Analytics Archives - gettectonic.com - Page 17
Self Service Customer Service
15Apr

Self Service Customer Service

The importance of effective customer service, particularly through self-service options, cannot be overstated. Both customers and organizations often prefer self-service solutions: customers to avoid waiting on hold and speaking with potentially uninformed agents, and organizations to reduce the load and cost associated with live agent interactions. Despite the clear benefits, the customer experience with self-service often falls short because it tends to prioritize business efficiencies over customer needs. For self-service to truly succeed, it must be mutually beneficial for both businesses and customers. According to Salesforce’s “State of the Connected Customer” study, 61% of customers prefer using self-service over live-agent phone calls for resolving simple issues. This trend is reflected in the growing use of self-service portals and chatbots, with 72% of customers utilizing self-service portals and 55% engaging with self-service chatbots. However, a significant barrier remains: 68% of customers would avoid using a company’s chatbot after a negative experience. The challenge lies in moving from a business-centric approach to a customer-centric one when deploying self-service technologies. Often, businesses implement these solutions primarily to cut costs, which can result in poorly designed interfaces that fail to meet customer expectations. This disconnect can harm customer satisfaction and loyalty in the long run. The integration of AI offers a promising solution. Unlike earlier iterations, today’s AI-driven chatbots can deliver personalized, context-aware interactions based on customer data and history. This capability ensures that customers receive timely, relevant assistance across multiple channels, enhancing the overall self-service experience. When deploying self-service capabilities, organizations should adopt a customer-first mindset: Successful self-service implementation hinges on these considerations, aiming not only to deflect calls but also to elevate customer satisfaction through intuitive, responsive self-service experiences. For further insights on optimizing self-service strategies, join our upcoming webinar discussing holistic CX strategies on July 10. We look forward to exploring how to empower customers to self-serve effectively, ensuring mutual benefits for organizations and their clientele. Customers Expect a Lot from Self-Service, and Too Few Get What They Want or Need Customers expect a lot from self-service channels — more than them just being available 24/365. They want answers to myriad questions or issues, and information about products and services. But the average self-service success rate today is just 14%. Improving this rate is a significant or moderate priority for 90% of customer service and support leaders Gartner recently surveyed. Customer support teams must provide always-on problem-solving across all of the self-service channels they offer — from site search to AI chatbots, to the portal to IVR and messaging apps. To think about the entirety of the modern service delivery model — even as customer demands evolve — focus on a few key areas: Gartner recommends that to meet the support organization’s goals and objectives, the self-service experience should include 11 foundational capabilities. Each improves some aspects of CX and elements of the search-to-resolution process. Together they drive significantly more business value, create effortless customer experiences, and improve overall self-service adoption and success. Here are the 11 capabilities: Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
April 15, 2024in Salesforce
Gen AI Depends on Good Data
13Apr

Gen AI Depends on Good Data

Accelerate Your Generative AI Journey: A Call to Action for Data Leaders Generative AI is generating immense excitement across organizations, with boards of directors conducting educational workshops and senior management teams brainstorming potential use cases. They need to keep in mind, Gen AI Depends on Good Data. Individuals and departments are already experimenting with the technology to enhance productivity and effectiveness. There needs to be as much effort into data quality as to the technology. The critical work required for generative AI success falls to chief data officers (CDOs), data engineers, and knowledge curators. Unfortunately, many have yet to begin the necessary preparations. A survey in late 2023 of 334 CDOs and data leaders, sponsored by Amazon Web Services and the MIT Chief Data Officer/Information Quality Symposium, coupled with interviews, reveals a gap between enthusiasm and readiness. While there’s a shared excitement about generative AI, much work remains to get organizations ready for it. The Current State of Data Preparedness Most companies have yet to develop new data strategies or manage their data to effectively leverage generative AI. This insight outlines the survey results and suggests next steps for data readiness. Maximizing Value with Generative AI Historically, AI has worked with structured data like numbers in rows and columns. Generative AI, however, utilizes unstructured data—text, images, and video—to generate new content. This technology offers both assistance and competition for human content creators. Survey findings show that 80% of data leaders believe generative AI will transform their business environment, and 62% plan to increase spending on it. Yet, many are not yet realizing substantial economic value from generative AI. Only 6% of respondents have a generative AI application in production deployment. A significant 16% have banned employee use, though this is decreasing as companies address data privacy issues with enterprise versions of generative AI models. Focus on Core Business Areas Experiments with generative AI should target core business areas. Universal Music, for instance, is aggressively experimenting with generative AI for R&D, exploring how it can create music, write lyrics, and imitate artists’ voices while protecting intellectual property rights. Gen AI Depends on Good Data For generative AI to be truly valuable, organizations need to customize vendors’ models with their own data and prepare their data for integration. Generative AI relies on well-curated data to ensure accuracy, recency, uniqueness, and other quality attributes. Poor-quality data yields poor-quality AI responses. Data leaders in our survey cited data quality as the greatest challenge to realizing generative AI’s potential, with 46% highlighting this issue. Jeff McMillan, Chief Data, Analytics, and Innovation Officer at Morgan Stanley Wealth Management, emphasizes the importance of high-quality training content and the need to address disparate data sources for successful generative AI implementation. Current Efforts and Challenges Most data leaders have not yet made significant changes to their data strategies. While 93% agree that a data strategy is critical for generative AI, 57% have made no changes, and only 11% strongly agree their organizations have the right data foundation. Organizations making progress are focusing on specific tasks like data integration, cleaning datasets, surveying data, and curating documents for domain-specific AI models. Walid Mehanna, Group Chief Data and AI Officer at Merck Group, and Raj Nimmagadda, Chief Data Officer for R&D at Sanofi, stress the importance of robust data foundations, governance, and standards for generative AI success. Focus on High-Value Data Domains Given the monumental effort required to curate, clean, and integrate all unstructured data for generative AI, organizations should focus on specific data domains where they plan to implement the technology. The most common business areas prioritizing generative AI development include customer operations, software engineering, marketing and sales, and R&D. The Time to Start is Now While other important data projects exist, including improving transaction data and supporting traditional analytics, the preparation for generative AI should not be delayed. Despite some slow pivoting from structured to unstructured data management, and competition among CDOs, CIOs, CTOs, and chief digital officers for leadership in generative AI, the consensus is clear: generative AI is a transformative capability. Preparing a large organization’s data for AI could take several years, and the time to start is now. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
April 13, 2024in Generative AI
Salesforce Einstein Features
13Apr

