Gaming Archives - gettectonic.com
09Apr

AI Agents Explained

AI agents represent a transformative technological advancement that is reshaping business dynamics, going beyond simple automation to address more complex challenges. This insight provides an in-depth exploration of AI agents, covering their functions, operations, and types, such as reflex, goal-based, utility-based, and learning agents. The commercial advantages of AI agents, including cost-effectiveness, scalability, and efficiency, are highlighted, with examples and applications across various industries to demonstrate their impact on business operations and customer experiences. What Are AI Agents? AI agents are sophisticated computer programs designed to autonomously make decisions based on inputs, enabling them to execute tasks independently. These agents are particularly adept at managing operations in uncertain environments, positioning them as critical steps toward artificial general intelligence—where machines can perform any intellectual task comparable to humans. Modern AI agents offer flexible solutions that significantly enhance business efficiency and customer service. How AI Agents Operate AI agents function as more than just tools; they are dynamic participants redefining how organizations interact with both digital and physical environments. Their core functions include learning, reasoning, and planning, which empower them to make informed decisions and take actions in complex scenarios. For companies aiming to fully leverage these capabilities, AI agents are indispensable. Components of AI Agents AI agents consist of several key components that enable them to function effectively in their environments. These components are crucial for developing intelligent agents capable of operating independently across various contexts: Types of AI Agents Understanding the different types of AI agents is crucial for businesses to select the most appropriate agent for their specific needs: Benefits of AI Agents for Businesses Incorporating AI agents into business operations can deliver numerous benefits, significantly impacting the bottom line. AI agents are revolutionizing corporate operations by enhancing customer experiences and operational efficiency, helping businesses thrive and stay competitive in today’s economy. Key benefits include: Applications of AI Agents AI agents are versatile tools with applications across various sectors: Examples of AI Agents AI agents are revolutionizing various industries with specialized applications: Future Trends in AI Agents The evolution of AI agents continues to shape industries, with future trends expected to redefine their capabilities and applications: AI Agents Transforming Customer Experience (CX) AI agents are key drivers in transforming customer experience (CX), offering more personalized, efficient, and seamless interactions. The integration of natural language processing (NLP) in AI agents enhances automation and personalization in customer engagements. Chatbots and voice assistants provide quick, accurate responses, strengthening brand presence and customer loyalty. AI agents also gather and analyze customer data to offer tailored services, predict customer needs, and provide proactive support. Conclusion AI agents are powerful tools for businesses, offering numerous benefits and applications across industries. They enhance customer experiences, streamline operations, and enable intelligent decision-making. Organizations should stay informed about the different types, benefits, applications, and examples of AI agents to fully leverage their potential for growth and innovation. Tectonic, a leading AI development company, provides customized solutions to meet the unique needs of clients across various industries. Their expertise includes integrating AI-powered chatbots, implementing predictive analytics, and exploring generative AI for creative content generation. Businesses can partner with Tectonic to embark on their AI journey and unlock new opportunities for success. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more

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April 9, 2025in Agentforce Platform, Agentic AI, Data, Generative AI
03Jan

