Salesforce Einstein - gettectonic.com - Page 12
Salesforce Copilot
19Nov

Spring ’24 Release in Salesforce Einstein and Data Cloud

Revolutionizing Data Management with Salesforce’s Einstein and Data Cloud While Artificial Intelligence (AI) has taken the spotlight, there’s an unsung hero eagerly waiting for its moment – Salesforce’s Data Cloud, formerly known as Genie. This product, a source of pride for Salesforce, is set to play a crucial role in powering AI on the Einstein 1 platform, offering a glimpse into the future at World Tour New York, accompanied by some exciting updates. Salesforce Einstein and Data Cloud appear to be in for a makeover. Spring ‘24 Release Highlights: The anticipation for Einstein Copilot & Search, part of the Einstein GPT products, is set to end in February 2024. Additionally, Data Cloud’s unstructured support will enter its pilot phase in the same month. While there are no indications of new AI products in the upcoming Salesforce Spring ‘24 release, the main release dates in February raise expectations for potential surprises when the Spring ‘24 Release Notes are unveiled. Salesforce may have Salesforce Einstein and Data Cloud enhancements up their sleeves. Elevating Einstein 1 Platform: Salesforce unveiled a major update to the Einstein 1 platform at World Tour New York, combining the prowess of Data Cloud and AI. This alliance empowers users to manage unstructured data efficiently through Data Cloud. While enabling Einstein Copilot to search, retrieve, and comprehend vast amounts of information seamlessly. According to IDC, 90% of business data is unstructured, including PDFs, emails, social media posts, and audio files. Forrester predicts that the volume of unstructured data managed by enterprises will double by 2024. Which is just around the corner, folks. The GenAI solution addresses the need to sift through this unstructured data. GenAI is offering a comprehensive platform for organizations. Key Benefits for Organizations of Salesforce Einstein and Data Cloud: This innovative offering by Salesforce, integrated into Data Cloud, requires the use of a Vector Database provided by Salesforce. Familiar automation tools like Flow and Apex can be employed to monitor changes in this data. Additionally it can trigger workflows. Data Cloud Vector Databases – A Closer Look: Salesforce’s Data Cloud, in conjunction with AI capabilities, largely will transform how organizations manage and leverage unstructured data. Thereby opening new possibilities for innovation and customer satisfaction. The age of Salesforce Einstein and Data Cloud are upon us! Contact Tectonic today to learn more about Data Cloud. 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 19, 2023in Salesforce Einstein
Salesforce Chat GPT
17Nov

Chat GPT and Salesforce

It’s more likely that you vacationed on Gilligan’s island than remain unfamiliar with ChatGPT. Since its introduction, this tool has emerged as an essential extension to Salesforce solutions, owing to its remarkable generative AI capabilities. From automating content creation to validating rules ChatGPT excels in some areas. By crafting Apex code, developing Lightning Web Components (LWC), summarizing conversations, and composing knowledge articles, ChatGPT has demonstrated its value. The integration of Chat GPT and Salesforce holds the potential to streamline substantial segments of organizational processes, leading to significant time savings. This not only enhances CRM data management, now fortified by generative AI, but also elevates the customer experience to unprecedented heights. While Salesforce has its own Einstein AI tools, Chat GPT and Salesforce remain a viable tool combination. This article will explore in detail how ChatGPT can seamlessly integrate with Salesforce. Understanding ChatGPT: When I questioned ChatGPT about its nature, and its response is as follows: ChatGPT is a language model created by OpenAI. It operates as a deep generative language neural network, having been trained on an extensive volumes of text to generate coherent and meaningful responses to user queries and commands. In essence, ChatGPT is essentially a chatbot, akin to Einstein bots. However, it distinguishes itself with its robust capabilities. Developed on the OpenAI GPT-3.5 family of large language models, incorporating both supervised and reinforcement learning techniques, ChatGPT has been trained on an extensive dataset of internet text up until January 2022. It excels in natural language processing tasks such as text generation, question answering, translation, and text classification. The underlying technology of ChatGPT is sophisticated, evolving continually through the application of machine learning and deep learning techniques. In 2023, OpenAI introduced two new models: ChatGPT-3.5 Turbo and ChatGPT-4, with the latter being notably superior. For instance, GPT-4 is multimodal, adept at processing both textual and visual inputs, comprehending and describing images effectively. It has also reduced the likelihood of generating nonsensical or “AI hallucinations” by 19-29%. In terms of security, GPT-4 incorporates robust measures from the outset, generating only 0.73% “toxic” responses compared to GPT-3.5’s 6.48%. It excels in maintaining context, with enhanced memory of the conversation, and improved context length to handle more extensive inputs. In summary, the mentioned improvements position GPT-4 as a more advanced and versatile option. ChatGPT Models: Every AI tool relies on models to discern patterns and make decisions from data. The OpenAI API is powered by a family of models with distinct capabilities and pricing scales. Users can also tailor their base models for specific use cases. The primary models include: This serves as a brief overview, and further exploration is possible here. OpenAI retires old models to introduce safer and more advanced versions. When a model becomes obsolete, it is promptly deactivated, with a specified shutdown date. Legacy models, those not receiving updates, are clearly labeled, signaling developers to transition to newer alternatives. Salesforce Consulting Services: Tectonic provides Salesforce consulting services geared toward catalyzing your company’s growth with Chat GPT and Salesforce, or by implementing a customized business solution or enhancing an existing implementation. Use Cases for ChatGPT in the Salesforce Ecosystem: The million-dollar question arises: how can ChatGPT be effectively utilized in Salesforce? Here are several Chat GPT and Salesforce use cases: Precautions with ChatGPT: While ChatGPT proves beneficial in areas like marketing, sales, and service, its application across the organization should be approached with caution. It’s a powerful tool, offering coherent and logical responses, but it cannot replace every role in every area. As of now, it functions as a valuable virtual assistant for specific tasks, but it shouldn’t be the sole source of information. Proper utilization of this tool can positively impact your Salesforce solution, but precautions must be taken. According to the “Generative AI Trends for Sales” report, 73% of sales professionals express concerns about the security risks associated with this technology, with 49% admitting to not knowing how to use it safely at work. Additionally, ChatGPT often lacks context, leading to potential inaccuracies in responses. Therefore, Salesforce recommends taking the following precautions before adopting any integration of Chat GPT and Salesforce: It’s essential not to overestimate ChatGPT while still leveraging its advantages. Currently, it can serve as a virtual assistant to assist with specific tasks, but it should not be the sole source of information. Prudent use of this tool can positively impact your Salesforce solution, but precautions should be taken. It’s advisable to make the most of generative AI capabilities within Salesforce, especially considering the developments in 2023. Salesforce has introduced multiple solutions incorporating this technology, not only with a comprehensive suite of GPT products but also a significant leap in Einstein. Since the past Dreamforce, Einstein Platform 1 has evolved into the CRM’s trusted AI, offering a high-performance real-time conversational assistant, Einstein Copilot, and an Infinite Capability Studio. Being part of Salesforce’s ecosystem eliminates potential data security and privacy gaps, facilitating integration with other platform solutions. ChatGPT marked the inception of the generative AI revolution and stands as a powerful tool. It’s crucial to remember that OpenAI releases versions frequently, addressing major issues promptly. There’s undoubtedly much more to explore and experience with ChatGPT. If you’re considering implementing it in your organization, Tectonic is prepared to assist you in achieving this goal. 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 17, 2023in Salesforce, Salesforce Einstein
Generative AI Regulations
07Nov

