Integrations Archives - gettectonic.com - Page 3

Customization and Configuration in Salesforce

Salesforce Customization vs. Configuration: Choosing the Right Approach for Your Business Salesforce has become a top choice for businesses aiming to strengthen customer relationships and achieve their goals. Its flexibility to adapt to diverse needs through customization and configuration makes it stand out. While both approaches aim to tailor your Salesforce Org to meet specific business requirements, they differ in methodology and use cases. This insight will help you understand their differences and provide insights into when and how to choose between them. Let’s get the insight! What is Salesforce Customization? Salesforce customization involves enhancing your Salesforce Org by introducing tailored features, functionalities, and applications through coding. It goes beyond the out-of-the-box capabilities, enabling you to extend your platform to meet unique and complex business requirements. This approach requires expertise from a Salesforce developer who leverages tools such as Apex, Lightning Components, and the Salesforce Code Builder to create custom solutions. Examples of Customization: What is Salesforce Configuration? Salesforce configuration refers to adapting Salesforce’s native features to meet business needs without modifying the underlying code. By using tools such as drag-and-drop builders, configuration allows users—even those without technical expertise—to optimize the platform’s functionality. Examples of Configuration: Key Differences Between Customization and Configuration Basis Customization Configuration Level of Personalization High personalization, tailored to unique needs Limited to Salesforce’s native capabilities Implementation Requires coding expertise and detailed development Simpler, relies on drag-and-drop tools Time to Deploy Longer development cycles Faster implementation and deployment Maintenance Can require ongoing updates and compatibility adjustments during Salesforce upgrades Easier to maintain, as it aligns with standard platform updates Cost Higher costs due to skilled developer involvement Cost-effective; can be handled by in-house admins Risk Higher risks due to potential code conflicts or errors Lower risks, but over-configuration can lead to complexity Best Practices for Customization and Configuration Choosing the Right Approach The decision to opt for customization or configuration depends on factors like business requirements, budget, timeline, and project complexity. Sometimes, a hybrid approach that combines customization and configuration is the best solution, providing flexibility while optimizing costs and implementation speed. Why Partner with Salesforce Experts? Partnering with experienced Salesforce consultants at Tectonic ensures your Org is tailored to meet your specific business needs. They analyze your workflows, processes, and challenges to recommend the most effective approach—whether it’s customization, configuration, or a blend of both. At Tectonic, our team of 200+ Salesforce experts specializes in delivering tailored solutions that maximize ROI. From development to ongoing maintenance, we ensure your Salesforce Org aligns with your long-term goals. Ready to transform your Salesforce platform? Let’s discuss how we can help. Like Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
November 8, 2024in Data, Salesforce, Salesforce Development Services, Salesforce Implementation Services, Salesforce Optimization
user q and a
08Nov

Handling Duplicate Phone Numbers in Salesforce Automations

I’m working with a list of customers in Salesforce, where duplicate detection is enabled for the phone field. When manually creating a new customer with an existing phone number, Salesforce displays a warning prompt asking if I want to proceed. The warning doesn’t block the save; it simply alerts me to the duplicate. However, when attempting to insert a record with the same phone number using MAKE, I encounter the following error: RuntimeError[400]: A duplicate record was found. Are you sure you want to create the record? This indicates that the automation is being blocked due to the duplicate detection rules. Solution Options Here are a few strategies to address this and allow the record to be inserted: Best Practices If you need help setting up any of these solutions, let Tectonic know! Like Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
November 8, 2024in Data, Salesforce
Tectonic Salesforce Customization
07Nov