Digital Marketing for Casinos

Unveiling the Casino Experience: Harnessing the Power of Digital Marketing In the exciting world of casinos—where entertainment meets sophistication—the influence of digital marketing is undeniable. A strategic approach is packed with actionable insights designed to boost online presence, engage audiences, and drive sustained success. Discover how to craft compelling content, wield social media’s dynamic power, utilize a customer relationship platform, and optimize visibility to ensure your casino stands out in an increasingly competitive digital arena. Tectonic has a successful, winning track record in Salesforce implementation for casinos. Whether you’re promoting exclusive guest events, captivating diverse demographics, or showcasing the unique experience of your casino, this insight equips you to master the digital space with Salesforce. With these strategies, casinos can create a ever-growing online presence that not only strengthens bonds with existing patrons but also entices new guests. Generating New and Repeat Guest Traffic with Salesforce Key Takeaways Why Digital Marketing is a Critical Component for Casinos Digital marketing serves as a critical driver of customer engagement, loyalty, and revenue growth in the casino industry. Strategies such as SEO, email marketing, and social media engagement empower casinos to connect with target audiences and continuously refine their efforts to remain competitive. In today’s crowded and competitive gaming world, leveraging data-driven marketing offers the competitive edge needed to captivate and retain customers. Winning Strategies for Casino Marketing 1. Search Engine Optimization (SEO):Ensure your casino is easy to find with these tactics: 2. Pay-Per-Click Advertising (PPC):Drive traffic with targeted PPC campaigns by: 3. Social Media Marketing:Create buzz with engaging social media campaigns: 4. Email Marketing:Maintain direct communication with: 5. Salesforce 360 Degree Guest View:Maintain personalized communication with: Reaching the Right Audience with Precision Audience Segmentation:Segmenting your audience by behavior, demographics, and preferences ensures more effective marketing. Navigating Legal and Ethical Challenges in Casino Marketing Compliance is essential in maintaining trust and navigating complex regulations. Measuring Success: Metrics and Optimization Key Metrics to Monitor: Campaign Optimization: Addressing Industry Challenges with Marketing 1. Rising Competition:Stand out by delivering unmatched gaming experiences and innovative promotions. 2. High Player Churn:Combat churn with data-driven marketing and personalized offerings to boost player lifetime value. 3. ROI Challenges:Optimize your mix of games and services to balance player satisfaction and profitability. The Road Ahead: Commitment to Digital Transformation With the global online gaming market projected to grow at a significant pace, casinos must embrace a future grounded in digital and data-driven marketing. Investments in technology, analytics, and talent will be pivotal in securing long-term profitability and differentiation. In an industry where chance often rules, success lies in a deliberate, strategic approach to digital marketing. This insight equips you with the tools to not only compete but thrive in this dynamic landscape. Contact Tectonic today to explore Salesforce tools to better reach, engage, and serve your guests. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more

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January 3, 2025in Agentforce Platform, Data, Einstein 1 Platform, Einstein Insights, Enterprise, Laws, Marketing Automation, Salesforce, Salesforce Data Cloud, Salesforce Development Services, Salesforce Experience Cloud, Salesforce Hospitality Travel and Transportation, Salesforce Implementation Services, Technology
Training and Testing Data
21Sep

