Salesforce Einstein Archives - gettectonic.com - Page 16
Salesforce for Manufacturing, Automotive, and Energy
07Mar

Improve Manufacturing Sales by Improving Partner Engagement

Did you know that half of all B2B revenue is generated through channel partner sales? A recent 2022 survey revealed that 80% of B2B executives find their partner programs ineffective, potentially slowing or even blocking product sales. The obstacles to improvements with manufacturing partners include siloed systems and data, fragmented processes, inconsistent programs, and outdated communication channels. You can improve manufacturing partner engagement. Learn more. To overcome these challenges and enhance manufacturing partner relationships. For increased sales, consider focusing on three key areas: By prioritizing transparency, investing in technology, and streamlining processes, manufacturers can strengthen partner relationships, leading to increased sales and improved customer satisfaction. Focus on making it easier for partners to do business with you, build trust, and create a unified, shared view of data to achieve mutually beneficial relationships. Is it time to explore how Tectonic and Salesforce can improve your partner engagement? Contact us today. Like1 Related Posts 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 Salesforce Government Cloud: Ensuring Compliance and Security Salesforce Government Cloud public sector solutions offer dedicated instances known as Government Cloud Plus and Government Cloud Plus – Defense. Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Integration of Salesforce Sales Cloud to Google Analytics 360 Announced In November 2017, Google unveiled a groundbreaking partnership with Salesforce, outlining their commitment to develop innovative integrations between Google Analytics Read more

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March 7, 2023in Salesforce Experience Cloud, Salesforce Manufacturing Automotive Energy, Salesforce Manufacturing Cloud, Salesforce Sales Cloud, Unified Knowledge
Salesforce Solutions for Communications
14Feb

Salesforce Solutions for Communications

Salesforce has recently unveiled tailored Salesforce Solutions for Communications aimed at enhancing customer experiences through advanced analytics, automation tools, and AI-driven customer service. These innovations empower providers to gain deeper insights into their customers, automate interactions, and effectively monitor customer satisfaction levels. For communication providers looking to elevate their customer service, Salesforce’s automation solutions offer significant benefits. They ensure seamless and efficient handling of every customer interaction, leading to improved satisfaction and increased loyalty. Salesforce Solutions for Communication Providers Salesforce offers a comprehensive suite of solutions specifically designed for communication providers to enhance customer experiences. This suite includes: Salesforce Customer Engagement Platform (CXP) and Tools The Salesforce Customer Engagement Platform equips communication providers with a suite of tools to enhance customer experiences, including: Salesforce Einstein AI Platform for Communication Providers The Salesforce Einstein AI Platform is tailored to improve customer experiences for communication providers by analyzing extensive datasets to identify patterns, understand customer needs, and optimize interactions, ultimately enhancing overall customer experience. Benefits of the Salesforce Customer Engagement Platform The Salesforce Customer Engagement Platform offers communication providers various benefits, including enhanced customer experience, increased sales, and improved operational efficiencies. Features such as social listening and engagement analytics enable providers to understand customer sentiment and behavior, facilitating personalized interactions and driving sales growth. New Salesforce Innovations for Communication Providers New innovations from Salesforce include analytics, AI, and prebuilt solutions that automate processes, improve customer experiences, and reduce operational costs. Integrations with WhatsApp enable richer customer engagements, personalized experiences, and automated interactions through chatbots. In conclusion, Salesforce’s solutions for communication providers represent a significant advancement in improving customer experiences, revolutionizing how providers interact with their clientele to achieve positive outcomes and business growth. Contact Tectonic today to explore Salesforce communications 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

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February 14, 2023in Salesforce, Salesforce Communications Cloud, Salesforce Einstein, Salesforce Sales Cloud, Salesforce Service Cloud
Cloud Based Business Solutions
09Feb