Salesforce Einstein Features

Salesforce Einstein Discover the power of the #1 AI for CRM with Einstein. Built into the Salesforce Platform, Einstein uses powerful machine learning and large language models to personalize customer interactions and make employees more productive. With Einstein powering the Customer 360, teams can accelerate time to value, predict outcomes, and automatically generate content within the flow of work. Einstein is for everyone, empowering business users, Salesforce Admins and Developers to embed AI into every experience with low code. Salesforce Einstein Features. Einstein Copilot Sales Actions: Sell faster with an AI assistant in the flow of work.Call Exploration: Ask Einstein to synthesize important call information in seconds. Ask Einstein to identify important takeaways and customer sentiment, so you have the context you need to move deals forward.

 Sales Summaries: Summarize records to identify likelihood the deal will close, the competitors involved, key activities, and more. Forecast Guidance: Ask Einstein to inform your forecast and help you identify which deals need your attention. Close Plan: Generate a customized action plan personalized to your customer and sales process. Increase conversion rates with step-by-step guidance and milestones grounded in CRM data. Salesforce Einstein Features Sales Generative AI features: ° Knowledge Creation: ° Search Answers for Agents and Customers: Einstein Copilot Service Actions: Streamline service operations by drafting Knowledge articles and surfacing answers, grounded in knowledge, to the most commonly asked questions. Summarize support interactions to save agent time and formalize institutional knowledge. Surface generated answers to agents’ & customers’ questions that are grounded in your trusted Knowledge base directly into your search page. Search Answers for Agents is included in the Einstein for Service Add-on SKU and Search Answers for Customers is included in the Einstein 1 Service Edition.
Empower agents to deliver more personalized service and reach resolutions faster with an AI assistant built into the flow of work. You can leverage out-of-the-box actions like summarize conversations or answer questions with Knowledge or you can build custom actions to fit your unique business needs. Service Salesforce Einstein Features This Release Einstein CopilotSell faster with an AI assistant. No data requirements
Included in Einstein 1 Sales Edition.hEinstein Copilot: Sales ActionsSell faster with an AI assistant.No data requirements. 
 Call explorer and meeting follow-up requires Einstein Conversation Insights.
Included in Einstein 1 Sales Edition. Generative AIBoost productivity by automating time-consuming tasks.No data requirements. 
 Call summaries and call explorer requires Einstein Conversation Insights.
Included in Einstein 1 Sales Edition. Einstein will use a global model until enough data is available for a local model. For a local model: ≥1,000 lead records created and ≥120 of those converted in the last 6 monthsEinstein Automated Contacts Automatically add new
contacts & events to your CRM≥ 30 business accounts. If you use Person Accounts, >= 50 percent of accounts must be business accounts Einstein Recommended ConnectionsGet insights about your teams network to see who knows your customers and can help out ona deal ≥ 2 users to be connected to Einstein Activity Capture
and Inbox (5 preferred) Einstein Forecasting Easily predict sales forecasts inside
of Salesforce Collaborative Forecasting enabled; use a standard fiscal year; measure forecasts by opportunity revenue; forecast hierarchy must include at least one forecasting enabled user who reports to a forecast manager; opportunities must be in Salesforce ≥ 24 months;Einstein Email Insights Prioritize your inbox with actionable intelligence Einstein Activity Capture enabledEinstein Activity Metiics (Activity 360) Get insight into the activities you enter
manually and automatically from Einstein
Activity Capture Einstein Activity Capture enabled Sales Analytics Get insights into the most common sales KPIs No data requirements. User specific requirements like browser and device apply Einstein Conveisation Insights Gain actionable insights from your sales calls with conversational intelligenceCall or video recordings from Lightning Dialer, Service Cloud Voice, Zoom and other supported CTI audio and video partners.Buyer Assistant Replace web-to-lead forms with real-time conversations. No data requirements – Sales Cloud UE or Sales Engagement. Einstein Opportunity ScoringEinstein Activity CaptuiePrioritize the opportunities most likely to convertAutomatically capture data & add to your CRMEinstein will use a global model until enough data is available for a local model. For a local model: ≥ 200 closed won and ≥ 200 closed lost opportunities in the last 2 years, each with a lifespan of at least 2 days≥ 30 accounts, contacts, or leads; Requires Gmail, Microsoft Exchange 2019, 2016, or 2013 Einstein Relationship Insights Speed prospecting with AI that researches for you. No data requirements. Einstein Next Best Action Deliver optimal recommendations at the point of maximumimpactNo data requirements. User specific requirements like browser and device apply Sales AIGenerate emails, prioritize leads & opportunities most likely to convert, uncover pipeline trends, predict sales forecasts, automate data capture, and more with Einstein for Sales. Generative AIPrompt BuilderEinstein Lead ScoringEinstein Opportunity ScoringEinstein Activity CaptureEinstein Automated ContactsEinstein Recommended ConnectionsEinstein ForecastingEinstein Email InsightsEinstein Activity Metrics (Activity 360)Sales AnalyticsEinstein Conversation InsightsBuyer Assistant Sales AIGenerative AI: 
Feature Why is it so Great? What do I need? Automate common questions and business processes to solve customer requests fasterBoost productivity by auto-generating service replies, summarizing conversations during escalations andtransfers or closed interactions, drafting knowledge articles, and surfacing relevant answers grounded inknowledge for agents’ and customers’ commonly asked questions. Deliver optimal recommendations at the point of maximum impactEliminate the guesswork with AI-powered recommendations for everyoneDecrease time spent on manual data entry for incoming cases and improve case field accuracy and completionAutomate case triage and solve customer requests fasterDecrease time spent selecting field values needed to close a case with chat conversations and improved field accuracySurface the best articles in real time to solve any customer’s questionEliminate time spent typing responses to the most common customer questionsGet insights into contact center operations, understand customers, and deliver enhanced customerexperiencesChat or Messaging channels, minimum of 20 examples for most languagesNo data requirements. User specific requirements like browser and device apply Make sure that your dataset has the minimum records to build a successful recommendation. Recipient Records need a minimum of 100 records,Recommended Item Records need a minimum of 10 records, andPositive Interaction Examples need a minimum of 400 records