Salesforce Customization Requests

The Most Commonly Requested Salesforce Customizations Salesforce’s flexibility is one of its biggest strengths, allowing businesses to tailor the platform to meet their unique needs. Here are the most frequently requested types of customizations: 1. Declarative Customization Make adjustments using Salesforce’s built-in tools—no coding required. Examples: Ideal For:Businesses looking for straightforward changes to enhance usability without requiring programming expertise. 2. Integration Customization Connect Salesforce with third-party systems to streamline workflows and centralize data. Examples: Benefits:Boost operational efficiency by enabling seamless communication between systems. 3. Custom Code Development Go beyond standard functionality with tailored solutions using Apex, Visualforce, or Lightning Web Components. Examples: Best For:Organizations with advanced or highly specific requirements that declarative tools can’t fulfill. 4. User Interface (UI) Customization Adapt the look and feel of Salesforce to improve user experience and align with your brand. Examples: Goal:Create an intuitive, visually appealing interface that boosts productivity and user adoption. 5. Workflow Automation Save time by automating repetitive tasks and processes. Examples: Impact:Streamline operations, reduce manual workloads, and improve efficiency. 6. Reporting and Analytics Customization Provide actionable insights with tailored reports and dashboards. Examples: Advantage:Empower teams to make data-driven decisions with clear, relevant insights. 7. Mobile Optimization Ensure a seamless Salesforce experience for users on mobile devices. Examples: Purpose:Keep teams connected and productive, regardless of location. Conclusion Salesforce customization goes beyond CRM—it transforms the platform into a tailored solution that aligns with your unique business processes. Whether you’re looking for simple adjustments or advanced integrations, these customizations unlock Salesforce’s full potential to drive operational success. Ready to Get Started?Discover how our Salesforce customization services can help tailor the platform to your specific needs. Let’s work together to maximize your investment and achieve your business goals! Like Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
November 7, 2024in Data, Salesforce, Salesforce Development Services, Salesforce Flow, Salesforce Implementation Services, Salesforce Optimization
Mulesoft
04Nov

MuleSoft Empowering AI Agents

Empowering AI Agents with Real-Time Data: MuleSoft’s Full Lifecycle AsyncAPI Support MuleSoft has officially launched full lifecycle AsyncAPI support, providing organizations with the tools to connect real-time data to AI agents via event-driven architectures (EDAs). This integration empowers businesses to deploy AI agents that can autonomously act on dynamic, real-time events across various operations. MuleSoft Empowering AI Agents. AI Agents in Action with AsyncAPI The integration of Agentforce, Salesforce’s AI agent suite, with AsyncAPI takes automation to a new level. By utilizing real-time data streams, businesses can create AI agents capable of immediate, autonomous decision-making. Why AsyncAPI Matters Event-driven architectures are critical for real-time data processing, yet 43% of IT leaders struggle to integrate existing systems with their EDAs. AsyncAPI provides a scalable, standardized way to connect applications and AI agents, overcoming these challenges. Key Features of MuleSoft’s AsyncAPI Support Why It’s a Game-Changer for AI Agents AsyncAPI integration enables AI agents to function asynchronously within EDAs, meaning they can process tasks without waiting for updates. For example: Driving Innovation Across Industries Organizations in sectors like retail, IT, and financial services can leverage these capabilities: Expert Insights Andrew Comstock, VP of Product, Integration at Salesforce:“AI is reshaping how we think about modern architectures, but connectivity remains foundational. By supporting AsyncAPI, we’re empowering businesses to build event-driven, autonomous systems on a flexible and robust platform.” Maksim Kogan, Solution Architect, OBI Group Holding:“Integrating AsyncAPI into Anypoint Platform simplifies the developer experience and increases resilience, enabling real-time services that directly enhance customer satisfaction.” Availability MuleSoft’s full lifecycle AsyncAPI support is now available via the Anypoint Platform, with compatibility for Kafka, Solace, Anypoint MQ, and Salesforce Platform Events. Tools like Anypoint Code Builder and Anypoint Exchange further streamline the development process. MuleSoft Empowering AI Agents With full AsyncAPI support, MuleSoft unlocks the potential for AI agents to operate seamlessly within real-time event-driven systems. From improving customer experiences to enhancing operational efficiency, this innovation positions businesses to thrive in today’s fast-paced digital landscape. Learn more about empowering your AI agents with MuleSoft’s AsyncAPI capabilities today. Like Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
November 4, 2024in Data, Mulesoft, Salesforce
Cohere-Powered Slack Agents
21Oct

