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AI Powered Education Cloud

AI Powered Education Cloud

Salesforce Unveils New AI-Powered Features for Education Cloud Salesforce recently announced a suite of new features for its Education Cloud platform designed to simplify tasks for educators and students. AI Powered Education Cloud is here. These features range from student-focused tools, such as helping students stay on track with their degree requirements, to admin-focused capabilities like standardizing an institution’s entire student data corpus. New AI Capabilities Available in June Features Coming in October Enhancing Education with AI Salesforce positions these new features as tools to alleviate teacher burnout and enhance student career prospects. “With industry-specific AI and data tools, Education Cloud will help K-12 and higher ed institutions provide more individualized support for every student while increasing efficiency and helping to reduce staff burnout,” said Salesforce Vice President Bala Subramanian in a prepared statement. “This will free educators and staff to focus on improving student outcomes like career readiness, well-being, and graduation rates.” 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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salesforce unified knowledge

Unified Knowledge to Salesforce Service Agents

Salesforce Introduces Unified Knowledge to Enhance Service Agent Intelligence Salesforce has unveiled Unified Knowledge, a new solution designed to enrich service agents’ ability to resolve customer inquiries. By aggregating information from third-party sources and integrating it into Salesforce, Unified Knowledge complements the data already available in Salesforce’s Data Cloud, creating a more comprehensive knowledge base for service agents. Within Salesforce Service Cloud, Einstein for Service leverages AI to provide service agents with real-time information when addressing customer queries. Previously, this information was drawn from Data Cloud. Now, with Unified Knowledge, data from sources such as SharePoint, Confluence, Google Drive, and brand websites is incorporated, further enhancing the breadth of information available to agents. Expanding Beyond Service Cloud While Service Cloud is the primary use case for Unified Knowledge, the solution is also designed to integrate with other Salesforce platforms, including Sales Cloud, Field Service, Health Cloud, and Financial Services Cloud. Developed in collaboration with Zoomin Software, Unified Knowledge allows for greater cross-platform data accessibility and more efficient workflows across various service touchpoints. Why It Matters While the exact reasoning behind Salesforce’s decision to create a separate data channel for Unified Knowledge, rather than consolidating everything into Data Cloud, remains somewhat unclear, the broader availability of data to service agents could enhance service quality and efficiency. At its core, Unified Knowledge uses generative AI to provide dynamic, context-aware responses to agent and customer queries. Key features of the solution include: With these advancements, Unified Knowledge brings generative AI capabilities into the hands of service agents and workers, allowing for quicker, more accurate decision-making and enhanced customer interactions. The Unified Knowledge feature offers significant potential in revolutionizing how companies provide customer support by improving access to critical data from a wide array of sources, ultimately leading to more informed, efficient, and personalized service. 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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Service Cloud Digital Engagement

Service Cloud Digital Engagement

Salesforce Enhances Service Cloud Digital Engagement for Unified Customer Interactions Salesforce has unveiled new enhancements to Service Cloud Digital Engagement, aimed at unifying unstructured conversational data from various digital channels, departments, and devices within a single platform. Built on the Einstein 1 Platform, these enhancements enable service leaders to gain a more holistic view of customers, enhancing the value delivered in every interaction. Importance of Enhancements Detailed Enhancements Service Cloud Digital Engagement is designed to deliver seamless, personalized conversational experiences across channels at scale. By connecting to Salesforce Data Cloud, which unifies structured and unstructured enterprise and customer data, companies can engage in more meaningful conversations. Key enhancements include: With Service Cloud built on the Einstein 1 Platform, companies can integrate sales, service, and marketing data into one platform, facilitating more relevant customer experiences and driving business growth. Salesforce’s Perspective Kishan Chetan, EVP & GM of Service Cloud, commented, “As customers interact with companies across more touch points and channels, they are looking for more personalization and a higher-touch experience. With Service Cloud built on the Einstein 1 Platform, companies can bring in sales, service, and marketing data on one platform to deliver more relevant customer experiences and drive business growth.” Customer Reactions Olivia Boles, Director of Operations Projects at PenFed, said, “Being able to see all the communication — chat transcripts, emails, phone calls — on the member’s profile page has totally transformed the agent and member experiences.” Availability 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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Generative AI and Service Cloud