Read More
April 13, 2024in Generative AI, Salesforce Einstein, Snowflake
Harnessing Sales Data
12Apr

Harnessing Sales Data

Harnessing Sales Data for Better Insights and Rapid Deal Closure Sales data is a critical asset for gaining insights and closing deals swiftly. With the ever-expanding data footprint, including customer response rates, leads in the pipeline, and quota attainment, tracking these metrics is essential. Ignoring them can be detrimental, as nearly all sales professionals recognize the importance of real-time data in meeting customer expectations, according to the Trends in Data and Analytics for Sales Report. However, concerns about data setup for generative AI and data accuracy persist. Sixty-three percent of sales professionals report that their company’s data isn’t optimized for AI, and only 42% are confident in their data’s accuracy. The demand for sales data has become a focal point for sales leaders and representatives, who increasingly rely on data to enhance customer engagement and productivity through trusted sources and AI integration. What is Sales Data? Sales data encompasses two main categories: external data, which includes information about prospects such as demographics, interests, behavior, and engagement; and internal sales data, including deal attributes and sales performance metrics. This data helps inform deal actions, assess progress toward sales targets or key performance indicators (KPIs), and supports tools like AI to enhance efficiency. Why is Sales Data Important? Sales data provides a measurable framework for all sales activities, enabling the setting of performance benchmarks and targets. It helps identify risks in the pipeline and highlights opportunities for upselling or fostering competition among sales reps. The data is also crucial for leveraging generative AI, which can automate tasks such as email drafting and sales pitch creation, provided the data is accurate and well-organized. Types of Sales Data Collecting and Utilizing Sales Data To effectively collect and utilize sales data, invest in a CRM system that serves as a centralized data repository with analytics capabilities. Automate data collection within the CRM, integrate data from other tools, and prioritize the security of sensitive information. Visualizing data through dashboards can help track progress toward business goals and make informed decisions. Real-Life Application: A Case Study A global consulting firm used sales data to enhance win rates and accelerate deal velocity. By integrating CRM analytics with data from various sources, the firm identified key deal attributes impacting success and adjusted strategies accordingly. The use of AI-driven “opportunity scores” further enabled the firm to monitor deal health and optimize resource allocation. Essential Tools for Harnessing Sales Data Turning Sales Data into Actionable Insights Regularly reviewing CRM-generated insights and adjusting strategies based on these insights is crucial for closing more deals and delivering consistent value to customers. By focusing on data-driven decision-making, sales teams can stay competitive and meet evolving customer needs. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
April 12, 2024in Generative AI
Government CRM System
12Apr