How Generative AI Regulations Could Effect You

If you’re considering integrating generative artificial intelligence (GAI) tools into your business operations, you’re not alone. While these tools have the potential to boost employee productivity, concerns regarding their safety have been voiced by business leaders. Despite their utility in areas like marketing, customer service, and data insights, there is a growing demand for generative AI regulations due to apprehensions about its societal impact and potential risks. Key Points to Consider: Global AI Regulatory Response: A global response to AI regulation is taking shape, with U.S. lawmakers engaging tech leaders and expressing unanimous agreement on the necessity of AI regulation. In the EU, audits of AI algorithms and underlying data from major platforms meeting specific criteria have already begun. Business Decision Makers’ Role: As a decision-maker in your business, it is crucial to understand GAI and its implications for interactions with other companies and consumers. Countries worldwide are working to ensure that generative AI adheres to existing measures related to privacy, transparency, copyright, and accountability. What Your Company Can Do Now: Recent Developments: Concerns and Background on Generative AI Regulations: Considerations for Businesses: The rapidly evolving landscape of generative AI calls for ongoing awareness and adaptation, with the need for businesses to stay informed and engage in proactive discussions with trusted advisers. As regulatory efforts focus on privacy, content moderation, and copyright concerns, the conversations around generative AI regulations are crucial in navigating the ever-changing technological landscape. Like 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 Sales Cloud Einstein Forecasting Salesforce, the global leader in CRM, recently unveiled the next generation of Sales Cloud Einstein, Sales Cloud Einstein Forecasting, incorporating Read more

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November 7, 2023in Salesforce Einstein
Salesforce Einstein Product Recommender
06Nov

Configure Einstein Wait Before Recommending an Already Purchased Product

Customize the Duration for Einstein’s Recommendation of Past Purchases. If you Configure Einstein Wait proactively you can effectively send recommendations to customer. And send them right as they are about to run out of your product. What is Einstein product recommendations? Einstein Recommendations deliver the next-best product, content, or offer to every individual through product and content recommendations for email and web. Enable every customer interaction to be an insight. With every click, download, view, and purchase, customers are giving data about their preferences. Data that you can use. Data that teaches Einstein. Einstein Web Recommendations use Einstein’s capabilities to observe behavior, build preference profiles, and deliver the next-best personalized content to each website visitor. Configure Einstein Wait allows you to deliver the next best content. Provide the right content at the right time. Use scenarios within the application to refine recommendations to match your specific business scenarios. The Einstein Configurator: The Einstein Configurator now enables you to specify the number of days a recommender should wait before suggesting products that were previously purchased. You have the flexibility to choose between 7, 30, 60, or 90 days, with the default option set at 30 days. Previously, the Einstein Configurator had a fixed 30-day waiting period for recommending previously purchased products, which was not always suitable for all customers in terms of wait time. How do you Configure Einstein Wait: Adjust the wait time for product recommendations on an individual recommender basis using the Einstein Configurator. Utilize the “Don’t recommend products purchased” option in the General tab of any recommender to make your preferred selection. Like 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 Sales Cloud Einstein Forecasting Salesforce, the global leader in CRM, recently unveiled the next generation of Sales Cloud Einstein, Sales Cloud Einstein Forecasting, incorporating Read more

