions toward data-driven, evidence-based optimization, ensuring that models are grounded in experimental validation and that A/B tests are strategically designed based on robust analytical frameworks.
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