Government CRM System

Explore How Governments Can Modernize Services for Citizens with Government CRM System What is CRM in Government? CRM (Customer Relationship Management) systems in government streamline administrative tasks, allowing public servants to concentrate on enhancing citizens’ daily lives. Does the US Government Use Salesforce? Salesforce is valuable to the US federal government due to its highly customizable nature, catering to diverse agency needs and projects. Understanding AI in Government: Reshaping Public Sector Services Enhancing Workforce Skills for Better Constituent Experiences and Efficient Agency Operations The AI revolution presents opportunities for governments to enhance efficiency and service delivery. AI technologies can significantly improve data processing, cybersecurity, public planning, and other critical areas. Government agencies must raise awareness about the benefits of AI and upskill employees to bridge the AI skills gap. This transformation enables workers to better serve the public and foster trust between sectors. However, the rapid adoption of AI also raises concerns about a potential skills crisis, as highlighted by a survey revealing insufficient high-quality AI and machine learning resources. While AI promises to create new jobs, it may also displace certain roles. Organizations must prepare employees for this shift, ensuring they transition to higher-value work and acquire the necessary AI skills. Data Modernization: Paving the Way for an AI-Optimized Future Modernizing data infrastructure is essential for leveraging AI effectively. Employees can upskill in data science and AI, facilitating this transition from traditional workflows to AI-driven processes. Applications of AI in Government AI offers transformative potential across various government functions, such as traffic management, healthcare delivery, and administrative tasks like paperwork processing. Government agencies can enhance operations through AI-driven insights, improving efficiency, and service delivery for citizens. Challenges and Opportunities in AI Adoption Despite the promise of AI, many public agencies lack sufficient AI and data management capabilities among their workforce. Effective Education and Training for AI Implementation Organizations must prioritize AI education and responsible usage to better serve the public while upholding stringent security standards. Understanding Government Cloud Salesforce Salesforce Government Cloud and Government Cloud Plus provide dedicated instances of Salesforce’s Customer 360 suite, tailored to meet government requirements. Enhancing Government Efficiency with Modern CRM Solutions Explore How CRM Software Can Revolutionize Citizen Engagement and Government Operations CRM systems empower local governments to establish meaningful connections with citizens, improving service delivery and operational efficiency. Key Features of Local Government CRM Software Discover essential CRM features for local government agencies, including workflow automations, communication tools, data security, citizen contact management, real-time analytics, and business intelligence reporting. Evaluating CRM Data-Quality Solutions Evaluate CRM solutions based on security, flexibility, scalability, interoperability, ease of use, and customization capabilities to enhance government operations effectively. Strategies for Implementing CRM Workflows in Government Implement CRM systems strategically to improve service delivery and constituent engagement, focusing on data integration and minimizing the need for complex coding during deployment. By embracing modern CRM technologies and AI solutions, governments can enhance efficiency, transparency, and citizen satisfaction, ushering in a new era of effective public service delivery. Government CRM System. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
April 12, 2024in Mulesoft, Salesforce Experience Cloud, Salesforce Public Sector Solutions
Predictive Lead Scoring
11Apr

Predictive Lead Scoring

Traditional lead scoring relies on predefined criteria and subjective assumptions, whereas predictive lead scoring (PLS) harnesses machine learning algorithms to analyze extensive data and identify key predictors of lead quality. Traditional lead scoring only learns from data if you revise your scoring methodology for it. Predictive lead scoring constantly reworks the machine learning model based on more and newer data. Traditional lead scoring can be impacted by human error and bias. PLS analyzes from historical data eliminating bias and error. PLS employs a machine learning model to assign scores to open leads based on historical data, enabling sales teams to prioritize effectively and improve lead qualification rates while reducing the time spent on lead qualification. Discover how AI can elevate PLS to new heights and transform various organizational functions amidst shrinking budgets and heightened performance expectations across sales and marketing teams. Key Benefits of Predictive Lead Scoring: PLS leverages data science and machine learning to analyze and predict future outcomes based on historical and current data, guiding businesses in identifying high-potential leads and optimizing resource allocation. Implementing Predictive Lead Scoring: AI CRM and PLS: AI-enabled CRM platforms like Salesforce’s Einstein Lead Scoring automate lead scoring processes, leveraging extensive data to predict lead quality and prioritize effectively for sales and marketing teams. Benefits of Predictive Lead Scoring: AI and Machine Learning in Lead Scoring: AI and machine learning enhance lead scoring by analyzing vast data sets, identifying patterns, and predicting behaviors for more accurate lead qualification and prioritization. A data-driven enterprise is a smarter enterprise acting on data and insights. Salesforce’s Intelligent Lead Scoring: Salesforce’s Einstein Lead Scoring automates lead scoring processes within Sales Cloud and Marketing Cloud, providing tailored metrics and insights for informed decision-making. Generative AI and Predictive Lead Scoring: Generative AI streamlines processes like email personalization and content creation, enhancing marketing effectiveness and productivity. Good PLS with AI and machine learning transforms lead management by leveraging data insights for efficient and accurate lead qualification, ultimately driving improved sales and marketing performance. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
April 11, 2024in Einstein Insights
What is Salesforce Einstein 1
09Apr

What is Salesforce Einstein 1?

