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May 06.2025
3 Minutes Read

Transforming Audience Targeting with AI: SAS and Amazon Bedrock Explained

Generative AI audience targeting concept with digital overlay.

Unlocking the Power of Generative AI in Marketing

The marketing landscape is evolving rapidly, necessitating advanced audience targeting methods for brands eager to capture consumer attention amidst an overwhelming sea of content. Traditional approaches often falter under the weight of complexity, inhibiting organizations from efficiently segmenting their audiences. Enter the groundbreaking integration of SAS Customer Intelligence 360 and Amazon Bedrock's generative AI, which springboards marketers into a new era of data-driven decision-making.

Revolutionizing Audience Targeting with AI Learning

Imagine being able to express your target audience's characteristics and behaviors in natural language, just as you would speak to a colleague. This is now possible thanks to the potent capabilities of SAS Customer Intelligence 360 combined with Amazon Bedrock's advanced Generative AI (GenAI). Instead of wrestling with technical jargon or complex programming, marketers simply describe their ideal targets—be it the age, spending habits, or purchasing history—directly.

This innovation reveals a broader shift towards simplified AI learning and natural language processing, bridging the gap between marketing intent and operational action. With this system, marketers can efficiently create precise audience segments while adhering to essential data governance protocols, ensuring that consumer privacy and ethical standards remain intact.

Streamlining Marketing Operations

Integrating SAS Customer Intelligence 360 with Amazon Bedrock doesn’t just introduce convenience; it reshapes the marketing paradigm. Teams experience significant gains in operational efficiency, as tasks that typically required hours or even days of technical groundwork can now be handled within minutes. This newfound speed enables rapid response to shifts in consumer behavior and market conditions, supporting the idea of an evolving AI learning path within the organization.

Moreover, teams can test and refine multiple audience targeting strategies almost instantaneously. Leveraging real-time insights, they can adapt their campaigns on-the-fly, embracing a culture of fast experimentation and agility—a hallmark of modern marketing success.

Case Studies: Real-World Applications

Many organizations have begun integrating similar technologies to drive efficiency and engagement. For instance, a retail brand successfully deployed these tools to boost their engagement rates by over 20%, thanks to the ability to tailor messages to specific demographics with unprecedented speed and precision. Furthermore, they were able to conduct A/B tests on different audience segments and immediately pivot their strategies based on the results, a powerful use of AI science and analytics.

Looking Ahead: Future Trends in AI Marketing

The integration of SAS and Amazon Bedrock signals a pivotal trend towards democratizing access to sophisticated marketing tools. As more organizations leverage these capabilities, we can anticipate a significant shift in the marketing landscape. AI learning will become a foundational aspect of marketing strategy, empowering professionals at all levels to leverage data intelligently.

Moreover, predictive analytics derived from machine learning will further refine audience insights, making it possible to anticipate consumer needs more accurately than ever before. Companies that prioritize AI technologies in their marketing frameworks will likely be the ones leading the charge in their respective industries.

The Power of Knowledge: Why This Matters

Understanding how to harness generative AI in marketing is not merely valuable—it's essential for businesses that aim to thrive in a competitive marketplace. With the integration of SAS Customer Intelligence 360 and Amazon Bedrock, marketers can unlock unprecedented capabilities in audience segmentation, leading to targeted campaigns that resonate profoundly with consumers.

If you’re keen to stay ahead of the curve, embracing these technologies and concepts is a critical step toward modernizing your marketing efforts. The more familiar you become with AI learning paths and their applications, the better equipped you'll be to navigate future challenges and opportunities in the digital marketing arena.

As this technology continues to evolve, remaining informed and adaptive will be paramount. For those ready to delve into the innovative world of AI-driven marketing, now is the time to act and explore the possibilities that lie ahead.

