Analytics

23Sep

Oracle Advertising Sundown

Oracle Shifts Focus to B2B CX, Introduces New Fusion Cloud Features Despite winding down its online advertising products, Oracle is doubling down on its investment in customer experience (CX) technology, particularly in enabling B2B buying and supporting subscription and consumption models. During the Oracle CloudWorld conference on Wednesday, the company unveiled new capabilities for its Fusion Cloud Customer Experience and Unity Customer Data Platform. These enhancements empower Oracle CX users to analyze customer profiles to assemble B2B buying teams, leverage generative AI tools like native analytics, and utilize industry-specific accelerators to speed up the adoption of customer data tools. Key features include the ability to create self-service sites for individual accounts, enabling customers to review and summarize contracts using generative AI, receive quotes, and renew subscriptions. Other features enhance “assisted buying experiences,” blending self-service and human interaction, while tools like account onboarding and AI-powered email drafting simplify full-service sales processes. Subscription models, though still in their early stages for B2B, offer a streamlined alternative to traditional procurement processes. As Liz Miller, an analyst at Constellation Research, noted, subscription-based buying is easier and quicker, avoiding the lengthy procurement cycles many B2B buyers are familiar with. “The pain of traditional B2B buying is still fresh in everyone’s mind,” she said. Oracle Advertising Shuts Down Oracle’s advertising product support will end on September 30, as confirmed by CEO Safra Catz during the company’s June earnings call. The Oracle Advertising Data Management Platform (DMP), built from its BlueKai acquisition, is being retired, following in the footsteps of Salesforce, which discontinued its Audience Studio in 2021. Despite Oracle winding down its ad platform, this move shouldn’t be seen as a shift away from customer experience. Oracle founder Larry Ellison remains deeply involved in shaping the company’s CX strategy, with a focus on marketing tools and Apex low-code platforms, said Rob Pinkerton, Oracle’s senior vice president. Oracle’s modernized CX suite, built on the Fusion Cloud platform, has evolved significantly in recent years, though questions remain about whether it’s too late to regain market share. “Oracle as a CX platform has fallen off the radar for many buyers,” said Miller, adding that customers are no longer debating between Oracle, Microsoft, and Salesforce in the CX space. New Industry-Specific Tools for CX Oracle has also expanded its CX platform with industry-specific tools designed to accelerate the adoption of its customer data platform (CDP) across sectors such as high tech, manufacturing, professional services, telecommunications, utilities, financial services, travel, and retail. According to Rebecca Wettemann, CEO of research firm Valoir, Oracle’s Fusion platform has matured significantly and now supports the complexity of modern customer needs. Wettemann highlighted how common components like customer interaction summaries can be adapted for multiple industries, delivering faster results than traditional applications. Oracle’s Clinical Digital Assistant is one such example of this approach, illustrating the platform’s versatility and AI-driven enhancements. With these developments, Oracle continues to refine its CX offerings to better meet the unique demands of B2B customers, providing tools that streamline operations and enhance customer experiences across various industries. 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

September 23, 2024in Generative AI

23Sep

Dreamforce 24 Insights

Three Key Insights You Might Have Missed from Dreamforce ’24 In today’s digital-driven world, interconnected systems are commonplace and essential, making platform integration and unified operations critical. As AI becomes more central, technologies like Salesforce Agentforce AI are drawing increased attention. At Dreamforce ’24, automation and AI were the event’s stars, particularly Salesforce’s plans for Agentforce AI. Dreamforce 24 Insights. Here are three key insights from Dreamforce ’24 that you might have missed: 1. Salesforce’s Automation Plans Could Reshape Its Future Salesforce has a solid reputation for business automation, but now, with agentic systems entering the picture, the company is looking at a transformative opportunity. John Furrier of theCUBE noted during Dreamforce, “Salesforce is positioned to use generative AI to simplify complexity and reduce the steps required to get things done.” As Salesforce integrates generative AI, the emphasis on securing and utilizing data becomes paramount. Christophe Bertrand of theCUBE pointed out that many organizations are not fully utilizing their data. The introduction of Agentforce AI, which aims to leverage this untapped potential, could bring automation to new heights and fundamentally transform how businesses operate. 2. Salesforce Agentforce AI Aims to Integrate Seamlessly Into Business Workflows A major focus of Dreamforce was Salesforce’s new AI offering—Agentforce. According to Muralidhar Krishnaprasad, Salesforce’s CTO, this represents the next stage of AI for the company. While earlier efforts focused on predictive AI (Einstein) and generative AI copilots, Agentforce moves toward more autonomous AI agents. “Our platform will be one of the most comprehensive for agent development,” Krishnaprasad explained. He highlighted that Agentforce will allow businesses to deploy AI agents across various functions—advertising, sales, service, and analytics—creating a seamless AI-driven ecosystem within the Salesforce platform. David Schmaier, president and CPO of Salesforce, added that Agentforce will transform customer interactions by integrating AI agents with Salesforce Data Cloud to deliver more personalized and efficient experiences. 3. Strategic Partnerships Are Streamlining Business and Enhancing Customer Solutions At Dreamforce, partnerships played a key role in Salesforce’s strategy for the future. A collaboration between Salesforce and AWS is streamlining procurement for joint customers through AWS Marketplace. This partnership allows companies to optimize their spend management and simplify the purchasing process for Salesforce products. IBM is also leveraging Agentforce to drive new outcomes through watsonx Orchestrate, as Nick Otto, IBM’s head of global strategic partnerships, explained. Automation and orchestration have been focal points for both IBM and Salesforce. Another partnership with Canva showcased AI-driven data autofill capabilities that integrate with Salesforce CRM. This allows sales teams to create personalized presentations at scale, automating workflows and increasing efficiency, as noted by Canva’s Chief Customer Officer, Rob Giglio. These insights from Dreamforce ’24 highlight the growing importance of AI, automation, and strategic partnerships in shaping the future of business operations with Salesforce at the forefront. 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