Cohere-Powered Slack Agents

Salesforce AI and Cohere-Powered Slack Agents: Seamless CRM Data Interaction and Enhanced Productivity Slack agents, powered by Salesforce AI and integrated with Cohere, enable seamless interaction with CRM data within the Slack platform. These agents allow teams to use natural language to surface data insights and take action, simplifying workflows. With Slack’s AI Workflow Builder and support for third-party AI agents, including Cohere, productivity is further enhanced through automated processes and customizable AI assistants. By leveraging these technologies, Slack agents provide users with direct access to CRM data and AI-powered insights, improving efficiency and collaboration. Key Features of Slack Agents: Salesforce AI and Cohere Productivity Enhancements with Slack Agents: Salesforce AI and Cohere AI Agent Capabilities in Slack: Salesforce and Cohere Data Security and Compliance for Slack Agents FAQ What are Slack agents, and how do they integrate with Salesforce AI and Cohere?Slack agents are AI-powered assistants that enable teams to interact with CRM data directly within Slack. Salesforce AI agents allow natural language data interactions, while Cohere’s integration enhances productivity with customizable AI assistants and automated workflows. How do Salesforce AI agents in Slack improve team productivity?Salesforce AI agents enable users to interact with both CRM and conversational data, update records, and analyze opportunities using natural language. This integration improves workflow efficiency, leading to a reported 47% productivity boost. What features does the Cohere integration with Slack AI offer?Cohere integration offers customizable AI assistants that can help generate workflows, summarize channel content, and provide intelligent responses to user queries within Slack. How do Slack agents handle data security and compliance?Slack agents leverage cloud-native DLP solutions, automatically detecting sensitive data across different file types and setting up automated remediation processes for enhanced security and compliance. Can Slack agents work with AI providers beyond Salesforce and Cohere?Yes, Slack supports AI agents from various providers. In addition to Salesforce AI and Cohere, integrations include Adobe Express, Anthropic, Perplexity, IBM, and Amazon Q Business, offering users a wide array of AI-powered capabilities. Like Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
October 21, 2024in Data, Salesforce Slack
How Skechers Solved Its Ecommerce Challenges
21Oct

How Skechers Solved Its Ecommerce Challenges

Skechers Boosts Direct-to-Consumer Sales with Ecommerce Platform Upgrades Skechers, now a global brand in 2024, credits its recent ecommerce platform upgrades for saving time and increasing direct-to-consumer sales. However, it wasn’t always equipped with the right technology to support its massive growth. During Salesforce’s Dreamforce conference in San Francisco, Eric Cheng, Skechers USA Inc.’s director of ecommerce architecture, shared insights into how key technology decisions helped the brand expand and enhance its website and content capabilities. “Today, we’re present in over 180 countries worldwide,” Cheng said, speaking on stage at the Moscone Center. Skechers’ journey began in 1992, and its expansion has taken the brand across borders, reaching millions of customers worldwide. “We connect hundreds of millions of customers through our retail stores and ecommerce platform to deliver a unique experience,” Cheng noted, emphasizing the need to meet the diverse demands of each market. Skechers ranks No. 273 in the Top 1000, Digital Commerce 360’s ranking of the largest North American e-retailers by online sales, where it is categorized as an Apparel & Accessories retailer. Digital Commerce 360 projects that Skechers will reach 0.65 million in online sales by 2024. Ecommerce Platform Challenges Cheng acknowledged that Skechers’ digital transformation wasn’t immediate: “The journey did not just happen overnight; it took time and effort.” Skechers faced challenges in three key areas: content management, scalability, and customer experience. The legacy system was inadequate, lacking robust tools for efficient content delivery, previewing scheduled content, and handling localization. As Cheng described, launching a marketing page often required the content team to be on standby at midnight—an unsustainable approach for 17 countries. How Skechers Solved Its Ecommerce Challenges To overcome these hurdles, Skechers partnered with Astound Digital. Together, they implemented Salesforce Service Cloud and Manhattan Active Omni for order management. Kyle Montgomery, senior vice president of commerce at Astound Digital, joined Cheng on stage and highlighted the goal: “Their vision was to unify, supply, and scale.” This transformation enabled Skechers to bring 17 countries in Europe, Japan, and North America onto a single platform. Jennifer Lane, Salesforce’s director of success guides, also emphasized the flexibility achieved using Salesforce’s Page Designer and localization solutions from Salesforce’s AppExchange. Integrations with Thomson Reuters for tax, CyberSource for payments, and Salesforce Marketing Cloud for personalization further enhanced Skechers’ capabilities. The Results Cheng highlighted three key improvements after the ecommerce overhaul. First, content creation and localization tools improved operational efficiency by over 500%. The time to launch in new markets was dramatically reduced from five months to just a few weeks. Additionally, Skechers saw a notable sales boost, with a 24.5% increase in its direct-to-consumer segment during Q1 2023. Skechers’ success demonstrates the significant impact of a well-executed ecommerce platform upgrade, allowing the brand to scale globally while improving customer experience and operational efficiency. Contact Tectonic to learn what Salesforce can do for you. Like Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
October 21, 2024in Google, Salesforce, Salesforce Marketing Cloud, Salesforce Service Cloud, Technology
Generative AI Replaces Legacy Systems
15Oct