Training and Testing Data

Data plays a pivotal role in machine learning (ML) and artificial intelligence (AI). Tasks such as recognition, decision-making, and prediction rely on knowledge acquired through training. Much like a parent teaches their child to distinguish between a cat and a bird, or an executive learns to identify business risks hidden within detailed quarterly reports, ML models require structured training using high-quality, relevant data. As AI continues to reshape the modern business landscape, the significance of training data becomes increasingly crucial. What is Training Data? The two primary strengths of ML and AI lie in their ability to identify patterns in data and make informed decisions based on that data. To execute these tasks effectively, models need a reference framework. Training data provides this framework by establishing a baseline against which models can assess new data. For instance, consider the example of image recognition for distinguishing cats from birds. ML models cannot inherently differentiate between objects; they must be taught to do so. In this scenario, training data would consist of thousands of labeled images of cats and birds, highlighting relevant features—such as a cat’s fur, pointed ears, and four legs versus a bird’s feathers, absence of ears, and two feet. Training data is generally extensive and diverse. For the image recognition case, the dataset might include numerous examples of various cats and birds in different poses, lighting conditions, and settings. The data must be consistent enough to capture common traits while being varied enough to represent natural differences, such as cats of different fur colors in various postures like crouching, sitting, standing, and jumping. In business analytics, an ML model first needs to learn the operational patterns of a business by analyzing historical financial and operational data before it can identify problems or recognize opportunities. Once trained, the model can detect unusual patterns, like abnormally low sales for a specific item, or suggest new opportunities, such as a more cost-effective shipping option. After ML models are trained, tested, and validated, they can be applied to real-world data. For the cat versus bird example, a trained model could be integrated into an AI platform that uses real-time camera feeds to identify animals as they appear. How is Training Data Selected? The adage “garbage in, garbage out” resonates particularly well in the context of ML training data; the performance of ML models is directly tied to the quality of their training data. This underscores the importance of data sources, relevance, diversity, and quality for ML and AI developers. Data SourcesTraining data is seldom available off-the-shelf, although this is evolving. Sourcing raw data can be a complex task—imagine locating and obtaining thousands of images of cats and birds for the relatively straightforward model described earlier. Moreover, raw data alone is insufficient for supervised learning; it must be meticulously labeled to emphasize key features that the ML model should focus on. Proper labeling is crucial, as messy or inaccurately labeled data can provide little to no training value. In-house teams can collect and annotate data, but this process can be costly and time-consuming. Alternatively, businesses might acquire data from government databases, open datasets, or crowdsourced efforts, though these sources also necessitate careful attention to data quality criteria. In essence, training data must deliver a complete, diverse, and accurate representation for the intended use case. Data RelevanceTraining data should be timely, meaningful, and pertinent to the subject at hand. For example, a dataset containing thousands of animal images without any cat pictures would be useless for training an ML model to recognize cats. Furthermore, training data must relate directly to the model‘s intended application. For instance, business financial and operational data might be historically accurate and complete, but if it reflects outdated workflows and policies, any ML decisions based on it today would be irrelevant. Data Diversity and BiasA sufficiently diverse training dataset is essential for constructing an effective ML model. If a model’s goal is to identify cats in various poses, its training data should encompass images of cats in multiple positions. Conversely, if the dataset solely contains images of black cats, the model’s ability to identify white, calico, or gray cats may be severely limited. This issue, known as bias, can lead to incomplete or inaccurate predictions and diminish model performance. Data QualityTraining data must be of high quality. Problems such as inaccuracies, missing data, or poor resolution can significantly undermine a model’s effectiveness. For instance, a business’s training data may contain customer names, addresses, and other information. However, if any of these details are incorrect or missing, the ML model is unlikely to produce the expected results. Similarly, low-quality images of cats and birds that are distant, blurry, or poorly lit detract from their usefulness as training data. How is Training Data Utilized in AI and Machine Learning? Training data is input into an ML model, where algorithms analyze it to detect patterns. This process enables the ML model to make more accurate predictions or classifications on future, similar data. There are three primary training techniques: Where Does Reinforcement Learning Fit In? Unlike supervised and unsupervised learning, which rely on predefined training datasets, reinforcement learning adopts a trial-and-error approach, where an agent interacts with its environment. Feedback in the form of rewards or penalties guides the agent’s strategy improvement over time. Whereas supervised learning depends on labeled data and unsupervised learning identifies patterns in raw data, reinforcement learning emphasizes dynamic decision-making, prioritizing ongoing experience over static training data. This approach is particularly effective in fields like robotics, gaming, and other real-time applications. The Role of Humans in Supervised Training The supervised training process typically begins with raw data since comprehensive and appropriately pre-labeled datasets are rare. This data can be sourced from various locations or even generated in-house. Training Data vs. Testing Data Post-training, ML models undergo validation through testing, akin to how teachers assess students after lessons. Test data ensures that the model has been adequately trained and can deliver results within acceptable accuracy and performance ranges. In supervised learning,

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September 21, 2024in Artificial Intelligence, Data
Microsoft Copilot
21Sep

Microsoft Copilot

The fundamental capabilities of collaboration platforms have remained largely unchanged since the pandemic began. These platforms typically offer video conferencing, desktop sharing, and text chat, creating a virtual approximation of in-person meetings. While this setup effectively allows teams to collaborate across distances, it raises the question: Is this all there is to the collaboration experience? Enter Copilot. Microsoft is pioneering a new era of collaboration, where AI assistants help users prioritize meetings, manage follow-ups on action items, and integrate meeting outputs into future tasks. This evolution is particularly promising for knowledge workers who are overwhelmed by constant meetings. Copilot aims to redefine the collaboration experience, promising increased productivity and a more strategic approach to meetings. However, OpenAI, Microsoft’s prominent AI partner, is making moves to disrupt the enterprise space as well. OpenAI recently launched ChatGPT Enterprise, which now boasts 600,000 users, including clients from 93% of the Fortune 500. This week, OpenAI also acquired the videoconferencing startup Multi, sparking speculation that the company may integrate collaboration features directly into ChatGPT. Multi’s unique approach to videoconferencing—described as “multiplayer” and drawing parallels to gaming rather than traditional meetings—hints at a potential shift in how meetings are experienced. The Multi tool, set to be discontinued in July following the acquisition, was tailored for software developers, focusing on screen sharing and leveraging Zoom’s video capabilities. Yet, the concept of enhanced document collaboration extends beyond software developers. Integrating document collaboration with AI-driven features like summarization, and linking this to advanced language models, could revolutionize the collaboration experience. This approach promises to streamline the collaborative process, focusing on the work at hand with new functionalities. That said, not all meetings revolve around documents. Many are simply conversations—often the ones people prefer to avoid. Therefore, refining how meetings are managed and integrating them into users’ work lives will remain crucial, even as new technologies enhance screen sharing and video capabilities. So, where does this leave traditional video services? The quest for meeting equity and AI-enhanced directors will likely continue to refine the experience, striving for the “next best thing to being there.” As the collaboration platform evolves, any outdated elements will become more apparent. Ultimately, collaboration is a multifaceted experience, and technology will play a key role in its continued advancement. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more