Data Strategies in AI

The demand for reliable data has been a longstanding requirement. Driven by the continuous pursuit of real-time personalization and improved business efficiencies. The advent of Generative AI has further intensified these needs, prompting analytics and IT leaders to strengthen their data foundations. Data Strategies in AI A significant 86% of analytics and IT leaders acknowledge that the effectiveness of AI’s outputs hinges on the quality of its data inputs. Encouragingly, technical leaders express confidence in their standing. Assessing factors such as data capabilities, processes, sponsorship, investment, and vision, more than a third of analytics and IT leaders categorize their data maturity as best-in-class. Despite this optimism, only 6% of these leaders admit to having data maturity below industry standards or nonexistent. This statistic may reflect the challenges of benchmarking maturity against peers or, at worst, an overconfidence in data strategy and capabilities. Data Strategies in AI In today’s fast-paced work environment, characterized by a flood of data and constant communications, individuals are grappling to keep up. Simultaneously, business leaders face mounting pressure to enhance productivity amid unpredictable economic conditions. The list of challenges continues to expand: An AI Data assessment offers several benefits, empowering organizations to: Four key strategies for AI impact are: vision, value-realization, risk, and adoption plans. 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

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February 9, 2023in Salesforce Einstein, Salesforce Marketing Cloud, Salesforce Sales Cloud, Salesforce Service Cloud
Salesforce Service Cloud
01Dec

Salesforce Service Cloud Explained

Service Cloud, developed by Salesforce, functions as a powerful customer relationship management (CRM) tool explicitly tailored to bolster a business’s customer service operations. This platform streamlines communication between customers and companies, utilizing channels such as email support, live chat, and phone interactions to aid customer agents in promptly identifying and resolving customer issues. Service Cloud, as a comprehensive customer service platform, empowers businesses to effectively manage and resolve customer inquiries and issues. Its huge array of features encompasses case tracking, social networking plug-ins for conversion, and analytics. If you’ve recently engaged with a business through live chat or created a support ticket, chances are they were leveraging a system like SFDC Service Cloud. Revolutionize the way your service teams provide value across every customer touchpoint, encompassing customer experience, engagement, processes, automation, and service operations. By reimagining service with Service Cloud, businesses can enhance customer lifetime value, reduce operational costs, and optimize efficiency—from the contact center to the field. Achieve personalized support at scale with trustworthy Artificial Intelligence. Integrate every customer service process into the Einstein 1 Platform to decrease costs and boost productivity. Service Cloud provides businesses with a 360-degree view of their customers, enabling the delivery of faster, smarter, and more tailored experiences. Businesses can establish a connected knowledge base, manage case interactions, and facilitate live agent chat—all within a unified platform. Leveraging customer’s past activity data allows for personalized interactions and opportunities for upselling services or products. It’s important to note the distinction between Salesforce Sales Cloud and Service Cloud. Sales Cloud focuses on the sales process, managing leads, opportunities, and sales forecasts. On the other hand, Service Cloud is dedicated to addressing customer inquiries and cases, emphasizing exceptional customer service and support. Important Features of Service Cloud Custom Reports and Dashboards Utilizing real-time reports within SFDC Service Cloud empowers business owners to access a comprehensive case history of customers, facilitating informed decision-making. The associated dashboard provides professionals with a 360-degree view, enabling easy tracking of customer information and cases for efficient problem-solving. Salesforce Lightning Service Console The Salesforce Service Cloud console consolidates customer data across multiple devices and channels. It provides a unified view of customer interactions, encompassing emails, chats, social media mentions, and phone calls. Salesforce Service Cloud Case Management A case management system collects and organizes customer service interactions from various channels, allowing agents to respond promptly. Automated Case Routing Automation in case routing expedites resolutions by assigning tasks to personnel based on qualifications and skills, managing tasks across multiple service channels. Social Customer Service Empowering customer teams, Social Customer Service handles user queries on social media channels and directs them to relevant agents, offering a 360-degree view of customers. Asset and Order Management Tracking orders, assets, and support history in one place provides a comprehensive view, enabling personalized services and enhancing customer satisfaction. Telephony Integration Integrating with Computer Telephony Integration (CTI) systems boosts agent productivity, allowing viewing of customer information without picking up the phone. Benefits of Salesforce Service Cloud: Supplementary Products: Digital Engagement, Service Cloud Einstein, Service Cloud Voice, Customer Lifecycle Analytics, Salesforce Surveys Response Pack. Are you ready to achieve the best in customer service with Salesforce Service Cloud?  Contact Tectonic today to learn more. Tectonic is please to announce Salesforce Service Cloud Implementation Solutions. Content updated September 2023. Like2 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 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 Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust 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