The bold new future of enterprise AI requires a new type of platform. What is Salesforce Einstein 1? One platform that can handle terabytes of disconnected data, gives you the freedom to choose your AI models, and connects directly into the flow of work, all while maintaining customer trust. The Einstein 1 Platform unifies your data, AI, CRM, development, and security into a single, comprehensive platform. It empowers IT, admins, and developers with an extensible AI platform, facilitating fast development of generative apps and automation. What is Salesforce Einstein 1 Einstein 1 has a mixture of artificial intelligence tools on the platform, and it kind of mirrors the way the core Salesforce platform is built, standardized and custom. We have out of the box AI features such as sales email generation in Sales Cloud, and service replies in Service Cloud. What is Einstein One? Einstein One seamlessly integrates our robust data platform with Data Cloud, offering a comprehensive AI and data-driven solution within our CRM service. As the leading comprehensive AI for CRM, Salesforce Einstein transforms the Customer Success Platform by incorporating a suite of integrated AI technologies, making it more intelligent and accessible to innovators worldwide. Introducing the Einstein One Platform The future of enterprise AI demands a groundbreaking platform capable of managing terabytes of diverse data, providing the flexibility to select AI models, and seamlessly integrating into daily workflows while maintaining customer trust. The Einstein One Platform consolidates data, AI, CRM, development, and security into a singular, comprehensive platform. It empowers IT, administrators, and developers with an extensible AI platform, facilitating rapid development of generative apps and automation. Salesforce has made significant investments in enhancing the Einstein One platform by introducing two prompt engineering features – a testing center and prompt engineering suggestions, demonstrating the company’s commitment to advancing AI capabilities. Data-driven and AI-driven customer experiences are vital in constructing Data Cloud. Salesforce’s predictive AI, Einstein, has been operational since 2016, conducting over a trillion predictions per week. The company continuously evolves its capabilities, introducing generative AI products like Einstein GPT, discussed at Dreamforce [2023]. ‘Customer 360′ now represents Salesforce customers’ ability to transform into customer-centric entities, delivering integrated and cohesive experiences across sales, marketing, service, and commerce. Leveraging Data Cloud, structured and unstructured engagement data comes alive, offering a personalized perspective of customer interactions. Einstein is accessible through various Salesforce products, including Sales Cloud, Service Cloud, Marketing Cloud, Salesforce Platform, Analytics Cloud, and Community Cloud. What is the Einstein One platform? Salesforce’s Einstein One is a transformative platform that securely integrates data, connects multiple Salesforce products, and empowers customer-centric businesses to create AI-driven apps, revolutionizing CRM interactions. What applications are connected with the Einstein One platform? Einstein Trust Layer: Ensuring the security of generative AI applications within Salesforce, this application acts as a safeguard for data, preventing unauthorized access. Data Cloud: Unifying structured and unstructured data, Data Cloud completes the customer 360 profile, integrating seamlessly with the Einstein One platform and making data actionable. Einstein Copilot: A built-in conversational assistant, Einstein Copilot enables users to ask questions in Natural Language and receive intelligent answers, actions, and more. Copilot Studio: An essential component of Einstein One, Copilot Studio manages Einstein One and Einstein Copilot, allowing businesses to control user access and use generative AI in their workspace. Prompt Builder, Skill Builder, Model Builder: Tools within Copilot Studio, these components empower businesses to create trusted AI prompts, add skills to user access, and choose AI models. What is Data Cloud for Einstein One? Data Cloud, combined with the Einstein Trust Layer, provides secure access to a unified data repository, forming a reliable resource for complete customer profiles and supporting AI assistants. Data Cloud facilitates valuable visualizations through reports, dashboards, and tools like Tableau, offering insights to enhance understanding of customers and business operations. What will Einstein One do for businesses? Einstein One aims to: Increase productivity Reduce operational costs Enhance the delivery of exceptional customer experiences The debut of Einstein One marks a significant stride for Salesforce and businesses, offering a secure path to boost productivity through AI technologies, ensuring continuous access to cutting-edge technologies for businesses. Where can generative AI impact businesses today? Generative AI has the potential to impact businesses by enhancing repetitive processes, making them faster. Exploring text-based processes, such as repetitive emails or form filling, is a great starting point for businesses looking to enhance capabilities. What can generative AI do for sales, service, field service teams? Generative AI can positively impact teams by automating processes, offering intelligent insights, and improving overall efficiency. Specific use cases for generative AI in sales, service, and field service teams can be explored through Salesforce’s 101 video series. What does Einstein One Tableau do? Einstein One Data Cloud, formerly known as Genie, serves as a metadata management tool connecting data from various sources. It makes data usable for AI and analysis, integrating with Einstein One, Tableau, and Salesforce’s CRM applications. Is Salesforce Einstein the same as Tableau? Formerly known as Einstein Analytics, Tableau CRM is Salesforce’s premium analytic platform, offering a powerful way to explore data, especially for current Salesforce users. Salesforce has renamed Einstein Analytics to Tableau CRM, reflecting its commitment to empowering users to make better decisions faster. In essence, the Einstein One platform combines Salesforce Data Cloud, Einstein Copilot, and the Einstein Trust Layer, providing a comprehensive solution for secure and connected data, facilitating generative AI on a comprehensive platform. Salesforce’s Einstein One Platform & the Data-Driven Revolution This innovation has the potential to revolutionize the way businesses interact with their customers. In this insight, we delve deeper into this transformative development, offering valuable insights, considerations for implementation, and the indispensable expertise of Tectonic as a data-oriented technology partner. Salesforce Data Cloud and Einstein AI are now seamlessly integrated into the Einstein One Platform. This integration empowers companies to effortlessly connect their diverse data sources, enabling the creation of AI-driven applications with minimal coding effort. Moreover, it forges a cohesive view of your data across