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September 21, 2024in Enterprise, Salesforce, Technology
Proper Programmers Desk
10Jul

Proper Programmers Desk

You’ve probably come across those generic “Proper Programmers Desk Items Under $100” lists. For some reason, there’s always that pointless monitor light and back support included. Let me be the first to call you out: if you need a monitor light to type, you’re not really a programmer. And if you spend money on back support but don’t use a random box to store your adapters and cables, you’re probably a poser. Now that we’ve got that out of the way, let’s look into some genuinely indispensable gadgets that every developer should consider. Simple Silicone USB-C 120W CableEven Apple, nudged by EU regulations, has transitioned to USB-C. Owning a robust, full-pin cable that ensures speedy connections between your devices is crucial. Ideal for app developers and tinkering enthusiasts alike, this cable is a steal for its price and versatility. Price: $2.68Where: Aliexpress Baseus 65W GaN ChargerSince everything is wireless and needs charging, a good charger is essential. The Baseus 65W GaN Charger is the best one we’ve come across. No more fumbling under the table for your USB or dealing with dead batteries. Just plug the cable in, and you’re good to go. Price: $26.04Where: Aliexpress External Power ButtonGot your setup under the desk or obscured by monitors? An external power button is your new best friend. Just watch out for curious cats! And touch-driven toddlers! Price: $3.09Where: Aliexpress USB 3.2 HubThe era when USB connectors were used for more than just charging might be fading, but for now, we still need to set up USB drives, keep a mouse receiver nearby, and connect various other USB devices. This hub is the fastest and most affordable option available from a reputable company. Think you are the exception to the rule? Don’t forget headsets, grab-and-go charging blocks, your Vape, the monitor light referenced above. Price: $24.90Where: Aliexpress HydrationWhether you’re into plain water or cutting-edge nootropics, staying hydrated is key. Snowmonkey flasks are my go-to: durable, excellent at maintaining temperature, and backed by fantastic customer service. They even offered a discount code just for you after hearing about this article! Promo Code: SuperShort15Where: Snowmonkey EarplugsWhile noise-canceling headphones are a game-changer, on a budget, simple earplugs are a miracle of their own. Whether foam that expands to fit, kneadable silicone, or rigid types, they’re affordable enough to try them all and find your perfect fit. Price: $1Where: Aliexpress Software EssentialsAs developers, our toolkit is incomplete without some top-notch software. While JetBrains or VIM might top your list for IDEs, let’s not spark a war over it. Here are a few essentials: Flow LauncherFlow is the ultimate Spotlight open-source alternative for Windows, surpassing everything I’ve used before. Need a fast, on-the-go translation? No problem—just choose a plugin from your settings, and you’re all set. It truly is magical. Where: Flow Launcher TickTickManaging your time becomes essential eventually. TickTick’s straightforward interface lets you jot tasks down and tick them off without fuss. Where: TickTick ObsidianA second brain for storing everything from code snippets to comprehensive project notes. Dive into tutorials on YouTube and explore its vast capabilities. Where: Obsidian CamoDitch the subpar sub-$100 webcams and use your smartphone instead. Crisp, clear, and cost-effective. Where: Camo Consider These Upgrades for the Proper Programmers Desk Good Mechanical KeyboardWe are living in a golden era for mechanical keyboard enthusiasts! You could spend years on YouTube exploring the countless options. Choose your favorite wisely. We’ve opted for the SteelSeries Apex Pro because its keys are analog, allowing you to adjust the sensitivity, making it a dream to type on. Of course, there are other viable options at this price point. Light MouseAfter extended use, you might start to feel pain and a sense of fullness in your wrists, eventually leading to sharp pain. But don’t worry. Choosing a mouse under 70 grams, like the Logitech SUPERLIGHT 2, can alleviate these issues. You could opt for the first edition, which is cheaper, but it uses a micro USB, and that’s a dealbreaker for some. Noise-Canceling HeadphonesIf you enjoy a bit of music or podcasts while programming (though they’re not the best for concentration), you might want to consider noise-canceling headphones. The best we’ve encountered are the Sony WH-1000XM4. We would suggest the newer version, but some tests indicate the previous model performs better and is more affordable. Good OLED TVWith TVs now boasting 120Hz refresh rates and various gaming modes, there’s no reason not to own a 55″+ monitor. Believe us, an OLED from LG makes all the difference. Android PhoneFinally, consider this scenario: You’re outdoors without your laptop, and suddenly, your customer’s service goes down. If you were prepared, you’d simply launch a Linux instance on your phone, open your IDE, and start coding a patch. Of course, you could also rush home, risk using a random computer, or just panic. And if money really is no object, add a Universal Robots UR20 Collaborative Arm to your desk for just south of $60,000. While marketed for moving pallets, handling packaging, and the like, we think it would be pretty cool running back and forth from the Keurig to your desk with steaming hot coffee. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more