Generative AI and Service Cloud

Salesforce Service Cloud users are set to receive more Einstein 1 generative AI tools in June and October. A key development is the expansion of automated customer conversations across more sales and marketing platforms. Generative AI and Service Cloud family of tools is growing. This insight aims to uncover the numerous use cases of generative AI in the modern contact center. We’ll help you understand how generative AI can fast track your contact center’s efficiency, improve data analysis capabilities, streamline QA and coaching processes, and make customers’ experiences better. Today, Salesforce launched Unified Conversations for WhatsApp, which automates bot responses to customer inquiries related to targeted marketing messages on the popular messaging app. Additionally, Salesforce plans to extend support to Line, a messaging app popular in Japan, later this year. These services are built on Salesforce’s Einstein 1 generative AI platform. The platform’s bots aggregate structured and unstructured CRM, product, service, and other data through Salesforce Data Cloud to generate personalized responses. These new features enable conversations to be routed to the digital channels where a Salesforce user’s customers are the most active. And to move omnichannel as customers needs change. Salesforce is also introducing a “bring your own channel” connector to support digital channels not natively covered by the platform. Current examples might include TikTok, Discord, and South Korea’s KakaoTalk, according to Ryan Nichols, Chief Product Officer for Salesforce Service Cloud. Generative AI and Service Cloud “It’s about getting data from all your conversations with customers from Service Cloud into Data Cloud and using that to not just deliver excellent customer service, but also grow your business,” Nichols said. Salesforce Einstein Conversation Mining, a Service Cloud feature currently in beta, aggregates conversations across customer channels to surface insights on the topics customers need help with. This aims to turn inbound customer service from a cost center into a revenue center, a goal long pursued at conferences like Dreamforce and ICMI. This massive change drives more than revenue, it drives ROI. Performance metrics such as time-to-answer and hold-time reduction have traditionally pressured agents to minimize call duration to retain their jobs. Now Salesforce is going to help them. While some skeptics question if generative AI can achieve this ambitious goal, Constellation Research analyst Liz Miller suggests it might be possible. Having previously managed a contact center herself, Miller recognizes the transformative potential of generative AI. With the aid of data, bots, and copilot counterparts assisting humans, agents could save time and access the right information to upsell customers during service engagements. Here are some of the ways Generative AI will change customer service forever. 1. Monitor and Ensure Compliance Maintaining compliance is crucial for fostering customer trust, preserving a positive brand image, and avoiding hefty privacy and compliance fines. In a contact center, compliance mistakes can quickly escalate into costly lawsuits and revenue losses. Generative AI allows your compliance team to proactively manage compliance by quickly identifying trends and addressing issues in real time. Instead of waiting for a compliance issue to escalate, you can fine-tune your AI model to provide compliance insights whenever necessary. For instance, you can ask: This approach offers more comprehensive insights than scorecards, which often lack context and accuracy. Generative AI’s analytical capabilities provide actionable insights to improve compliance across your contact center. 2. Get Insights About Your Call Center Performance at a Glance Generative AI language models make it easier than ever to gain insights into your contact center’s performance. Simply ask the model for the information you need. For example, you can inquire about the real-time average handling time (AHT) by asking, “What is the average handling time today?” But that’s just the beginning. With an advanced language model, you can compare metrics across different quarters or generate ideas for coaching plans by asking for each agent‘s strengths and weaknesses and suggestions for improvement. 3. Automate Post-Call Work Generative AI assistants can act as real-time notetakers, summarizing 100% of calls and freeing agents from manual note-taking. This automation makes after-call work effortless, generating comprehensive and compliant notes with a single click. 4. Capture Coachable Moments Easily Incorporating real-world coachable moments into your sessions is essential for tangible performance improvements. Generative AI can identify areas where agents typically struggle without requiring hours of call listening and note-checking. Traditional methods mean compromising on the specificity of coaching due to time constraints, especially when managing large teams. Generative AI solutions, however, enable call center managers to obtain detailed insights about each agent’s performance quickly. This allows for personalized coaching plans that address individual shortcomings efficiently. You can ask: 5. Improve Decision Making With Efficient Root-Cause Analysis Effective decision-making can transform your contact center. However, many managers struggle to identify the root causes of performance issues. Generative AI algorithms can analyze vast amounts of data and customer interactions, uncovering patterns and trends in customer and agent behavior. These insights help pinpoint the issues most impacting performance and customer satisfaction, allowing you to make informed decisions. The process is nearly fully automated, freeing your team from time-consuming data collection tasks. 6. Reduce Manual Work and Focus on Improvement Improving contact center performance requires extensive data, which is resource-intensive to collect manually. Generative AI simplifies this by analyzing customer interactions and providing actionable insights on demand. This saves time and money, allowing you to focus on improvements that deliver a higher ROI. 7. Scale What Works Discovering and scaling best practices is essential for team-wide success. Generative AI and Natural Language Processing (NLP) models can analyze customer interactions to identify effective strategies and coaching opportunities. For example, if a representative handles challenging situations well, AI can generate tips for other team members based on these successful interactions. Generative AI can identify top-performing agents and analyze their calls to extract best practices, providing a more comprehensive approach than focusing on a single agent. Queries you might use include: 8. Generate Agent Scripts Generative AI enables you to draft and fine-tune agent scripts for various customer interactions. Instead of relying

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TEFCA could drive payer-provider interoperability