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December 1, 2022in Salesforce Development Services, Salesforce Einstein, Salesforce Service Cloud
Einstein Chatbot Salesforce
23Nov

Einstein Chatbot Salesforce

Salesforce users have two options for creating a chatbot: either using an external platform and integrating the final product through Salesforce Lightning, or directly developing it within Salesforce Einstein Bot. Salesforce prioritizes empowering customers to create applications without the need for coding. Einstein Chatbot Salesforce. Einstein Bots utilize Artificial Intelligence (AI) to enhance the customer service experience across various channels such as SMS, Chat, Slack, Facebook Messenger, and WhatsApp, supporting conversations in multiple languages. They offer personalized and intelligent service by leveraging AI-powered chatbots integrated directly into the CRM. With just a few clicks, users can expedite issue resolution and boost productivity by utilizing AI-generated responses or automating routine tasks with bots integrated into Salesforce data. These secure, multilingual bots can be deployed on enhanced messaging channels, including in-app, web, and third-party platforms, as well as Slack and the Einstein Bots API. Creating an Einstein Bot: Enhanced Features for Einstein Bots: Einstein Bots offer various benefits: Salesforce Einstein Chatbots provide a valuable solution for organizations aiming to streamline customer service, reduce case volume, and enhance overall customer experience across diverse channels and languages. 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

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November 23, 2022in Salesforce Einstein
tectonic logo
14Nov

AI Large Language Models

What Exactly Constitutes a Large Language Model? Picture having an exceptionally intelligent digital assistant that extensively combs through text, encompassing books, articles, websites, and various written content up to the year 2021. Yet, unlike a library that houses entire books, this digital assistant processes patterns from the textual data it undergoes. This digital assistant, akin to a large language model (LLM), represents an advanced computer model tailored to comprehend and generate text with humanlike qualities. Its training involves exposure to vast amounts of text data, allowing it to discern patterns, language structures, and relationships between words and sentences. How Do These Large Language Models Operate? Fundamentally, large language models, exemplified by GPT-3, undertake predictions on a token-by-token basis, sequentially building a coherent sequence. Given a request, they strive to predict the subsequent token, utilizing their acquired knowledge of patterns during training. These models showcase remarkable pattern recognition, generating contextually relevant content across diverse topics. The “large” aspect of these models refers to their extensive size and complexity, necessitating substantial computational resources like powerful servers equipped with multiple processors and ample memory. This capability enables the model to manage and process vast datasets, enhancing its proficiency in comprehending and generating high-quality text. While the sizes of LLMs may vary, they typically house billions of parameters—variables learned during the training process, embodying the knowledge extracted from the data. The greater the number of parameters, the more adept the model becomes at capturing intricate patterns. For instance, GPT-3 boasts around 175 billion parameters, marking a significant advancement in language processing capabilities, while GPT-4 is purported to exceed 1 trillion parameters. While these numerical feats are impressive, the challenges associated with these mammoth models include resource-intensive training, environmental implications, potential biases, and more. Large language models serve as virtual assistants with profound knowledge, aiding in a spectrum of language-related tasks. They contribute to writing, offer information, provide creative suggestions, and engage in conversations, aiming to make human-technology interactions more natural. However, users should be cognizant of their limitations and regard them as tools rather than infallible sources of truth. What Constitutes the Training of Large Language Models? Training a large language model is analogous to instructing a robot in comprehending and utilizing human language. The process involves: Fine-Tuning: A Closer Look Fine-tuning involves further training a pre-trained model on a more specific and compact dataset than the original. It is akin to training a robot proficient in various cuisines to specialize in Italian dishes using a dedicated cookbook. The significance of fine-tuning lies in: Versioning and Progression Large language models evolve through versions, with changes in size, training data, or parameters. Each iteration aims to address weaknesses, handle a broader task spectrum, or minimize biases and errors. The progression is simplified as follows: In essence, large language model versions emulate successive editions of a book series, each release striving for refinement, expansiveness, and captivating 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

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November 14, 2022in Data, Salesforce, Salesforce Einstein, Technology
ChatGPT and Einstein GPT
08Nov