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July 10, 2024in Salesforce
Gemma 2 Available
26Jun

Gemma 2 is now available to researchers and developers

News from Google – Gemma 2 is now available! Introducing Gemma 2: Advanced AI for Everyone Expanding Access to AI AI has the potential to solve some of humanity’s most pressing issues, but this can only happen if everyone has the tools to build with it. Earlier this year, Google introduced Gemma, a family of lightweight, state-of-the-art open models based on the same research and technology used to create the Gemini models. They’ve since expanded the Gemma family with CodeGemma, RecurrentGemma, and PaliGemma, each offering unique capabilities for various AI tasks. These models are easily accessible through integrations with partners like Hugging Face, NVIDIA, and Ollama. Launching Gemma 2 Google is now officially releasing Gemma 2 to researchers and developers worldwide. Available in both 9 billion (9B) and 27 billion (27B) parameter sizes, Gemma 2 offers higher performance and greater efficiency than its predecessor, along with significant safety enhancements. The 27B model provides competitive alternatives to models more than twice its size, achieving performance levels that were only possible with proprietary models as recently as last December. This performance is now achievable on a single NVIDIA H100 Tensor Core GPU or TPU host, significantly reducing deployment costs. Setting a New Standard for Efficiency and Performance Gemma 2 is built on a redesigned architecture, engineered for exceptional performance and inference efficiency. Here’s what sets it apart: Designed for Developers and Researchers Gemma 2 is not only more powerful but also easier to integrate into your workflows: Supporting Responsible AI Development Google is committed to providing resources for responsible AI development, including their Responsible Generative AI Toolkit. The recently open-sourced LLM Comparator helps with in-depth evaluation of language models. You can now use its companion Python library to run comparative evaluations and visualize the results. Additionally, we are working on open-sourcing our text watermarking technology, SynthID, for Gemma models. When training Gemma 2, Google followed rigorous safety processes, filtering pre-training data and performing extensive testing and evaluation to identify and mitigate potential biases and risks. They publish their results on public benchmarks related to safety and representational harms. Projects Built with Gemma The first Gemma launch led to over 10 million downloads and numerous inspiring projects. For instance, Navarasa used Gemma to create a model rooted in India’s linguistic diversity. Looking Ahead Gemma 2 will enable even more ambitious projects, unlocking new levels of performance and potential in AI creations. We will continue to explore new architectures and develop specialized Gemma variants for a broader range of AI tasks and challenges, including an upcoming 2.6B parameter model designed to bridge the gap between lightweight accessibility and powerful performance. Getting Started Gemma 2 is now available in Google AI Studio, allowing you to test its full performance capabilities at 27B without hardware requirements. You can also download Gemma 2’s model weights from Kaggle and Hugging Face Models, with Vertex AI Model Garden coming soon. To support research and development, Gemma 2 is acessable free of charge through Kaggle or a free tier for Colab notebooks. First-time Google Cloud customers may be eligible for $300 in credits. Academic researchers can apply for the Gemma 2 Academic Research Program to receive Google Cloud credits to accelerate their research with Gemma 2. Applications are open now through August 9. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more