TEFCA could drive payer-provider interoperability

Bridging the Interoperability Gap: TEFCA’s Role in Payer-Provider Data Exchange The electronic health information exchange (HIE) between healthcare providers has seen significant growth in recent years. However, interoperability between healthcare providers and payers has lagged behind. The Trusted Exchange Framework and Common Agreement (TEFCA) aims to address this gap and enhance data interoperability across the healthcare ecosystem. TEFCA could drive payer-provider interoperability with a little help from the world of technology. TEFCA’s Foundation and Evolution TEFCA was established under the 21st Century Cures Act to improve health data interoperability through a “network of networks” approach. The Office of the National Coordinator for Health Information Technology (ONC) officially launched TEFCA in December 2023, designating five initial Qualified Health Information Networks (QHINs). By February 2024, two additional QHINs had been designated. The Sequoia Project, TEFCA’s recognized coordinating entity, recently released several key documents for stakeholder feedback, including draft standard operating procedures (SOPs) for healthcare operations and payment under TEFCA. During the 2024 WEDI Spring Conference, leaders from three QHINs—eHealth Exchange, Epic Nexus, and Kno2—discussed the future of TEFCA in enhancing provider and payer interoperability. ONC released Version 2.0 of the Common Agreement on April 22, 2024. Common Agreement Version 2.0 updates Common Agreement Version 1.1, published in November 2023, and includes enhancements and updates to require support for Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) based transactions. The Common Agreement includes an exhibit, the Participant and Subparticipant Terms of Participation (ToP), that sets forth the requirements each Participant and Subparticipant must agree to and comply with to participate in TEFCA. The Common Agreement and ToPs incorporate all applicable standard operating procedures (SOPs) and the Qualified Health Information Network Technical Framework (QTF). View the release notes for Common Agreement Version 2.0 The Trusted Exchange Framework and Common AgreementTM (TEFCATM) has 3 goals: (1) to establish a universal governance, policy, and technical floor for nationwide interoperability; (2) to simplify connectivity for organizations to securely exchange information to improve patient care, enhance the welfare of populations, and generate health care value; and (3) to enable individuals to gather their health care information. Challenges in Payer Data Exchange Although the QHINs on the panel have made progress in facilitating payer HIE, they emphasized that TEFCA is not yet fully operational for large-scale payer data exchange. Ryan Bohochik, Vice President of Value-Based Care at Epic, highlighted the complexities of payer-provider data exchange. “We’ve focused on use cases that allow for real-time information sharing between care providers and insurance carriers,” Bohochik said. “However, TEFCA isn’t yet capable of supporting this at the scale required.” Bohochik also pointed out that payer data exchange is complicated by the involvement of third-party contractors. For example, health plans often partner with vendors for tasks like care management or quality measure calculation. This adds layers of complexity to the data exchange process. Catherine Bingman, Vice President of Interoperability Adoption for eHealth Exchange, echoed these concerns, noting that member attribution and patient privacy are critical issues in payer data exchange. “Payers don’t have the right to access everything a patient has paid for themselves,” Bingman said. “This makes providers cautious about sharing data, impacting patient care.” For instance, manual prior authorization processes frequently delay patient access to care. A 2023 AMA survey found that 42% of doctors reported care delays due to prior authorization, with 37% stating that these delays were common. Building Trust Through Use Cases Matt Becker, Vice President of Interoperability at Kno2, stressed the importance of developing specific use cases to establish trust in payer data exchange via TEFCA. “Payment and operations is a broad category that includes HEDIS measures, quality assurance, and provider monitoring,” Becker said. “Each of these requires a high level of trust.” Bohochik agreed, emphasizing that narrowing the scope and focusing on specific, high-value use cases will be essential for TEFCA’s adoption. “We can’t solve everything at once,” Bohochik said. “We need to focus on achieving successful outcomes in targeted areas, which will build momentum and community support.” He also noted that while technical data standards are crucial, building trust in the data exchange process is equally important. “A network is only as good as the trust it inspires,” Bohochik said. “If healthcare systems know that data requests for payment and operations are legitimate and secure, it will drive the scalability of TEFCA.” By focusing on targeted use cases, ensuring rigorous data standards, and building trust, TEFCA has the potential to significantly enhance interoperability between healthcare providers and payers, ultimately improving patient care and operational efficiency. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more 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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Gen AI and Test Automation