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06.14.2025

Transform Spare Parts Inventory Management with AI Technology Insights

Update Understanding the Need for Optimized Inventory Management In today's fast-paced industrial environment, managing spare parts inventory effectively is essential for maintaining asset performance and minimizing downtime. The costs associated with equipment failure can escalate quickly, making predictive maintenance strategies critical. However, uncertainties surrounding equipment failures pose a significant challenge, driving industries to hold more inventory than necessary. This article explores how integrating AI technology into spare parts inventory management can streamline processes, reduce waste, and enhance service delivery. A Simulated Approach to Optimize Inventory Levels At the heart of optimizing spare parts inventory lies a simulation-based approach, particularly leveraging SAS's programming capabilities. By employing tools such as PROC LIFEREG and PROC LIFETEST, organizations can accurately model asset lifetimes using statistical distributions, specifically the Weibull distribution. This modeling not only helps predict failures but also incorporates uncertainty in failure events, allowing companies to tailor their inventory strategies effectively. Practical Insights on Stochastic Simulation The integration of stochastic simulation into inventory planning allows decision-makers to visualize various failure scenarios. This predictive capability is essential for understanding key performance metrics such as availability and backlog. For instance, an airline fleet might set a target of having 95% of asset failures fulfilled from its inventory without delay—an important performance indicator known as the Type 1 service level or fill rate. By achieving this balance, businesses can significantly cut costs while meeting operational needs. The Data-Driven Analytics Advantage Utilizing a comprehensive approach that combines descriptive, predictive, and prescriptive analytics can dramatically enhance inventory decisions. Descriptive analytics sheds light on historical trends, while predictive analytics helps forecast future needs. Most importantly, prescriptive analytics offers recommendations on optimal inventory levels based on these insights. Real-World Application: Case Study in Fleet Optimization Consider a fleet of aircraft where each asset must be replaced upon failure. By analyzing historical removal data, businesses can track operational hours and failure incidents to robustly predict inventory requirements. The data-driven methods enable firms to optimize spare parts inventory levels—leading to improved asset uptime and diminished costs. Future Predictions: The Impact of AI on Inventory Management As AI technology continues to evolve, the future of spare parts inventory management will likely see even greater integration of machine learning algorithms. By automating inventory decisions and utilizing real-time data analytics, businesses can enhance efficiency and reduce reliance on manual interventions. This continual improvement may redefine industry standards and results in unprecedented gains in asset performance management. Conclusion: Embracing the Future of Spare Parts Management The journey towards effective spare parts inventory optimization is crucial for industries reliant on high asset performance. By leveraging simulation-based techniques and evolving AI technologies, companies can meet the challenges posed by uncertainties in equipment performance. Those looking to modernize their inventory practices should focus on integrating these technologies, which not only foster innovation but also yield substantial cost savings and improved service levels. To learn more about how to optimize your spare parts inventory and implement AI technologies effectively, explore the available resources in AI learning paths that cater to innovators in your industry.

06.14.2025

Unlock Accuracy in Machine Learning with Hyperparameter Tuning Fun

Update Understanding Hyperparameter Tuning in AI Building a machine learning model isn’t just about plugging in data and hitting the train button. It’s deeply nuanced, requiring fine-tuning to achieve that coveted accuracy. Hyperparameter tuning serves as a crucial step in enhancing the performance of your machine learning model. But how can you ensure that your model reaches its peak potential while navigating complex variables? Let’s decode that mystery while exploring the fun aspects of tuning! Making Sense of Hyperparameters Think of hyperparameters like the ingredients in a recipe. Just as changing the oven temperature can alter how a cake turns out, tweaking hyperparameters will affect how well your model learns from data. Hyperparameters are values you set before training a model; they dictate how the model will be trained. For example, consider parameters like learning rate, batch size, and the number of estimators in a tree-based model. Setting these values appropriately is vital, as it can significantly impact your model’s performance. AI Learning and Its Potential As we delve into the world of AI learning, understanding hyperparameter tuning brings us closer to harnessing artificial intelligence’s full potential. Even a 1% boost in accuracy can notably sway results in various applications, from predicting stock trends to diagnosing medical conditions. As our reliance on AI grows, so does the need for sophisticated models that learn effectively without overfitting or becoming overly complex. Hyperparameter Autotuning: The Smart Approach Manual tuning of hyperparameters can be a cumbersome process. Imagine trying to bake a cake again and again, adjusting the temperature and time for each attempt. Not very efficient, right? However, with hyperparameter autotuning, you can let your computer handle the tedious work. It tests multiple combinations of hyperparameters and identifies what works best for your model. This saving of time and effort allows data scientists to focus on more strategic tasks, leveraging AI’s capabilities to automate decisions about model parameters. Overfitting: A Key Challenge While tuning is essential for model improvement, there's a flip side. Overfitting your model to your training data can yield excellent results on paper but fails spectacularly in real applications. It’s like making a cake that tastes amazing to you, but when your friends try to recreate it with their ingredients, it can go awry. A successful model must generalize well across different datasets, maintaining accuracy without being overly tailored to the training data. The Future of Hyperparameter Tuning Looking ahead, innovations in AI science suggest that hyperparameter tuning will become more user-friendly with the integration of AI itself. Imagine algorithms that can predict optimal hyperparameters based on previous learning experiences! As we advance, we may also see educational pathways in AI aiming to make hyperparameter tuning accessible even to beginners. Who knows, the next ground-breaking AI tool could be just around the corner, simplifying yet another layer of complexity. Actionable Insights for Better Models As you embark on your AI journey, remember: tuning is not just a technical necessity but an opportunity for creativity. Don’t shy away from experimentation! Gather feedback from each model iteration and use the insights to fine-tune your process. Whether it’s in finance, healthcare, or any other sector, improved accuracy can translate to significant gains—both financially and operationally. Join the AI Learning Path Today The landscape of AI is rapidly changing, and by understanding hyperparameter tuning, you're gaining crucial insights that could reshape your AI learning path. Whether you’re a seasoned data scientist or just dipping your toes into the AI waters, mastering these concepts will empower you to create more effective machine learning models. Embrace the fun of the learning journey and explore how your newfound skills can impact your career path!

