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einstein discovery dictionary
06Sep

Einstein Discovery Dictionary

Familiarize yourself with terminology that is commonly associated with Einstein Discovery. Actionable VariableAn actionable variable is an explanatory variable that people can control, such as deciding which marketing campaign to use for a particular customer. Contrast these variables with explanatory variables that can’t be controlled, such as a customer’s street address or a person’s age. If a variable is designated as actionable, the model uses prescriptive analytics to suggest actions (improvements) the user can take to improve the predicted outcome. Actual OutcomeAn actual outcome is the real-world value of an observation’s outcome variable after the outcome has occurred. Einstein Discovery calculates model performance by comparing how closely predicted outcomes come to actual outcomes. An actual outcome is sometimes called an observed outcome. AlgorithmSee modeling algorithm. Analytics DatasetAn Analytics dataset is a collection of related data that is stored in a denormalized, yet highly compressed, form. The data is optimized for analysis and interactive exploration. AttributeSee variable. AverageIn Einstein Discovery, the average represents the statistical mean for a variable. BiasIf Einstein Discovery detects bias in your data, it means that variables are being treated unequally in your model. Removing bias from your model can produce more ethical and accountable models and, therefore, predictions. See disparate impact. Binary Classification Use CaseThe binary classification use case applies to business outcomes that are binary: categorical (text) fields with only two possible values, such as win-lose, pass-fail, public-private, retain-churn, and so on. These outcomes separate your data into two distinct groups. For analysis purposes, Einstein Discovery converts the two values into Boolean true and false. Einstein Discovery uses logistic regression to analyze binary outcomes. Binary classification is one of the main use cases that Einstein Discovery supports. Compare with multiclass classification. CardinalityCardinality is the number of distinct values in a category. Variables with high cardinality (too many distinct values) can result in complex visualizations that are difficult to read and interpret. Einstein Discovery supports up to 100 categories per variable. You can optionally consolidate the remaining categories (categories with fewer than 25 observations) into a category called Other. Null values are put into a category called Unspecified. Categorical VariableA categorical variable is a type of variable that represents qualitative values (categories). A model that represents a binary or multiclass classification use case has a categorical variable as its outcome. See category. CategoryA category is a qualitative value that usually contains categorical (text) data, such as Product Category, Lead Status, and Case Subject. Categories are handy for grouping and filtering your data. Unlike measures, you can’t perform math on categories. In Salesforce Help for Analytics datasets, categories are referred to as dimensions. CausationCausation describes a cause-and-effect relationship between things. In Einstein Discovery, causality refers to the degree to which variables influence each other (or not), such as between explanatory variables and an outcome variable. Some variables can have an obvious, direct effect on each other (for example, how price and discount affect the sales margin). Other variables can have a weaker, less obvious effect (for example, how weather can affect on-time delivery). Many variables have no effect on each other: they are independent and mutually exclusive (for example, win-loss records of soccer teams and currency exchange rates). It’s important to remember that you can’t presume a causal relationship between variables based simply on a statistical correlation between them. In fact, correlation provides you with a hint that indicates further investigation into the association between those variables. Only with more exploration can you determine whether a causal link between them really exists and, if so, how significant that effect is .CoefficientA coefficient is a numeric value that represents the impact that an explanatory variable (or a pair of explanatory variables) has on the outcome variable. The coefficient quantifies the change in the mean of the outcome variable when there’s a one-unit shift in the explanatory variable, assuming all other variables in the model remain constant. Comparative InsightComparative insights are insights derived from a model. Comparative insights reveal information about the relationships between explanatory variables and the outcome variable in your story. With comparative insights, you isolate factors (categories or buckets) and compare their impact with other factors or with global averages. Einstein Discovery shows waterfall charts to help you visualize these comparisons. CorrelationA correlation is simply the association—or “co-relationship”—between two or more things. In Einstein Discovery, correlation describes the statistical association between variables, typically between explanatory variables and an outcome variable. The strength of the correlation is quantified as a percentage. The higher the percentage, the stronger the correlation. However, keep in mind that correlation is not causation. Correlation merely describes the strength of association between variables, not whether they causally affect each other. CountA count is the number of observations (rows) associated with an analysis. The count can represent all observations in the dataset, or the subset of observations that meet associated filter criteria.DatasetSee Analytics dataset. Date VariableA date variable is a type of variable that contains date/time (temporal) data.Dependent VariableSee outcome variable. Deployment WizardThe Deployment Wizard is the Einstein Discovery tool used to deploy models into your Salesforce org. Descriptive InsightsDescriptive insights are insights derived from historical data using descriptive analytics. Descriptive insights show what happened in your data. For example, Einstein Discovery in Reports produces descriptive insights for reports. Diagnostic InsightsDiagnostic insights are insights derived from a model. Whereas descriptive insights show what happened in your data, diagnostic insights show why it happened. Diagnostic insights drill deeper into correlations to help you understand which variables most significantly impacted the business outcome you’re analyzing. The term why refers to a high statistical correlation, not necessarily a causal relationship. Disparate ImpactIf Einstein Discovery detects disparate impact in your data, it means that the data reflects discriminatory practices toward a particular demographic. For example, your data can reveal gender disparities in starting salaries. Removing disparate impact from your model can produce more accountable and ethical insights and, therefore, predictions that are fair and equitable. Dominant ValuesIf Einstein Discovery detects dominant values in a variable, it means that the data is unbalanced. Most values are in the same category, which can limit the value of the analysis. DriftOver time, a deployed model’s performance can drift, becoming less accurate in predicting outcomes. Drift can occur due to changing factors in the data or in your business environment. Drift also results from now-obsolete assumptions built into the story