Gen AI and Test Automation

Generative AI has brought transformative advancements across industries, and test automation is no exception. By generating code, test scenarios, and even entire suites, Generative AI enables Software Development Engineers in Test (SDETs) to boost efficiency, expand test coverage, and improve reliability. 1. Enhanced Test Case Generation One of the biggest hurdles in test automation is generating diverse, comprehensive test cases. Traditional methods often miss edge cases or diverse scenarios. Generative AI, however, can analyze existing data and automatically generate extensive test cases, including potential edge cases that may not be apparent to human testers. Example: An SDET can use Generative AI to create test cases for a web application by feeding it requirements and user data. This enables the AI to produce hundreds of test cases, capturing diverse user behaviors and interactions that manual testers may overlook. pythonCopy codeimport openai openai.api_key = ‘YOUR_API_KEY’ def generate_test_cases(application_description): response = openai.Completion.create( engine=”text-davinci-003″, prompt=f”Generate comprehensive test cases for the following application: {application_description}”, max_tokens=500 ) return response.choices[0].text app_description = “An e-commerce platform for browsing products, adding to cart, and checking out.” test_cases = generate_test_cases(app_description) print(test_cases) Sample Output: 2. Intelligent Test Script Creation Writing test scripts manually can be labor-intensive and error-prone. Generative AI can simplify this by generating test scripts based on an application’s flow, ensuring consistency and precision. Example: If an SDET needs to automate tests for a mobile app, they can use Generative AI to generate scripts for various scenarios, significantly reducing manual work. pythonCopy codeimport hypothetical_ai_test_tool ui_description = “”” Login Page: – Username field – Password field – Login button Home Page: – Search bar – Product listings – Add to cart buttons “”” test_scripts = hypothetical_ai_test_tool.generate_selenium_scripts(ui_description) Sample Output for test_login.py: pythonCopy codefrom selenium import webdriver from selenium.webdriver.common.keys import Keys def test_login(): driver = webdriver.Chrome() driver.get(“http://example.com/login”) username_field = driver.find_element_by_name(“username”) password_field = driver.find_element_by_name(“password”) login_button = driver.find_element_by_name(“login”) username_field.send_keys(“testuser”) password_field.send_keys(“password”) login_button.click() assert “Home” in driver.title driver.quit() 3. Automated Maintenance of Test Suites As applications evolve, maintaining test suites is critical. Generative AI can monitor app changes and update test cases automatically, keeping test suites accurate and relevant. Example: In a CI/CD pipeline, an SDET can deploy Generative AI to track code changes and update affected test scripts. This minimizes downtime and ensures tests stay aligned with application updates. pythonCopy codeimport hypothetical_ai_maintenance_tool def maintain_test_suite(): changes = hypothetical_ai_maintenance_tool.analyze_code_changes() updated_scripts = hypothetical_ai_maintenance_tool.update_test_scripts(changes) for script_name, script_content in updated_scripts.items(): with open(script_name, ‘w’) as file: file.write(script_content) maintain_test_suite() Sample Output:“Updating test_login.py with new login flow changes… Test scripts updated successfully.” 4. Natural Language Processing for Test Case Design Generative AI with NLP can interpret human language, enabling SDETs to create test cases from plain-language descriptions, enhancing collaboration across technical and non-technical teams. Example: An SDET can use an NLP-powered tool to translate a feature description from a product manager into test cases. This speeds up the process and ensures that test cases reflect intended functionality. pythonCopy codeimport openai openai.api_key = ‘YOUR_API_KEY’ def create_test_cases(description): response = openai.Completion.create( engine=”text-davinci-003″, prompt=f”Create test cases based on this feature description: {description}”, max_tokens=500 ) return response.choices[0].text feature_description = “Allow users to reset passwords via email to regain account access.” test_cases = create_test_cases(feature_description) print(test_cases) Sample Output: 5. Predictive Analytics for Test Prioritization Generative AI can analyze historical data to prioritize high-risk areas, allowing SDETs to focus testing on critical functionalities. Example: An SDET can use predictive analytics to identify areas with frequent bugs, allocating resources more effectively and ensuring robust testing of high-risk components. pythonCopy codeimport hypothetical_ai_predictive_tool def prioritize_tests(): risk_areas = hypothetical_ai_predictive_tool.predict_risk_areas() prioritized_tests = hypothetical_ai_predictive_tool.prioritize_test_cases(risk_areas) return prioritized_tests prioritized_test_cases = prioritize_tests() print(“Prioritized Test Cases:”) for test in prioritized_test_cases: print(test) Sample Output: Gen AI and Test Automation Generative AI has the potential to revolutionize test automation, offering SDETs tools to enhance efficiency, coverage, and reliability. By embracing Generative AI for tasks like test case generation, script creation, suite maintenance, NLP-based design, and predictive prioritization, SDETs can reduce manual effort and focus on strategic tasks, accelerating testing processes and ensuring robust, reliable software systems. 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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Sales Prospecting Tools