September 23, 2024in Data, Generative AI, Salesforce, Salesforce Data Cloud

23Sep

Case Study: Salesforce Advanced Forcasting and Streamline Operations Yields Big Change and Bigger Results

Case Study: Salesforce Advanced Forcsting and Streamline Operations Yields Big Change and Bigger Results

September 23, 2024in Data, Enterprise, Google, Mulesoft, Public Sector Solutions, Salesforce, Salesforce Einstein, Salesforce Experience Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Implementation Services, Salesforce Manufacturing Automotive Energy, Salesforce Marketing Cloud, Salesforce Success Stories, Technology

22Sep

Tableau Einstein is Here

Tableau Einstein marks a new chapter for Tableau, transforming the analytics experience by moving beyond traditional reports and dashboards to deliver insights directly within the flow of a user’s work. This new AI-powered analytics platform blends existing Tableau and Salesforce capabilities with innovative features designed to revolutionize how users engage with data. The platform is built around four key areas: autonomous insight delivery through AI, AI-assisted development of a semantic layer, real-time data access, and a marketplace for data and AI products, allowing customers to personalize their Tableau experience. Some features, like Tableau Pulse and Tableau Agent, which provide autonomous insights, are already available. Additional tools, such as Tableau Semantics and a marketplace for AI products, are expected to launch in 2025. Access to Tableau Einstein is provided through a Tableau+ subscription, though pricing details remain private. Since being acquired by Salesforce in 2019, Tableau has shifted its focus toward AI, following the trend of many analytics vendors. In February, Tableau introduced Tableau Pulse, a generative AI-powered tool that delivers insights in natural language. In July, it also rolled out Tableau Agent, an AI assistant to help users prepare and analyze data. With AI at its core, Tableau Einstein reflects deeper integration between Tableau and Salesforce. David Menninger, an analyst at Ventana Research, commented that these new capabilities represent a meaningful step toward true integration between the two platforms. Donald Farmer, founder of TreeHive Strategy, agrees, highlighting that while the robustness of Tableau Einstein’s AI capabilities compared to its competitors remains to be seen, the platform offers more than just incremental add-ons. “It’s an impressive release,” he remarked. A Paradigm Shift in Analytics A significant aspect of Tableau Einstein is its agentic nature, where AI-powered agents deliver insights autonomously, without user prompts. Traditionally, users queried data and analyzed reports to derive insights. Tableau Einstein changes this model by proactively providing insights within the workflow, eliminating the need for users to formulate specific queries. The concept of autonomous insights, represented by tools like Tableau Pulse and Agentforce for Tableau, allows businesses to build autonomous agents that deliver actionable data. This aligns with the broader trend in analytics, where the market is shifting toward agentic AI and away from dashboard reliance. Menninger noted, “The market is moving toward agentic AI and analytics, where agents, not dashboards, drive decisions. Agents can act on data rather than waiting for users to interpret it.” Farmer echoed this sentiment, stating that the integration of AI within Tableau is intuitive and seamless, offering a significantly improved analytics experience. He specifically pointed out Tableau Pulse’s elegant design and the integration of Agentforce AI, which feels deeply integrated rather than a superficial add-on. Core Features and Capabilities One of the most anticipated features of Tableau Einstein is Tableau Semantics, a semantic layer designed to enhance AI models by enabling organizations to define and structure their data consistently. Expected to be generally available by February 2025, Tableau Semantics will allow enterprises to manage metrics, data dimensions, and relationships across datasets with the help of AI. Pre-built metrics for Salesforce data will also be available, along with AI-driven tools to simplify semantic layer management. Tableau is not the first to offer a semantic layer—vendors like MicroStrategy and Looker have similar features—but the infusion of AI sets Tableau’s approach apart. According to Tableau’s chief product officer, Southard Jones, AI makes Tableau’s semantic layer more agile and user-friendly compared to older, labor-intensive systems. Real-time data integration is another key component of Tableau Einstein, made possible through Salesforce’s Data Cloud. This integration enables Tableau users to securely access and combine structured and unstructured data from hundreds of sources without manual intervention. Unstructured data, such as text and images, is critical for comprehensive AI training, and Data Cloud allows enterprises to use it alongside structured data efficiently. Additionally, Tableau Einstein will feature a marketplace launching in mid-2025, which will allow users to build a composable infrastructure. Through APIs, users will be able to personalize their Tableau environment, share AI assets, and collaborate across departments more effectively. Looking Forward As Tableau continues to build on its AI-driven platform, Menninger and Farmer agree that the vendor’s move toward agentic AI is a smart evolution. While Tableau’s current capabilities are competitive, Menninger noted that the platform doesn’t necessarily set Tableau apart from competitors like Qlik, MicroStrategy, or Microsoft Fabric. However, the tight integration with Salesforce and the focus on agentic AI may provide Tableau with a short-term advantage in the fast-changing analytics landscape. Farmer added that Tableau Einstein’s autonomous insight generation feels like a significant leap forward for the platform. “Tableau has done great work in creating an agentic experience that feels, for the first time, like the real deal,” he said. Looking ahead, Tableau’s roadmap includes a continued focus on agentic AI, with the goal of providing each user with their own personal analyst. “It’s not just about productivity,” said Jones. “It’s about changing the value of what can be delivered.” Menninger concluded that Tableau’s shift away from dashboards is a reflection of where business intelligence is headed. “Dashboards, like data warehouses, don’t solve problems on their own. What matters is what you do with the information,” he said. “Tableau’s push toward agentic analytics and collaborative decision-making is the right move for its users and the market as a whole.” 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