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November 6, 2023in Salesforce Einstein
ethical ai consumer trust vs expectations
06Nov

Ethical AI-Consumer Trust Vs Expectations

Consumer Trust and Responsible AI Implementation Ethical AI Consumer Trust vs Expectations Research indicates that while consumers have low trust in AI systems, they expect companies to use them responsibly. Around 90% of consumers believe that companies have a duty to contribute positively to society. However, despite guidance on responsible technology use, many consumers remain apprehensive about how companies are deploying technology, particularly AI. ethical ai consumer trust vs expectations A global survey conducted in March 2021 revealed that citizens lack trust in AI systems but still hold organizations accountable for upholding principles of trustworthy AI. To earn customers’ trust in AI and mitigate brand and legal risks, companies need to adopt ethical AI practices centered around principles such as Transparency, Fairness, Responsibility, Accountability, and Reliability. Developing an Ethical AI Practice Over the past few years, industry professionals like have focused on maturing AI ethics practices within companies like Salesforce. This journey toward ethical AI maturity often begins with an ad hoc approach. Ad Hoc Stage In the ad hoc stage, individuals within organizations start recognizing unintended consequences of AI and informally advocate for considering bias, fairness, accountability, and transparency. These early advocates spark awareness among colleagues and managers, prompting discussions on the ethical implications of AI. Some advocates eventually transition to full-time roles focused on building ethical AI practices within their companies. Organized and Repeatable Stage With executive buy-in, companies progress to the organized and repeatable stage, establishing a culture where responsible AI practices are valued. This stage involves: Achieve Ethical AI Consumer Trust vs Expectations During this stage, companies must move beyond superficial “ethics washing” by actively integrating ethical principles into their operations and fostering a culture of responsibility. Additionally, the independence and empowerment of individuals in responsible AI roles are crucial for maintaining integrity and honesty in ethical AI practices. Final Insight Thoughts As companies progress through the maturity model for ethical AI practices, they strengthen consumer trust and mitigate risks associated with AI deployment. By prioritizing transparency, fairness, and accountability, organizations can navigate the ethical complexities of AI implementation and contribute positively to society. ethical ai consumer trust vs expectations 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 6, 2023in Salesforce Einstein
Fine Tune Your Large Language Model
04Nov

Fine Tune Your Large Language Model

Revamping Your LLM? There’s a Superior Approach to Fine Tune Your Large Language Model. The next evolution in AI fine-tuning might transcend fine-tuning altogether. Vector databases present an efficient means to access and analyze all your business data. Have you ever received redundant emails promoting a product you’ve already purchased or encountered repeated questions in different service interactions? Large language models (LLMs), like OpenAI’s ChatGPT and Google’s Bard, aim to alleviate such issues by enhancing information-sharing and personalization within your company’s operations. However, off-the-shelf LLMs, built on generic internet data, lack access to your proprietary data, limiting the nuanced customer experience. Additionally, these models might not incorporate the latest information—ChatGPT, for instance, only extends up to January 2022. To customize off-the-shelf LLMs for your company, fine-tuning requires integrating your proprietary data, but this process is costly, time-consuming, and may raise trust concerns. A superior alternative is a vector database, described as “a new kind of database for the AI era.” This database offers the benefits of fine-tuning while addressing privacy concerns, promoting data unification, and saving time and resources. Fine-tuning involves training an LLM for specific tasks, such as analyzing customer sentiment or summarizing a patient’s health history. However, it is resource-intensive and fails to resolve the fundamental issue of fragmented data across your organization. A vector database, organized around vectors that describe different types of data, can seamlessly integrate with an LLM or the prompt. By storing and organizing data with an emphasis on vectors, this database streamlines access to relevant information, eliminating the need for fine-tuning and unifying enterprise data with your CRM. This is pivotal for the accuracy, completeness, and efficiency of AI outputs. Unstructured data, comprising 90% of corporate data, poses a challenge for LLMs due to its varied formats. A vector database resolves this by allowing AI to process unstructured and structured data, delivering enhanced business value and ROI. Ultimately, a company’s proprietary data serves as the cornerstone for constructing an enterprise LLM. A vector database ensures seamless storage and processing of this data, facilitating better decision-making across all business applications. 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 4, 2023in Salesforce Einstein
Salesforce Service Cloud Intelligence
03Nov