ChatGPT and Einstein GPT

Artificial intelligence (AI) has been rapidly advancing globally, with breakthroughs captivating professionals across various sectors. One milestone that has gained significant attention is the emergence of ChatGPT, a cutting-edge language model revolutionizing the tech landscape. This development has profoundly impacted businesses relying on Salesforce for their customer relationship management (CRM) needs. In March 2023, Salesforce unveiled its latest AI innovation, Einstein GPT, promising to transform how companies engage with their clientele. In this article, we explore what Salesforce Einstein GPT entails and how it can benefit teams across diverse industries. When OpenAI introduced ChatGPT in November 2022, they didn’t expect the overwhelming response it received. Initially positioned as a “research preview,” this AI chatbot aimed to refine existing technology while soliciting feedback from users. However, ChatGPT quickly became a viral sensation, surpassing OpenAI’s expectations and prompting them to adapt to its newfound popularity. Developed on the foundation of the GPT-3.5 language model, ChatGPT was specifically tailored to facilitate engaging and accessible conversations, distinguishing it from its predecessors. Its launch attracted a diverse user base keen to explore its capabilities, prompting OpenAI to prioritize addressing potential misuse and enhancing its safety features. As ChatGPT gained traction, it caught the attention of Salesforce, a leading CRM provider. In March 2023, Salesforce unveiled Einstein GPT, its own AI innovation, poised to transform customer engagement. Built on the GPT-3 architecture and seamlessly integrated into Salesforce Clouds, Einstein GPT promised to revolutionize how businesses interact with their clientele. Einstein GPT boasts a range of features designed to personalize customer experiences and streamline workflows. From generating natural language responses to crafting personalized content and automating tasks, Einstein GPT offers versatility and value across industries. By leveraging both Einstein AI and GPT technology, businesses can unlock unprecedented efficiency and deliver superior customer experiences. Despite its success, OpenAI acknowledges the need for ongoing refinement and vigilance, emphasizing the importance of responsible deployment and transparency in the development of AI technology. Exploring Einstein GPT Salesforce presents Einstein GPT as the premier generative AI tool for CRM worldwide. Utilizing the advanced GPT-3 architecture, Einstein GPT seamlessly integrates into all Salesforce Clouds, including Tableau, MuleSoft, and Slack. This groundbreaking technology empowers users to generate natural language responses to customer inquiries, craft personalized content, and compose entire email messages on behalf of sales personnel. With its high degree of customization, Einstein GPT can be finely tuned to meet the specific needs of various industries, use cases, and customer requirements, delivering significant value to businesses of all sizes and sectors. Objectives of Salesforce AI Einstein GPT Salesforce AI Einstein GPT is designed to achieve several key objectives: Distinguishing Einstein GPT from Einstein AI Einstein GPT represents the latest evolution of Salesforce’s Einstein artificial intelligence technology. Unlike its predecessors, Einstein GPT integrates proprietary Einstein AI models with ChatGPT and other leading large language models. This integration enables users to interact with CRM data using natural language prompts, resulting in highly personalized, AI-generated content and triggering powerful automations that enhance workflows and productivity. By leveraging both Einstein AI and GPT technology, businesses can achieve unparalleled efficiency and deliver exceptional customer experiences. Features of Einstein GPT in Salesforce CRM Key features and capabilities of Salesforce Einstein chatbot GPT include: Utilizing Einstein GPT for Business Improvement Einstein GPT can be leveraged across various domains to enhance business operations: Integration with Salesforce Data Cloud Salesforce Data Cloud, a cloud-based data management system, enables real-time data aggregation from diverse sources. Einstein GPT utilizes unified customer data profiles from the Salesforce Data Cloud to personalize interactions throughout the customer journey. OpenAI on ChatGPT Methods We trained this model using Reinforcement Learning from Human Feedback (RLHF), using the same methods as InstructGPT, but with slight differences in the data collection setup. We trained an initial model using supervised fine-tuning: human AI trainers provided conversations in which they played both sides—the user and an AI assistant. We gave the trainers access to model-written suggestions to help them compose their responses. We mixed this new dialogue dataset with the InstructGPT dataset, which we transformed into a dialogue format. To create a reward model for reinforcement learning, we needed to collect comparison data, which consisted of two or more model responses ranked by quality. To collect this data, we took conversations that AI trainers had with the chatbot. We randomly selected a model-written message, sampled several alternative completions, and had AI trainers rank them. Using these reward models, we can fine-tune the model using Proximal Policy Optimization. We performed several iterations of this process. ChatGPT is fine-tuned from a model in the GPT-3.5 series, which finished training in early 2022. You can learn more about the 3.5 series here. ChatGPT and GPT-3.5 were trained on an Azure AI supercomputing infrastructure. Limitations ChatGPT and Einstein GPT Salesforce Einstein GPT signifies a significant advancement in AI technology, empowering businesses to deliver tailored customer experiences and streamline operations. With its integration into Salesforce CRM and other platforms, Einstein GPT offers unprecedented capabilities for personalized engagement and automated insights, ensuring organizations remain competitive in today’s dynamic market landscape. When OpenAI quietly launched ChatGPT in late November 2022, the San Francisco-based AI company didn’t anticipate the viral sensation it would become. Initially viewed as a “research preview,” it was meant to showcase a refined version of existing technology while gathering feedback from the public to address its flaws. However, the overwhelming success of ChatGPT caught OpenAI off guard, leading to a scramble to capitalize on its newfound popularity. ChatGPT, based on the GPT-3.5 language model, was fine-tuned to be more conversational and accessible, setting it apart from previous iterations. Its release marked a significant milestone, attracting millions of users eager to test its capabilities. OpenAI quickly realized the need to address potential misuse and improve the model’s safety features. Since its launch, ChatGPT has undergone several updates, including the implementation of adversarial training to prevent users from exploiting it (known as “jailbreaking”). This technique involves pitting multiple chatbots against each other to identify and neutralize malicious behavior. Additionally,