Sales Prospecting Tools

The Complete Guide to Sales Prospecting Tools Sales prospecting tools: Two men examining a touchscreen displaying dashboards and charts. With the right tools, you can spend more time building relationships that convert prospects into loyal customers. Learn how technology can help you identify and engage the right prospects more efficiently. Selling has become more challenging, with 69% of sales professionals agreeing that their jobs are harder now. That’s why sales prospecting tools are crucial—they streamline the process, making it faster and more accurate. When equipped with the right tools, you can focus more on nurturing customer relationships, turning prospects into long-term clients. In this guide, we’ll explore what sales prospecting tools are, key features to look for, and the biggest benefits they provide. What Are Sales Prospecting Tools? Sales prospecting tools are software solutions designed to help sales teams identify, engage, and convert potential customers. These tools enhance the sales prospecting process, enabling sales reps to quickly and effectively reach new buyers. They often integrate with existing platforms, such as Customer Relationship Management (CRM) software and email marketing systems, to optimize outreach and engagement. Typically, prospecting tools focus on outbound marketing, helping sales reps connect with potential customers who may not yet be familiar with the company or product. Types of Sales Prospecting Tools Selecting the right sales prospecting tool depends on your current prospecting methods and future goals. Below are the most common categories of prospecting tools: Lead Generation Tools Lead generation tools help sales teams identify prospects who are ready to purchase. These tools streamline workflows, enhance productivity, and flag potential buyers based on their online activity. For example, they might alert a rep when a prospect searches for solutions related to your product or service. Some lead generation tools also enable mass outreach, such as power dialers that allow sales reps to call multiple prospects simultaneously. Choosing the right lead generation tool depends on how your target customers prefer to engage. For instance, if you have better results from social media interactions than phone calls, a power dialer may not be the best fit. Evaluate your analytics and future goals to determine which tool will maximize your success. CRM Software CRM software manages all customer and prospect interactions across sales, service, marketing, and more. Acting as a single source of truth, CRM platforms centralize all sales activity in one location, allowing leaders to assign prospects and track progress more effectively. With AI-powered features, CRM tools can guide reps on the next best steps and personalize workflows, improving conversion rates. CRMs also provide critical insights for targeting prospects more likely to convert. Social Media Prospecting Tools Social media has become a powerful channel for sales prospecting. Specialized tools scrape social platforms for data to help sales reps identify prospects ready for outreach. For instance, they can track user activity related to the business problem your product solves and notify reps when users engage with relevant content. The integration of AI in social media prospecting tools has further boosted their effectiveness. As AI continues to evolve, expect more sophisticated features in this space. Why Are Sales Prospecting Tools Important? In today’s competitive market, your prospects are also being contacted by your competitors—most of whom are using advanced sales prospecting tools. If you’re not using similar tools, you risk falling behind. Sales prospecting tools help level the playing field by streamlining research and outreach, allowing reps to connect with the right prospects at the right time. However, these tools must be used strategically. Simply contacting more people won’t guarantee more sales. Personalization and targeting remain key. Using the insights provided by these tools, sales reps can tailor their messages and approaches, making each outreach effort more effective. Benefits of Using Sales Prospecting Tools When fully integrated into your sales processes, prospecting tools can deliver substantial benefits, including: Key Features to Look for in Sales Prospecting Tools To ensure your sales prospecting tool adds value to your business, consider the following features: Compliance Keeping up with constantly changing rules around prospecting—especially across different channels—can be daunting. A good prospecting tool automates compliance, ensuring your emails, calls, and social media outreach meet best practices and regulations. Ease of Use Your prospecting tool should simplify your workflow, not complicate it. Look for intuitive interfaces and tools that can automate repetitive tasks, such as dialing multiple numbers or sending emails in bulk. AI-Powered Analytics Tools with AI capabilities can generate valuable insights, such as identifying the best time to call a prospect or suggesting which channel is most likely to yield a response. System Integration Your prospecting tool should seamlessly integrate with existing systems, such as CRMs and marketing automation platforms, to ensure data flows smoothly and insights are actionable across your entire workflow. Customizable and Scalable Your sales process is unique to your business. Opt for customizable and scalable tools that can adapt as your needs change, ensuring you get maximum ROI from your investment. Make Prospecting Work for Your Business Without the right tools, your team is at a disadvantage compared to competitors using advanced sales prospecting technologies. Finding a tool with the right features and customizing it for your specific needs—such as pricing structures and campaign strategies—can empower your team to prospect more efficiently, yielding better results in less time. Content updated October 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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AI Design Beyond the Chatbot

AI Design Beyond the Chatbot

As AI continues to advance, designers, builders, and creators are confronted with profound questions about the future of applications and how users will engage with digital experiences. AI Design Beyond the Chatbot. Generative AI has opened up vast possibilities, empowering people to utilize AI for tasks such as writing articles, generating marketing materials, building teaching assistants, and summarizing data. However, alongside its benefits, there are challenges. Sometimes, generative AI produces unexpected or biased responses, a phenomenon known as hallucination. In response, approaches like retrieval augmented generation (RAG) have emerged as effective solutions. RAG leverages a vector database, like SingleStore, to retrieve relevant information and provide users with contextually accurate responses. AI Design Beyond the Chatbot Looking ahead, the evolution of AI may lead to a future where users interact with a central LLM operating system, fostering more personalized and ephemeral experiences. Concepts like Mercury OS offer glimpses into this potential future. Moreover, we anticipate the rise of multimodal experiences, including voice and gesture interfaces, making technology more ubiquitous in our lives. Imran Chaudhri’s demonstration of a screen-less future, where humans interact with computers through natural language, exemplifies this trend. However, amidst these exciting prospects, the current state of AI integration in businesses varies. While some are exploring innovative ways to leverage AI, others may simply add AI chat interfaces without considering contextual integration. To harness AI effectively, it’s crucial to identify the right use cases and prioritize user value. AI should enhance experiences by reducing task time, simplifying tasks, or personalizing experiences. Providing contextual assistance is another key aspect. AI models can offer tailored suggestions and recommendations based on user context, enriching the user experience. Notion and Coda exemplify this by seamlessly integrating AI recommendations into user workflows. Furthermore, optimizing for creativity and control ensures users feel empowered in creation experiences. Tools like Adobe Firefly strike a balance between providing creative freedom and offering control over generated content. Building good prompts is essential for obtaining quality results from AI models. Educating users on how to construct effective prompts and managing expectations regarding AI limitations are critical considerations. Ultimately, as AI becomes more integrated into daily workflows, it’s vital to ensure seamless integration into user experiences. Responsible AI design requires ongoing dialogue and exploration to navigate this rapidly evolving landscape effectively. 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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Einstein Web Recommendations