Salesforce Service Cloud Service Intelligence Enhancements

Service Intelligence, a data-centric solution, showcases vital performance metrics within the contact center. Recent Service Intelligence Enhancements have given even greater insights. In a significant launch, Salesforce introduced Service Intelligence. Service Intelligence an advanced analytics app for Service Clou. It is aimed at boosting agent efficiency, reducing costs, and elevating customer satisfaction. Service Intelligence Enhancements and updates within this Salesforce product aim to bolster operational efficiency and take customer satisfaction to unprecedented heights. Representing a new milestone for Service Cloud, the Winter ’24 innovations redefine how businesses approach customer service and optimize operations. Fueled by Data Cloud, Salesforce’s real-time hyperscale data engine, Service Intelligence provides users with direct access to all their data within Service Cloud, eliminating the need to switch between screens for information. Pre-built and customizable dashboards in Service Intelligence offer a comprehensive view of essential metrics, including customer satisfaction and individual and team workloads. With Einstein Conversation Mining, service professionals can leverage AI to analyze customer chat and email conversations, uncover insights, assess the likelihood of complaint escalation, and proactively address issues with customers. The relevance of AI is emphasized, with an 88% increase in AI adoption among service professionals from 2020 to 2022. With 63% acknowledging that AI will help them serve customers faster. Service professionals are embracing AI enabling to make informed decisions swiftly, gaining a competitive edge. Service Intelligence Enhancements As of 2023, Service Intelligence is now generally available. Key features include pre-built service dashboards offering AI-powered insights through Einstein Conversation Mining, providing visibility into key metrics across cases. Einstein Conversation Mining employs AI to analyze customer conversations. Einstein Conversation mining enables quick identification of trends and top customer issues. Tableau integration allows users to seamlessly explore data in Tableau directly from a Service Intelligence dashboard, maintaining data context from their service console. Service Intelligence encompasses Data Cloud, CRM Analytics, and Einstein Conversation Mining. There by offering a wealth of information such as customer data and key performance indicators (KPIs) to help service teams enhance operations and reduce costs. Einstein for Service accelerates customer communication and satisfaction by generating email responses based on knowledge articles. The Winter ’24 release introduces the Lightning Article Editor and Article Customization in Salesforce Service Cloud, marking a significant advancement in knowledge management. The Lightning Article Editor simplifies content creation and editing. Thus enabling support and customer service teams to produce informative material efficiently. These enhancements in Service Cloud streamline operations, empower agent performance, and usher in a new era of customer satisfaction. Explore these exciting updates to transform your organization’s strategy. Embrace the future of service excellence with the Winter ’24 release for Service Cloud. 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 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 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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November 3, 2023in Salesforce Data Cloud, Salesforce Einstein, Salesforce Sales Cloud, Salesforce Service Cloud, Salesforce Tableau
Learn AI
31Oct

Learn AI

Achieving Excellence in Artificial Intelligence: The Path to Success-Learn AI In the rapidly evolving world of Artificial Intelligence (AI), quality and core skills are paramount for building a rewarding career. Merely possessing credentials won’t suffice in the highly competitive AI landscape. Employers are seeking knowledgeable employees. This is an industry so new, anyone can get involved. To embark on a trajectory of lifelong growth, investing in the right AI certification course is imperative. According to the Access Partnership Survey, 42% of employers seek individuals with AI development qualifications, a figure expected to rise to 51% in the next five years. This underscores the trust placed by global recruiters in renowned AI certification programs. Various specializations such as computer vision, machine learning, large language models, natural language processing, robotics, and AI software are witnessing significant profitability in the global market. For those seeking premier training in AI, a myriad of options awaits exploration, ranging from Generative AI to nuanced AI courses, paving the way for a flourishing career. Businesses across industries are actively seeking specialized AI professionals to drive amplified growth, while the workforce is keen on upskilling to seize lucrative AI job opportunities. As we gaze into 2024 and beyond, certain AI skills and roles will undoubtedly be in high demand, with AI emerging as one of the hottest job sectors. With Chat GPT’s rapid rise, mastering these leading AI skills has become essential. Let’s delve into the top free online AI certification programs for 2024, offering the best avenues for an illustrious AI career: While free courses are appealing, it’s essential to recognize that paid credentials often hold more weight. Enrolling in a rewarding paid AI certification program can provide a significant career boost. The United States Artificial Intelligence Institute (USAII®) stands out as a trusted choice among global recruiters. About the United States Artificial Intelligence Institute (USAII®): Renowned for its top-tier AI certification programs, USAII® is highly regarded among industry recruiters. Offering tailored certifications catering to diverse skill sets, it serves as a launchpad for AI aspirants worldwide. Explore the following AI certifications from USAII® to elevate your career: Embrace the best AI skills with globally recognized credentials, whether free or paid. Invest in an online AI certification to chart a course towards long-term career success. 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

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October 31, 2023in Generative AI, Salesforce Einstein
How Good is Our Data
28Oct