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November 8, 2022in Generative AI, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein
Salesforce Lightning
03Nov

Why Migrate From Salesforce Classic to Salesforce Lightning?

Why Switch from Salesforce Classic to Lightning? Salesforce is actively advancing the Lightning interface, and the reasons are compelling. The Lightning platform offers numerous advantages, including an improved user interface, quicker development times, and extensive customization options. With user-friendly features like drag-and-drop functionality, Lightning enhances the overall user experience (UX). Many companies are opting to migrate from Salesforce Classic to Salesforce Lightning to leverage these benefits and more. Salesforce Classic Salesforce Classic served as the primary interface until 2016, but today, it is considered outdated. Investing in Lightning Experience is driven by the desire to deliver a superior user experience, adopt features solving business challenges, enhance processes, and gain access to Lightning innovation and rapid app development technology. Given that Salesforce Lightning is faster, provides advanced customization features, and boasts easier navigation compared to Classic, transitioning to Lightning in 2023 can be a strategic decision for your business. Lightning also integrates Salesforce Einstein, offering reporting, analytics, and generative AI capabilities. Salesforce Lightning In contrast to Classic, the Lightning development platform enables non-technical users to effortlessly create customized apps without programming knowledge. Due to its sleek UI, faster performance, regular feature releases, and comprehensive usability, Salesforce Lightning has become the preferred CRM for businesses. Migration However, migrating from Salesforce Classic to Lightning, or any other CRM to Lightning, presents challenges, including high implementation time. Opting for a phased rollout is an effective approach to overcome these challenges. While challenges may arise, a strategic plan executed by experienced Salesforce Lightning Support can ensure a seamless and timely migration. If you’re ready to migrate to Salesforce Lightning, especially if your Salesforce is highly customized, it’s crucial to document all changes made in Classic and identify corresponding solutions in Lightning. If handling the complexities of Classic seems daunting, consider engaging a Salesforce Migration partner like Tectonic to conduct a full Salesforce audit before migrating to Salesforce Lightning, ensuring a smooth transition. Content updated December 2023. 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

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November 3, 2022in Data, Generative AI, Salesforce, Salesforce Einstein, Technology
Salesforce Genie
20Sep