Einstein Web Recommendations

Einstein Web Recommendations leverage Einstein’s capabilities to analyze user behavior, construct preference profiles, and deliver personalized content tailored to each website visitor. Utilize application scenarios to fine-tune recommendations according to your specific business rules. Web Recommendations are provided through two methods: a JSON response or HTML/JS. While the JSON response is the recommended delivery method due to its flexibility, HTML/JS can be used if the web team is unable to work with JSON. As the JSON method allows for greater flexibility, you are responsible for parsing and styling the recommendations within your web environment. Marketing Cloud Einstein Recommendations enable the creation of product or content recommendations for display on your website. The Einstein Recommendation Engine necessitates a minimum of three active items in your product catalog. Incorporate any catalog field into the web recommendation call, emphasizing a clear understanding of the data driving recommendations during catalog setup. A unique web recommendation call is generated for each page type, with Home, Product, Category, Cart, and Conversion pages recommended as a best practice. However, there is no restriction on the number of pages that can be configured in the UI. Page templates are utilized by Einstein Recommendations, treating a Product Page as a template for building personalized recommendations specific to a product page. Different types of page templates may have distinct scenarios and contexts. For instance, recommendations on a product page may be based on the viewed product, adding context. Conversely, homepage recommendations rely on overall user affinity, lacking specific context. Integrate the Einstein Web Recommendations code into the designated page’s code, incorporating both the JavaScript for the recommendation call and the HTML recommendation zone placeholder provided. Select and configure the content to be included in your web recommendations: 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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mulesoft and healthcare

MuleSoft and Healthcare

Driving Innovation in Healthcare Through Data Interoperability Healthcare organizations are navigating an unprecedented surge in patient data, which is critical for communication, research, and management. This data plays a pivotal role in modernizing healthcare and improving outcomes, particularly with the shift toward a Value-Based Care Model. However, 81% of IT leaders report that much of this data remains trapped in silos, hindering innovation and negatively impacting patient satisfaction. The Importance of Interoperability in Healthcare Improving Patient Outcomes and Managing RiskLeading healthcare organizations understand that achieving interoperability—seamless data exchange across clinical and non-clinical systems—is vital. Beyond supporting Value-Based Care, interoperability drives patient satisfaction, loyalty, and cost-efficiency. By enabling accurate data sharing, healthcare providers can: Interoperability also supports proactive preventative care, reducing long-term healthcare costs and boosting life expectancy. Regulatory Mandates: The Interoperability and Patient Access Final RuleSince May 1, 2020, the Centers for Medicare & Medicaid Services (CMS) have mandated interoperability through the Interoperability and Patient Access Final Rule. This legislation holds U.S. healthcare providers accountable for: Non-compliance can result in significant fines and public reporting of violations, further emphasizing the criticality of achieving interoperability. The Challenge of ImplementationDespite its clear benefits—improved patient outcomes, compliance, and cost savings—achieving interoperability poses challenges. Technological complexities and siloed data structures hinder seamless integration. This is where MuleSoft, a Salesforce company, provides a powerful solution. How MuleSoft Enables Interoperability in Healthcare Breaking Down Silos with API-Led IntegrationMuleSoft is a trusted partner for leading healthcare organizations, offering secure, scalable solutions to eliminate data silos. Recognized as a Leader in Gartner’s Magic Quadrant for iPaaS, MuleSoft empowers providers with its HIPAA-compliant Anypoint Platform, facilitating interoperability through API-led integration. Key Features and Benefits Transforming Healthcare with MuleSoft The digital transformation of healthcare is accelerating, driven by evolving regulations, patient expectations, and a dynamic global environment. MuleSoft stands at the forefront of this shift, empowering healthcare organizations to: By partnering with MuleSoft, healthcare organizations can embrace innovation and build healthier connections—one integration at a time. Contact Tectonic today to get started. 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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Education Cloud AI Innovations

Education Cloud AI Innovations

Salesforce AI Innovations: Empowering Student Success and Faculty Efficiency Salesforce is introducing new Education Cloud AI Innovations, AI-powered tools designed to streamline the educational journey, enabling students to chart clear paths to graduation, translate their coursework into resume-ready skills, and connect with mentors who can guide them toward their career goals. Enhancing Faculty and Staff Efficiency with AI New generative AI capabilities are set to automate time-consuming tasks for faculty and staff, allowing them to focus on what matters most—driving student success. Personalizing Student Experiences with AI Institutions like the University of Nevada Las Vegas and Texas Tech are leveraging Salesforce for Education to create personalized student experiences and improve staff efficiency through AI-driven solutions. Salesforce Introduces AI-Powered Student Success Tools for Education Cloud Today, Salesforce unveiled cutting-edge AI tools for Education Cloud, including Intelligent Degree Planning and Skills Generator. These innovations are designed to help institutions craft personalized graduation pathways, translate coursework into tangible skills, and facilitate impactful mentorship programs. Additionally, Salesforce introduced Data Cloud for Education and Einstein Copilot Recruitment & Admissions Actions. These tools will enable institutions to automate routine tasks, enhance recruitment and enrollment processes, and bolster student support. Why It Matters Education professionals face the highest burnout rates across industries, and students are feeling the impact—only 11% of college students believe they are workforce-ready. As the education sector approaches an enrollment cliff, confidence in the value of a college degree is declining, and educators are leaving the profession in significant numbers. Schools must find ways to reduce staff workload while improving student experiences and outcomes. Explore Education Cloud Elevate the educational experience with the #1 AI CRM for learner and institution success. DIVE IN AI Innovation for Lifelong Student Success The new AI capabilities for Education Cloud, built on Salesforce’s Einstein 1 Platform, will help higher education and K-12 institutions unlock the power of their data to deliver trusted AI solutions. These innovations are designed to improve staff efficiency while enhancing student experiences and learning outcomes. Key features include: AI in Action for Faculty and Staff Salesforce is delivering new AI and data tools to automate tasks related to recruitment, enrollment, and student experience management. New features include: With these industry-specific AI and data tools, Education Cloud is poised to help K-12 and higher education institutions offer more individualized support to every student while increasing operational efficiency and reducing staff burnout. The Salesforce Perspective “Every institution wants to provide the best possible experiences for their students and staff. With industry-specific AI and data tools, Education Cloud will help K-12 and higher ed institutions offer more personalized support to every student while increasing efficiency and helping to reduce staff burnout. This will free educators and staff to focus on improving student outcomes, such as career readiness, well-being, and graduation rates.”— Bala Subramanian, VP & GM of Education 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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Why Education Cloud