Generative AI Replaces Legacy Systems

Generative AI Will Overtake Legacy Stack Vendors With the rise of generative AI, legacy software vendors like Appian, IBM, Salesforce, SAP, Pegasystems, IFS, Oracle, Software AG, TIBCO, and UIPath are becoming increasingly obsolete. These vendors represent the old guard, clinging to outdated business process automation systems, while the future clearly belongs to AI-driven innovation. Back in the early 2010s, discussions around dynamic processes—self-assembling workflows created by artificial intelligence—were already gaining traction. The vision was to bypass the need for traditional process mapping or manually designing new interfaces. Instead, AI would dynamically generate processes in response to specific tasks, allowing for far greater flexibility and adaptability. However, business rules within BPMS (Business Process Management Systems) often imposed constraints that limited decision-making flexibility. Today, this vision is finally within reach. Many traditional stack vendors are scrambling to integrate generative AI into their offerings in a desperate bid to remain relevant. But the truth is, generative AI renders these vendors largely unnecessary. For instance, Pegasystems, like many others, now incorporates generative AI into its software, but users are still bound to old workflows and low-code development systems. The reliance on building processes, regardless of AI assistance, keeps them stuck in the past. Across the board—whether it’s ERP, CRM, or RPA—vendors such as Salesforce, SAP, and IFS remain tethered to their outdated systems, even though they possess all the necessary data, both structured and unstructured, to benefit from a simpler, AI-powered approach. All that’s needed is a generative AI layer on top to handle tasks like customer complaints. Consider a customer complaint scenario: traditionally, a complaint is processed through a defined workflow, often requiring the creation of expensive, custom SaaS solutions. But what if an LLM (Large Language Model) could handle this instead? The LLM could analyze the complaint, extract key information, assess urgency through sentiment analysis, and generate a custom workflow on the fly. It could even generate backend code in real-time to process refunds or update databases, all without relying on legacy front-end systems. The LLM’s ability to create and execute dynamic workflows eliminates the need for static business processes. The AI generates temporary code and UI elements to handle a specific interaction, then discards them once the task is complete. This shifts the focus away from traditional, bloated enterprise systems and towards dynamic, JIT (Just-In-Time) interactions that are tailored to each individual customer. The efficiency gains are not in cutting jobs but in eliminating the need for costly, antiquated software and lengthy digital transformation programs. Generative AI doesn’t require massive ERP or CRM implementations, and businesses can converse directly with customer data through AI, bypassing the need for complex system integrations. Master Data Management, which once consumed millions of dollars and years of effort, is now positioned to become a simple, AI-powered solution. Enterprises already have well-structured and clean data, and adding a generative AI layer could remove the need for integrating or syncing legacy systems. The era of major vendors selling AI-enhanced solutions built on top of decaying software stacks is coming to an end. The idea of using generative AI as the foundation for a new business operating system, without the need for bloated, legacy software, is increasingly appealing. With the global workflow automation market projected to grow to .4 billion by 2030, the future clearly belongs to AI-driven solutions. Like Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
October 15, 2024in Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce
Gap Between Data and Decisions
14Oct