Read More
April 9, 2024in Artificial Intelligence, Data, Einstein 1 Platform, Einstein Insights, Enterprise, Generative AI, Google, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein, Salesforce Sales Cloud, Technology
Einstein Copilot for Tableau in Public Beta
06Apr

Einstein Copilot for Tableau in Public Beta

Salesforce Introduces Einstein Copilot for Tableau in Public Beta In early April, Salesforce unveiled the public beta availability of Einstein Copilot for Tableau, an innovative AI-powered assistant aimed at assisting users across various roles and functions in exploring and interacting with data within Tableau. This groundbreaking tool enables deep dives into data by leveraging Tableau’s analytical engine through natural language queries, accessing data from spreadsheets, cloud and on-premises data warehouses, and Salesforce Data Cloud. The public release of Copilot for Tableau is anticipated to be widely available to customers by summer 2024. Key Features of Einstein Copilot for Tableau Einstein Copilot for Tableau offers several features tailored to enhance user experience and streamline data exploration: Recommended Questions: The assistant automatically analyzes data and suggests relevant questions, allowing users to interact with data effortlessly without the need for specialized data analysis skills. Conversational Data Exploration: Users can iterate and refine their data exploration process seamlessly while maintaining context, enabling them to ask follow-up questions and delve deeper into insights as if they were engaging in a conversation with their data. Guided Calculation Creation: Copilot guides users through the process of creating calculations and parsing information, simplifying complex tasks such as extracting specific data elements from text fields. Enhancing Accuracy and Trust To ensure accuracy and contextual relevance, Einstein Copilot for Tableau leverages trusted company data from Data Cloud, fostering trust among users by delivering precise and relevant outputs based on internal data sources. Future Outlook Salesforce’s approach to introducing generative AI assistants for specific product types and use cases underscores the importance of function-specific training to meet users’ specific needs. As the technology matures, vendors may transition from premium license fees to consumption-based models, reflecting the evolving landscape of AI assistant technology adoption. The rollout of Einstein Copilot for Tableau represents a significant step forward in making data analysis accessible to a broader audience, reinforcing Salesforce’s commitment to innovation and customer-centric solutions in the realm of AI-powered analytics. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
April 6, 2024in Einstein 1 Platform, Salesforce, Salesforce Data Cloud, Salesforce Tableau
Build a Culture of Data
04Apr

Build a Culture of Data

What is a Data Culture? A Data Culture is the collective behaviors and beliefs of people who value, practice, and encourage the use of data to improve decision-making. As a result, data is woven into the operations, mindset, and identity of an organization. Why is a data culture important?  It enables more informed decision-making. With a data culture in place, decisions at all levels of the organization are based on data-driven insights rather than intuition or guesswork. This leads to more effective strategies and better outcomes. What is the difference in data culture and data strategy? Gartner defines data strategy as “a highly dynamic process employed to support the acquisition, organization, analysis, and delivery of data in support of business objectives.” In contrast, the culture around data comes together with data talent, data literacy, and data tools. Build a Culture of Data Building a data culture is crucial for companies to unlock valuable insights and make smarter, more strategic decisions. Here’s what leaders need to know to foster a data-driven environment: By following these steps and prioritizing the development of a data culture, leaders can empower their organizations to make informed decisions, drive growth, and stay ahead of the competition in today’s data-driven world. Data Maturity Understanding data maturity is crucial for organizations as it provides a framework for assessing their current state of data management and analytics capabilities. It serves as a tool to guide decision-making and prioritize initiatives aimed at advancing the organization’s data capabilities. By evaluating data maturity, organizations can identify gaps, set goals, and determine the necessary steps to progress along their data journey. Data maturity assessment typically involves evaluating various aspects of data management, including data governance, data quality, data infrastructure, analytics capabilities, and organizational culture around data. Based on the assessment, organizations can identify areas of strength and weakness and develop a roadmap for improvement. Furthermore, understanding data maturity enables organizations to track their progress over time. By periodically reassessing data maturity, organizations can measure how much they have advanced and identify areas that still require attention. This iterative process allows organizations to continuously improve their data capabilities and adapt to evolving business needs and technological advancements. In summary, understanding data maturity allows organizations to: 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
April 4, 2024in Einstein 1 Platform, Salesforce
Salesforce's Get Ready for AI Report
01Apr

Salesforce’s Get Ready for AI Report

Welcome to the future of business – Get Ready for AI is for analytics and data leaders. The tools for those who are interested in positioning themselves for AI success. From strategy to governance, you’ll learn what’s top-of-mind with other thought leaders, and see what actions you can take to be a more effective leader in a rapidly changing technology and business environment.  Salesforce’s Get Ready for AI Report This insight introduces four topics that are essential for data leaders beginning their AI journey: Access the full report here. Salesforce’s Get Ready for AI Report Data is at the center of any AI initiative, and organizations that are leading the way are focused on ensuring their data sources are current, authoritative, and complete. From talent, to strategy, to infrastructure, organizations that are prioritizing data across every business unit are ready to ride the AI wave. Positioning themselves for a significant competitive advantage over their peers. Salesforce’s Get Ready for AI Report As with any digital transformation, success depends on an enterprise-wide commitment. Data leaders are in a unique position to help guide their organizations through this transition, and achieve the benefits that AI can deliver. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
April 1, 2024in Salesforce, Salesforce Einstein
Data Cloud and Snowflake Bidrectional Data Sharing
01Apr