Why Education Cloud

Salesforce Education Cloud is a comprehensive solution designed for higher education institutions, offering powerful tools to manage every aspect of the student lifecycle—from recruitment and admissions to student success and alumni engagement. However, to fully maximize the benefits of Salesforce Education Cloud, institutions need a partner that understands the unique challenges and needs of higher education. Our team specializes in implementing Salesforce Education Cloud, ensuring that your institution has the right tools to drive success at every stage of the student journey. Student 360 Customization We can help tailor Salesforce’s Student 360 to fit your institution’s specific needs, providing a unified, comprehensive view of each student. By integrating all your data sources seamlessly, we empower your staff with actionable insights that enhance student success and support. Customized Solutions for Your Institution Our Tectonic team focuses on delivering custom configurations aligned with your institution’s unique processes and goals. This tailored approach ensures you have the precise tools needed to support and optimize every stage of the student lifecycle. Continuous Optimization As your institution evolves, so should your Salesforce Education Cloud solution. We’re committed to helping your system grow with you, optimizing it to meet changing demands and driving ongoing success and improvement. AI and Automation to Streamline Processes From automating admissions workflows to using predictive analytics for student retention, Salesforce’s AI and automation features keep your institution ahead of the curve. We collaborate closely with your team to streamline repetitive tasks, set up AI-driven insights, and ensure your staff can leverage these tools to improve efficiency and make data-informed decisions. Alumni Engagement and Fundraising Salesforce Education Cloud offers advanced tools to foster alumni relationships and manage donor relations. We help you customize these tools to segment alumni databases, execute targeted outreach campaigns, and strengthen fundraising efforts, ensuring lasting engagement with graduates. By partnering with Tectonic, your institution will harness the full potential of Salesforce Education Cloud, driving student success and strengthening alumni connections for the long term. Content updated August 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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UX Principles for AI in Healthcare

Agentic Era of UX

The Agentic Era of UX The future of digital experience has arrived, but it’s fragmenting into countless micro-applications. The missing piece in AI user experience? The experience itself. It’s been almost a year and a half since generative AI burst onto the scene, heralded as transformative. But what have we actually seen in terms of user experience? Many companies released AI-powered summaries or search features, claimed them as revolutionary, and received applause—until the applause faded. The so-called “next era” of tech hasn’t yet delivered on its promise. We were given “the most profound technology since fire,” yet many implementations feel like candles that barely flicker. Many UX designers continue advocating for AI to solve genuine user needs. Technology must serve users, not just exist for its own sake. The core issue now is broader: AI has often been treated as a quick fix rather than a true UX transformation. Where user experience traditionally supports the entire journey, AI is being wedged into small, isolated tasks, losing the holistic perspective. For most companies, AI feels like a string of individual “use cases” rather than a full, cohesive UX meal. Many consulting firms push companies to prioritize use cases in terms of complexity and value, often resulting in chatbots that address a handful of user needs. There are notable exceptions, though. For example, Loom went beyond simple AI features to enhance the user’s entire workflow, supporting end-to-end functionality for video recording, transcription, editing, and even task management. Welcome to the Agentic Era of AI We’re now on the verge of the “agentic” era of AI. Industry leaders are abuzz with the potential of AI agents. OpenAI’s Sam Altman calls agents AI’s “killer function,” while other leaders predict this future is within reach, possibly within 3–18 months. The agentic promise is profound: AI agents, or “agentic workflows,” break down complex tasks into manageable steps, helping users complete intricate projects with autonomy. As Ezra Klein describes, imagine telling an AI to plan your child’s dragon-themed birthday party in Brooklyn, and the agent handles everything from booking to ordering the cake—transforming a casual AI prompt into tangible results. Today’s general-purpose models can’t handle this level of complexity independently. But agentic workflows make this possible by chaining AI actions, allowing systems to execute tasks step-by-step. A Vision for Agentic UX Design’s role in this era is to bring a vision of agentic UX to life. In traditional digital experiences, we build systems that assist users along their journey, but we still expect users to navigate the journey themselves. With an agentic UX, an AI partner supports the user at every step. This vision means UX will be defined by three pillars: Early examples are emerging, like Adobe’s Gen Studio, Intercom’s Copilot, and Dovetail’s Magic Experience, each taking steps toward a future where AI provides ongoing, meaningful support to users. An agentic UX doesn’t necessarily need to label itself “agent-powered.” Dovetail, for instance, offers a suite of “Magic” features where the AI partner plays a supporting role, from summarizing transcripts to highlighting key points. Over time, as AI evolves, these agents will assume greater responsibility in user journeys, shifting from supportive to proactive. Strategically Reinvent for the Agentic Era Adapting to the agentic era presents an opportunity—and a risk for those who ignore it. Currently, organizations are focused on laying the infrastructure for “AI readiness.” While that’s essential, it can obscure the longer-term vision of what’s possible. Until business leaders fully grasp the agentic UX’s potential, it’s up to design to step into a strategic role and make this vision vivid, relatable, and exciting. This requires more than launching a quick proof of concept; it demands a reimagining of digital experience. Here’s a recommended approach: It’s been a challenging year for design, with layoffs and value debates. But with the agentic era approaching, the strategic potential for UX is immense. Now is the time to rally, to guide organizations into a new era of digital experience where users are truly supported every step of the way. 4ox Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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Net Promoter Score Explained