September 22, 2024in Data, Enterprise, Generative AI, Salesforce

21Sep

State Loan Processing Software by Salesforce

State Loan Processing Software: A Salesforce-Powered Solution Introduction In today’s fast-paced financial environment, efficient loan management is critical for lending institutions to succeed. Traditional loan processing methods are often inefficient, prone to errors, and unable to meet the demands of modern financial services. These outdated techniques lead to delays, compliance issues, and lost revenue. The answer lies in adopting advanced loan management software that leverages technology to streamline processes and enhance customer experiences. Current Challenges Many lenders continue to rely on outdated tools like spreadsheets and manual workflows, hindering productivity and increasing the potential for human error. A study by the National Association of Federal Credit Unions found that 60% of credit unions reported inefficiencies in their loan processes, negatively impacting member satisfaction. Key challenges faced by lending institutions include: Types of Loan Management Software To address these challenges, a variety of loan management software solutions have emerged, each designed to optimize specific aspects of the lending process. Loan Management Software Description: Automates essential loan processes like origination and payment processing. Main Features: Customer Relationship Management (CRM) Software Description: Platforms like Salesforce enable lenders to efficiently manage borrower relationships. Main Features: Compliance Management Software-State Loan Processing Software by Salesforce Description: Ensures lending practices adhere to state and federal regulations. Main Features: Analytics and Reporting Tools Description: Offers data-driven insights to guide strategic decision-making. Main Features: Integrated Payment Solutions Description: Streamlines payment processing across various channels. Main Features: Final Thoughts Adopting modern loan management software brings a host of advantages, including enhanced efficiency, improved compliance, and higher customer satisfaction. Platforms like Salesforce enable lenders to revolutionize their loan processing and management, making their operations more competitive in an evolving market. For lenders seeking to transform their approach to loan management, innovative solutions like Salesforce and Tectonic offer a path to operational excellence and business growth. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 21, 2024in Salesforce Financial Services Solutions, Salesforce Nonprofit Cloud, Salesforce Optimization

21Sep

Salesforce Certified AI Associate

The Salesforce Certified AI Associate certification is a professional credential that demonstrates your knowledge of artificial intelligence (AI) and its application within Salesforce platforms. This certification is perfect for individuals aiming to enhance their ability to use AI to drive business outcomes. Key topics covered in the certification include: Trailblazer Trailhead for Salesforce Certified AI Associate 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

September 21, 2024in AI Tools, Artificial Intelligence, Salesforce

21Sep

Training and Testing Data

Data plays a pivotal role in machine learning (ML) and artificial intelligence (AI). Tasks such as recognition, decision-making, and prediction rely on knowledge acquired through training. Much like a parent teaches their child to distinguish between a cat and a bird, or an executive learns to identify business risks hidden within detailed quarterly reports, ML models require structured training using high-quality, relevant data. As AI continues to reshape the modern business landscape, the significance of training data becomes increasingly crucial. What is Training Data? The two primary strengths of ML and AI lie in their ability to identify patterns in data and make informed decisions based on that data. To execute these tasks effectively, models need a reference framework. Training data provides this framework by establishing a baseline against which models can assess new data. For instance, consider the example of image recognition for distinguishing cats from birds. ML models cannot inherently differentiate between objects; they must be taught to do so. In this scenario, training data would consist of thousands of labeled images of cats and birds, highlighting relevant features—such as a cat’s fur, pointed ears, and four legs versus a bird’s feathers, absence of ears, and two feet. Training data is generally extensive and diverse. For the image recognition case, the dataset might include numerous examples of various cats and birds in different poses, lighting conditions, and settings. The data must be consistent enough to capture common traits while being varied enough to represent natural differences, such as cats of different fur colors in various postures like crouching, sitting, standing, and jumping. In business analytics, an ML model first needs to learn the operational patterns of a business by analyzing historical financial and operational data before it can identify problems or recognize opportunities. Once trained, the model can detect unusual patterns, like abnormally low sales for a specific item, or suggest new opportunities, such as a more cost-effective shipping option. After ML models are trained, tested, and validated, they can be applied to real-world data. For the cat versus bird example, a trained model could be integrated into an AI platform that uses real-time camera feeds to identify animals as they appear. How is Training Data Selected? The adage “garbage in, garbage out” resonates particularly well in the context of ML training data; the performance of ML models is directly tied to the quality of their training data. This underscores the importance of data sources, relevance, diversity, and quality for ML and AI developers. Data SourcesTraining data is seldom available off-the-shelf, although this is evolving. Sourcing raw data can be a complex task—imagine locating and obtaining thousands of images of cats and birds for the relatively straightforward model described earlier. Moreover, raw data alone is insufficient for supervised learning; it must be meticulously labeled to emphasize key features that the ML model should focus on. Proper labeling is crucial, as messy or inaccurately labeled data can provide little to no training value. In-house teams can collect and annotate data, but this process can be costly and time-consuming. Alternatively, businesses might acquire data from government databases, open datasets, or crowdsourced efforts, though these sources also necessitate careful attention to data quality criteria. In essence, training data must deliver a complete, diverse, and accurate representation for the intended use case. Data RelevanceTraining data should be timely, meaningful, and pertinent to the subject at hand. For example, a dataset containing thousands of animal images without any cat pictures would be useless for training an ML model to recognize cats. Furthermore, training data must relate directly to the model‘s intended application. For instance, business financial and operational data might be historically accurate and complete, but if it reflects outdated workflows and policies, any ML decisions based on it today would be irrelevant. Data Diversity and BiasA sufficiently diverse training dataset is essential for constructing an effective ML model. If a model’s goal is to identify cats in various poses, its training data should encompass images of cats in multiple positions. Conversely, if the dataset solely contains images of black cats, the model’s ability to identify white, calico, or gray cats may be severely limited. This issue, known as bias, can lead to incomplete or inaccurate predictions and diminish model performance. Data QualityTraining data must be of high quality. Problems such as inaccuracies, missing data, or poor resolution can significantly undermine a model’s effectiveness. For instance, a business’s training data may contain customer names, addresses, and other information. However, if any of these details are incorrect or missing, the ML model is unlikely to produce the expected results. Similarly, low-quality images of cats and birds that are distant, blurry, or poorly lit detract from their usefulness as training data. How is Training Data Utilized in AI and Machine Learning? Training data is input into an ML model, where algorithms analyze it to detect patterns. This process enables the ML model to make more accurate predictions or classifications on future, similar data. There are three primary training techniques: Where Does Reinforcement Learning Fit In? Unlike supervised and unsupervised learning, which rely on predefined training datasets, reinforcement learning adopts a trial-and-error approach, where an agent interacts with its environment. Feedback in the form of rewards or penalties guides the agent’s strategy improvement over time. Whereas supervised learning depends on labeled data and unsupervised learning identifies patterns in raw data, reinforcement learning emphasizes dynamic decision-making, prioritizing ongoing experience over static training data. This approach is particularly effective in fields like robotics, gaming, and other real-time applications. The Role of Humans in Supervised Training The supervised training process typically begins with raw data since comprehensive and appropriately pre-labeled datasets are rare. This data can be sourced from various locations or even generated in-house. Training Data vs. Testing Data Post-training, ML models undergo validation through testing, akin to how teachers assess students after lessons. Test data ensures that the model has been adequately trained and can deliver results within acceptable accuracy and performance ranges. In supervised learning,