Salesforce Announces Genie

San Francisco – September 20, 2022 – Salesforce (NYSE: CRM), the global leader in CRM, today unveiled Salesforce Genie, a hyperscale real-time data platform that powers the entire Salesforce Customer 360 platform. With Genie, every company can turn data into customer magic, delivering seamless, highly personalized experiences across sales, service, marketing, and commerce that continuously adapt to changing customer information and needs in real time. Today, 71% of customers expect every interaction with a business to be personalized. However, data remains siloed and duplicative across most companies. Companies on average have 976 separate applications to run their business — that’s potentially 976 versions of a single customer, leading to disconnected digital experiences. Responding to customer needs in real time is more critical than ever, but is becoming more challenging as the amount of data created, captured, replicated, and consumed each year is expected to more than double by 2026. Companies must seamlessly connect digital and real-world customer interactions in order to provide the most relevant, personalized, and compelling experiences, in every moment. A sales rep can no longer rely on in-person communication to close a large deal — reps need real-time insights about every customer, based on real-time and historical data, to act on the most relevant information in the moment, every time. A service agent runs the risk of losing a valuable customer if they lack a complete, real-time view across every touchpoint — every agent needs a source of real-time customer truth. With real-time patient data, healthcare providers can deliver proactive guidance and care recommendations.  “Genie makes every part of Customer 360 more automated, intelligent, and real time.” DAVID SCHMAIER, PRESIDENT AND CHIEF PRODUCT OFFICER, SALESFORCE “Every business leader wants to take advantage of real-time data to create compelling, personalized customer experiences — milliseconds matter in this new digital-first world,” said David Schmaier, President and Chief Product Officer, Salesforce. “That’s why we built Genie, our most significant innovation ever on the Salesforce Platform. Genie makes every part of Customer 360 more automated, intelligent, and real time.” Salesforce Genie: Unify data to build a real-time customer graph and single source of truth As the heart of real-time Customer 360, Salesforce Genie ingests and stores real-time data streams at massive scale and combines it with Salesforce transactional data. Genie includes built-in connectors that bring in data from every channel (mobile, web, APIs), legacy data through MuleSoft, and historical data from proprietary data lakes. 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

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September 20, 2022in Mulesoft, Salesforce, Salesforce Einstein
Salesforce BI
25Aug

Analytics Studio Salesforce

Analytics Studio Salesforce provides the foundational tool for building CRM Analytics assets—the lenses and dashboards that make up Analytics apps. Use Analytics Studio to explore your data and build lenses and dashboards with data visualizations that give you the insights to help you run your business. Store everything in apps— the containers of assets that tell a story about an aspect of your business through data visualizations. Analytics Studio lets you do most of your work declaratively through its intuitive graphical user interface. You can also access the JSON, XMD, SaQL, and other files that control how Analytics consumes and displays data in dashboards and apps. Open those files with your editor of choice to make fine-grained changes to Analytics business user functionality. To open Analytics Studio, navigate to the Salesforce app picker and select Analytics Studio. Sometimes you must scroll or click View All to find it. From there, click Create, and start building. The Analytics Learning Map provides pointers to help get you going and master Analytics visualizations. Is Salesforce analytics Studio free? You have to be aware that Einstein Analytics is not free even if you’re using other Salesforce products such as Sales, Service, or Financial Cloud. You have to buy a separate license for Einstein Analytics Plus or Einstein Analytics Growth, depending on the components you plan to use in your workflow. What is CRM Analytics Salesforce? Salesforce CRM analytics is a powerful, accurate, and expansive CRM analytics solution, designed to provide users with a single, unified system capable of collecting and analyzing customer data to turn into valuable insights. Content updated December 2023. 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

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August 25, 2022in Salesforce Einstein, Salesforce Implementation Services
Salesforce Revenue Intelligence
23Jul

Salesforce Revenue Intelligence

Achieve Sales Targets and Ensure Forecast Accuracy with Revenue Intelligence Salesforce Revenue Intelligence is a powerful data-driven sales solution designed to help sales teams hit their targets, grow pipelines, and ensure accurate forecasts. By combining Tableau CRM dashboards and analytics with a visual pipeline progress flow chart in Pipeline Inspection, Revenue Intelligence offers a comprehensive view of your sales pipeline. Key Features of Revenue Intelligence Unified Sales Insights Revenue Intelligence integrates several advanced tools, including: These features leverage AI, forecasting, and customer engagement data to help your sales team confidently close deals and drive revenue growth. Available for an Additional Cost Revenue Intelligence and its robust features are available as an add-on. For detailed pricing information, please contact your Salesforce account executive. Enhance Revenue and Build Stronger Pipelines with Revenue Insights Comprehensive Performance Insights Revenue Insights empowers sales teams with crucial performance insights to build stronger pipelines, improve forecast accuracy, and generate more revenue. It offers three embedded dashboards: Dynamic Pipeline Visualization With Pipeline Inspection, users can track changes in their pipeline dynamically. The Flow tab displays changes to opportunities across different forecast categories over time, allowing users to filter and focus on the most relevant data. By leveraging Salesforce Revenue Intelligence, sales teams can achieve unprecedented clarity and control over their sales processes, ensuring consistent growth and success. 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