Gap Between Data and Decisions

Bridging the Gap Between Data and Decisions One of the biggest challenges in enterprise technology is the fragmentation of data across various systems, commonly referred to as data silos. Salesforce research shows that 81% of IT leaders believe these silos prevent their organizations from fully utilizing data, while 94% of business leaders feel they should be extracting more value from their data. Despite the wealth of available information, only 30% of employees report using data to make decisions. Tableau Einstein addresses this challenge by enabling self-service analytics through a unified metadata framework. This framework ensures consistent data definitions and structures across an organization, allowing data architects and analysts to create semantic models. These models simplify complex datasets, making them more accessible to business users. Additionally, the platform integrates AI-powered agents into the data authoring process, automatically suggesting relationships between data objects and providing business context to improve the reliability of insights. Integrating AI for Proactive Insights Southard Jones, Chief Product Officer of Tableau, highlights the need for analytics tools to deliver proactive and autonomous insights within users’ workflows. “Employees often miss critical insights because they lack the time or expertise to analyze complex data sets,” Jones said. “With predictive and generative AI, Tableau Einstein can explore your data and surface insights you might not have considered.” Features like Pulse and Tableau Agent provide insights within familiar platforms like Salesforce, Slack, and third-party applications such as Workday. For example, a manager using Workday might receive real-time notifications about team performance metrics or potential indicators of employee attrition, allowing for timely intervention. Enhancing Trust Through Transparency Building trust in data analytics is key to adoption. Tableau Einstein introduces Tableau Semantics, which acts as a bridge between raw data and business terminology. “With Tableau Semantics, we provide clear explanations and data lineage, showing users exactly how metrics are created,” Jones explained. This transparency demystifies complex calculations, making it easier for users to understand metrics like campaign ROI. By aligning technical data with business understanding, the platform seeks to make analytics more accessible and trustworthy. Potential Challenges While AI-driven analytics holds great promise, it raises questions about data privacy, implementation complexity, and the learning curve for employees. Although AI-powered insights can revolutionize how organizations use data, it’s essential to address data governance concerns and ensure employees are trained to interpret AI-generated insights accurately. The competitive landscape for AI analytics tools is growing, with alternatives like Microsoft’s Power BI offering similar capabilities. Organizations will need to compare Tableau Einstein with other options in terms of features, integration potential, and overall cost. Facilitating Collaboration and Customization Tableau Einstein’s open, API-driven architecture enables developers to create customized analytical agents tailored to specific enterprise needs. “By opening our APIs, we allow developers to interact with the platform programmatically,” Jones noted. This flexibility supports the creation of custom analytics solutions that can seamlessly integrate with other business applications. The platform also introduces marketplace capabilities for sharing and discovering data models and analytics assets, fostering collaboration and innovation while reducing redundant efforts across organizations. Real-World Applications and Early Feedback Early adopters are already exploring how Tableau Einstein enhances decision-making processes. Leslie Bercher, Senior Director of Performance Marketing & Analytics at Arvest Bank, noted, “Embedding these insights into our daily applications empowers teams to make informed decisions quickly. It’s not just about data; it’s about having actionable insights when and where we need them.” Despite the potential, adopting advanced analytics tools may require investment in training and change management to ensure successful implementation. Without the right support, organizations may struggle to unlock the full potential of these technologies. Looking Ahead: The Future of AI-Powered Analytics Tableau Einstein is available through Tableau+, with future integrations planned across Salesforce products, including Sales, Service, Marketing, and Commerce Cloud. Salesforce is offering expert guidance, migration support, and a reimagined partner network to help businesses adopt the platform efficiently. As companies continue their digital transformation journeys, AI-powered analytics tools like Tableau Einstein will be pivotal in making data more accessible and actionable. Success will depend on how well organizations address challenges related to data privacy, employee training, and system integration. “The autonomous revolution in analytics is just beginning,” Jones observed. “The key will be how companies use these tools to gain insights and drive strategic actions that lead to measurable business outcomes.” Salesforce’s launch of Tableau Einstein marks a significant step in democratizing data analytics within organizations. By embedding AI-driven insights into daily workflows and increasing transparency in data metrics, the platform has the potential to transform operations. However, realizing this potential will require thoughtful implementation, strong employee engagement, and ongoing support. As AI and analytics evolve, companies that harness these technologies effectively will gain a competitive advantage in a data-driven world. Like Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
October 14, 2024in Generative AI
Build Launch and Track Campaigns
10Oct