September 21, 2024in Artificial Intelligence, Data

21Sep

Embedded Salesforce Einstein

In a world where data is everything, businesses are constantly seeking ways to better understand their customers, streamline operations, and make smarter decisions. Enter Salesforce Einstein—a powerful AI solution embedded within the Salesforce platform that is revolutionizing how companies operate, regardless of size. By leveraging advanced analytics, automation, and machine learning, Einstein helps businesses boost efficiency, drive innovation, and deliver exceptional customer experiences. Embedded Salesforce Einstein is the answer. Here’s how Salesforce Einstein is transforming business: Imagine anticipating customer needs, market trends, or operational challenges before they happen. While it’s not magic, Salesforce Einstein’s AI-powered insights and predictions come remarkably close. By transforming vast amounts of data into actionable insights, Einstein enables businesses to anticipate future scenarios and make well-informed decisions. Industry insight: In financial services, success hinges on anticipating market shifts and client needs. Banks and investment firms leverage Einstein to analyze historical market data and client behavior, predicting which financial products will resonate next. For example, investment advisors might receive AI-driven recommendations tailored to individual clients, boosting engagement and satisfaction. Manufacturers also benefit from Einstein’s predictive maintenance tools, which analyze data from machinery to anticipate equipment failures. A car manufacturer, for instance, could use these insights to schedule maintenance during off-peak hours, minimizing downtime and preventing costly disruptions. Personalization is now a necessity. Salesforce Einstein elevates personalization by analyzing customer data to offer tailored recommendations, messages, and services. Industry insight: In e-commerce, personalized recommendations are often the key to converting browsers into loyal customers. An online bookstore using Einstein might analyze browsing history and past purchases to suggest new releases in genres the customer loves, driving repeat sales. In healthcare, Einstein’s personalization can improve patient outcomes by providing customized follow-up care. Hospitals can use Einstein to analyze patient histories and treatment data, offering reminders tailored to each patient’s needs, improving adherence to care plans and speeding recovery. Salesforce Einstein’s sales intelligence tools, such as Lead Scoring and Opportunity Insights, enable sales teams to focus on the most promising leads. This targeted approach drives higher conversion rates and more efficient sales processes. Industry insight: In real estate, Einstein helps agents manage numerous leads by scoring potential buyers based on their engagement with property listings. A buyer who repeatedly views homes in a specific area is flagged, prompting agents to prioritize their outreach, accelerating the sales process. In the automotive industry, Einstein identifies leads closer to purchasing by analyzing behaviors such as online vehicle configuration and test drive bookings. This allows sales teams to focus on high-potential buyers, closing deals faster. Automation is at the heart of Salesforce Einstein’s ability to streamline processes and boost productivity. By automating repetitive tasks like data entry and customer inquiries, Einstein frees employees to focus on strategic activities, improving overall efficiency. Industry insight: In insurance, Einstein Bots can handle routine tasks like policy inquiries and claim submissions, freeing up human agents for more complex issues. This leads to faster response times and reduced operational costs. In banking, Einstein-powered chatbots manage routine inquiries such as balance checks or transaction histories. By automating these interactions, banks reduce the workload on call centers, allowing agents to provide more personalized financial advice. Einstein Discovery democratizes data analytics, making it easier for non-technical users to explore data and uncover actionable insights. This tool identifies key business drivers and provides recommendations, making data accessible for all. Industry insight: In healthcare, predictive insights are helping providers identify patients at risk of chronic conditions like diabetes. With Einstein Discovery, healthcare providers can flag at-risk individuals early, implementing targeted care plans that improve outcomes and reduce long-term costs. For energy companies, Einstein Discovery analyzes data from sensors and weather patterns to predict equipment failures and optimize resource management. A utility company might use these insights to schedule preventive maintenance ahead of storms, reducing outages and enhancing service reliability. More Than a Tool – Embedded Salesforce Einstein Salesforce Einstein is more than just an AI tool—it’s a transformative force enabling businesses to unlock the full potential of their data. From predicting trends and personalizing customer experiences to automating tasks and democratizing insights, Einstein equips companies to make smarter decisions and enhance performance across industries. Whether in retail, healthcare, or technology, Einstein delivers the tools needed to thrive in today’s competitive landscape. Tectonic empowers organizations with Salesforce solutions that drive organizational excellence. Contact Tectonic today. 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