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July 23, 2022in Salesforce, Salesforce Einstein
Data Management for AI
05Jul

Data Management for AI

AI Data Management is the strategic and systematic handling of an organization’s data assets through the integration of AI technology. The primary goal is to enhance data quality, analysis, and decision-making processes. This encompasses the implementation of procedures, guidelines, and technical methodologies for the efficient collection, organization, storage, and utilization of data. While Generative AI receives considerable attention, more established AI applications, such as predictive analytics and chatbots, have long proven beneficial for organizations. Technical leaders leveraging AI report notable improvements in decision-making speed and operational efficiency. Beyond speed, analytics and IT leaders find more time to address strategic challenges rather than being immersed in routine tasks. Customers also experience significant enhancements in satisfaction due to AI. With AI outcomes heavily reliant on data quality, nearly nine in 10 analytics and IT leaders prioritize data management as a high concern amidst new AI developments. Artificial Intelligence quietly contributes to data management by addressing aspects like quality, accessibility, and security. As organizations accelerate digital transformation, AI and Machine Learning are increasingly harnessed to maximize data value. Effective data management is pivotal in creating an environment where data becomes a valuable asset throughout the organization. It mitigates issues arising from poor data, such as friction, inaccurate predictions, and accessibility challenges, ideally preventing them proactively. The labor-intensive nature of data management involves cleaning, extracting, integrating, cataloging, labeling, and organizing data. AI plays a crucial role in organizing data by analyzing extensive datasets and identifying relevant and high-quality content based on predefined criteria. It assists in tagging, categorizing, and summarizing content, simplifying user access to needed information. AI significantly contributes to various data management areas, including classification, cataloging, quality improvement, security, and data integration. It excels in tasks such as obtaining, extracting, and structuring data, locating data, reducing errors, ensuring security, and building master lists. In the realm of database management systems, AI is integrated, particularly machine learning, for automatic diagnosis, monitoring, alerting, and protection of databases. This advancement allows software to manage these tasks autonomously. ML data management applies data quality practices and debugging solutions to machine learning processes. Techniques such as embeddings/similarity search, active learning, meta-learning, and reinforcement learning are utilized for understanding data. AI databases play a crucial role in meeting the complex querying needs of AI systems, providing flexibility and power to enhance innovation and progress. AI-powered solutions contribute to data management by analyzing access patterns, detecting anomalies, and ensuring compliance with privacy regulations through anonymization or pseudonymization of sensitive data. Like1 Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more Salesforce Data Studio Data Studio Overview Salesforce Data Studio is Salesforce’s premier solution for audience discovery, data acquisition, and data provisioning, offering access Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more

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July 5, 2022in Salesforce Einstein
Salesforce and Google Calendar
22Jun

Salesforce, Gmail, and Google Calendar Integration

Facilitate a seamless integration of Gmail and Google Calendar with Salesforce to optimize your sales representatives’ workflow. According to McKinsey & Company, sales reps dedicate up to 28% of their day to email and calendars, emphasizing the need to integrate these applications. Integration yields several benefits: Integration aids key sales activities: Address common challenges faced by sales reps: Integration features, along with Einstein Activity Capture, enhance sales reps’ capabilities: Empower sales teams with increased productivity. Integrate Gmail and Google Calendar with Salesforce. Like1 Related Posts Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Salesforce and Zendesk Integration Zendesk Connection Salesforce and Zendesk integration. Create a remote connection using the Zendesk connector to sync data from Zendesk to Read more

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June 22, 2022in Salesforce Einstein
Salesforce AI Propensity Scores
08Jun