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September 6, 2023in Artificial Intelligence, Data, Salesforce
vector index
25Aug

Choosing the Right Vector Index

Finding the Needle in the Digital Haystack: Choosing the Right Vector Index Imagine searching for a needle in a vast digital haystack of millions of data points. In AI and machine learning, selecting the right vector index is like equipping yourself with a magnet—it transforms your search into a faster, more precise process. Whether you’re building a recommendation system, chatbot, or Retrieval-Augmented Generation (RAG) application, the vector index you choose significantly impacts your system’s performance. So how do you pick the right one? Let’s break it down. What Is Similarity Search? At its core, similarity search is about finding items most similar to a query item based on a defined metric. These items are often represented as high-dimensional vectors, capturing data like text embeddings, images, or user preferences. This process enables applications to deliver relevant results efficiently and effectively. What Is a Vector Index? A vector index is a specialized organizational system for high-dimensional data. Much like a library catalog helps locate books among thousands, a vector index enables algorithms to retrieve relevant information from vast datasets quickly. Different techniques offer varying trade-offs between speed, memory usage, and accuracy. Popular Vector Indexing Techniques 1. Flat Index The Flat Index is the simplest method, storing vectors without alteration, like keeping all your files in one folder. 2. Inverted File Index (IVF) The IVF improves search speed by clustering vectors, reducing the number of comparisons. 3. Product Quantization (PQ) PQ compresses high-dimensional vectors, reducing memory requirements and speeding up calculations. 4. Hierarchical Navigable Small World Graphs (HNSW) HNSW offers a graph-based approach that excels in balancing speed and accuracy. Composite Indexing Techniques Blending techniques can help balance speed, memory efficiency, and accuracy: Conclusion Choosing the right vector index depends on your specific needs—speed, memory efficiency, or accuracy. By understanding the trade-offs of each indexing technique and fine-tuning their parameters, you can optimize the performance of your AI and machine learning models. Whether you’re working with small, precise datasets or massive, high-dimensional ones, the right vector index is your key to efficient, accurate searches. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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August 25, 2023in Data
Benefits of Salesforce Experience Cloud
11Aug