September 21, 2024in Salesforce Einstein

20Sep

Salesforce Underwriting Solutions

Merchant Cash Advance Solutions: Enhancing Underwriting with Salesforce In today’s fast-paced financial services industry, efficient and effective underwriting is more crucial than ever. Merchant cash advances (MCAs) have emerged as a popular alternative funding option for businesses that might not qualify for traditional loans. This insight explores how integrating Salesforce with MCA software can streamline underwriting, strengthen lender-borrower relationships, and boost overall operational efficiency. Understanding Merchant Cash Advances Merchant cash advances offer businesses upfront capital in exchange for a portion of future sales. Unlike traditional loans, MCAs are often easier to secure and come with flexible repayment options tied to daily credit card receipts. However, the unique structure of MCAs brings challenges to underwriting, due to the diversity in business models and cash flow patterns. The Role of Underwriting in MCA Underwriting is a vital step in the lending process, assessing the risk associated with providing funds to a borrower. For MCAs, underwriting involves evaluating a business’s revenue streams, creditworthiness, and overall financial health. Traditional underwriting methods can be cumbersome and slow, often causing delays in funding. Challenges in Traditional Underwriting Methods The Power of Salesforce in Streamlining Underwriting Salesforce offers powerful solutions that integrate seamlessly with MCA software, effectively addressing these challenges: Benefits of Integrating MCA Software with Salesforce Key Features to Look for in MCA Software Integrated with Salesforce When choosing an MCA solution integrated with Salesforce, consider features such as: Conclusion Integrating merchant cash advance solutions with Salesforce offers a transformative approach to streamlining underwriting processes in this niche financing sector. By automating workflows, centralizing data management, enhancing communication channels, and improving overall efficiency—all while ensuring compliance—lenders can gain a competitive edge and deliver exceptional service to their clients. If you are searching for a Merchant Cash Advance, Underwriting, or financial services solution contact Tectonic today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 20, 2024in Salesforce Development Services, Salesforce Financial Services Solutions

20Sep

Agentforce Advances Copilot and Prompt Builder

Agentforce was the highlight of the week in San Francisco during Salesforce’s annual Dreamforce conference—and for good reason! Agentforce Advances Copilot and Prompt Builder and that is truly exciting. Agentforce represents a groundbreaking solution that promises to transform how individuals and organizations interact with their CRM. However, as with any major product announcement, it raises many questions. This was evident during Dreamforce, where admins and developers, eager to dive into Agentforce, had numerous queries. Here’s an in-depth look at what Agentforce is, how it operates, and how organizations can leverage it to automate processes and drive value today. Agentforce Advances Copilot and Prompt Builder Many Dreamforce attendees who anticipated hearing more about Einstein Copilot were surprised by the introduction of Agents just before the event. However, understanding the distinctions between the legacy Einstein Copilot and the new Agentforce is crucial. Agentforce Advances Copilot and Prompt Builder. Agentforce Agents are essentially a rebranding of Copilot Agents but with an essential enhancement: they expand the functionality of Copilot to create autonomous agents capable of tasks such as summarizing or generating content and taking specific actions. Here are some key changes in terminology: Just like Einstein Copilot, Agents use user input—an “utterance”—entered into the Agentforce chat interface. The agent translates this utterance into a series of actions based on configurable instructions, and then executes the plan, providing a response. Understanding Agents: Topics A key difference between Einstein Copilot and Agentforce is the addition of “Topics.” Topics allow for greater flexibility and support a broader range of actions. They organize tasks by business function, helping Agents first determine the appropriate topic and then identify the necessary actions. This topic layer reduces confusion and ensures the correct action is taken. With this structure, Agentforce can support many more custom actions compared to Copilot’s 15-20, significantly expanding capabilities. Understanding Agents: Actions Actions in Agentforce function similarly to those in Einstein Copilot. These are the tasks an agent executes once it has identified the right plan. Out-of-the-box actions are available right away, providing a quick win for organizations looking to implement standard actions like opportunity summarization or sales emails. For more customized use cases, organizations can create bespoke actions using Apex, Flows, Prompts, or Service Catalog items (currently in beta). Understanding Agents: Prompts Whenever an LLM is used, prompts are necessary to provide the right input. Thoughtfully engineered prompts are essential for getting accurate, useful responses from LLMs. This is a key part of leveraging Agent Actions effectively, ensuring better results, reducing errors, and driving productive agent behavior. Prompt Builder plays a crucial role, allowing users to build, test, and refine prompts for Agent Actions, creating a seamless experience between generative AI and Salesforce workflows. How Generative AI and Agentforce Enhance CRM GenAI tools like Agentforce offer exciting enhancements to Salesforce organizations in several ways: However, these benefits are realized only when CRM users adopt and adapt to AI-assisted workflows. Organizations must prioritize change management and training, as most users will need to adjust to this new AI-powered way of working. If your company has already embraced AI, then you are halfway there. If AI hasn’t been introduced to the workforce you need to get started yesterday. Getting Started with Agentforce With all the buzz around Dreamforce, it’s no surprise that many organizations are eager to start using Agentforce. Fortunately, there are immediate opportunities to leverage these tools. The recommended approach is to begin with standard Agent actions, testing out-of-the-box features like opportunity summarization or creating close plans. From there, organizations can make incremental tweaks to customize actions for their specific needs. We have all come to expect that just as quickly as we include agentic ai into our processes and flows, Salesforce will add additional features and capabilities. As teams become more familiar with developing and deploying Agent actions, more complex use cases will become manageable, transforming the traditional point-and-click Salesforce experience into a more intelligent, agent-driven platform. Already I find myself asking, “is this an agent person or an ai-agent”? The day is coming, no doubt, when the question will be reversed. Tectonic’s AI Experts Can Help Interested in learning more about Agentforce or need guidance on getting started? Tectonic specializes in AI and analytics solutions within CRM, helping organizations unlock significant productivity gains through AI-based tools that optimize business processes. We are excited to enable you to enable Agentforce to Advance Copilot and Prompt Builder By Tectonic’s Solutions Architect, Shannan Hearne 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