How Data Cloud and Salesforce Success Depend on Data Quality

Optimizing AI’s Impact on Your Business: The Crucial Role of Data Quality in Salesforce In the ever-evolving digital landscape, the convergence of data quality and artificial intelligence (AI) is a linchpin for organizational success. Success depends on data quality within the Salesforce ecosystem. The synergy between Einstein, an advanced AI system, and Data Cloud underscores the pivotal role of high-quality, comprehensive, and real-time data. Thereby unleashing the full potential of AI-driven insights and interactions with customers and prospects. Let’s explore how data quality profoundly influences these two emerging features. This insight will be shedding light on the repercussions of poor data quality and how Einstein and Data Cloud can elevate your organization to greater levels of sales success. Understanding Data Value Depends on Data Quality: Quality data extends beyond merely addressing duplicate records or inaccurate phone numbers It isn’t just about ensuring the area code field doesn’t contain zip codes. It is more than aligning contacts to accounts. It encompasses factors such as completeness, accuracy, and timeliness in your CRM: Consequences of Bad Data: Poor-quality data leads to inefficiencies and wasted time. Oftentimes causing flawed decision-making and strains on organizational resources. More critically, these poor business decisions often lead to tangible financial losses.  Transforming bad data into quality data is imperative. Quality is key for relying on it to enhance company performance, requiring ongoing strategies rather than a one-stop solution. The Financial Impact of Accurate Data: Accurate data holds immense value. With data volumes projected to exceed 180 zettabytes by 2025, organizations must harness the power of their data. Proactive handling of data quality not only ensures higher data quality but also mitigates the financial impact of poor data quality. The sooner a plan is implemented to enhance and sustain data quality, the fewer negative repercussions organizations face in leveraging their data for growth.  Your next decision is based on your last data.  Is it going to help you or hurt you? Salesforce Einstein and the GIGO Principle: Salesforce Einstein, positioned as Artificial Intelligence for everyone, underscores trust as a core value. The system’s ability to create relevant and timely content and interactions is contingent on the quality of the data it operates on. Similar to the historical concept of “Garbage In, Garbage Out” (GIGO), AI results are only as reliable and valuable as the completeness and accuracy of the input data. No surprise, right? Introduction to Salesforce Data Cloud: Enter Salesforce Data Cloud, a platform allowing the organization and segmentation of customer data from any source. This open, extensible platform enables data enrichment from various sources, creating an optimal customer record. This enriched record empowers Sales, Service, and Marketing teams to perform intelligently and swiftly, ultimately driving enhanced results for the company. The WIIFM Factor: Amidst discussions about AI and Data Cloud, addressing the “What’s in it for me?” (WIIFM) question is crucial for organization adoption. Individual organizations must evaluate the reliability and accuracy of their data and determine forward-looking strategies for maintaining quality data, regardless of the source. The common theme remains: for data to yield valuable insights, it must be complete, timely, relevant, and accurate. Ultimately, success depends on data quality. Like2 Related Posts 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 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 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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October 28, 2023in Salesforce Data Cloud, Salesforce Einstein, Salesforce Financial Cloud, Salesforce Managed Services, Salesforce Sales Cloud
Salesforce Life Sciences Cloud
24Oct

Salesforce Life Sciences Cloud

Salesforce has unveiled Life Sciences Cloud, a secure and trusted platform tailored for pharmaceutical (pharma) and medical technology (medtech) organizations. This innovative solution aims to expedite drug and device development, streamline patient enlistment and retention throughout the clinical trial journey, and harness AI capabilities to deliver personalized customer experiences. The significance of this announcement lies in the life sciences industry’s urgent need for accurate and accessible data to advance research and development efforts and enhance clinical trials. Despite this need, the industry has been slow to adopt digital tools, with a staggering 88% of healthcare and life sciences organizations yet to achieve their digital transformation objectives. Amit Khanna, SVP & GM of Health and Life Sciences at Salesforce, emphasized the necessity for integrated, compliant, and data-driven solutions in the life sciences industry. He highlighted Salesforce’s commitment to enhancing stakeholder engagement across the R&D and commercialization spectrum by leveraging data, AI, and CRM capabilities. The Salesforce solution encompasses: Commercial Operations, available now, provides insights into the commercial lifecycle, including contract compliance, pricing, and inventory management. AI-powered bots offer timely alerts to field representatives and forecasting insights to optimize sales strategies. Clinical Operations offers tools to set up and execute efficient trials, including Data Cloud for Health, Chain of Custody Management, and Participant Management features, aiming to enhance patient recruitment, safety, and engagement. Pharma CRM facilitates personalized engagement with stakeholders, managing interactions and digital content while ensuring compliance with regulations. Features like Healthcare Professional (HCP) Engagement and Einstein for Life Sciences enhance engagement and automate tasks for streamlined operations. Customer testimonials, such as from SI-BONE, highlight the tangible benefits of digitizing processes and improving efficiency with Salesforce solutions. Availability details for various features are provided, with some features already generally available and others set to roll out in the coming months and years. To learn more about Salesforce’s offerings for healthcare and life sciences, access industry insights, and explore the potential of CRM and AI in this sector, interested parties are encouraged to dig into the available resources or contact Tectonic today. Additionally, it’s noted that sales automation functionality for pharma/biotech customers will be available from mid-2025 onward. Learn about Salesforce for healthcare and life sciences  Learn more about Salesforce Life Sciences Cloud 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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October 24, 2023in Life Sciences Cloud, Salesforce Einstein, Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services
Einstein Email Insights Explained
18Oct