Benefits of Salesforce Experience Cloud

Salesforce Experience Cloud: Transforming Digital Customer Engagement To understand the Benefits of Salesforce Experience Cloud we must understand what a customer or partner portal is intended to do. Salesforce Experience Cloud, previously known as Community Cloud, is a powerful digital experience platform (DXP) designed to help organizations create and deliver exceptional, customer-centric experiences across multiple channels. This platform goes beyond community management, offering tools for building and managing websites, portals, mobile apps, and integrating social media. Benefits of Salesforce Experience Cloud explored. Built on Salesforce Customer 360, Experience Cloud gives businesses a comprehensive view of their customers by connecting data from various sources. With these insights, businesses can create personalized experiences tailored to each customer’s preferences and needs. Organizations can use Experience Cloud to design portals, websites, and communities, providing seamless access to relevant information, collaboration tools, and resources. The platform’s flexibility allows businesses to enhance customer satisfaction, improve partner collaboration, and boost employee productivity. Key Benefits of Salesforce Experience Cloud Salesforce Experience Cloud offers numerous benefits that help businesses deliver seamless experiences across the customer journey. Here are some of its key advantages: 1. Seamless Integration Experience Cloud integrates effortlessly with other Salesforce products like Sales Cloud and Service Cloud, providing a unified platform for comprehensive customer management. 2. Scalability and Customization The platform is highly scalable, allowing businesses to expand their communities as they grow. With extensive customization options, businesses can tailor the platform to meet their specific needs and branding requirements. 3. Security and Trust Salesforce is known for its robust security features, ensuring customer data is protected at all times. Businesses can confidently manage sensitive customer information within Experience Cloud. 4. Extensive AppExchange Ecosystem Salesforce’s AppExchange marketplace provides access to a wide range of pre-built integrations and apps that enhance the functionality of Experience Cloud, allowing businesses to customize and extend their platform capabilities. Real-World Uses of Salesforce Experience Cloud Salesforce Experience Cloud is used by businesses across various industries to improve customer engagement, enhance collaboration, and boost productivity. Some key use cases include: 1. Partner Portals Experience Cloud enables businesses to create dedicated partner portals where partners can collaborate with internal teams, access resources, and share leads. This accelerates partner engagement and streamlines business processes. 2. Self-Service Portals Businesses can offer 24/7 self-service portals, allowing customers to access product information, troubleshoot common issues, and track their interactions. These portals help reduce the workload on support teams and enhance customer satisfaction. 3. Customer Communities Experience Cloud allows businesses to create customer communities where users can find personalized content, engage with other users, and access self-service resources. This promotes collaboration and reduces the strain on customer support teams. 4. Employee Communities Internal employee communities serve as hubs for company-wide communication, training, and collaboration. Employees can access resources, share knowledge, and seek support, ultimately boosting engagement and productivity. 5. Branded Mobile Apps Businesses can use Experience Cloud to develop branded mobile apps that give customers, partners, and employees convenient access to services, resources, and information on the go. 6. Social Media Integration Experience Cloud integrates with popular social media platforms, allowing businesses to engage with customers directly, share content, and respond to inquiries. Top Features of Salesforce Experience Cloud Salesforce Experience Cloud is packed with features that enhance customer engagement, streamline operations, and improve overall efficiency: Companies Using Salesforce Experience Cloud Nike and PUMA leverage Experience Cloud for personalization. Nike’s loyalty program and Puma’s mobile shopping experience are enhanced by the platform’s built-in mobile UX design and technical architecture, resulting in better customer engagement and increased sales. Bank of America and Wells Fargo use Experience Cloud to offer customer support through self-service portals and community forums, improving customer satisfaction and gathering valuable feedback. IBM uses the platform to create collaborative communities for employees and customers alike. With integrated tools like Salesforce Einstein and IBM Watson, the company has enhanced internal collaboration and customer service. Hulu uses Salesforce to power its Help Center, where customers can find answers, engage with other viewers, and leave feedback that shapes Hulu’s content. OpenTable relies on Experience Cloud for its Diner Help portal, a one-stop shop for dining-related queries, enhancing the user experience and operational efficiency. Choosing the Right Salesforce Experience Cloud Partner for Implementation When implementing Salesforce Experience Cloud, choosing the right partner is crucial to ensure success. Look for a partner with: With the right partner, like Tectonic, businesses can fully grasp the power of Salesforce Experience Cloud to deliver exceptional digital experiences that foster customer loyalty, drive business growth, and improve operational efficiency. Content updated October 2024. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 11, 2023in Data, Salesforce, Salesforce Development Services, Salesforce Einstein, Salesforce Experience Cloud
Salesforce Einstein and Einstein Automate
09Aug

Einstein Trust

Generative AI, Salesforce, and the Commitment to Trust The excitement surrounding generative AI is palpable as it unlocks new dimensions of creativity for individuals and promises significant productivity gains for businesses. Engaging with generative AI can be a great experience, whether creating superhero versions of your pets with Midjourney or crafting pirate-themed poems using ChatGPT. According to Salesforce research, employees anticipate saving an average of 5 hours per week through the adoption of generative AI, translating to a substantial monthly time gain for full-time workers. Whether designing content for sales and marketing or creating a cute version of a beloved story, generative AI is a tool that helps users create content faster. However, amidst the enthusiasm, questions arise, including concerns about the security and privacy of data. Users ponder how to leverage generative AI tools while safeguarding their own and their customers’ data. Questions also revolve around the transparency of data collection practices by different generative AI providers and ensuring that personal or company data is not inadvertently used to train AI models. Additionally, there’s a need for assurance regarding the accuracy, impartiality, and reliability of AI-generated responses. Salesforce has been at the forefront of addressing these concerns, having embraced artificial intelligence for nearly a decade. The Einstein platform, introduced in 2016, marked Salesforce’s foray into predictive AI, followed by investments in large language models (LLMs) in 2018. The company has diligently worked on generative AI solutions to enhance data utilization and productivity for their customers. The Einstein Trust Layer is designed with private, zero-retention architecture. Emphasizing the value of Trust, Salesforce aims to deliver not just technological capabilities but also a responsible, accountable, transparent, empowering, and inclusive approach. The Einstein Trust Layer represents a pivotal development in ensuring the security of generative AI within Salesforce’s offerings. The Einstein Trust Layer is designed to enhance the security of generative AI by seamlessly integrating data and privacy controls into the end-user experience. These controls, forming gateways and retrieval mechanisms, enable the delivery of AI securely grounded in customer and company data, mitigating potential security risks. The Trust Layer incorporates features such as secure data retrieval, dynamic grounding, data masking, zero data retention, toxic language detection, and an audit trail, all aimed at protecting data and ensuring the appropriateness and accuracy of AI-generated content. Salesforce proactively provided the ability for any admin to control how prompt inputs and outputs are generated, including reassurance over data privacy and reducing toxicity. This innovative approach allows customers to leverage the benefits of generative AI without compromising data security and privacy controls. The Trust Layer acts as a safeguard, facilitating secure access to various LLMs, both within and outside Salesforce, for diverse business use cases, including sales emails, work summaries, and service replies in contact centers. Through these measures, Salesforce underscores its commitment to building the most secure generative AI in the industry. Generating content within Salesforce can be achieved through three methods: CRM Solutions: Einstein Copilot Studio: Einstein LLM Generations API: An overarching feature of these AI capabilities is that every Language Model (LLM) generation is meticulously crafted through the Trust Layer, ensuring reliability and security. At Tectonic, we look forward to helping you embrace and utilize generative AI with Einstein save time. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 9, 2023in Artificial Intelligence, Data, Generative AI, Salesforce, Salesforce Einstein
Contact Builder is responsible for aggregating customer data from multiple source into a single customer view, known as the contact model in marketing cloud. For more information refer to the official documentation. Audience Builder is a segmentation tool, that abstracts the traditional SQL process for building segments and allows marketers to build segments by dragging and dropping them. For more information, please refer to the the documentation. Contact Builder has been moved to the Audience Builder tab
31Jul