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June 26, 2024in Generative AI
AI Hallucinations
18May

AI Hallucinations

Generative AI (GenAI) is a powerful tool, but it can sometimes produce outputs that appear true but are actually false. These false outputs are known as hallucinations. As GenAI becomes more widely used, concerns about these hallucinations are growing, and the demand for insurance coverage against such risks is expected to rise. The market for AI risk hallucination insurance is still in its infancy but is anticipated to grow rapidly. According to Forrester’s AI predictions for 2024, a major insurer is expected to offer a specific policy for AI risk hallucination. Hallucination insurance is predicted to become a significant revenue generator in 2024. AI hallucinations are false or misleading responses generated by AI models, caused by factors such as: These hallucinations can be problematic in critical applications like medical diagnoses or financial trading. For example, a healthcare AI might incorrectly identify a benign skin lesion as malignant, leading to unnecessary medical interventions. To mitigate AI hallucinations: AI hallucination, though a challenging phenomenon, also offers intriguing applications. In art and design, it can generate visually stunning and imaginative imagery. In data visualization, it can provide new perspectives on complex information. In gaming and virtual reality, it enhances immersive experiences by creating novel and unpredictable environments. Notable examples of AI hallucinations include: Preventing AI hallucinations involves rigorous training, continuous monitoring, and a combination of technical and human interventions to ensure accurate and reliable outputs. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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May 18, 2024in Generative AI
MC Personalization Tips and Tricks
08Nov