September 20, 2024in AI Tools, Einstein 1 Platform, Einstein Insights, Generative AI, Salesforce Einstein, Salesforce Implementation Services, Salesforce Marketing Automation, Salesforce Optimization

20Sep

Collaborative Business Intelligence

Collaborative Business Intelligence: Connecting Data and Teams In today’s data-driven world, the ability to interact with business intelligence (BI) tools is essential for making informed decisions. Collaborative business intelligence (BI), also known as social BI, allows users to engage with their organization’s data and communicate with data experts through the same platforms where they already collaborate. While self-service BI empowers users to generate insights, understanding the data’s context is critical to avoid misunderstandings that can derail decision-making. Collaborative BI integrates BI tools with collaboration platforms to bridge the gap between data analysis and communication, reducing the risks of misinterpretation. Traditional Business Intelligence Traditional BI involves the use of technology to analyze data and present insights clearly. Before BI platforms became widespread, data scientists and statisticians handled data analysis, making it challenging for non-technical professionals to digest the insights. BI evolved to automate visualizations, such as charts and dashboards, making data more accessible to business users. Previously, BI reports were typically available only to high-level executives. However, modern self-service BI tools democratize access, enabling more users—regardless of technical expertise—to create reports and visualize data, fostering better decision-making across the organization. The Emergence of Collaborative BI Collaborative BI is a growing trend, combining BI applications with collaboration tools. This approach allows users to work together synchronously or asynchronously within a shared platform, making it easier to discuss data reports in real time or leave comments for others to review. Whether it’s through Slack, Microsoft Teams, or social media apps, users can receive and discuss BI insights within their usual communication channels. This seamless integration of BI and collaboration tools offers a competitive edge, simplifying the process of sharing knowledge and clarifying data without switching between applications. Key Benefits of Collaborative Business Intelligence Leading Collaborative BI Platforms Here’s a look at some of the top collaborative BI platforms driving innovation in the market: Conclusion Collaborative BI empowers organizations by improving decision-making, democratizing data access, optimizing data quality, and ensuring data security. By integrating BI tools with collaboration platforms, businesses can streamline their operations, foster a culture of data-driven decision-making, and enhance overall efficiency. Choosing the right platform is key to maximizing the benefits of collaborative BI. 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

September 20, 2024in Data, Google, Salesforce, Technology

19Sep

Salesforce AI Agents and New Data Cloud Features

Salesforce has announced several major updates to its Data Cloud at the annual Dreamforce conference, aimed at enhancing data analysis and boosting the capabilities of its new AI agents, Agentforce. These updates include support for unstructured data in audio and video formats, 50 new data connectors, and a low-latency pipeline to ensure faster AI responses. Salesforce describes Data Cloud as a customer data platform designed to unify data from various sources, providing actionable insights for sales, service, and marketing teams. The latest features will further improve this functionality, according to Rahul Auradkar, Salesforce’s General Manager of Unified Data Services and Einstein. Key Updates 1. Unstructured Data Support: Audio and Video Formats Salesforce has introduced the ability to process unstructured audio and video data. This allows enterprises to analyze information from sources like customer calls, training sessions, product demos, and webinars. The unstructured data can enrich customer profiles, provide deeper insights into behavior, and enhance the performance of AI agents within Agentforce by improving response accuracy. IDC’s Hayley Sutherland highlighted that vectorization of this content enables retrieval-augmented generation (RAG), enhancing the use of large language models (LLMs) to generate more effective and context-aware responses. 2. 50 New Data Connectors Salesforce has expanded its connector library with 50 new options, raising the total to 200. This helps businesses integrate diverse data sources, facilitating improved personalization and decision-making. These connectors are also critical for feeding data to AI agents, enabling them to generate more accurate insights. 3. Sub-second Data Layer for Real-time Responses A new sub-second data layer has been rolled out, allowing enterprises to ingest, unify, analyze, and act on data in real time. This layer powers Einstein Personalization, enabling instant AI-driven recommendations and analytics. It’s especially important for the low-latency needs of Agentforce’s autonomous AI agents, ensuring faster, more accurate customer interactions. 4. Governance and Security Enhancements Salesforce has introduced AI tagging and classification features for unstructured data, automating labeling and organization to ensure secure access. Policy-based governance will offer granular control over data access based on tags, metadata, and user attributes. Additionally, customer-managed encryption keys ensure data security, while the newly available private connect feature allows secure data sharing between Data Cloud and public cloud environments. 5. Data Cloud One and Hybrid Search Data Cloud One, a new feature, will allow enterprises to connect multiple Salesforce accounts across different departments or regions into a single instance, simplifying data management. The hybrid search feature, which combines vector and keyword search, is designed to improve the speed and accuracy of finding industry-specific information. Other updates include Tableau Semantics, which helps organize data based on its meaning, and a new data community platform to foster collaboration among data professionals. These updates reflect Salesforce’s commitment to enhancing data-driven decision-making and delivering powerful AI capabilities for enterprises. 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