Audience Builder Contact Builder

Contact Builder, formerly known as Audience Builder, is a robust tool within Marketing Cloud designed to manage data associated with contacts, referred to as ‘people’ records. This platform provides access to both demographic and behavioral information, which is organized into ‘attribute groups’ (such as abandoned carts), ‘events,’ and ‘populations,’ allowing for efficient segmentation of data. The primary distinction between Audience Builder and Contact Builder lies in their functionalities within Marketing Cloud. While Audience Builder focuses on segmenting data, Contact Builder serves as the tool for defining the data model within the Marketing Cloud ecosystem. Contact Builder is integral to data management within Marketing Cloud and will continue to play a central role in the platform’s operations. Marketing Cloud Audience Builder has empowered marketers to create finely segmented audiences based on behavioral and demographic data. With its retirement, the question arises: “What comes next?” To address this, Marketing Cloud offers engagement marketing tools powered by AI, enabling marketers to activate their data and deliver personalized campaigns at scale to enhance customer lifetime value. These tools include: Email Marketing: Cross-Channel Analytics: “After adopting Salesforce Marketing Cloud and using it to hyper-target our audience, we are able to reduce the waste of our current marketing budget and become more efficient with spending on initiatives that deliver better results“ Kyall MaiSVP & Chief Innovation Officer, Esquire Contact Builder vs. Audience Builder Contact Builder serves as the central hub for managing attribute values associated with each contact within Marketing Cloud. It maintains a comprehensive database of contact information and facilitates the organization and linkage of data from various sources, including ERP systems, CRM systems, and POS systems. This tool offers a unified view of customer interactions with the brand, enabling personalized communication across channels such as email, SMS, and push notifications. Audience Builder, now known as Contact Builder, dynamically creates targeted audiences based on stored attribute and behavioral values of contacts. These audiences are generated according to specific rules and criteria defined by the user. Audience Builder helps marketers segment contacts effectively, allowing for precise targeting or exclusion from marketing activities within Marketing Cloud Engagement. Key Features and Functions: Contact Builder: Audience Builder: While both Contact Builder and Audience Builder are essential components of Marketing Cloud, Contact Builder takes precedence as the primary tool for managing contact data and enabling personalized customer interactions. Audience Builder, now integrated into Contact Builder, continues to play a vital role in audience segmentation and targeting within Marketing Cloud Engagement. Contact Builder is responsible for aggregating customer data from multiple source into a single customer view, known as the contact model in marketing cloud. For more information refer to the official documentation. Audience Builder is a segmentation tool, that abstracts the traditional SQL process for building segments and allows marketers to build segments by dragging and dropping them. For more information, please refer to the the documentation. Contact Builder has been moved to the Audience Builder tab. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 31, 2023in Salesforce Marketing Cloud
mopsey app
29Jul

Mopsy

Mopsy is an alternative to the default “Today’s Tasks” component found on the Salesforce Home page. Limitations of Today’s Tasks Component The default “Today’s Tasks” component often falls short in functionality. For instance, if a task is overdue, it may not be highlighted. In the example below, a task from yesterday isn’t shown in the list of overdue tasks: Today’s Tasks component showing no tasks due today. While the component does have a filter option to display overdue tasks, it fails to show tasks that are due today, as they are not yet overdue. How Mopsy Improves the Experience Mopsy addresses this issue effectively. It offers a “Today + Overdue” filter, which ensures that users can view both today’s tasks and those overdue from previous days: Mopsy component displaying tasks for Today + Overdue, including an overdue task from yesterday. Additionally, Mopsy includes a convenient button on the component for creating new tasks (the plus sign in the upper right corner). Administrators also have the option to configure the component to display some or all of the Comment field on tasks: Task showing the Subject and Comment fields. Considerations for Mopsy A notable drawback of Mopsy, similar to the “Today’s Tasks” component, is its user-selectable filter. This means that individual users may set different filters, potentially causing missed tasks. Unfortunately, administrators cannot set or lock the filter to “Today + Overdue” by default. Therefore, training for users to set and maintain this filter consistently is advisable. Final Thoughts If your organization actively uses tasks and needs a more functional task management tool, Mopsy is a worthwhile addition to the Home page. It helps users stay on top of their tasks and improve visibility into upcoming items. 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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July 29, 2023in Salesforce
Slack and Salesforce
21Jul