Einstein Email Insights Explained

In 2016, Salesforce ventured into the realm of artificial intelligence (AI) with the introduction of Salesforce Einstein. Far from a standalone product, Salesforce Einstein represents a technological layer seamlessly integrated into the Salesforce Lightning platform and various other Salesforce cloud products. Insights for Sales Specifically designed for sales professionals, Einstein Email Insights proves to be a valuable tool. It provides crucial information within the email interface, empowering salespeople to respond to customers more effectively and at the opportune moment. Einstein Email Insights offers sales representatives essential context related to sales alongside pertinent inbound emails. By specifying criteria for email content analysis, users can extract the most relevant insights. Einstein ensures that contextual sales information is surfaced while composing emails, facilitating the delivery of optimal responses at the right time. This feature is available with Sales Cloud Einstein, Inbox, High Velocity Sales, or Revenue Intelligence, albeit with an additional cost. It is accessible for Salesforce Enterprise, Performance, and Unlimited editions, subject to an extra charge. The Einstein Family If you are using Einstein Activity Capture with a Sales Cloud Einstein or Inbox license, Email Insights is automatically activated upon enabling Einstein Activity Capture. However, users without these licenses must read and agree to the Email Insights terms of service before utilizing the feature. The insights derived from Einstein Conversation Insights go beyond mere data points; they offer actionable intelligence. Businesses can leverage these insights to refine products, optimize customer service processes, and make informed decisions that positively impact the overall customer experience. Salesforce Einstein Insights ensures that every action taken by your sales representatives is calculated and meaningful, aiming to convert prospects into customers. Statistics show that Salesforce Einstein Insights can boost conversion rates by 43%. This AI-powered platform compiles data from accounts, opportunities, call histories, and even news sources to predict business outcomes, empowering your team to strategize with confidence at every stage of customer engagement. Salesforce has recently introduced new email features in its Marketing Cloud to enhance the efficiency of email capabilities. The new Einstein Email capabilities enable modern marketers to increase productivity, send more personalized emails, and drive greater customer engagement. Additionally, Salesforce’s acquisition of Rebel, an interactive email provider, brings forth the ability to create interactive emails, allowing recipients to take action directly within the email. This functionality, akin to AMP for Gmail, enables users to browse image carousels, fill out forms, register for events, and more, directly from their inbox. Salesforce plans to launch this pilot early in 2020, integrating it with Email Studio and Journey Builder. Like2 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 Public Sector Salesforce Solutions Public Sector Solutions revolutionize public service delivery through flexible and secure e-government tools supporting both service providers and constituents. Designing Read more

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October 18, 2023in Einstein Insights, Salesforce Einstein
Einstein 1 is Coming
09Oct

Einstein 1 is Coming

What Does the New Einstein 1 Data Cloud Mean for Your Organization? Einstein 1 is Coming One of the major announcements at Dreamforce was the exciting intro that Einstein 1 is Coming. The Einstein 1 Data Cloud is now natively integrated with the Einstein 1 Platform. This integration allows users to connect any data, create unified customer profiles, and enhance every customer experience with AI, automation, and analytics. This is a giant step for Salesforce-kind. It can revolutionize the ways businesses engage with their customers. While this announcement is exciting, what does it mean for organizations at different stages of their Salesforce journey? In this insight, we explore the announcement details, considerations for using the Einstein 1 Data Cloud in your company, and how Tectonic can assist in navigating this new offering. What’s New with the Platform? The integration of Salesforce Data Cloud and Einstein AI into the Einstein 1 Platform marks a significant enhancement. The platform integration enables companies to securely connect any data, build AI-powered apps with low code, and deliver superior CRM experiences. It unifies data across the enterprise by mapping it to Salesforce’s underlying metadata framework, regardless of how the data is structured in disparate systems. Regardless of how complex it is. What is Einstein 1 Data Cloud? The Key to Unified Data Salesforce Einstein 1 Data Cloud unifies customer data, enterprise content, telemetry data, Slack conversations, and other structured and unstructured data to create a single view of the customer. This integration unlocks otherwise siloed data and scales operations in new ways: Salesforce has announced that Enterprise Edition and above customers can use Data Cloud at no additional cost. However, organizations should consider their position on the Salesforce maturity curve before implementation. Data Cloud’s capabilities, while extensive, might not fully optimize data for organizations further along in their Salesforce journey without a thorough trial. What is the Einstein Conversational Assistant? An AI-Powered Shift Einstein now includes a generative AI-powered conversational assistant featuring Einstein Copilot and Einstein Copilot Studio. These tools operate within the Einstein Trust Layer, a secure AI architecture native to the Einstein 1 Platform that ensures data privacy and security. Why Should Organizations Consider Einstein 1? Customer data is often fragmented and siloed across disparate systems, preventing a unified view necessary for informed business decision-making. Data unification is essential for data-driven decision making and fully getting the full ROI of AI. AI is a major trend in technology, but effective AI requires comprehensive, aligned data. Without a unified data foundation, AI’s potential is limited. Einstein 1 with Data Cloud provides the solution by consolidating data, enabling the training of AI models to make optimal decisions and recommendations. How Can Tectonic Help You Transition? Tectonic brings extensive Salesforce expertise and industry-specific experience in sectors heavily reliant on data, such as healthcare, financial services, and travel and tourism. These industries face strict data regulations and often have siloed data in legacy systems. Einstein 1 helps organizations achieve a 360-degree view of their customers by unifying data. Tectonic can assist in maximizing AI on the Salesforce platform by building a robust data foundation and providing a roadmap for future scalability. While both Einstein 1 and AI Cloud are Salesforce terms that promise AI-driven capabilities, there are differences to consider. Einstein 1 Platform is a comprehensive suite that includes Data Cloud, AI tools, and automation capabilities. In contrast, AI Cloud is more of an overarching term that might encompass Einstein 1 as part of its suite, focusing on the broader application of AI across Salesforce’s entire range of products and services. Understanding these distinctions is critical in identifying which solution aligns with your organizational 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

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October 9, 2023in Einstein 1 Platform, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein
Generative AI Glossary
27Sep

