ADA - gettectonic.com - Page 10
Five9 Cautious Forward Looking
04Sep

Five9 Cautious Forward Looking

Recently, Five9 reported its second-quarter FY24 results, revealing a strong performance for the period. However, the company’s cautious forward-looking guidance led to a significant drop in its stock price, which fell by over 25%. In response to queries about the conservative outlook, a Five9 spokesperson attributed the reduced 2024 revenue guidance—a 3.8% decrease—to macroeconomic headwinds. This cautious forecast stands in contrast to the more optimistic outlooks of Five9’s publicly traded peers. Economic factors such as global issues, talent shortages, AI uncertainty, and the upcoming election are influencing customers’ decisions on IT investments, which likely contributed to the reduced guidance. Additionally, sales execution challenges have prompted the company to take corrective measures. While Five9 might face unique challenges that other CCaaS providers do not, the full impact will become clearer in the next quarter. In response to these challenges, Five9 has taken steps to stabilize its operations, including promoting Matt Tuckness from VP of Global Customer Success to EVP of Sales and Customer Success. This move, described by leadership as promoting a “dedicated sales leader” with a decade of experience at Five9, aims to enhance sales execution. Scott Berg from Needham questioned the timing of the promotion, suggesting it might be a reaction to a single quarter’s results. Dan Burkland, Five9’s President, defended the decision, emphasizing that having a dedicated EVP of Sales is crucial for focusing on enterprise deals, especially given Five9’s efforts to grow its enterprise base. Five9 has also announced a 7% workforce reduction, affecting approximately 185 employees. This marks the company’s first layoff in its history, which is notable given its history of growth through acquisitions, such as the recent planned acquisition of Acqueon, a real-time revenue execution platform. Typically, acquisitions lead to headcount adjustments, but Five9 had managed to avoid such cuts until now. The company stated that the reduction was necessary to focus on profitable growth and long-term business resilience while continuing to serve global customers and innovate. Although layoffs are challenging, they are sometimes necessary for business adaptation. Many UCaaS and CCaaS providers expanded their workforces during the pandemic and later faced the need to trim excess staff as the market softened. Five9’s adjustment in headcount reflects changing market conditions. The acquisition of Acqueon is expected to accelerate Five9’s vision by integrating expertise in inbound and outbound communications to enhance personalized customer experiences across marketing, sales, and service. Acqueon will operate as a separate business unit within Five9, with plans to eventually integrate its brand into the larger Five9 brand. Overall, despite the quarter’s challenges, Five9 had a strong performance. It achieved a record-breaking $1 billion ARR run rate for the first time, with total subscription revenue growing by 17%. The company maintains a robust balance sheet with over $1 billion in cash. The recent organizational changes, including new leadership and headcount adjustments, are indicative of Five9’s maturation and aim to return the company to its pattern of strong performance and growth. 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
September 4, 2024in Salesforce
2024 AI Glossary
02Sep