Flow Core Actions for Slack: Send Slack Message

Effortlessly send messages to Slack channels, direct messages, or the Messages tab of a Slack app using the Send Slack Message Flow Core Action. Required Editions Prerequisite: Ensure Salesforce for Slack integrations are enabled before using this action. Steps to Send a Slack Message in Flow 1. Add an Action to the Flow Set Connection Values for Slack Input Parameter Description Slack App Required. Specify the Slack app to execute the action. Only Slack apps installed in your org are available. The input value corresponds to the Slack app ID. Slack Workspace Required. Identify the Slack workspace where the app is installed. You can select a value or resource. The input value corresponds to the Slack workspace ID. Execute Action As Specify the entity executing the action: Set Slack Message Details Input Parameter Description Slack Conversation ID Required. The ID of the Slack channel, direct message, or user to send the message. Store Output Values Output Parameter Description Slack Message Timestamp The timestamp of the sent message. Use this for starting threads or as a resource in subsequent actions. Usage Notes By configuring the Send Slack Message Flow Core Action, you can streamline communication workflows and enhance collaboration directly from Salesforce to Slack. 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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July 21, 2023in Enterprise
The Evolution of Salesforce Data Cloud
21Jul

The Evolution of Salesforce Data Cloud

The Evolution of Salesforce Data Cloud Salesforce’s journey to Data Cloud started with its acquisition of Krux in 2016, which was later rebranded as Salesforce DMP. This transformation gained momentum in 2019 when Salesforce introduced its customer data platform (CDP), incorporating Salesforce DMP. Subsequent acquisitions of Datorama, MuleSoft, Tableau, and Evergage (now Interaction Studio) enriched Salesforce CDP’s functionality, creating today’s robust Data Cloud. Understanding Customer Data Platforms (CDPs) A customer data platform (CDP) aggregates customer data from multiple channels to create a unified customer profile, enabling deeper insights and real-time personalization. A CDP serves as a centralized customer data repository, merging isolated databases from marketing, service, and ecommerce to enable easy access to customer insights. Salesforce’s “State of Marketing” report highlights the impact of CDPs, noting that 78% of high-performing businesses use CDPs, compared to 58% of underperformers. This analysis explores the evolution of CDPs and their role in transforming customer relationship management (CRM) and the broader tech ecosystem, turning customer data into real-time interactions. Key Functions of a Customer Data Platform (CDP) CDPs perform four main functions: data collection, data harmonization, data activation, and data insights. Origins of Customer Data Platforms (CDPs) CDPs evolved as the latest advancement in customer data management, driven by the need for a unified marketing data repository. Unlike earlier tools that were often limited to specific channels, CDPs enable real-time data synchronization and cross-platform engagement. Advances in AI, automation, and machine learning have made this level of segmentation and personalization attainable. The Future of Customer Data Platforms (CDPs) The next generation of CDPs, like Salesforce’s Data Cloud, supports real-time engagement across all organizational functions—sales, service, marketing, and commerce. Data Cloud continuously harmonizes and updates customer data, integrating seamlessly with Salesforce products to process over 100 billion records daily. With Data Cloud, organizations gain: Benefits of a Customer Data Platform (CDP) CDPs provide comprehensive insights into customer interactions, supporting personalization and cross-selling. Beyond segmentation, they serve as user-friendly platforms for audience analysis and data segmentation, simplifying day-to-day data management. Data Cloud allows organizations to transform customer data into personalized, seamless experiences across every customer touchpoint. Leading brands like Ford and L’Oréal utilize Data Cloud to deliver connected, real-time interactions that enhance customer engagement. The Need for Customer Data Platforms (CDPs) CDPs address critical data management challenges by unifying disjointed data sources, resolving customer identities, and enabling seamless segmentation. These capabilities empower companies to maximize the potential of their customer data. CDP vs. CRM CDPs are an evolution of traditional CRM, focusing on real-time, highly personalized interactions. While CRMs store known customer data, CDPs like Data Cloud enable real-time engagement, making it the world’s first real-time CRM by powering Salesforce’s Customer 360. Selecting the Right CDP When choosing a CDP, the focus often falls into two areas: insights and engagement. An insights-oriented CDP prioritizes data integration and management, while an engagement-focused CDP leverages data for real-time personalization. Data Cloud combines both, integrating real-time CDP capabilities to deliver unmatched insights and engagement across digital platforms. Content updated October 2024. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 21, 2023in Data, Mulesoft, Salesforce, Salesforce Data Cloud
Generative AI Regulations
17Jul