The Salesforce Generative AI Glossary

Salesforce has built and maintains a fairly definitive glossary of generative Artificial Intelligence terminology, Tectonic thought was good enough to share in our insights. Salesforce Generative AI Glossary. Help everyone in your company understand key generative AI terms, and what they mean for your customer relationships. Fun fact: This article was (partially) written using generative AI. Bookmark this! This generative AI glossary will be updated regularly. Does it seem like everyone around you is casually tossing around terms like “generative AI,” “large language models,” or “deep learning”? Salesforce has created a primer on everything you need to know to understand the newest, most impactful technology that’s come along in decades. Let’s dive into the world of generative AI. Salesforce has built a list of the most essential terms that will help everyone in your company — no matter their technical background – understand the power of generative AI. Each term is defined based on how it impacts both your customers and your team. And to highlight the real-world applications of generative AI, we put it to work for this article. Salesforce experts weighed in on the key terms, and then let a generative AI tool lay the groundwork for this glossary. Each definition needed a human touch to get it ready for publication, but it saved loads of time. Anthropomorphism The tendency for people to attribute human motivation, emotions, characteristics or behavior to AI systems. For example, you may think the model or output is ‘mean’ based on its answers, even though it is not capable of having emotions, or you potentially believe that AI is sentient because it is very good at mimicking human language. While it might resemble something familiar, it’s essential to remember that AI, however advanced, doesn’t possess feelings or consciousness. It’s a brilliant tool, not a human being. Artificial intelligence (AI) AI is the broad concept of having machines think and act like humans. Generative AI is a specific type of AI (more on that below). Artificial neural network (ANN) An Artificial Neural Network (ANN) is a computer program that mimics the way human brains process information. Our brains have billions of neurons connected together, and an ANN (also referred to as a “neural network”) has lots of tiny processing units working together. Think of it like a team all working to solve the same problem. Every team member does their part, then passes their results on. In the end, you get the answer you need. Augmented intelligence Think of augmented intelligence as a melding of people and computers to get the best of both worlds. Computers are great at handling lots of data and doing complex calculations quickly. Humans are great at understanding context, finding connections between things even with incomplete data, and making decisions on instinct. Augmented intelligence combines these two skill sets. It’s not about computers replacing people or doing all the work for us. It’s more like hiring a really smart, well-organized assistant.  Customer Relationship Management (CRM) with Generative AI CRM is a technology that keeps customer records in one place to serve as the single source of truth for every department, which helps companies manage current and potential customer relationships. Generative AI can make CRM even more powerful — think personalized emails pre-written for sales teams, e-commerce product descriptions written based on the product name, contextual customer service ticket replies, and more. Deep learning Deep learning is an advanced form of AI that helps computers become really good at recognizing complex patterns in data. It mimics the way our brain works by using what’s called layered neural networks, where each layer is a pattern (like features of an animal) that then lets you make predictions based on the patterns you’ve learned before (ex: identifying new animals based on recognized features). It’s really useful for things like image recognition, speech processing, and natural-language understanding. Discriminator (in a GAN) In a Generative Adversarial Network (GAN), the discriminator is like a detective. When it’s shown pictures (or other data), it has to guess which are real and which are fake. The “real” pictures are from a dataset, while the “fake” ones are created by the other part of the GAN, called the generator (see generator below). The discriminator’s job is to get better at telling real from fake, while the generator tries to get better at creating fakes. This is the software version of continuously building a better mousetrap. Ethical AI maturity model An Ethical AI maturity model is a framework that helps organizations assess and enhance their ethical practices in using AI technologies. It maps out the ways organizations can evaluate their current ethical AI practices, then progress toward more responsible and trustworthy AI usage. It covers issues related to transparency, fairness, data privacy, accountability, and bias in predictions.  Explainable AI (XAI) Remember being asked to show your work in math class? That’s what we’re asking AI to do. Explainable AI (XAI) should provide insight into what influenced the AI’s results, which will help users to interpret (and trust!) its outputs. This kind of transparency is always important, but particularly so when dealing with sensitive systems like healthcare or finance, where explanations are required to ensure fairness, accountability, and in some cases, regulatory compliance. Generative AI Generative AI is the field of artificial intelligence that focuses on creating new content based on existing data. For a CRM system, generative AI can be used to create a range of helpful outputs, from writing personalized marketing content, to generating synthetic data to test new features or strategies. Generative adversarial network (GAN) One of two deep learning models, GANs are made up of two neural networks: a generator and a discriminator. The two networks compete with each other, with the generator creating an output based on some input, and the discriminator trying to determine if the output is real or fake. The generator then fine-tunes its output based on the discriminator’s feedback, and the cycle continues until it stumps the discriminator. Generative pre-trained transformer

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September 27, 2023in Generative AI, Salesforce Einstein
salesforce einstein analytics and hospitality
16Sep