Build Launch and Track Campaigns

Revolutionizing Campaigns: How Marketing Agents Empower Your Marketing Team Marketing agents are transforming how businesses create, launch, and track campaigns—delivering better results while boosting internal team productivity and cohesion. With the power of AI and data, these agents act as collaborative partners, enhancing marketing efficiency and creativity in unprecedented ways. A Smarter Approach to Campaign Challenges Marketers have long faced the challenge of creating quality content at scale. According to the Content Marketing Institute, 54% of B2B marketers struggle to meet this demand, while B2C marketers often lack the resources to make their efforts scalable and consistent. On top of this, they must ensure campaigns are efficient, customer-centric, and stand out in a competitive landscape. Enter marketing agents—AI-powered tools that help teams manage and optimize campaigns, from strategy to execution. At Dreamforce 2024, Salesforce unveiled Agentforce, a suite of intelligent agents integrated across the Customer 360 platform, including Agentforce Campaigns. With 71% of marketers planning to adopt generative and predictive AI within the next 18 months, as per Salesforce’s State of Marketing report, tools like Agentforce are poised to redefine how campaigns are built and delivered. How Humans and AI Agents Work Together Marketing agents are AI-powered virtual assistants that collaborate with humans to analyze data, generate insights, and execute marketing plans. Unlike traditional tools, they understand the context behind your needs and suggest actionable solutions—whether that’s creating content, optimizing campaigns, or analyzing results. By automating time-consuming tasks, marketing agents free teams to focus on high-value activities like strategy and personalization. But the key to maximizing their potential lies in shifting your mindset: instead of simply seeking efficiency, aim to transform how you deliver exceptional customer experiences. 8 Ways Agentforce Campaigns Elevates Your Marketing 1. Intelligent Recommendations Agentforce Campaigns turns insights into actions. For example, Marketing Cloud’s Einstein not only tracks your goals but also suggests adjustments or new campaigns tailored to your objectives, helping you stay ahead. 2. Instant Campaign Briefs Building a campaign starts with a solid brief. With Agentforce, you can create one in seconds using natural language prompts. The AI-generated brief incorporates your goals and guidelines, making collaboration and approvals seamless. 3. Contextual Content Creation Agentforce generates emails, landing pages, and calls to action directly aligned with your brand’s tone and campaign goals. Marketers can refine outputs with natural language prompts, ensuring a perfect fit for their strategy. 4. Effortless Audience Segmentation No SQL skills? No problem. Describe your ideal audience in natural language, and Agentforce will translate that into actionable segments—helping you target precisely the right customers. 5. Automated Journey Activation Agentforce simplifies multi-channel journey creation by drafting personalized campaign flows. You can refine, approve, and activate these journeys with ease, saving time while enhancing impact. 6. Unlimited Content Variations AI eliminates content constraints, allowing you to generate multiple variations for personalized campaigns. Target high-value customers, newcomers, or loyal fans with tailored messages—all at scale. 7. Explore Nuanced Segments Agentforce enables marketers to create segments without relying on overburdened data science teams. Dive into deeper audience insights, such as churn rates based on location, age, or past behavior, with just a prompt. 8. Embed Continuous Testing Testing is often deprioritized due to time limitations. Agentforce automates testing workflows, making it easier to incorporate A/B testing and iterative learning into every campaign. Getting Started with Agentforce Campaigns Agentforce Campaigns is available in Marketing Cloud Growth and Advanced Editions, designed to empower businesses of all sizes. By integrating AI-driven tools into your workflow, you can elevate your marketing to new heights—enhancing creativity, efficiency, and customer engagement. Ready to revolutionize your campaigns? Explore how Agentforce can help you win customers and foster a more productive, cohesive marketing team. Salesforce Disclaimer: Unreleased features mentioned here are subject to change and may not become available as described. Make purchasing decisions based on currently available features. Like1 Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
October 10, 2024in Agentforce Platform, Artificial Intelligence, Data, Google, Salesforce, Salesforce Personalization
dbt Labs and Salesforce
08Oct

dbt Labs and Salesforce

dbt Labs, a leader in analytics engineering, announced at Coalesce 2024 a groundbreaking partnership with Salesforce to integrate Salesforce Data Cloud’s AI, automation, and analytics capabilities with dbt Labs’ expertise in data transformation and metrics management. This collaboration aims to deliver a seamless, trustworthy, and comprehensive data experience for users. “Together, Salesforce and dbt Labs are redefining what’s possible with data,” said Ryan Segar, Chief Customer Officer at dbt Labs. “By integrating our solutions, we’re helping customers accelerate their analytics development journey, delivering powerful, flexible data insights that drive better business outcomes.” The partnership offers Salesforce Data Cloud, Tableau, and Agentforce users access to dbt Labs’ robust data transformation pipeline, ensuring high data accuracy, quality, and reliability. An independent metrics layer from dbt Labs will allow Salesforce and Tableau users to define, manage, and standardize key business metrics, providing consistent and comparable insights across platforms. This supports confident, data-driven decision-making directly within the flow of work. New integrations include the ability to connect dbt Semantic Layer with Tableau Pulse, export metrics from dbt Cloud to Tableau Cloud, and leverage dbt models within Tableau and Einstein. Future integrations will explore features such as alignment with Tableau Semantics and enabling instant Tableau analytics from the dbt Cloud console. Ali Tore, Senior Vice President of Advanced Analytics at Salesforce, emphasized the benefits of this collaboration: “By combining the strengths of dbt with Salesforce Data Cloud, we’re empowering customers with AI-powered insights built on a foundation of trusted, reliable data. This integration unlocks the full potential of their data to drive impactful business outcomes.” With over 50,000 teams already using dbt, Salesforce customers can now leverage advanced data modeling techniques trusted by leading global organizations. This partnership offers scalable, robust data modeling directly within Salesforce Data Cloud, benefiting both technical and non-technical users alike. Like Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
October 8, 2024in Data, Salesforce, Salesforce Data Cloud
Data Cloud Model
08Oct