September 19, 2024in Data, Enterprise

19Sep

Vonage Premier for Salesforce Service Cloud Voice

HOLMDEL, N.J., Sept. 18, 2024 /PRNewswire/ — Vonage, a global leader in cloud communications helping businesses accelerate their digital transformation and a part of Ericsson (NASDAQ: ERIC), is one of the first contact center providers to join Salesforce’s Bring Your Own Channel for Contact Center as a Service (BYOC for CCaaS) pilot program. With BYOC for CCaaS, Vonage Premier for Salesforce Service Cloud Voice customers will have the ability to integrate Vonage omnichannel and AI-powered capabilities into their existing contact center solutions, including voice, SMS, chat, social messaging apps like WhatsApp, and more – delivering faster resolution times and creating a more native, personalized and meaningful experience for customers by connecting with them on their channel of choice. “We are very excited to have Vonage, a leading Salesforce Service Cloud Voice partner, take this very important step to expand its deep Salesforce integration through BYOC for CCaaS, delivering the omnichannel capabilities – and the APIs to enable them – that create the kind of customer experiences that drive meaningful engagement,” said Tony Flores, Senior Director of Product Management for Salesforce. With BYOC for CCaaS, Vonage Premier for Service Cloud Voice customers will now be able to connect with customers across various communications channels, as well as access data insights and AI-based agent productivity tools, to create a better overall customer journey and a more productive and efficient agent experience. The solution’s single routing and agent capacity model also increases contact center capacity, leading to more customer interactions being resolved better and faster. Workforce Engagement Management (WEM) is also provided through Vonage’s seamless integrations with leading WEM solutions Verint, Calabrio Teleopti, Playvox and injixo, ensuring optimum planning, scheduling, tracking, and management of the contact center workforce. “Today’s contact center agents play a vital role in support of the businesses they represent and in meeting the increasing demands of tech-savvy customers who want to connect from anywhere, on their preferred communications channels,” said Reggie Scales, Acting Head of Applications for Vonage. “These agents are also frequently working from anywhere and need the tools to access critical information to troubleshoot common customer issues and provide real-time customer support. Having all of these capabilities in a single user interface – omnichannel modes of communication coupled with a 360 view of customer information and key knowledge bases – this is the contact center of the future.” A key differentiator for Vonage as a pilot partner in this program is its ability to source a single AI-based Virtual Agent solution for self-service automations across voice and digital channels using Vonage AI studio – while also leveraging Salesforce for all Live Agent Assist and Analytics needs. Vonage can also integrate its own Vonage Communications APIs to power pre-built programmable capabilities for voice, SMS, social and chat, directly into the contact center – all on one combined Salesforce and Vonage platform. This singular view also enhances efficiency by keeping agents and supervisors in a single Salesforce desktop to eliminate application switching and the need to toggle between screens. “Modern contact centers are experiencing increasing pressure and demand to deliver better, more personalized, omnichannel interactions, as well as quicker and more accurate responses to customer issues,” said Jim Lundy, CEO, Founder & Lead Analyst, Aragon Research. “With BYOC for CCaaS, Vonage aims to address the increasing demand for a unified and customizable customer experience across all communication channels, leveraging existing Salesforce platforms and AI-powered insights and automation.” Vonage Premier for Service Cloud Voice is currently available on the Salesforce AppExchange with Salesforce BYOC for CCaaS integrated capabilities now available for customers to pilot. To find out more about Vonage Premier for Service Cloud Voice, visit www.vonage.com. Salesforce, AppExchange, Service Cloud Voice, Einstein and others are among the trademarks of Salesforce, inc. About Vonage Vonage, a global cloud communications leader, helps businesses accelerate their digital transformation. Vonage’s Communications Platform is fully programmable and allows for the integration of Video, Voice, Chat, Messaging, AI and Verification into existing products, workflows and systems. The Vonage conversational commerce application enables businesses to create AI-powered omnichannel experiences that boost sales and increase customer satisfaction. Vonage’s fully programmable unified communications, contact center and conversational commerce applications are built from the Vonage platform and enable companies to transform how they communicate and operate from the office or remotely – providing the flexibility required to create meaningful engagements. Vonage is headquartered in New Jersey, with offices throughout the United States, Europe, Israel and Asia and is a wholly-owned subsidiary of Ericsson (NASDAQ: ERIC), and a business area within the Ericsson Group called Business Area Global Communications Platform. To follow Vonage on X (formerly known as Twitter), please visit twitter.com/vonage. To follow on LinkedIn, visit linkedin.com/company/Vonage/. To become a fan on Facebook, go to facebook.com/vonage. To subscribe on YouTube, visit youtube.com/vonage. SOURCE Vonage 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