2024 AI Glossary

Artificial intelligence (AI) has moved from an emerging technology to a mainstream business imperative, making it essential for leaders across industries to understand and communicate its concepts. To help you unlock the full potential of AI in your organization, this 2024 AI Glossary outlines key terms and phrases that are critical for discussing and implementing AI solutions. Tectonic 2024 AI Glossary Active LearningA blend of supervised and unsupervised learning, active learning allows AI models to identify patterns, determine the next step in learning, and only seek human intervention when necessary. This makes it an efficient approach to developing specialized AI models with greater speed and precision, which is ideal for businesses aiming for reliability and efficiency in AI adoption. AI AlignmentThis subfield focuses on aligning the objectives of AI systems with the goals of their designers or users. It ensures that AI achieves intended outcomes while also integrating ethical standards and values when making decisions. AI HallucinationsThese occur when an AI system generates incorrect or misleading outputs. Hallucinations often stem from biased or insufficient training data or incorrect model assumptions. AI-Powered AutomationAlso known as “intelligent automation,” this refers to the integration of AI with rules-based automation tools like robotic process automation (RPA). By incorporating AI technologies such as machine learning (ML), natural language processing (NLP), and computer vision (CV), AI-powered automation expands the scope of tasks that can be automated, enhancing productivity and customer experience. AI Usage AuditingAn AI usage audit is a comprehensive review that ensures your AI program meets its goals, complies with legal requirements, and adheres to organizational standards. This process helps confirm the ethical and accurate performance of AI systems. Artificial General Intelligence (AGI)AGI refers to a theoretical AI system that matches human cognitive abilities and adaptability. While it remains a future concept, experts predict it may take decades or even centuries to develop true AGI. Artificial Intelligence (AI)AI encompasses computer systems that can perform complex tasks traditionally requiring human intelligence, such as reasoning, decision-making, and problem-solving. BiasBias in AI refers to skewed outcomes that unfairly disadvantage certain ideas, objectives, or groups of people. This often results from insufficient or unrepresentative training data. Confidence ScoreA confidence score is a probability measure indicating how certain an AI model is that it has performed its assigned task correctly. Conversational AIA type of AI designed to simulate human conversation using techniques like NLP and generative AI. It can be further enhanced with capabilities like image recognition. Cost ControlThis is the process of monitoring project progress in real-time, tracking resource usage, analyzing performance metrics, and addressing potential budget issues before they escalate, ensuring projects stay on track. Data Annotation (Data Labeling)The process of labeling data with specific features to help AI models learn and recognize patterns during training. Deep LearningA subset of machine learning that uses multi-layered neural networks to simulate complex human decision-making processes. Enterprise AIAI technology designed specifically to meet organizational needs, including governance, compliance, and security requirements. Foundational ModelsThese models learn from large datasets and can be fine-tuned for specific tasks. Their adaptability makes them cost-effective, reducing the need for separate models for each task. Generative AIA type of AI capable of creating new content such as text, images, audio, and synthetic data. It learns from vast datasets and generates new outputs that resemble but do not replicate the original data. Generative AI Feature GovernanceA set of principles and policies ensuring the responsible use of generative AI technologies throughout an organization, aligning with company values and societal norms. Human in the Loop (HITL)A feedback process where human intervention ensures the accuracy and ethical standards of AI outputs, essential for improving AI training and decision-making. Intelligent Document Processing (IDP)IDP extracts data from a variety of document types using AI techniques like NLP and CV to automate and analyze document-based tasks. Large Language Model (LLM)An AI technology trained on massive datasets to understand and generate text. LLMs are key in language understanding and generation and utilize transformer models for processing sequential data. Machine Learning (ML)A branch of AI that allows systems to learn from data and improve accuracy over time through algorithms. Model AccuracyA measure of how often an AI model performs tasks correctly, typically evaluated using metrics such as the F1 score, which combines precision and recall. Natural Language Processing (NLP)An AI technique that enables machines to understand, interpret, and generate human language through a combination of linguistic and statistical models. Retrieval Augmented Generation (RAG)This technique enhances the reliability of generative AI by incorporating external data to improve the accuracy of generated content. Supervised LearningA machine learning approach that uses labeled datasets to train AI models to make accurate predictions. Unsupervised LearningA type of machine learning that analyzes and groups unlabeled data without human input, often used to discover hidden patterns. By understanding these terms, you can better navigate the AI implementation world and apply its transformative power to drive innovation and efficiency across your organization. Tectonic 2024 AI Glossary 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 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

Read More
September 2, 2024in Generative AI
Winter 25 Permission Set Groups
02Sep

Winter 25 Permission Set Groups

Salesforce’s Winter ’25 release introduces a host of updates across the platform, with a particular emphasis on security and user management improvements. Among these, the enhancements to Permission Set Groups stand out, offering more efficiency in managing user access and permissions. Let’s take a closer look at these updates and how they can benefit your Salesforce environment. What Are Permission Set Groups? Before diving into the new enhancements, it’s essential to understand Permission Set Groups. Salesforce created these groups to simplify the assignment of permissions to users. Instead of assigning multiple individual permission sets, administrators can bundle them into a Permission Set Group. This approach streamlines the process, making it easier to manage permissions for users with complex roles requiring access to multiple features and objects. What’s New in Winter ’25? The Winter ’25 release brings several key updates to Permission Set Groups, making them more robust and flexible. Here’s a breakdown of the major improvements: Key Benefits of the Winter ’25 Enhancements The Winter ’25 updates to Permission Set Groups offer several advantages for Salesforce admins and organizations: Getting Started To begin utilizing these new features, head to the Permission Set Group settings in Salesforce Setup. Review your current permission sets and explore how these new features can streamline your processes. The expiration date feature, in particular, will be valuable if you manage temporary roles or frequently changing project teams. Winter 25 Permission Set Groups The Winter ’25 Salesforce release delivers significant improvements to Permission Set Groups, equipping admins with enhanced tools to manage user permissions securely and efficiently. By incorporating these features into your Salesforce environment, you can strengthen security, optimize user access management, and ensure your organization operates smoothly. For a deeper dive into these updates, check the Salesforce Winter ’25 release notes or join discussions in Salesforce communities and forums. 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
September 2, 2024in Salesforce
guide to RAG
30Aug

