datamodellering
Definisjon
Prosessen med å lage en datamodell for å organisere og strukturere data i henhold til et spesifikt domene eller en applikasjon.
Synonymer3
Antonymer2
Eksempler på bruk1
Datamodellering is essential for database design; Effective datamodellering improves data consistency; The team used datamodellering to map business processes.
Etymologi og opprinnelse
Derived from the Norwegian compound word 'data' meaning information processed by computers, and 'modellering' from 'modellere' meaning to model or shape, originating from Latin 'modulus' meaning measure or standard. The term reflects the practice of shaping data into structured models.
Relasjonsmatrise
Utforsk forbindelser og sammenhenger
a/b-testing
A/B testing and data modeling are tightly linked in marketing, business, and digital strategy through the iterative process of hypothesis generation, experiment design, and result interpretation. Data modeling provides the statistical and predictive frameworks that define how variables and customer behaviors are represented, enabling marketers to segment audiences, identify key drivers of conversion, and predict outcomes. This modeling informs the design of A/B tests by specifying which variables to manipulate and which metrics to track, ensuring that experiments are focused and statistically valid. Conversely, the results of A/B tests feed back into data models by validating assumptions, refining predictive accuracy, and uncovering new patterns or causal relationships. For example, a data model might identify a high-impact customer segment, prompting targeted A/B tests on messaging or offers for that segment. The test results then update the model’s parameters to improve future targeting and personalization. This cyclical interaction enhances decision-making precision, optimizes resource allocation, and accelerates learning in digital strategies, making data modeling and A/B testing mutually reinforcing components of evidence-based marketing and business optimization.
Account based marketing (ABM)
Account Based Marketing (ABM) targets specific high-value accounts with personalized marketing efforts, requiring precise identification and understanding of these accounts' characteristics, behaviors, and needs. Datamodellering (data modeling) plays a crucial role in structuring, integrating, and analyzing diverse data sources—such as CRM data, firmographics, engagement metrics, and intent signals—to create comprehensive, actionable profiles of target accounts. By developing robust data models that unify and standardize this information, organizations can accurately segment accounts, predict buying signals, and tailor marketing strategies at an individual account level. This structured data foundation enables ABM teams to prioritize accounts, customize messaging, optimize channel selection, and measure campaign effectiveness with higher precision. In essence, datamodellering transforms raw, disparate data into a coherent framework that directly informs and enhances the execution and scalability of ABM strategies within marketing, business development, and digital transformation initiatives.
ad exchange
An ad exchange is a digital marketplace where advertising inventory is bought and sold programmatically, relying heavily on real-time bidding and data-driven decision-making. Datamodellering (data modeling) plays a critical role in this ecosystem by structuring and organizing vast amounts of user, contextual, and campaign data to enable precise targeting and optimization. Specifically, data models help define user segments, predict user behavior, and evaluate ad performance metrics, which are essential inputs for the algorithms that run on ad exchanges. Without robust data modeling, the ad exchange’s ability to match the right ads to the right users at the right time diminishes, leading to inefficient spend and lower ROI. Conversely, insights derived from ad exchange transactions feed back into data models to continuously refine targeting strategies and improve predictive accuracy. Thus, datamodellering underpins the operational intelligence of ad exchanges, making their integration vital for effective digital marketing strategies that depend on programmatic advertising.
a/b-test
is used for
Account executive
An Account Executive (AE) in marketing and business serves as the primary liaison between clients and the internal teams, responsible for understanding client needs, managing campaigns, and driving revenue growth. Datamodellering (data modeling) plays a crucial role in enabling AEs to make data-driven decisions and tailor their strategies effectively. Specifically, data modeling structures and organizes customer, campaign, and market data into actionable formats, allowing AEs to identify target segments, forecast campaign outcomes, and optimize resource allocation. By leveraging well-constructed data models, AEs can present clients with evidence-based recommendations, demonstrate ROI more convincingly, and customize marketing strategies to align with digital transformation goals. In digital strategy contexts, data modeling supports the AE’s ability to interpret complex datasets, enabling precise audience targeting and personalized messaging, which are essential for successful client engagement and campaign performance. Thus, the AE relies on datamodellering outputs to translate raw data into strategic insights that directly influence client satisfaction and business growth.
Ad copy
Ad copy creation benefits significantly from datamodellering (data modeling) by leveraging structured customer data and behavioral insights to craft highly targeted, personalized messaging. Specifically, datamodellering enables marketers to segment audiences based on attributes such as demographics, purchase history, and engagement patterns, which informs the tone, content, and call-to-action of ad copy to increase relevance and conversion rates. Additionally, predictive models can identify which messaging variants are likely to perform best for different segments, allowing for dynamic optimization of ad copy in real-time campaigns. This integration ensures that ad copy is not generic but data-driven, maximizing ROI by aligning creative elements with statistically derived customer profiles and preferences.
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