Salesforce AI Propensity Scores

AI-driven propensity scores take an existing data model and improve its predictions, speed, and analysis with AI. Salesforce AI Propensity Scores in CRM: In CRM, a propensity score is the model’s probabilistic estimate of a customer performing a specific action.  A propensity model is a mathematical formula that takes into account all of the known factors that are associated with conversion. The model then uses this information to estimate the likelihood that a given lead will convert to a customer. In super geeky terms, The propensity score is the probability of a unit (e.g., person, classroom, school) being assigned to a particular treatment given a set of observed covariates. How do you calculate propensity score? Propensity scores are generally calculated using one of two methods: a) Logistic regression or b) Classification and Regression Tree Analysis. a) Logistic regression: This is the most used method for estimating propensity scores. It is a model used to predict the probability that an event occurs. Why do we need propensity score? Propensity score analysis (PSA) arose as a way to achieve exchangeability between exposed and unexposed groups in observational studies without relying on traditional model building. Exchangeability is critical to our causal inference. Get Accurate Predictions by Defining the Target Variable Defining the target variable is crucial for accurate model predictions. Your model needs a primary focus for analysis and predictions. Scoring models uncover relationships between features and the target variable, providing insights on how to maximize or minimize this variable. For example, to predict the likelihood of opportunities converting into accounts, define a target variable that indicates this conversion. You can also apply custom logic to refine this target variable further. Make Informed Business Decisions Based on Historical Trends Generate predictions for specific periods to make informed business decisions and maximize revenue based on historical trends. For example, to determine which accounts sales representatives should focus on in the next 30 days, select a 30-day prediction duration. Then, use CRM Analytics datasets with historical data to identify revenue trends from accounts in the past 30 days. Effortlessly Build and Deploy Propensity Models Utilize the Scoring Framework to build and deploy generic propensity models for various industries without coding. Configure and deploy Einstein Discovery models through the AI Accelerator, which displays predictions and Einstein Next Best Action recommendations on record pages using the AI Accelerator—Einstein Predictions & Recommendations component. Scoring Framework Features AI Accelerator and Scoring Framework Integration Get real-time predictions across multiple industries by integrating AI Accelerator. Build generic propensity models without writing code using the Scoring Framework. Configure and deploy Einstein Discovery models, and showcase predictions and Next Best Action recommendations on record pages. AI Accelerator Functionality Salesforce Einstein’s Role Salesforce Einstein integrates robust AI technologies within the Lightning Platform, offering administrators and developers a comprehensive set of platform services to build smarter apps and customize AI for their businesses. Scoring Framework and CRM Analytics Use the Scoring Framework, based on CRM Analytics, to quickly build and deploy propensity models for various industries. Define template configurations, create CRM Analytics apps, and develop Einstein Discovery models and recipes effortlessly. Validating Input Features and Prediction Accuracy Train your model to validate input features and prediction accuracy. Then, deploy the model based on these predictions. Predictions Based on Standard or Custom Objects Build predictive models using standard or custom objects, enhancing business processes with smarter and more predictive capabilities. Making Business Decisions from Historical Trends Generate predictions based on historical trends to help make informed business decisions aimed at maximizing revenue. Enhancing Analysis with Additional Input Features Improve data analysis by incorporating features from CRM Analytics datasets along with object data. Focused Predictions with Defined Target Variables Improve prediction accuracy by defining the primary focus variable for your model. Customize input features to ensure valuable and accurate predictions for your use case. Targeted Predictions with Data Subsets Enhance prediction relevance by focusing on specific data subsets using filter conditions. Contextual Predictions by Storing in Records Store predictions in records to view them within the context of your use case, facilitating informed decision-making. Real-Time Predictions with AI Accelerator Integrate the Scoring Framework with AI Accelerator for real-time predictions, suggestions, and insights. Template Configuration and Data Requirements Ensure data requirements are met while configuring templates, confirming that there are enough records in the dataset. Quick Access to CRM Analytics and AI Accelerator Easily access CRM Analytics apps or AI Accelerator use cases by clicking the relevant button on the template configuration card. Handling Template Configuration and AI Accelerator Issues Retry template configuration activation or deactivation if unsuccessful. Similarly, retry creating or deleting AI Accelerator use cases as needed. Exploring New Metadata Types and Tooling API Objects Explore new metadata types and use the Tooling API to work with Scoring Framework setup objects, enhancing your capabilities within the Scoring Framework. Content updated April 2024. 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

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June 8, 2022in Salesforce Einstein

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