Tectonic Guide to Rag Part 2

The first notable change in the field of language models is the significant expansion of context window sizes and a reduction in token costs. For instance, Anthropic’s largest model, Claude, has a context window exceeding 200,000 tokens, while recent reports indicate that Gemini’s context window can reach up to 10 million tokens. Under such circumstances, Retrieval-Augmented Generation (RAG) may no longer be necessary for many tasks, as all required data can be accommodated within the expanded context window. Several financial and analytical projects have already demonstrated that tasks can be solved without needing a vector database as intermediate storage. This trend of reducing token costs and increasing context window sizes is likely to continue, potentially decreasing the need for external mechanisms in LLMs, although they are still relevant for the time being. If the context window remains insufficient, methods for summarization and context compression have been introduced. LangChain, for example, offers a class called ConversationSummaryMemory to address this challenge. pythonCopy codellm = OpenAI(temperature=0) conversation_with_summary = ConversationChain( llm=llm, memory=ConversationSummaryMemory(llm=OpenAI()), verbose=True ) conversation_with_summary.predict(input=”Hi, what’s up?”) Knowledge Graphs As the volume of data continues to grow, navigating through it efficiently becomes increasingly critical. In certain cases, understanding the structure and attributes of data is essential for effective use. For example, if the data source is a company’s wiki, an LLM might not recognize a phone number unless the structure or metadata indicates that it’s the company’s contact information. Humans can infer meaning from conventions, such as the subdirectory “Company Information,” but standard RAG may miss such connections. This challenge can be addressed by Knowledge Graphs, also known as Knowledge Maps, which provide both raw data and metadata that illustrates how different entities are interconnected. This method is referred to as Graph Retrieval-Augmented Generation (GraphRAG). Graphs are excellent for representing and managing structured, interconnected information. Unlike vector databases, they excel at capturing complex relationships and attributes among diverse data types. Creating a Knowledge Graph The process of creating a knowledge graph typically involves collecting and structuring data, which requires expertise in both the subject matter and graph modeling. However, LLMs can automate a significant portion of this process by analyzing textual data, identifying entities, and recognizing their relationships, which can then be represented in a graph structure. In many cases, an ensemble of vector databases and knowledge graphs can improve accuracy, as discussed previously. For example, search functionality might combine keyword search through a regular database (e.g., Elasticsearch) and graph-based queries. LangChain can also assist in extracting structured data from entities, as demonstrated in this code example: pythonCopy codedocuments = parse_and_load_data_from_wiki_including_metadata() graph_store = NebulaGraphStore( space_name=”Company Wiki”, tags=[“entity”] ) storage_context = StorageContext.from_defaults(graph_store=graph_store) index = KnowledgeGraphIndex.from_documents( documents, max_triplets_per_chunk=2, space_name=space_name, tags=[“entity”] ) query_engine = index.as_query_engine() response = query_engine.query(“Tell me more about our Company”) Here, searching is conducted based on attributes and related entities, instead of similar vectors. If set up correctly, metadata from the company’s wiki, such as its phone number, would be accessible through the graph. Access Control One challenge with this system is that data access may not be uniform. For instance, in a wiki, access could depend on roles and permissions. Similar issues exist in vector databases, leading to the need for access management mechanisms such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Relationship-Based Access Control (ReBAC). These access control methods function by evaluating paths between users and resources within graphs, such as in systems like Active Directory. To ensure the integrity of data during the ingestion phase, metadata related to permissions must be preserved in both the knowledge graph and vector database. Some commercial vector databases already have this functionality built in. Ingestion and Parsing Data needs to be ingested into both graphs and vector databases, but for graphs, formatting is especially critical since it reflects the data’s structure and serves as metadata. One particular challenge is handling complex formats like PDFs, which can contain diverse elements like tables, images, and text. Extracting structured data from such formats can be difficult, and while frameworks like LLama Parse exist, they are not always foolproof. In some cases, Optical Character Recognition (OCR) may be more effective than parsing. Enhancing Answer Quality Several new approaches are emerging to improve the quality of LLM-generated answers: While these advancements in knowledge graphs, access control, and retrieval mechanisms are promising, challenges remain, particularly around data formatting and parsing. However, these methods continue to evolve, enhancing LLM capabilities and efficiency. 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 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