Net Promoter Score Explained

When a friend or colleague takes the time to tell you about a product or service, you probably pay attention. Your friend is more reliable than a Yelp review, right? Word of mouth is the most common way people hear about brands. But how can you tell if your existing customers like your company enough to recommend it to their friends? One way is by tracking your Net Promoter Score (NPS). Is NPS really the best way to measure customer loyalty? Some service leaders aren’t convinced. We wanted to explore the pros and cons of this popular (and sometimes controversial) metric by reviewing what it is, why it’s important, and why some Service Trailblazers choose to measure loyalty in other ways. What is a Net Promoter Score? The Net Promoter Score is a customer experience metric that captures how likely a customer is to recommend your products, services, or brand. Created by Fred Reichheld in 2003, NPS has since been widely adopted. To find NPS, businesses ask customers: “On a scale from 0 to 10, how likely would you be to recommend our company to a friend or colleague?” Typically, companies follow up with an open-ended question to understand why a customer chose their score. Customers are categorized as promoters, passives, or detractors based on their scores: By understanding these categories, businesses can gain insights into customer loyalty and take action to improve customer experiences and foster stronger relationships. How to Calculate Net Promoter Score A company’s NPS is calculated by subtracting the percentage of detractors from the percentage of promoters. For instance, if you have responses from 100 customers, with 30 promoters and 18 detractors, your NPS would be 12. Why is the Net Promoter Score Important? Companies use NPS to gain insights into individual customer experiences and understand the overall perception of their products, services, and brand. NPS feedback helps address individual issues, enhance product offerings, and apply customer service principles effectively. An improving NPS indicates positive changes that matter to customers, while a sudden drop signals potential issues that need attention. What is a Good Net Promoter Score? NPS can range from -100 to +100. While anything above 0 indicates more promoters than detractors, industry-specific benchmarks provide a more nuanced view. For example, the average NPS in the insurance industry is 74, whereas in healthcare, it’s only 45. According to Bain & Company, the creators of NPS, a score above 0 is considered good. Scores over 20 are favorable, over 50 are excellent, and above 80 are world-class. Comparing your NPS to industry benchmarks helps gauge your customer experience relative to competitors, though it’s important to consider the context, such as company size and market scope. What is a Bad Net Promoter Score? Any NPS below zero is considered bad, as it means more customers are dissatisfied than satisfied. A significantly lower NPS compared to competitors may indicate the need to re-evaluate your customer service experience and address gaps that cause dissatisfaction. What Can You Measure Using NPS? NPS provides insights into: Bain & Company’s data shows that companies with long-term profitable growth have an NPS twice as high as the average company. How to Run Surveys and Collect Feedback Running an NPS survey involves asking a single question, collecting responses, and calculating your NPS. Surveys can be distributed via email, text, in-store, or online at checkout, using a survey builder or NPS app for automation. When to Run NPS Surveys The timing of NPS surveys varies: Staggering surveys over time can provide a more organic picture of customer satisfaction. How to Collect NPS Feedback Following up with an open-ended question like, “What’s the primary reason for your score?” helps gather actionable insights. This feedback can highlight areas for improvement and strengths to build on. How to Improve Your Net Promoter Score Improving NPS requires both individual and broad strategies: Implementing Net Promoter Score Invest in NPS survey software that integrates with your CRM platform. Automate survey distribution and workflows to manage responses efficiently. Designate responsibilities for customer follow-ups and NPS analysis. Measuring Your NPS is Just the Beginning Your NPS provides valuable insights into customer loyalty and satisfaction. However, the follow-through on these insights is crucial for engaging customers and delivering better experiences. Measuring your NPS is the first step towards enhancing your overall customer experience. 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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