MC Personalization Tips and Tricks

Salesforce Marketing Cloud Personalization, formerly Interaction Studio, offers incredible power for personalization. MC Personalization Tips and Tricks below will help you level up your game. Einstein Recipes: Enhancements and Challenges Multiple Dimensional Variations for Products in Einstein Recipes Einstein Recipes offer powerful and flexible tools for creating recommendations. However, the fourth step, Variations, falls short compared to other options. Currently, you can configure only a single Dimensional Variation. While multiple Item Types are available, once you select one, you cannot limit recommended products to specific numbers per category or brand. This limitation hinders control over product recommendations, especially for e-commerce sites with diverse catalogs. Unlike Dimensional Variations, multiple Boosters or Exclusions of the same type can be configured differently, which would be a valuable feature to add for Variations. Department Variation for Products in Einstein Recipes Einstein Recipes allow Dimensional Variations at the Category level, but only for primary categories. There is no option for Department (master category) level, which is limiting for e-commerce sites with broad category trees, such as: Recommendations with Category Variation set can still be dominated by similar products due to similar primary categories. Two solutions could address this: Price Reduction Ingredient in Einstein Recipes Triggered Campaigns in Journey Builder can target various events, including Catalog Triggers. Some triggers, like Product Expiring Soon, are available for Web with Einstein Recipes Ingredients. However, there is no Ingredient for the common e-commerce use case of Price Reduction. Marketing Cloud Personalization (Interaction Studio) has the required price and listPrice attributes for Triggered Campaigns. A workaround involves calculating price reductions externally and passing this information to a Related Catalog Object. More efficient solutions would be: Rating Count in Recipe’s Rating Exclusion Marketing Cloud Personalization offers Exclusions/Inclusions on Recipes to fine-tune recommendations. One option is to exclude/include items based on their rating, with an optional zero rating capture. It would be beneficial to include an option to filter based on rating count, allowing for: Currently, such filters can only be applied on the server side in the Template, which can limit recommendations. Having this feature at the recipe level would be more powerful. Abandoned Cart Retention Setting Marketing Cloud Personalization captures cart information for Einstein Recipes recommendations. However, cart content remains indefinitely unless managed proactively. A workaround involves a Web Campaign that checks cart age and pushes a clear cart action if necessary. A better solution would be a configurable option in MCP settings to automatically remove old cart data. Catalog Enhancements Full MCP Category Hierarchy Support for ETL Marketing Cloud Personalization can create a hierarchical tree of categories with automatic summing of views and revenue. However, this is currently possible only under specific conditions, such as having one Category per product and using a Sitemap format. This limitation is problematic, as ETL is often a better way to manage it. The Category ETL already provides detailed information using department and parentCategoryId attributes, but this data does not replicate the drill-down hierarchy in the Catalog UI or pass data from the bottom Category up. Ensuring feature parity between Sitemap and ETL would be beneficial. Segmentation Enhancements MCP Action Name Management Marketing Cloud Personalization captures actions from multiple sources but does not allow managing created actions. An option to view and remove unnecessary actions would improve user experience by reducing the number of options in the segmentation/targeting picklists. An even better solution would be to merge existing actions, preserving behavioral data after refactoring action names. MCP Hourly-Based Segmentation Rules Currently, segmentation rules in Marketing Cloud Personalization are based on days, limiting on-site campaign targeting. For example, to display an infobar for abandoned cart users, the current segmentation can only show users who have not performed a Cart Action today. Hourly-based segmentation rules would allow more precise targeting, showing users who have not performed a Cart Action in the last hour. Adding a picklist to choose between day or hour-based rules would enhance segmentation capabilities. Full MCP Catalog Export Marketing Cloud Personalization supports manual catalog export but only with limited data. The current export file lacks complete catalog data (e.g., promotable and archived attributes), making it unsuitable for ETL sources. An option to export the full catalog data, matching the ETL schema and including hidden items, would greatly benefit debugging and batch-modifying items for subsequent ETL import. Full MCP Catalog Metadata Visibility Marketing Cloud Personalization supports viewing custom attribute metadata in the Catalog but is limited to ETL updates. Extending this to built-in attributes and including origin and lastUpdated values for all sources (Sitemap, Mobile App, Manual update, API) would simplify debugging Catalog metadata issues, reducing admin/developer work and support tickets. ETL Enhancements External Email Campaign ETL Experience Name & ID External Email Campaign ETL allows passing behavioral data but is limited to Campaign ID and Campaign Name. To fully leverage this data in segmentation, it should also support Email ID and Email Name. Adding Experience ID and Experience Name fields to the ETL would enable targeted personalization, allowing segmentation on entire campaigns or specific emails within campaigns. External Email Campaign ETL Send Segmentation External Email Campaign ETL passes Send, Click, and Open data but does not support segmentation based on Send events. Enabling segmentation rules for Send events would unlock use cases like targeting Web or Push campaigns to users who received an email campaign but did not open it, fully leveraging cross-channel and real-time personalization. External Email Campaign ETL Unsubscription Event Type External Email Campaign ETL passes Send, Click, and Open data but cannot pass unsubscriptions. Including the Unsubscribe event would enable targeted campaigns like surveys about unsubscription reasons, win-back campaigns, or replacing email subscription prompts with other channel recommendations. By addressing these enhancements and challenges, Salesforce Marketing Cloud Personalization (Interaction Studio) can further improve its capabilities and provide more precise, effective, and user-friendly tools for personalized marketing. Reporting Enhancements: Direct Attribution at the MCP Campaign Level Current Reporting in Marketing Cloud Personalization (MCP) Marketing Cloud Personalization (Interaction Studio) offers various reports based on Activity, Results, and Visits. However, it

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November 8, 2023in Salesforce Development Services, Salesforce Financial Services Solutions, Salesforce for Nonprofits, Salesforce Health and Life Sciences Solutions, Salesforce Hospitality Travel and Transportation, Salesforce Implementation Services, Salesforce Marketing Cloud, Salesforce Personalization
Industry Cloud
09Aug

Tectonic’s Salesforce Industry Experience

Tectonic’s Salesforce Industry Experience spans 16+ years of work focused on certain industry areas. Tectonic has worked in all of the following industries implementing and customizing Salesforce: Put Tectonic’s Industry Experience to work for your organization. Our industry and Salesforce expertise will have you up and running in no time. Casino Case Study Government Case Study Healthcare Payer Case Study Tectonic’s Salesforce Industry Experience Implementing Salesforce can be complex. Getting it wrong can be costly. From 5 to 5000 users, across Sales, Service and Marketing clouds, Tectonic has the process and patterns to help you plan, design and implement your solution right the first time. Whether this is your first Salesforce implementation or expansion of users or additional clouds, we can guide you from inception to delivery. Misalignment of your business needs and your system configuration create low adoption and usage rates that erode your Salesforce ROI. Tectonic’s technology professionals use our proven industry expertise and collaborate with business partners to deliver business value. From minor tweaks and retraining to reimplementation, we assess your situation and execute a customized solution to meet your needs. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more