Read More
August 30, 2024in Data

When to use Flow

When and Why Should You Use a Flow in Salesforce? Flow is Salesforce’s premier tool for creating configurable automation and guided user experiences. If you need to build a process that doesn’t require the complexity of Apex code, Flow should be your go-to solution. It’s versatile, user-friendly, and equipped to handle a wide range of business automation needs. Legacy tools like Process Builder and Workflow Rules are being phased out, with support ending in December 2025. While you may choose to edit existing automations in these tools temporarily, migrating to Flow should be a top priority for future-proofing your Salesforce org. Capabilities of FlowFlows allow you to: When Should You Avoid Using a Flow?Although Flow is powerful, it’s not the right choice in every scenario. Here are situations where it may not be suitable: Creating a Flow in Salesforce Pro Tips for Flow Building Flow vs. Apex: Which to Choose?Flows are simpler, faster to deploy, and accessible to admins without coding expertise. Apex, on the other hand, is suited for complex use cases requiring advanced logic or integrations. Here’s when Apex should be used instead: Why Flows Are the FutureSalesforce has positioned Flow as the central automation tool by deprecating Workflow Rules and Process Builder. With every release, Flow’s capabilities expand, making it easier to replace tasks traditionally requiring Apex. For instance: Final ThoughtsSalesforce admins should prioritize building and migrating automation to Flow. It’s a scalable and admin-friendly tool that ensures your org stays up-to-date with Salesforce’s evolving ecosystem. Whether you’re automating basic processes or tackling complex workflows, Flow provides the flexibility to meet your needs. 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
August 28, 2024in Data, Salesforce Flow
Winter 25 Release Developer Enhancements
25Aug

Winter 25 Release Developer Enhancements

Developer Enhancements Salesforce Flow Enhancements Flow Orchestrator Enhancements How to Access and Use Key Salesforce Features View the Automation Lightning App: Manage Steps in an Orchestration Stage: Customize the Flow Orchestration Work Guide Component: Customize SMS One-Time Password Delivery for Experience Cloud Sites: Control Authenticated Callouts Permissions: Improve Data Transmission Speed and Security with TLS 1.3: Enable LWC Stacked Modals: Additional Enhancements: Salesforce Mobile App Enhancements Resolve Sync Issues with Offline Drafts: Access Hyperlinks in Enhanced Reports: Messaging in the Salesforce Mobile App: Seller-Focused Sales Mobile Experience: Stay Informed About Upcoming Releases: Check Out Resources: 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
August 25, 2024in Salesforce Shield
Apex Sharing Reasons
20Aug

Apex Sharing Reasons

Creating Apex Sharing Reasons Apex sharing reasons are essential for implementing Apex-managed sharing on custom objects. They define the purpose behind the sharing of records and streamline the process of managing sharing entries programmatically. By using Apex sharing reasons, you can: Why Use Apex Sharing Reasons? When Apex sharing reasons are configured, they appear in the Reason column of the sharing settings for a custom object record. This visibility helps users and administrators understand the intent behind each sharing entry. Key Considerations Steps to Create an Apex Sharing Reason Additional Information For more details about Apex-managed sharing, consult the Apex Developer Guide. This resource provides comprehensive insights into managing record sharing programmatically in Salesforce. By implementing Apex sharing reasons thoughtfully, you can enhance both the functionality and clarity of your record-sharing processes. 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
August 20, 2024in Data, Salesforce
Salesforce Data Snowflake and You
19Aug