Salesforce, Data Science, and Generative AI

Is Salesforce utilized in the field of data science? Salesforce data science and Generative AI Data Science-as-a-Service (DSaaS) democratizes access to machine learning through the Salesforce Data Management Platform, enabling widespread adoption of data science capabilities. Utilizing Salesforce for Data Science Empowerment: The integration of Salesforce into data science represents a transformative endeavor aimed at democratizing machine learning through Data Science-as-a-Service (DSaaS). By leveraging the Salesforce Data Management Platform, the objective is to empower individuals across various domains with the potential of data science. Democratization of Data Science: DSaaS introduces a versatile workbench that capitalizes on machine learning to refine segmentation, enhance activation strategies, and uncover deeper insights. Through robust analytics tools, users can gain profound insights into individual customer behaviors. Supported by a formidable 20-petabyte analytics environment and a real-time big data infrastructure, data-driven analytics are taken to unprecedented levels. Harnessing Modeling Resources: Data owners enjoy the flexibility to harness their data, algorithms, and models either within the Salesforce Data Management Platform or within their independent environments. Spearheading this initiative is the Salesforce Unified Intelligence Platform (UIP) team, constructing a centralized data intelligence platform aimed at enriching business insights, enhancing user experience, improving product quality, and optimizing operational efficiency, all while upholding the core value of trust embedded in the Salesforce platform. Salesforce Data Science and Generative AI Emphasizing Security and Design: Security stands as a cornerstone of the Salesforce platform, with the UIP’s evolution tracing back to a transition from a colossal Hadoop cluster to UIP in public clouds. The architectural journey prioritized data classification early on, engaging in meticulous reviews with legal and security experts to classify data intended for storage within UIP. Adopting the “zero-trust infrastructure” principle, the architecture is fortified against both internal and external threats, ensuring robust defense mechanisms against potential data breaches. Unlocking Data Science Potential through DSaaS: DSaaS serves as a catalyst in democratizing machine learning through the Salesforce Data Management Platform, spotlighting the pivotal role of data science in fostering generative AI and cultivating trustworthy AI. Data scientists play a critical role in ensuring data quality and organization to steer clear of issues such as biased or irrelevant outcomes. Navigating Data Science Challenges: Despite the transformative potential of data science, businesses encounter various challenges including managing diverse data sources, scarcity of skilled professionals, data privacy and security concerns, data cleansing complexities, and effectively communicating findings to non-technical stakeholders. Proposed Solutions: Addressing these challenges involves leveraging data integration tools, investing in the upskilling and reskilling of data professionals, implementing robust data privacy measures, employing data governance tools for data cleansing, and honing communication skills for reporting findings to non-technical stakeholders. The success of generative AI hinges on well-organized data, and data science is pivotal in achieving this. Whether utilizing AI tools built with the expertise of data scientists or building a data science team, businesses can navigate the evolving landscape of AI and data science with confidence. Content updated March 2024. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 17, 2023in AI Tools, Data, Generative AI, Salesforce, Salesforce Einstein, Salesforce Sales Cloud
Salesforce BI
15Jul

Salesforce Analytics Acronyms

Here is a helpful list of analytics acronyms you may encounter when discussing Salesforce. Salesforce Analytics Acronyms Acronym Meaning Definition AWS Amazon Web Services AWS offers “reliable, scalable, and inexpensive cloud computing services…the world’s most comprehensive and broadly adopted cloud, offering over 200 fully featured services from data centers globally.” (source)Multiple Salesforce products use AWS; however, it’s prominent in the analytics arena, where organizations rely on AWS to:”Extract, prepare, and analyze Salesforce.com data using Amazon AppFlow, AWS Glue DataBrew, and Amazon Athena.” (source) BYOAI Bring Your Own AI ‘Bring your own AI (model)’ is Salesforce’s way to meet their customers where they’re at. With Data Cloud, Salesforce not only supports Einstein (their own LLM), but also other ones like Sagemaker, Vertex AI, etc. BYOM Bring Your Own Model ‘Bring your own AI (model)’ is Salesforce’s way to meet their customers where they’re at. With Data Cloud, Salesforce not only supports Einstein (their own LLM), but also other ones like Sagemaker, Vertex AI, etc. CRMA CRM Analytics This is the analytics/business intelligence (BI) solution on the Salesforce platform, integrating Salesforce data with external data and provides the ability to analyze large datasets while performing predictive analytics. (Previously known as Wave/Einstein Analytics/Tableau CRM). EA Einstein Analytics The former name for CRM Analytics (also formerly Tableau CRM). You may still come across EA in outdated documentation. GPT Generative Pre-trained Transformer GPT is a technology concept, although it’s often mistaken for a product name (with the rise of the wildly popular ChatGPT). LLM Large Language Model An AI model that has been trained using lots of data. Salesforce has their own LLM that outperformed expectations during testing. Salesforce’s LLM leverages Data Cloud, or can be plugged into a different data lake. Organizations can also opt to their own LLM alongside their Salesforce data. NBA Einstein Next Best Action An AI-driven Salesforce product that makes recommendations to users when they are working with records in Salesforce. It appears within a Lightning component that’s typically placed on the record page sidebar. The user can choose to Accept or Reject the recommendation. NLP Natural Language Processing “Artificial intelligence that focuses on how computers can understand, interpret, and generate human language. NLP allows customers to interact with systems using normal human language rather than complex commands.” (source) SAQL Structured Query Language “Use SAQL to access data in Tableau CRM dataset. Tableau CRM uses SAQL behind the scenes in lenses, dashboards, and explorer to gather data for visualizations.” (source) TCRM Tableau CRM Tableau CRM is a self-service data visualization and business intelligence (BI) platform that integrates Salesforce data with external data. Formerly known as Einstein Analytics, and now CRM Analytics, this product boasts some of the most powerful data analysis capabilities in the market. XMD Extended Metadata “Enables you to customize the formatting of dataset fields and their values in Analytics dashboards and lenses.” (source) 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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July 15, 2023in Salesforce
Salesforce AI
14Jul

