data model
Definisjon
En abstrakt representasjon som organiserer dataelementer og standardiserer hvordan de relaterer til hverandre og til egenskaper i den virkelige verden.
Synonymer3
Antonymer2
Eksempler på bruk1
The database uses a relational data model to organize information; A good data model improves the efficiency of data retrieval; Developers designed a data model to represent customer interactions.
Etymologi og opprinnelse
The term 'data model' combines 'data', from Latin 'datum' meaning 'something given', and 'model', from Latin 'modulus' meaning 'a measure or standard', reflecting a structured framework for organizing data.
Relasjonsmatrise
Utforsk forbindelser og sammenhenger
"ABC-Analyse (Strategic Method of Inventory Management)"
is used for
Ad monitoring software
Ad monitoring software collects detailed data on ad performance metrics such as impressions, clicks, conversions, spend, audience demographics, and placement effectiveness across multiple channels. This raw data needs to be structured and interpreted to derive actionable insights, which is where a data model becomes critical. A well-designed data model organizes and standardizes the disparate data points from ad monitoring tools into coherent entities and relationships—such as campaigns, creatives, audience segments, and time periods—enabling advanced analytics, trend identification, and predictive modeling. By applying a data model, marketing teams can accurately attribute performance, detect patterns in ad effectiveness, and optimize budget allocation dynamically. In digital strategy, this integration allows for automated decision-making processes, such as programmatic bidding adjustments or personalized content delivery, based on the structured insights derived from the data model. Therefore, the data model acts as the backbone that transforms raw ad monitoring data into strategic intelligence, making the monitoring actionable and scalable within marketing operations.
ad exchange
An ad exchange functions as a real-time marketplace where digital advertising inventory is bought and sold programmatically, relying heavily on sophisticated data models to optimize bidding strategies, audience targeting, and pricing. Data models in this context analyze vast amounts of user data—such as demographics, browsing behavior, and purchase intent—to predict which ad impressions are most valuable and likely to convert. This predictive capability enables the ad exchange to dynamically match advertisers’ bids with the most relevant inventory, maximizing ROI for advertisers and yield for publishers. Without robust data models, ad exchanges would lack the precision needed to efficiently allocate impressions, leading to suboptimal ad placements and wasted spend. Conversely, the ad exchange provides the environment where data models are applied in real-time to execute automated buying decisions, making the relationship cyclical and essential for effective digital advertising strategies.
a/b-test
is used for analyzing results in
Ad copy
Ad copy effectiveness depends heavily on insights derived from a data model that structures and interprets customer data, behaviors, and preferences. Specifically, data models segment audiences based on demographics, purchase history, engagement patterns, and psychographics, enabling marketers to tailor ad copy with precise messaging, tone, and offers that resonate with each segment. Additionally, predictive data models forecast customer responses to different messaging strategies, allowing iterative optimization of ad copy through testing and machine learning feedback loops. This integration ensures that ad copy is not generic but dynamically aligned with data-driven customer insights, improving conversion rates and ROI. In digital strategy, this relationship enables scalable personalization at the campaign level, where data models inform which ad copy variants to deploy across channels and timing, thus making marketing efforts more efficient and measurable.
Ad creative testing
Ad creative testing involves systematically experimenting with different versions of advertisements—such as variations in imagery, copy, calls-to-action, and formats—to identify which creative elements drive the best audience engagement and conversion outcomes. A data model, in this context, serves as a structured analytical framework that ingests the performance data from these tests (click-through rates, conversion rates, engagement metrics, etc.) and applies statistical or machine learning techniques to quantify the impact of each creative variable. By integrating ad creative testing results into a data model, marketers can move beyond intuition-based decisions to data-driven optimization, enabling predictive insights about which creative combinations will perform best for specific audience segments or channels. This relationship is crucial because the data model transforms raw test results into actionable intelligence, guiding iterative creative development and budget allocation with measurable ROI improvements. Without a robust data model, ad creative testing risks producing fragmented insights that are difficult to scale or generalize across campaigns and markets.
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