Salesforce Data Snowflake and You

Unlock the Full Potential of Your Salesforce Data with Snowflake At Tectonic, we’ve dedicated years to helping businesses maximize their Salesforce investment, driving growth and enhancing customer experiences. Now, we’re expanding those capabilities by integrating with Snowflake.Imagine the power of merging Salesforce data with other sources, gaining deeper insights, and making smarter decisions—without the hassle of complex infrastructure. Snowflake brings this to life with a flexible, scalable solution for unifying your data ecosystem.In this insight, we’ll cover why Snowflake is essential for Salesforce users, how seamlessly it integrates, and why Tectonic is the ideal partner to help you leverage its full potential. Why Snowflake Matters for Salesforce Users Salesforce excels at managing customer relationships, but businesses today need data from multiple sources—e-commerce, marketing platforms, ERP systems, and more. That’s where Snowflake shines. With Snowflake, you can unify these data sources, enrich your Salesforce data, and turn it into actionable insights. Say goodbye to silos and blind spots. Snowflake is easy to set up, scales effortlessly, and integrates seamlessly with Salesforce, making it ideal for enhancing CRM data across various business functions.The Power of Snowflake for Salesforce Users Seamless Data IntegrationSnowflake’s cloud-native architecture lets you combine structured, semi-structured, and unstructured data effortlessly. Salesforce users can extend their data capabilities by integrating insights from external sources, such as web analytics, other CRMs, or real-time IoT data, all while keeping the setup simple. This provides a comprehensive view of each customer and enables faster, data-driven decisions. Scalability without ComplexitySnowflake is a fully managed, cloud-native platform that scales to meet your growing data needs without heavy infrastructure demands. This allows Salesforce users to expand their data strategy without altering their CRM setup or adding IT resources. Advanced Analytics and AI/ML ReadinessFrom predictive lead scoring to customer churn analysis, Snowflake supports AI and machine learning workloads, enabling Salesforce users to develop models that deliver actionable insights. By unifying data across platforms, Snowflake allows for more accurate and robust AI/ML models, driving smarter decisions across areas like customer support and product recommendations. Enterprise-Grade Security & GovernanceSnowflake ensures that your data is secure and compliant. With top-tier security and data governance tools, your customer data remains protected and meets regulatory requirements across platforms, seamlessly integrating with Salesforce. Cross-Cloud Data SharingSnowflake’s Snowgrid feature makes it easy for Salesforce users to share and collaborate on data across clouds. Teams across marketing, sales, and operations can access the same up-to-date information, leading to better collaboration and faster, more informed decisions. Real-Time Data ActivationCombine Snowflake’s data platform with Salesforce Data Cloud to activate insights in real-time, enabling enriched customer experiences through dynamic insights from web interactions, purchase history, and service touchpoints. Tectonic + Snowflake: Elevating Your Salesforce Experience Snowflake offers powerful data capabilities, but effective integration is key to realizing its full potential—and that’s where Tectonic excels. Our expertise in Salesforce, now combined with Snowflake, ensures that businesses can maximize their data strategies. How Tectonic Helps: Strategic Integration Planning: We assess your current data ecosystem and design a seamless integration between Salesforce and Snowflake to unify data without disrupting operations. Custom Data Solutions: From real-time dashboards to data enrichment workflows, we create solutions tailored to your business needs. Ongoing Support and Optimization: Tectonic provides continuous support, adapting your Snowflake integration to meet evolving data needs and business strategies. Real-World Applications Retail: Integrate in-store and e-commerce sales data with Salesforce for real-time customer insights. Healthcare: Unify patient data from wearables, EMRs, and support interactions for a holistic customer care experience. Financial Services: Enhance Salesforce data with third-party risk assessments, enabling quicker, more accurate underwriting. Looking Ahead: The Tectonic Advantage Snowflake opens up new possibilities for Salesforce-powered businesses. Effective integration, however, requires strategic planning and hands-on expertise. Tectonic has a long-standing track record of helping clients get the most out of Salesforce, and now, Snowflake adds an extra dimension to our toolkit. Whether you want to better manage data, unlock insights, or enhance AI initiatives, Tectonic’s combined Salesforce and Snowflake expertise ensures you’ll harness the best of both worlds. Stay tuned as we dive deeper into Snowflake’s features, such as Interoperable Storage, Elastic Compute, and Cortex AI with Arctic, and explore how Tectonic is helping businesses unlock the future of data and AI. Ready to talk about how Snowflake and Salesforce can transform your business? Contact Tectonic 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
August 19, 2024in Data, Enterprise, Salesforce, Salesforce Data Cloud, Snowflake
Salesforce Thematic Personalization
16Aug

Salesforce Thematic Personalization

Thematic Personalization Made Simple with Salesforce Leverage the power of thematic personalization to tailor your messaging and creative assets for each target audience directly within Salesforce. If you’re a Faraday user, integrating thematic personalization predictions into your CRM is a seamless way to elevate your outreach. With predictions accessible in Salesforce, you can shape your content to better resonate with your leads and contacts. This integration helps you understand what appeals to your audience, ensuring your communications are more relevant and impactful—all while working within the tools you already know. It’s an effortless way to enhance personalization and make the most of your data. Step-by-Step Integration Guide Step 1: Connect Your Data SourcesUse the link below to connect Salesforce to Faraday. Alternatively, you can skip this step and upload your data using CSV files to get started.👉 Connect to Salesforce Step 2: Ingest Data into Event StreamsStream your data into Faraday to enable the platform to interpret its meaning. Follow the link below for guidance on setting up event streams to power this template.👉 Ingest Data Step 3: Organize Your Customer DataGroup your data into cohorts—key building blocks in Faraday. These cohorts enable you to predict customer behavior with precision.👉 Define Cohorts Step 4: Declare Your Prediction ObjectivesOnce your cohorts are ready, instruct Faraday to predict the behaviors you care about. Follow the documentation using the link below.👉 Set Prediction Goals Step 5: Build and Deploy Your Personalization PipelineCreate a content personalization pipeline and deploy it to Salesforce to use predictions for shaping creative and messaging.👉 Deploy Content Personalization Step 6: Finalize Deployment to SalesforceComplete your setup by creating a deployment target within Salesforce or, if preferred, export your results as a CSV file.👉 Deploy to Salesforce Why Integrate Thematic Personalization?This integration empowers you to seamlessly incorporate predictive insights into your CRM workflow, enabling more personalized, effective communications. With minimal effort, you can connect with your audience on a deeper level, enhance engagement, and achieve better results. 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
August 16, 2024in Data, Salesforce
benefits of salesforce flow automation
15Aug