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August 9, 2022in Mulesoft, Salesforce, Salesforce Managed Services
The Evolution of Industrial Revolutions
03Jul

The Evolution of Industrial Revolutions

History of First Four Industrial Revolutions Throughout history, humanity has always relied on technology. Although the technology of each era looked different from today’s, it was groundbreaking for its time. People consistently used available technology to simplify their lives while striving to enhance and advance it. This ongoing pursuit of innovation laid the groundwork for the industrial revolutions. Today, we are in the midst of the fourth industrial revolution, also known as Industry 4.0, marked by the rise of tech and web design companies. The Evolution of Industrial Revolutions. Here’s an overview of the three previous industrial revolutions that have led us to this point: The First Industrial Revolution (1765) The first industrial revolution followed the proto-industrialization period, starting in the late 18th century and extending into the early 19th century. This era was characterized by mechanization, which transformed industries and shifted the economic backbone from agriculture to industry. The massive extraction of coal and the invention of the steam engine introduced a new type of energy, accelerating manufacturing and economic growth through the expansion of railroads. This led to the enlarging of cities where factories and industry took place. The Second Industrial Revolution (1870) Nearly a century after the first, the second industrial revolution began in the late 19th century, marked by significant technological advancements. New sources of energy—electricity, gas, and oil—emerged, leading to the development of the internal combustion engine. This period also saw the rise of steel demand, chemical synthesis, and new communication methods like the telegraph and telephone. The invention of the automobile and airplane at the turn of the 20th century solidified the second industrial revolution’s profound impact on modern society. This led to the growing mobility of humanity. The Third Industrial Revolution (1969) In the latter half of the 20th century, the third industrial revolution introduced nuclear energy as a new power source. This revolution brought forth the rise of electronics, telecommunications, and computers, paving the way for space exploration, advanced research, and biotechnology. In the industrial sector, the advent of Programmable Logic Controllers (PLCs) and robots led to an era of high-level automation, revolutionizing manufacturing processes. This, in turn, led to a time of greater lesiure and freedom. Industry 4.0 Many consider Industry 4.0 to be the fourth industrial revolution, unfolding right before our eyes. Beginning at the dawn of the third millennium with the widespread use of the Internet, Industry 4.0 represents a shift from physical to virtual innovations. It encompasses developments in virtual reality, augmented reality, and other digital technologies that reshape our interaction with the physical world. The four industrial revolutions have fundamentally shaped global economies. Numerous programs and projects are being implemented worldwide to help people harness the benefits of the fourth revolution in their daily lives. From digital flipbooks to augmented reality gaming, the future is bright. For instance, the EU-funded RESTART project aims to transform vocational education and training (VET) systems to meet the digital skill demands of modern industries, ensuring that the workforce is equipped to thrive in this new technological landscape. What’s next? Look out as we are already into the Fifth Industrial Revolution. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more

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July 3, 2019in Salesforce, Technology
Salesforce Certifications
29Sep

Web Pages That Helped With My Google Data Engineer Exam

Google Data Engineer Exam It seems like every day more resources appear to help you study for the Google Data Engineer certification, so I thought in the interests of being helping you aspiring data engineers with some URLs I found helpful. Life of a BigQuery streaming insert When to use different types of storage in Google cloud The Google Study Guide for the Data Engineer Exam A quick guide on Apache Hadoop (several questions on this) The content on this mobile gaming scenario was covered extensively. This Spotify architecture is helpful because if similar to the scenarios you will deal with on the test Data Transfer Options for Google Cloud Lots and Lots of Case Studies Look for more helpful posts on studying for your Data Engineer Exam! A Professional Data Engineer makes data usable and valuable for others by collecting, transforming, and publishing data. This individual evaluates and selects products and services to meet business and regulatory requirements. A Professional Data Engineer creates and manages robust data processing systems. This includes the ability to design, build, deploy, monitor, maintain, and secure data processing workloads. Recommended experience: 3+ years of industry experience including 1+ years designing and managing solutions using Google Cloud. Like1 Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more

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September 29, 2017in Data, Google, Google Analytics, Salesforce

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