06.13.2025

Unlock AI Potential: Master Smarter Data Management with SAS Viya

Update Understanding the Backbone of AI: Data Management In the world of artificial intelligence (AI), a fundamental principle remains: AI is only as powerful as the data behind it. Often encapsulated in the phrase "garbage in, garbage out," the means by which data is collected, managed, and utilized greatly influences the quality of AI outcomes. Poor data management leads to flawed models, biased predictions, and opaque decision-making processes, consequently impacting organizational effectiveness. As businesses transition to more AI-integrated operations, a solid data management foundation becomes essential. The Reality of Data Management Challenges Despite the urgency for AI readiness, many organizations face significant hurdles in basic data management. A recent study by DBTA revealed that only 40.9% of enterprises take a proactive approach to their data management strategies. This discrepancy exposes various issues, such as siloed data, inefficient data pipelines, and inconsistent governance practices, which collectively hinder the potential of AI projects. Casper Pedersen, SAS’ Data Lead in EMEA, identifies six prevalent challenges disrupting effective data management frameworks across industries, including data infrastructure complexity and storage inefficiency. The inability to efficiently manage data often leads to stalled AI pilots, frustrating teams eager to demonstrate the ROI of their technological investments. Common Challenges in Data Management Let’s further explore the specific challenges organizations face in making their data fit for AI initiatives: Data Infrastructure Complexity: As different teams utilize various data formats, complexities emerge in data structures. Schema drift can break models and bog down pipelines, resulting in increased cloud costs and sluggish performance. SAS SpeedyStore addresses these issues by unifying different data types into a single, efficient engine, allowing data engineers to focus more on developing models rather than managing chaotic data flows. Storage Inefficiency: Many teams are caught between the high cost of fast storage and the performance issues associated with cheap storage. Legacy storage solutions often struggle to efficiently manage data, often leading to organizations effectively 'paying through the nose' for their data, even when it is infrequently used. The Integral Role of SAS Viya SAS Viya offers various tools to tackle these common pain points through innovative data management solutions. By streamlining processes and reducing reliance on multiple tools, organizations can create more efficient AI frameworks. With SAS Viya, enterprises can: Consolidate different data types for a holistic view. Optimize AI performance through in-memory computing. Achieve real-time analytics capabilities, ensuring timely decision-making. Future Perspectives on Data Management and AI The evolving landscape of AI necessitates a shift towards smart data management practices. Companies focused on integrating solutions like SAS Viya are likely to experience a competitive edge as they better manage their data inputs. As AI continues to develop, firms that prioritize establishing robust data infrastructures may find themselves at the forefront of industry innovation. Actionable Insights for Businesses Organizations should evaluate their current data management strategies against the challenges outlined above. Companies are encouraged to: Conduct a data maturity assessment to identify existing gaps. Invest in integrated data management solutions such as SAS Viya. Empower data teams with tools that reduce complexity and promote efficiency. As enterprises grapple with evolving technologies, understanding the interplay between data management and AI capabilities can shape their future successes. Transforming how data is managed is not just about remaining relevant; it's about survival in a technology-driven marketplace. Businesses that take steps now to create a robust data management foundation can position themselves to leverage the potential of AI fully, aligning themselves with future industry standards. Conclusion In a world where AI technologies play an increasing role, recognizing and overcoming data management challenges can unlock new potentials for businesses. To navigate the complexities and reap the benefits of AI, companies should proactively seek solutions that offer clarity and efficiency in their data management strategies.

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#721","city":"Greenville","state":"SC","zip":"29341","email":"support@divinewebconsultants.com","tos":"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","privacy":"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