Data Cloud and Snowflake Bidrectional Data Sharing

Salesforce Data Cloud and Snowflake are excited to announce that bidirectional data sharing between Snowflake, the Data Cloud company, and Salesforce Data Cloud is now generally available. In September, we introduced the ability for organizations to leverage Salesforce data directly in Snowflake via zero-ETL data sharing, enabling unified customer and business data, accelerating decision-making, and streamlining business processes. Today, we’re thrilled to share that customers can now also share Snowflake data into the Salesforce Data Cloud, using the same zero-ETL innovation to reduce friction and quickly surface powerful insights across sales, service, marketing, and commerce applications. Data Cloud and Snowflake Bidrectional Data Sharing. Data Cloud and Snowflake Bidrectional Data Sharing Enterprises generate valuable customer data within Salesforce applications, while increasingly relying on Snowflake as their preferred data platform for storing, modeling, and analyzing their full data estate. This integration between Salesforce and Snowflake minimizes friction, data latency, scale limitations, and data engineering costs associated with using these two leading platforms. The Snowflake Marketplace also offers customers the opportunity to acquire new data sets to enhance or fill gaps in their existing business data, driving innovation. By combining enterprise data and third-party data from Snowflake Marketplace with valuable customer data from Salesforce applications, organizations can unify their data and build powerful AI solutions to surface rich insights, driving superior and differentiated customer experiences. “Zero-ETL data sharing between Salesforce Data Cloud and Snowflake is game-changing. It has opened up new frontiers of data collaboration. We’re excited to see how customers are powering their customer data analytics and developing innovative AI solutions with near real-time data from Salesforce and Snowflake, generating incredible business value. Now that this integration is generally available, this kind of innovation will be broadly accessible,” says Christian Kleinerman, SVP of Product, Snowflake. Power Personalized Experiences with Salesforce and Snowflake Data sharing between Salesforce Data Cloud and Snowflake brings together holistic insights, empowering multiple customer-facing departments within any organization to create a truly robust customer 360. As Snowflake’s Chief Marketing Officer, Denise Persson, often states, a true, enterprise-wide customer 360 is the beating heart of a modern, customer-facing organization. The applicability of this integration spans various industries and unlocks new growth opportunities. For example: The bidirectional integration enables data sharing across business systems, Salesforce clouds, and operational systems, facilitating data set analysis and future action planning. This brings actionable insights and drives actions, unleashing a new level of customer experience and business productivity. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
April 1, 2024in Salesforce Data Cloud, Salesforce Marketing Cloud, Snowflake
Salesforce Education Cloud for Educational Challenges
24Mar

Salesforce Education Cloud for Educational Challenges

Educational institutions today confront a multitude of complex challenges, ranging from disjointed information systems to the need for agility in meeting evolving educational demands. Salesforce Education Cloud presents a unified solution aimed at overcoming these obstacles by enhancing operational efficiencies, boosting student engagement, and ensuring compliance with ever-changing educational standards. Below is an in-depth examination of the prevalent challenges faced by educational institutions and the tailored solutions provided by Salesforce Education Cloud. Key Challenges in the Education Sector Salesforce Education Cloud: Tailored Solutions for Education Salesforce Education Cloud addresses these challenges through a suite of customized features and tools designed to streamline operations, enhance student services, and promote effective communication. Real-World Impact of Salesforce Education Cloud Implementation of Salesforce Education Cloud yields transformative benefits across educational institutions: Conclusion Salesforce Education Cloud offers a comprehensive solution to the diverse challenges faced by educational institutions. By integrating this robust platform, schools, colleges, and universities can enhance operational efficiency, improve student outcomes, and cultivate a collaborative educational environment. Institutions seeking to explore the benefits of Education Cloud or enhance their existing systems are encouraged to consult with a Salesforce Education Cloud Consultant for tailored guidance and implementation strategies. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
March 24, 2024in Salesforce, Salesforce Education Cloud, Salesforce Education Solutions, Salesforce Einstein
Top AI Tools Shaping Business Success
19Mar