Salesforce Einstein and Social Studio

The digital era (sometimes considered the third industrial revolution) has transformed the dynamics of customer-business interactions, demanding a proactive approach to customer engagement across diverse channels. Navigating this ever-evolving world is essential for business success, and Social Studio is a valuable asset in this endeavor. Social Studio, in conjunction with Einstein AI, plays a pivotal role in achieving social media success. Recognizing the significance of social media in shaping brand identity and fostering customer relationships, marketers are tasked with maintaining an active and responsive presence across various social media platforms. This challenge is met by Social Studio, offering a centralized hub for comprehensive social media management. This platform facilitates content planning and publication, team collaboration, content approval, audience engagement, and performance analysis. Equipped with advanced scheduling tools, user-friendly content creation features, and customizable approval rules, Social Studio ensures the safeguarding of your brand’s integrity across multiple platforms and messaging. Notably, Social Studio seamlessly integrates with Salesforce Marketing Cloud, providing an all-encompassing solution for efficient social media management. Its capabilities extend to user role management, image classification through Einstein Vision, and process automation using macros. With Social Studio, users gain access to a unified platform for content creation, scheduling, and monitoring, audience interaction, and performance analysis. Whether collaborating within a team or managing multiple accounts, Social Studio streamlines social media efforts, empowering users to achieve their goals. Embrace the advantages of this robust tool for an enhanced social media experience! Social Studio is a one-stop solution to manage, schedule, create, and monitor posts. You can organize posts by brand, region, or multiple teams and individuals in a unified interface. Social Studio offers powerful real-time publishing and engagement. Social Studio offers powerful real-time publishing and engagement platform for content marketers, plus the comprehensive content performance by social network and time frame. A single interface offers a fully customizable team-based collaboration platform that analyzes channel and content performance. Analyze current trends and recommend new content ideas. With Social Studio you can: Social Studio Components Social Studio is made up of these components: Note: Salesforce will sundown Social Studio on November 18, 2024, but some users will lose access before then if their contract expires sooner.  Salesforce recommends retrieving your Social Studio data at least 90 days before the Order End Date of your Marketing Cloud Social Studio Product(s) or November 18, 2024, whichever is sooner.  The digital, or third industrial revolution is the shift from mechanical and analogue electronic technologies from the Industrial Revolution towards digital electronics which began in the latter half of the 20th century.  This was prompted with the adoption and evolution of digital computers. (source) 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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July 14, 2023in Salesforce Einstein, Salesforce Implementation Services, Salesforce Marketing Cloud
Salesforce Marketing Cloud Journey Builder
13Jul

Journey Builder Explained

In B2C marketing, the focus on Journey Builder within the Marketing Cloud framework is essential to take customers on journeys with personalized interactions depending on where they are at in the buying cycle. This tool empowers marketers to craft intricate marketing journeys that deliver personalized experiences to customers. Operating within Marketing Cloud, the journey tool orchestrates comprehensive customer journeys, facilitating interactions across multiple platforms such as email, mobile, advertising, and websites. It stands as a foundational element of Marketing Cloud, primarily tailored for B2C initiatives. Salesforce Journey Builder facilitates a deeper understanding of customers by triggering actions based on their unique behaviors and ensuring consistent messaging across channels. As consumers navigate seamlessly between platforms and devices, brands must offer personalized and seamless journeys to maximize customer lifetime value. To achieve this, marketers must address key questions: Answering these questions requires a comprehensive view of the customer journey, with actions aligned to evolving customer expectations. With Salesforce Marketing Cloud Journey Builder, marketers can attain a unified view of all customer interactions, optimizing end-to-end journeys. Journey Builder provides visibility into consumer interactions across marketing channels, including email, mobile, social ads, and more. By connecting these interactions, marketers gain insights for improved message crafting, campaign design, and automation, fostering seamless customer experiences and fostering loyalty. Interactions a customer may have with the brand throughout their journey include clicking on an ad, opening an email, making a purchase, conversing with customer support, and more. Journey Builder, as an event-driven tool, initiates conversations based on customer history, preferences, and real-time behavior, supporting visual mapping of simple or complex journeys. However, Journey Builder operates within Marketing Cloud and utilizes content and audiences from Email Studio, Mobile Studio, Advertising Studio, Content Builder, and Audience Builder. It leverages event-driven triggers to react to customer actions, such as downloading an app or leaving a shopping cart abandoned, thus enabling timely and relevant responses. Key features of Journey Builder include a user-friendly drag-and-drop interface, entry and filter criteria for swift actions, and powerful add-ons for enhanced functionality. Ultimately, Salesforce Journey Builder facilitates a seamless customer experience by guiding journeys, ensuring consistent messaging, adapting to evolving needs, and maintaining brand consistency across channels. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 13, 2023in Data, Google, Salesforce, Salesforce Marketing Cloud
08Jul

Is Prompt Engineering Dying?