Benefits of Salesforce Flow Automation

Salesforce Flow Automation offers robust tools to streamline operations, enhance productivity, and improve accuracy. Whether you’re new to Salesforce or refining existing workflows, here are five top tips for maximizing the benefits of Salesforce Flow Automation. 1. Define Clear Objectives Before creating any flows, clearly define your automation goals, whether it’s reducing manual data entry, accelerating approval processes, or ensuring consistent customer follow-ups. Having specific objectives will keep your flow design focused and help you measure the impact of your automation. 2. Leverage Pre-Built Flow Templates Salesforce provides a range of pre-built flow templates tailored to common business needs, saving time and effort. Start with these templates and customize them to suit your unique requirements, allowing you to implement efficient solutions without building from scratch. 3. Optimize Decision Elements Decision elements in Salesforce Flow enable branching logic based on set conditions. Use them to direct the flow according to specific criteria, such as routing different approval paths based on deal value or service type. This targeted approach ensures each scenario is handled effectively. 4. Thoroughly Test Before Deployment Testing is a critical part of the automation process. Before launching a new flow, test it in a sandbox environment to catch any issues. Cover a range of scenarios and edge cases to confirm that the flow works as expected, helping avoid disruptions and ensuring a smooth transition into live use. 5. Monitor and Continuously Improve Automation is an evolving process. After deploying flows, monitor their performance to ensure they’re achieving desired outcomes. Use Salesforce’s reporting tools to track metrics like completion rates and processing times. With this data, you can fine-tune your flows to boost efficiency and adapt to changing business needs. By following these tips, you can unlock the full potential of Salesforce Flow Automation, leading to streamlined processes and better business outcomes. Embrace automation to reduce manual work and keep focus on driving core business growth. 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
August 15, 2024in Data, Salesforce, Salesforce Implementation Services, Salesforce Optimization
When The Customers Prefer Self-Service
14Aug

When The Customers Prefer Self-Service

Assistance is crucial for complex issues, but for simpler problems, customers typically prefer the convenience of self-service tools like account portals, FAQs, and chatbots. This preference is especially strong among digital natives, such as millennials and Gen Z. However, deploying self-service tools requires careful planning. For instance, over two-thirds of customers abandon a company’s chatbot after a single negative experience, underscoring the importance of a positive initial interaction. Statistics show that 72% of customers use self-service portals, and 55% engage with self-service chatbots. The willingness of nearly half of all customers, including 60% of millennials, to pay more for superior customer service highlights the importance of customer experience in an era of price sensitivity. Customers expect instant responses, creating a scalability challenge for service teams but also an opportunity to offer premium service. Instant responses can set a company apart, as even well-regarded brands often struggle to maintain quick and seamless connections between customers and agents. Self-service platforms must be easily adjustable, not only to address areas needing improvement but also to adapt to changing market demands. Customers now expect proactive service rather than the traditional reactive approach. Despite this, customer service is often perceived as reactive. The time and effort customers spend resolving service issues are significant, especially when service teams are inconsistently trained and equipped, leading to a perception that quality service is a matter of luck. Consistency across channels, devices, and departments is highly valued but often lacking. Many customers find themselves repeating information to different representatives, indicating a fragmented information environment. Poorly integrated technology and processes leave 55% of customers feeling as if they interact with separate departments rather than a unified company. Disconnected experiences are a major source of frustration. Prompt resolution of issues is a top priority for customers, and many find it quicker to search for answers themselves than to contact the company. Self-service not only facilitates quick problem-solving but also empowers customers to address issues at their own pace and learn as much or as little as they wish. In terms of preferences, over 67% of customers prefer some form of self-service over speaking with a representative. Additionally, 73% prefer using the company’s website for support rather than relying on social media, SMS, or live chat apps. Don’t always assume the “latest and greatest” solutions available are the best solutions for your customers. A self-service strategy involves providing customers with tools to resolve their needs independently, reducing the need for representative assistance. Reduce staffing needs and increase speed to answers for customers. Its a win win. However, implementing self-service can face challenges, such as confusing navigation, lack of ongoing attention, inflexibility, failure to incorporate feedback, constraints on users, extra work, lack of human interaction, difficulty in personalization, and the need for continuous analysis and monitoring. Successful self-service integration requires addressing these factors to meet customer expectations. Contact Tectonic for assistance bringing your self-service solutions to your customers. 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 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 Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