Top AI Tools Shaping Business Success

Top AI Tools Shaping Business Success in 2024 In the dynamic world of business, staying ahead means embracing the latest technologies. Artificial Intelligence (AI) is no longer just a buzzword—it’s a transformative force that helps businesses operate more efficiently, make smarter decisions, and enhance customer experiences. As we move through 2024, the AI tool ecosystem is rapidly expanding, offering innovative solutions to automate tasks, gain deep insights, and improve customer engagement. Below, we explore the top AI tools that are shaping the future of business. StoryChief is a comprehensive content marketing platform that simplifies the creation and distribution of content through AI. From ideation to optimization, it leverages machine learning to help businesses generate high-quality, engaging content at scale. Key Features: Pricing: Plans start with a free tier, with paid options ranging from $40 to $500 per month. Developed by OpenAI, ChatGPT is a versatile language model capable of generating human-like text. It excels in content creation, customer support, and data analysis. Key Use Cases: Pricing: API access with usage-based pricing. Perplexity AI is an advanced search engine that provides accurate, summarized answers to complex queries using natural language processing (NLP). Key Features: Pricing: Free version available, with Pro version at $20/month offering enhanced features. Zapier connects over 5,000 apps, enabling automation of repetitive tasks across your tech stack with AI-powered tools that simplify complex automations. Key Features: Pricing: Free plan available for up to 100 tasks per month; paid plans start at $19.99/month. Grammarly is an AI-driven writing assistant that enhances the quality of written communication, ensuring clarity, conciseness, and error-free content. Key Features: Pricing: Free version available; Premium plans start at $12/month for individuals and $25/user/month for businesses. Typeframes simplifies video creation with AI, turning scripts or images into professional-quality videos with animations, transitions, and voiceovers. Key Features: Pricing: Plans start at $29/month, with higher-tier options available. Chatbase enables businesses to build intelligent chatbots and virtual assistants that handle a wide range of customer service inquiries. Key Features: Pricing: Free plan available with limited message credits; paid plans start at $19/month. Secta is an AI-powered headshot generator that creates professional-quality headshots from user-submitted photos, ideal for businesses needing polished profile pictures. Key Features: Pricing: Pay-as-you-go at $49 per headshot session. Voicenotes is an AI-driven transcription tool that converts voice memos into concise summaries and action items, perfect for capturing important information efficiently. Key Features: Pricing: Free plan available; paid plans start at $10/month, with lifetime payment options. Notion AI enhances the popular Notion productivity platform with AI-powered writing assistance, content summarization, and database management. Key Features: Pricing: Available as an add-on at $10 per user per month, with discounts for annual plans. Choosing the Right AI Tools for Your Business Selecting the right AI tools involves considering several factors: By evaluating these aspects, you can effectively leverage AI to enhance efficiency, drive growth, and maintain a competitive edge in 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
March 19, 2024in Salesforce
LLMs Beyond Generative AI
16Mar

LLMs Beyond Generative AI

Beyond Text Generation: The Versatile Capabilities of Large Language Models While large language models (LLMs) and generative AI have dominated the conversation over the past year, the spotlight has largely been on their text generation capabilities. There’s no denying the value of LLMs in generating answers to questions. However, focusing solely on this use case overlooks other valuable applications. This insight will explore several primary uses of LLMs, ensuring you recognize their broader potential beyond just generative purposes. Creation and Generation This is the most publicized use case for LLMs today. Applications like ChatGPT can answer questions with detailed responses, and tools like DALL-E generate images based on user prompts. Similar generators exist for code, video, and 3D virtual worlds. Interestingly, these generators share fundamental algorithmic approaches despite producing different content types—text, images, videos. Since they all process prompts, they require training to understand and decompose these prompts to guide the generation process, necessitating the use of LLMs. However, generating new content is just one aspect of what LLMs can achieve. Summarization LLMs excel at summarizing information. For instance, if you have a list of papers on your to-read list, an LLM can summarize their key themes, common points, and differences. This provides a clear baseline, helping you focus on essential aspects as you read. Summarizing content with AI tends to have a lower error risk compared to generating new content because the LLM works within the boundaries of the provided information. While it might occasionally miss a pattern or emphasize the wrong details, it’s unlikely to produce completely incorrect summaries. Translation Often underrated, translation might be one of the most impactful uses of LLMs. For example, LLMs can translate old code from obsolete languages into modern ones. An LLM generates a draft translation, which, although imperfect, can be refined by a programmer who understands the goal of the code even with limited knowledge of the original language. Human language translation also stands to benefit significantly. Soon, we’ll be able to communicate in our preferred languages, with LLMs instantly translating our words into the listener’s language. This will eliminate the need for a common language and help preserve uncommon languages by removing the communication barriers associated with them. Interpretation and Extraction LLMs are also adept at interpreting statements and triggering subsequent actions. Image generators use this approach, as do tools that handle analytical queries. For instance, asking “Please summarize this year’s sales by region and subtotal by product” allows an LLM to interpret the request, extract key parameters, and pass them to a query generator for the answer. Companies like Quaeris, which I advise, focus on this capability. Additionally, LLMs can handle tasks like sentiment analysis and customer service inquiries. They can ingest inquiries and extract relevant details, such as the product in question, the issue raised, and the requested action, to route the inquiry to the appropriate person more effectively. LLMs Beyond Generative AI The examples discussed are not exhaustive but represent some common and powerful uses of LLMs. They highlight that LLMs offer far more than just text generation. Exploring these other applications can provide significant benefits for you and your organization. Originally posted in the Analytics Matters newsletter on LinkedIn. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
March 16, 2024in Generative AI, Salesforce Einstein

Subscribe to our mailing list. Join our mail list to receive our newsletter

We'll keep you up to date with our latest news, insights, free resources, and much more.
Don't worry, we won't spam you.
gettectonic.com