The Rise and Fall of Prompt Engineering Prompt engineering is everywhere—it’s the hot topic in the AI world. The World Economic Forum, OpenAI’s Sam Altman, and the Twitterverse can’t stop talking about it. My feeds are filled with ads promoting courses that promise to make you a fortune with minimal effort. But here’s the uncomfortable truth: prompt engineering is already facing its decline. Don Giannatti originally wrote on this topic in June of 2023. Whic got us thinking, is prompt engineering dying? Why Is Prompt Engineering Fading? Reason 1: AI Is Getting Smarter AI is advancing rapidly. Machines are starting to understand our words and phrases just like we do, similar to a child learning to talk. The need for finely tuned prompts is decreasing because AI is developing the ability to generate its own prompts simply by interpreting questions. It’s learning all the time. Reason 2: AI Crafts Its Own Prompts SDon already usea minimal nudge prompts, which AI then expands into detailed, contextually accurate prompts. GPT-4 can do this, and GPT-5 will have it even more integrated. While prompt engineering has been trendy among marketers and tech enthusiasts, its relevance is quickly waning. Reason 3: Prompts Are Limited in Versatility Prompts are tailored for specific AI models and versions, limiting their flexibility. AI can overcome these limitations more efficiently than humans. Machine learning excels in reducing input and friction, and AI is quickly learning and improving upon human-made prompts. The Future: Problem Formulation The enduring skill in the AI age is problem formulation—how we identify, analyze, and define problems. When we can clearly illustrate a problem, AI can provide efficient solutions. AI cannot identify unquantifiable problems that aren’t part of existing systems—that’s still a human strength, for now. Prompt Engineering vs. Problem Formulation Prompt engineering focuses on the words, sentence structure, and punctuation. Problem formulation is about defining the problem—seeing the bigger picture and broader strokes. Without a well-defined problem, even the best-crafted prompt is just a set of words. Why Problem Formulation Matters Problem formulation has been overshadowed by problem-solving. It’s not easy, isn’t taught in universities, and isn’t popularized by futurists. Yet, it’s essential. Executives often struggle with diagnosing problems—85% of them say so. To stay ahead, we need better problem formulation. Four Ways to Enhance Problem Formulation Embracing AI Wisely AI is evolving quickly. To leverage its potential, we must clearly identify problems. Once defined, AI can generate prompts to find solutions. A Take on AI While one can appreciate the educational and helpful capabilities of GPT and other language models, be cautious about the rapid integration of AI into our lives without adequate discussion or input from society. I trust AI more than the billionaires driving its adoption, but be wary of their motivations. 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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July 8, 2023in Salesforce Einstein
Leverage AI and Machine Learning in Your Data Warehouse
06Jul

Leverage AI and Machine Learning in Your Data Warehouse

5 Reasons to Leverage AI and Machine Learning in Your Data Warehouse Incorporating AI and machine learning (ML) into a data warehouse transforms it into a powerful tool for decision-making and insight generation across the entire organization. Here are five key benefits of integrating AI and ML into your data warehouse: 1. Improved Efficiency AI and ML streamline data warehouse operations by automating time-consuming tasks like data validation and cleansing. These technologies can manage repetitive processes, such as extraction, transformation, and loading (ETL), freeing data teams to focus on higher-priority tasks that drive business value. AI and ML ensure that inconsistencies are addressed automatically, which boosts overall operational efficiency. 2. Faster Performance ML can monitor query performance in real time, identifying bottlenecks and optimizing processes to increase speed and accuracy. Automating data ingestion and delivery enables users to act on insights faster, making real-time decision-making possible. Faster data processing leads to more timely and effective business strategies. 3. Increased Accessibility for All Users AI and ML enhance data quality and simplify data queries, making insights accessible even to non-technical users. By allowing natural language inputs and generating easy-to-understand visualizations, these technologies empower employees at all skill levels to interact with data. When everyone in the organization works from the same data foundation, decision-making becomes more aligned and consistent. 4. More Accurate Forecasting ML’s predictive capabilities allow data warehouses to anticipate trends and proactively solve problems before they arise. Predictive models and anomaly detection help prevent downtime, improve customer demand forecasting, and enhance overall accuracy. The more these algorithms are used, the more refined and effective they become, improving insights and forecasts over time. 5. Reduced Data Storage Costs AI and ML analyze data usage to optimize storage solutions, identifying and eliminating redundant data to free up space. These technologies can also optimize data architecture, making the warehouse more efficient and reducing operational costs. As an organization scales, AI and ML help manage growing data volumes without increasing expenses, ensuring cost-effective data storage and processing. By integrating AI and ML into a data warehouse, organizations can enhance speed, efficiency, and accuracy, driving better decision-making and improving business outcomes. Content updated October 2024. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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July 6, 2023in Data

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