Read More
August 14, 2024in Salesforce Commerce Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Hospitality Travel and Transportation, Salesforce Manufacturing Automotive Energy, Salesforce Optimization, Salesforce Service Cloud, Technology
AI All Grown Up
10Aug

Generative AI Tools

One of the most significant use cases for generative AI in business is customer service and support. Most of us have likely experienced the frustration of dealing with traditional automated systems. However, today’s advanced AI, powered by large language models and natural language chatbots, is rapidly improving these interactions. While many still prefer human agents for complex or sensitive issues, AI is proving highly capable of handling routine inquiries efficiently. Here’s an overview of some of the top AI-powered tools for automating customer service. Although the human element will always be essential in customer experience, these tools free up human agents from repetitive tasks, allowing them to focus on more complex challenges requiring empathy and creativity. Cognigy Cognigy is an AI platform designed to automate customer service voice and chat channels. It goes beyond simply reading FAQ responses by delivering personalized, context-sensitive answers in multiple languages. Cognigy’s AI Copilot feature enhances human contact center workers by offering real-time AI assistance during interactions, making both fully automated and human-augmented support possible. IBM WatsonX Assistant IBM’s WatsonX Assistant helps businesses create AI-powered personal assistants to streamline tasks, including customer support. With its drag-and-drop configuration, companies can set up seamless self-service experiences. The platform uses retrieval-augmented generation (RAG) to ensure that responses are accurate and up-to-date, continuously improving as it learns from customer interactions. Salesforce Einstein Service Cloud Einstein Service Cloud, part of the Salesforce platform, automates routine and complex customer service tasks. Its AI-powered Agentforce bots manage “low-touch” interactions, while “high-touch” cases are overseen by human agents supported by AI. Fully customizable, Einstein ensures that responses align with your brand’s tone and voice, all while leveraging enterprise data securely. Zendesk AI Zendesk, a leader in customer support, integrates generative AI to boost its service offerings. By using machine learning and natural language processing, Zendesk understands customer sentiment and intent, generates personalized responses, and automatically routes inquiries to the most suitable agent—be it human or machine. It also provides human agents with real-time guidance on resolving issues efficiently. Ada Ada is a conversational AI platform built for large-scale customer service automation. Its no-code interface allows businesses to create custom bots, reducing the cost of handling inquiries by up to 78% per ticket. By integrating domain-specific data, Ada helps improve both support efficiency and customer experience across omnichannel support environments. More AI Tools for Customer Service There are numerous other AI tools designed to enhance automated customer support: While AI tools are transforming customer service, the key lies in using them to complement human agents, allowing for a balance of efficiency and personalized care. 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
August 10, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Salesforce Einstein
Small Language Models
10Aug