Data Cloud Model

While many Salesforce products like Sales Cloud, Service Cloud, Education Cloud, Health Cloud, and various industry clouds are built on a common ‘core’ platform that shares the same datastore (an Oracle relational database), Salesforce Data Cloud operates on (Data Cloud Model) a distinct architecture and technology stack. This insight explores the data models and related concepts that underpin the platform. Data Cloud Architecture and Storage Starting with the storage layer, Salesforce Data Cloud integrates multiple services: This combination enables Data Cloud to provide a petabyte-scale data store, overcoming the scalability and performance constraints of traditional relational databases. The physical architecture of Data Cloud is represented as a set of data objects, which form the foundation for how data is ingested, harmonized, and activated within the platform. Key Data Cloud Objects Data Source A Data Source is the initial data layer in Data Cloud, representing an external platform or system from which your data originates. Data Sources can include: Data Stream A Data Stream is an entity extracted from a Data Source, such as ‘Orders’ from Commerce Cloud, ‘Contacts’ from Sales Cloud, or ‘Subscribers’ from Marketing Cloud. Once a Data Source is connected to Data Cloud, Data Streams provide access to these entities and require assignment to a category: Profile, Engagement, or Other. A single Data Source can contain multiple Data Streams. Data Source Object (DSO) Data Streams are ingested into Data Source Objects (DSOs), which serve as temporary staging stores containing data in its raw, native format (e.g., CSV files). Minor transformations can be applied to fields during data ingestion. Data Lake Object (DLO) The next step in the data flow is the Data Lake Object (DLO). DLOs are typed, schema-based, materialized views that reside in the data lake (Amazon S3) and are generally stored as Apache Parquet files. This format, along with Apache Iceberg’s abstraction layer, supports efficient data storage and retrieval. DLOs are the first objects available for inspection and preparation of data, including field mapping and additional transformations. Data Model Object (DMO) Unlike DSOs and DLOs, which use a physical data store, Data Model Objects (DMOs) provide a virtual, non-materialized view into the data lake. Queries against a DMO return results based on the current data snapshot in DLOs, without storing them. DMOs can aggregate attributes from different Data Streams, Calculated Insights, and other sources. DMOs inherit their category from the first DLO mapped to them, and only DLOs with the same category can be mapped to the DMO. Like Salesforce objects, DMOs offer a canonical data model with pre-defined attributes (standard objects), and you can also create custom DMOs (custom objects). DMOs can have one-to-one or many-to-one relationships with other DMOs. Currently, there are 89 standard DMOs in Data Cloud, and this number is growing to support more use cases. DMOs are organized into various subject areas, including: Data Spaces Data Spaces offer logical partitions, allowing data to be segregated between different brands, regions, or departments, without the need for multiple Data Cloud instances. They also align with a Software Development Lifecycle (SDLC), enabling you to stage and test Data Objects in separate environments without affecting production data. Data Sources, Data Streams, and DLOs can be shared across Data Spaces, while DMOs and other platform features are isolated based on user permissions. Final Thoughts With the drop in storage costs and advancements in technology, companies now have vast datasets at their disposal. Every customer interaction, whether it’s a purchase, an email open, or a web page view, can be captured and stored. When properly organized, this data allows businesses to understand customers better, predict their needs, personalize interactions, and much more. However, one of Salesforce’s challenges is that its original platform, built over 20 years ago, was designed for a different era and struggles with handling voluminous, “big” data. Salesforce Data Cloud addresses these limitations with a modern architecture that overcomes the constraints of relational databases. Data Cloud is poised to become the backbone of the Salesforce platform, supporting the data needs of customers and products for the next two decades. If you’re interested in learning how to implement and integrate Salesforce Data Cloud in your organization, following best practices, reach out today. Like Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
October 8, 2024in Data, Google, Salesforce, Salesforce Data Cloud, Snowflake, Technology, Unified Knowledge
AI Era Demands an Intelligent Data Infrastructure
05Oct