Trust in AI Data

As companies rapidly embrace Artificial Intelligence and realize its benefits, trust must be their top priority. And to instill trust in AI, they must first instill trust in the data that powers it. Think about data as a well-balanced diet for AI — you’re healthiest when you avoid junk food and consume all the proper nutrients. Simply put, organizations can only harness the full power of AI when it is fueled by accurate, comprehensive data.  The Future is Here AI is no longer a futuristic concept; it has become a reality in our living rooms, cars, and frequently, in our pockets. As this technology continues to play an ever-expanding role in our daily lives, a crucial question arises: To what extent can, and should, we place our trust in these AI systems? Trust in AI data comes more naturally for some than others. As the prevalence of AI increases, so does the concern about ensuring that it aligns with human values. A frequently cited example illustrating this challenge involves the moral decision an autonomous car may face when confronted with a collision scenario. Consider a situation where a driver must swerve to avoid being hit and seriously injured by an oncoming bus. However, the dilemma arises as the car faces the prospect of hitting a baby if it swerves left or an elderly person if it swerves right—posing a complex ethical question for the autonomous car. Arvind Krishna, Senior Vice President of Hybrid Cloud and Director of IBM Research, emphasizes the importance of careful programming in AI systems to prevent biases introduced by programmers from influencing outcomes. Recognizing the complexity of such issues, he discusses the need to develop frameworks for addressing these ethical challenges, a task IBM is tackling through its participation in the Partnership on AI alongside other technology organizations. Trust in AI data vs bias: Instances of machines demonstrating bias have already garnered attention, eroding trust in AI systems. AI technicians are actively working to identify and mitigate the origins of bias, acknowledging that machines can become biased due to inadequate representation in their training data. Guru Banavar, IBM Chief Science Officer for Cognitive Computing, notes that unintentional bias may arise from a lack of care in selecting the right training dataset, while intentional bias can result from a malicious attacker manipulating the dataset. James Hendler, Director of the Institute for Data Exploration and Applications at Rensselaer Polytechnic Institute, reminds us that while AI can be a force for social good, it also holds the potential for diverse social impacts, where actions deemed good by one may be perceived as harmful by another. Hence, an awareness of these complexities is essential in navigating the ethical landscape of AI applications. Artificial Intelligence (AI) is revolutionizing work processes and service delivery, empowering organizations to harness its formidable capabilities for data-driven predictions, product and service optimization, innovation augmentation, increased productivity, and cost reduction. While the benefits of AI adoption are immense, it also introduces risks and challenges, prompting concerns about the current level of trustworthiness in AI applications. Public Trust in AI data Unlocking the full potential and return on investment from AI necessitates a sustained commitment to building and upholding public trust. For widespread adoption, people must have confidence that AI development and utilization adhere to responsible and trustworthy practices. In a pioneering initiative, KPMG Australia, in collaboration with the University of Queensland, conducted a world-first in-depth exploration of trust and global attitudes toward AI across 17 countries. The resulting report, “Trust in Artificial Intelligence: A Global Study 2023,” delivers comprehensive insights into the factors influencing trust, the perceived risks and benefits of AI utilization, community expectations regarding AI governance, and the entities considered trustworthy in AI development, usage, and regulation. This report, titled “Trust in Artificial Intelligence: 2023 Global Study on the Shifting Public Perceptions of AI,” presents key findings from the global study and offers individual country snapshots, serving as a valuable resource for those leading, creating, or governing AI systems. Importantly, it outlines four critical pathways for policymakers, standards setters, governments, businesses, and non-governmental organizations (NGOs) to navigate the challenges associated with trust in the development and deployment of AI. Like 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 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

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September 16, 2023in Salesforce Einstein, Salesforce Public Sector Solutions
Google 360 Analytics Dashboard in Marketing Cloud
13Sep

Salesforce Audience Insights

Salesforce Audience Insights By Tectonic’s Marketing Consultant, Shannan Hearne Salesforce Marketing Cloud is so much more than just an email sending platform. This insight explores how it can power your advertising.; Marketing Cloud offers robust audience segmentation capabilities, empowering marketers to effectively segment their customer base. The integration of AI through Audience Insights enhances this power. Formerly known as Advertising Studio, the platform is now recognized as Marketing Cloud Advertising. Audience Insights provides a deeper understanding of customers by unveiling unique characteristics, interests, and behaviors of user groups interacting with your ads and converting. By connecting Marketing Cloud Advertising to paid media channels, you can optimize your audience strategy, gaining a unified, cross-channel view and assessing the effectiveness of first-party audiences through a dedicated dashboard. Key features of Audience Insights include: To leverage Audience Insights, your Marketing Cloud Intelligence admin needs to configure it before connecting Advertising to paid media channels. This integration allows you to analyze the effectiveness of first-party audiences with a single, cross-channel perspective. As a Marketing Cloud Advertising customer with a Marketing Cloud Intelligence license, you gain access to comprehensive audience and campaign analytics through Audience Insights for Marketing Cloud Advertising. This application is conveniently available in the Marketing Cloud Intelligence Marketplace. Utilize the Audience Insights for Advertising Studio dashboard to refine campaigns using first-party audiences. Additionally, the Marketing Insights for Sales Cloud solution facilitates a deeper understanding of how marketing efforts and spend translate into revenue, offering insights into the sales funnel and guiding strategic decisions. Salesforce Audience Insights The Marketing Insights for Sales Cloud solution utilizes objects such as Leads, Opportunities, Accounts, Contacts, Campaigns, and Campaign Members to provide a holistic view of marketing and sales alignment. Setting up your digital advertising strategy within Salesforce, particularly through Advertising Studio, yields significant benefits. Integration with Google Analytics 360 expands your digital marketing and analytics possibilities. Advertising Studio seamlessly connects with advertising platforms like Google Display Ads, Facebook Ads, Instagram, Pinterest, Twitter, LinkedIn, AdWords, Gmail, and YouTube. Are you ready to employ the full power of Salesforce Marketing Cloud and Advertising Studio? Contact Tectonic today. Like3 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 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

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September 13, 2023in Marketing Automation, Salesforce Einstein, Salesforce Marketing Cloud, Salesforce Marketing Cloud Advertising, Salesforce Sales Cloud

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