Small Language Models

Large language models (LLMs) like OpenAI’s GPT-4 have gained acclaim for their versatility across various tasks, but they come with significant resource demands. In response, the AI industry is shifting focus towards smaller, task-specific models designed to be more efficient. Microsoft, alongside other tech giants, is investing in these smaller models. Science often involves breaking complex systems down into their simplest forms to understand their behavior. This reductionist approach is now being applied to AI, with the goal of creating smaller models tailored for specific functions. Sébastien Bubeck, Microsoft’s VP of generative AI, highlights this trend: “You have this miraculous object, but what exactly was needed for this miracle to happen; what are the basic ingredients that are necessary?” In recent years, the proliferation of LLMs like ChatGPT, Gemini, and Claude has been remarkable. However, smaller language models (SLMs) are gaining traction as a more resource-efficient alternative. Despite their smaller size, SLMs promise substantial benefits to businesses. Microsoft introduced Phi-1 in June last year, a smaller model aimed at aiding Python coding. This was followed by Phi-2 and Phi-3, which, though larger than Phi-1, are still much smaller than leading LLMs. For comparison, Phi-3-medium has 14 billion parameters, while GPT-4 is estimated to have 1.76 trillion parameters—about 125 times more. Microsoft touts the Phi-3 models as “the most capable and cost-effective small language models available.” Microsoft’s shift towards SLMs reflects a belief that the dominance of a few large models will give way to a more diverse ecosystem of smaller, specialized models. For instance, an SLM designed specifically for analyzing consumer behavior might be more effective for targeted advertising than a broad, general-purpose model trained on the entire internet. SLMs excel in their focused training on specific domains. “The whole fine-tuning process … is highly specialized for specific use-cases,” explains Silvio Savarese, Chief Scientist at Salesforce, another company advancing SLMs. To illustrate, using a specialized screwdriver for a home repair project is more practical than a multifunction tool that’s more expensive and less focused. This trend towards SLMs reflects a broader shift in the AI industry from hype to practical application. As Brian Yamada of VLM notes, “As we move into the operationalization phase of this AI era, small will be the new big.” Smaller, specialized models or combinations of models will address specific needs, saving time and resources. Some voices express concern over the dominance of a few large models, with figures like Jack Dorsey advocating for a diverse marketplace of algorithms. Philippe Krakowski of IPG also worries that relying on the same models might stifle creativity. SLMs offer the advantage of lower costs, both in development and operation. Microsoft’s Bubeck emphasizes that SLMs are “several orders of magnitude cheaper” than larger models. Typically, SLMs operate with around three to four billion parameters, making them feasible for deployment on devices like smartphones. However, smaller models come with trade-offs. Fewer parameters mean reduced capabilities. “You have to find the right balance between the intelligence that you need versus the cost,” Bubeck acknowledges. Salesforce’s Savarese views SLMs as a step towards a new form of AI, characterized by “agents” capable of performing specific tasks and executing plans autonomously. This vision of AI agents goes beyond today’s chatbots, which can generate travel itineraries but not take action on your behalf. Salesforce recently introduced a 1 billion-parameter SLM that reportedly outperforms some LLMs on targeted tasks. Salesforce CEO Mark Benioff celebrated this advancement, proclaiming, “On-device agentic AI is here!” 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 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 Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more

Read More
August 10, 2024in Generative AI
Chatbots in Healthcare
10Aug

Chatbots in Healthcare

Not all medical chatbots are created equal, as a recent JAMA Network Open study reveals. The study found that some chatbots are better at tailoring health information to patient health literacy than others. Chatbots in Healthcare may not be ready for prime time. The report compared the free and paid versions of ChatGPT, showing that while the paid version initially provided more readable health information, the difference was minimal once researchers asked the chatbots to explain things at a sixth-grade reading level. The findings suggest that both versions of ChatGPT could potentially widen health disparities in terms of information access and literacy. Chatbots like ChatGPT are becoming increasingly prominent in healthcare, showing potential in improving patient access to health information. However, their quality can vary. The study evaluated the free and paid versions of ChatGPT using the Flesch Reading Ease score for readability and the DISCERN instrument for consumer health information quality. Researchers tested both versions using the five most popular cancer-related queries from 2021 to 2023. They found that while the paid version had slightly higher readability scores (52.6) compared to the free version (62.48) on a 100-point scale, both scores were deemed suboptimal. The study revealed that prompting the free version of ChatGPT to explain concepts at a sixth-grade reading level improved its readability score to 71.55, outperforming the paid version under similar conditions. Even so, when both versions were asked to simplify answers to a sixth-grade reading level, the paid version scored slightly higher at 75.64. Despite these improvements, the overall readability of responses was still problematic. Without the simplification prompt, responses were roughly at a 12th-grade reading level. Even with the prompt, they remained closer to an eighth- or tenth-grade level, possibly due to chatbot confusion about the request. The study raises concerns about health equity. If the paid version of ChatGPT provides more accessible information, individuals with the means to purchase it might have a clear advantage. This disparity could exacerbate existing health inequities, especially for those using the free version. The researchers concluded that until chatbots consistently provide information at a lower reading level, clinicians should guide patients on how to effectively use these tools and encourage them to request information at simpler reading levels. Like Related Posts 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 Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to Read more 50 Advantages of Salesforce Sales Cloud According to the Salesforce 2017 State of Service report, 85% of executives with service oversight identify customer service as a Read more

Read More
August 10, 2024in Generative AI, Salesforce Health and Life Sciences Solutions, Salesforce Optimization

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