September 19, 2024in Data, Salesforce Service Cloud

18Sep

CXM

XM Software Providers Set to Replace Point Solutions with Multifunction Suites By 2028, Enterprises Will Transition to Comprehensive CXM Solutions, According to New ISG Research According to new research from Information Services Group (ISG) (Nasdaq: III), companies are expected to shift from various customer experience (CX) point solutions to comprehensive, cross-functional suites by 2028. This transition aims to manage CX at the enterprise level more effectively. Keith Dawson, Director of Customer Experience Research at ISG, explains, “Enterprises recognize the need for platforms that manage the entire customer lifecycle. We are witnessing the rise of tools that integrate communication components with analytic assessments of customer value, loyalty, and intent, marking a significant shift in the marketplace.” The ISG Buyers Guide™ for Customer Experience Management (CXM) defines CXM as a suite of applications on a unified platform that provides a comprehensive view of customer activity and enables management of that activity across departments. The report notes that the mix of applications in a software provider’s suite often reflects their historical expertise and origins. CXM addresses the limitations of traditional Customer Resource Management (CRM) software, which has been more departmental and application-centric. In contrast, CXM focuses on the customer journey and interactions across all channels. The report highlights challenges in comparing similar offerings from different providers due to their varied origins and expertise. The range of functionality across CXM products often reflects their diverse components, users, and use cases. ISG identifies five core areas of platform functionality in CXM software: knowledge management, resource management, automation, analytics, and customer journey management. However, CXM software is still developing, and it is uncommon to find a single solution that excels in all five areas. Many providers start with their core strengths and expand their capabilities over time. The ISG Buyers Guide evaluates CXM software providers based on support for analytics, customer journey management, knowledge management, CRM platform support, operational resource management, and process control and optimization. To be included in the CXM Buyers Guide, products must cover at least three of the four areas: resource management, automation, analytics, and customer journey management. Separate guides on Customer Journey Management (CJM) and Knowledge Management (KM) are available for more specific analysis. For its 2024 Buyers Guides, ISG assessed 19 providers, including Adobe, eGain, Emplifi, Freshworks, Genesys, HubSpot, Microsoft, Nextiva, NICE, Oracle, Qualtrics, Salesforce, SAP, ServiceNow, Sprinklr, SugarCRM, Verint, Zendesk, and Zoho. The top three software providers in each category are: Mark Smith, Partner at ISG Software Research, notes, “Managing customer experience is crucial for every organization, yet many lack the technology to orchestrate the customer journey across channels. The Buyers Guide for CXM offers insights to help businesses understand, optimize, and select software providers that move beyond the limitations of traditional CRM systems.” The ISG Buyers Guides for CXM, CJM, and KM are based on over a year of market research. The research is independent and not influenced by software providers, aiming to help enterprises optimize their software investments. For more details, visit the ISG Buyers Guides to read executive summaries and request full reports. About ISG Software Research ISG Software Research, formerly Ventana Research, delivers expert market research and analysis on business and IT software. The firm provides consulting, advisory, research, and education services for enterprises, software and service providers, and investment firms. For more information and to join the community, visit Ventana Research. About ISG ISG (Information Services Group) (Nasdaq: III) is a global technology research and advisory firm specializing in digital transformation services. With a client base of over 900 organizations, ISG helps clients achieve operational excellence and growth. The firm’s expertise spans AI and automation, cloud and data analytics, sourcing advisory, and more. Founded in 2006 and based in Stamford, Conn., ISG employs 1,600 professionals in over 20 countries. For more information, visit ISG. 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

September 18, 2024in Salesforce

18Sep

Pendo Salesforce and Productivity

Increasing productivity is an ongoing challenge for businesses. A recent McKinsey study reveals that over half of employees feel they are relatively unproductive at work. This widespread issue underscores a deeper problem. Historically, productivity was seen as a measure of individual employee performance—high performers versus low performers. But with the growing reliance on software for daily tasks, leading companies now view productivity differently. While employees remain essential, productivity increasingly hinges on process efficiency. To boost employee productivity, businesses need to focus on optimizing workflows and processes, ensuring they are well-designed, intuitive, and easy to execute. The key to achieving this? An intelligent digital adoption solution. Why optimizing processes boosts productivity Effective workflows are critical for achieving consistent service delivery, maintaining data integrity, enhancing efficiency, and supporting scalability. By streamlining processes, businesses can unlock productivity gains across the board. Pendo’s intelligent digital adoption platform integrates analytics, data, automation, and AI to drive optimized work outcomes, surpassing the capabilities of traditional digital adoption platforms (DAPs). Enhancing workflows with AI-powered analytics Pendo’s platform is designed to help employees work smarter by leveraging comprehensive analytics. For instance, revenue operations teams using Pendo can gain insights into how tasks are performed in Salesforce, identify common bottlenecks, and discover alternative approaches employees might take. The platform’s AI capabilities further enhance this process. Pendo’s machine learning model analyzes both quantitative and qualitative data from user feedback and analytics to offer AI-driven recommendations for workflow improvements. As the AI learns from user interactions, its suggestions become increasingly effective. Providing targeted support at the right time Understanding workflow challenges is only the first step. Employees may face different issues based on their roles or experience. For example, a customer success representative may require different support in Salesforce compared to a regional sales rep. Pendo’s platform can cater to these diverse needs by creating in-app guides and tooltips that offer tailored support based on user metadata and behavior. This allows high performers and superusers to work independently while providing targeted help where needed. Teams can monitor progress through Pendo Analytics, tracking metrics like workflow completion rates and time to completion. This data helps refine in-app guidance and enhance effective strategies. Empowering employees for greater achievement Industries such as media, retail, non-profit, and financial services often struggle with visibility into their sales processes and potential breakdowns. Pendo’s platform enables IT and product teams to identify operational gaps in software usage, leading to more satisfied, productive employees and improved business outcomes. As digital adoption advances, Pendo remains at the forefront of driving a smarter, more efficient future. Explore how Pendo can enhance workflow completion and efficiency in Salesforce. 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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