AI Era Demands an Intelligent Data Infrastructure

Why the AI Era Demands an Intelligent Data Infrastructure NetApp aims to bridge the gap between cloud-based AI models and on-premises data management systems, addressing a major challenge many organizations face as they explore emerging AI capabilities, such as generative AI. AI Era Demands an Intelligent Data Infrastructure. While the most advanced AI tools often reside in hyperscale clouds, the key data of most organizations remains on-premises. As companies move from AI experimentation to scaling their initiatives, they face the complex task of closing the divide between their data and AI. At first glance, the solution seems simple: Organizations can integrate their proprietary data with cloud-based AI services to enhance large language models (LLMs) and other AI tools, leveraging their industry-specific expertise to generate unique insights and drive value. In fact, a recent Enterprise Strategy Group (ESG) survey found that 84% of respondents believed incorporating enterprise data was critical to supporting their generative AI efforts. However, achieving this at scale is far more difficult than it appears. Many organizations are reluctant to send their valuable data to public clouds due to concerns about security, such as exposing sensitive intellectual property or personal information. Additionally, managing the process of transferring large datasets, creating data copies, updating models with fresh data, and keeping track of everything over time adds cost and complexity. Beyond these logistical issues, many organizations struggle with a fundamental challenge: poor data management. Rapid data growth, fragmentation, and a lack of understanding of their own data forces business users and data scientists to spend excessive time “data wrangling”—the process of identifying, gathering, and preparing data for AI models. This inefficiency delays training and deploying models, pushing back the point of inference, which is the key to unlocking value from AI. Increased latency also raises the risk that inferencing is based on outdated data, leading to repeated cycles and further inefficiency. This challenge has made data management a critical obstacle in AI adoption. ESG research identified poor data quality as the top challenge organizations face when implementing AI. Inadequate data is rapidly becoming a roadblock that hampers AI initiatives across industries. To address these challenges, NetApp has shifted its focus toward building an “intelligent data infrastructure” for the AI era. At the company’s Insight 2024 conference, NetApp outlined a strategy to support AI success by bridging the gap between cloud-based AI models and on-premises data environments. The company’s vision includes simplifying, automating, and mitigating risks in the data management workflow needed to scale AI in the enterprise. NetApp’s approach includes innovations like a global metadata namespace and enhancements to its OnTap software, which enable exploration, classification, and management of data across the NetApp ecosystem. These capabilities integrate directly into AI data pipelines, enabling scalable searches and retrieval-augmented generation inferencing. Additionally, NetApp’s new “disaggregated” storage architecture will support more cost-effective scaling for compute-heavy AI tasks such as model training. NetApp’s strategy also extends to the public cloud, where it already has strong partnerships with all three hyperscalers, offering NetApp services as first-party options in their clouds. The company is expanding this by developing additional cloud-native capabilities for AI, such as integrating Azure NetApp Files with Microsoft Azure AI services, FSx for NetApp OnTap with Amazon Bedrock, and Google Cloud NetApp Volumes with Google Vertex AI and BigQuery. These integrations will allow organizations to securely enrich public cloud-based AI models with their on-premises data, creating a scalable and secure environment for AI development. While some aspects of NetApp’s strategy will be delivered over the next year, many cloud integrations will be available sooner. NetApp is also building a partner ecosystem, including hardware partners like Lenovo and Nvidia, software ISVs, and service providers like Domino Data Labs, in addition to its cloud alliances. With this vision for intelligent data infrastructure, NetApp is positioning itself as a key enabler of AI-driven innovation, and it will be intriguing to watch how this strategy unfolds in the coming months. Like Related Posts 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
October 5, 2024in AI Tools, Data, Enterprise, Generative AI, Google

Subscribe to our mailing list. Join our mail list to receive our newsletter

We'll keep you up to date with our latest news, insights, free resources, and much more.
Don't worry, we won't spam you.
gettectonic.com