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What is BI in Salesforce?

Salesforce BI helps to create fast, digestible reports to help you make informed decisions at the right time. Salesforce Einstein is a leading business intelligence software solution that will help streamline your operations. Read on in this insight to learn how Salesforce BI capabilities including Tableau rank in the Gartner Magic Quadrant. Make the right decision every time using analytics that go beyond business intelligence software. See why Gartner named Salesforce (Tableau) a Leader in the Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms for the 11th consecutive year. Data and analytics leaders must use analytics and BI platforms to support the needs of IT, analysts, consumers and data scientists. While integration with cloud ecosystems and business applications is a key selection requirement, buyers also need platforms to support openness and interoperability. Analytics and business intelligence (ABI) platforms enable less technical users, including business people, to model, analyze, explore, share and manage data, and collaborate and share findings, enabled by IT and augmented by artificial intelligence (AI). For several years, the Magic Quadrant for Analytic and Business Intelligence Platforms has emphasized visual self-service for end users augmented by AI to deliver automated insights. While this remains a significant use case, the ABI platform market will increasingly need to focus on the needs of the analytic content consumer and business decision makers. To achieve this, automated insights must be relevant in context of a user’s goals, actions and workflow. Many platforms are adding capabilities for users to easily compose low-code or no-code automation workflows and applications. This blend of capabilities is helping to expand the vision for analytics beyond simply delivering datasets and presenting dashboards. Today’s ABI platforms can deliver enriched contextualized insights, refocus attention on decision-making processes and ultimately take actions that will deliver business value. In addition to the increasing consumer design focus trend, we see other key market trends, including the need for improved governance of analytic content creation and dissemination, and the demand for a headless, open architecture. For example, a headless ABI platform would decouple the metrics store from the front-end presentation layer, enabling more interoperability with competitive products. ABI platform functionality includes the following 12 critical capabilities, which have been updated to reflect areas of market change, differentiation and customer demand: Gartner added three new critical capabilities as part of our metrics store evaluation criteria this year:  ABI platforms have always been about measurement. For decades, the slicing and dicing of measures by their dimensional attributes was synonymous with the act of performing business intelligence. However, over the last decade, the focus on metrics and measurement was overshadowed by data visualization. As data visualization became the most conspicuous capability, some business executives began to conflate ABI platforms with data visualization — as if ABI platforms are glorified chart wizards. This misconception minimizes much of the work performed and the business value delivered by ABI platforms. Establishing metrics stores as a critical capability to execute makes it clear that defining and communicating performance measures throughout an organization is one of the key purposes of an ABI platform. Analytics collaboration is a combination of many features (such as Slack/Teams integration, action frameworks) that collectively improve an organization’s ability to make decisions with consensus. Data science integration reflects the increasing likelihood that a business analyst may want to use data science to test certain hypotheses, and that data scientists will need to leverage features such as data prep and data visualization. In addition, Gartner is changing “catalogs” to “analytic catalogs” to emphasize a set of requirements that are not being met by ABI platform vendors today. Most large enterprises have thousands of reports built across multiple ABI platforms, but consumers in these organizations have no easy way to access these reports. The name change to analytic catalogs reflects the need for ABI platform vendors to deliver analytic content with the consumer in mind. Three critical capabilities were removed from our evaluation criteria: security, natural language generation (NLG; rolled into data storytelling) and cloud analytics (which will no longer be considered a platform capability, but instead a go-to-market strategy covered in the Magic Quadrant). And one of the security sub-criteria, about the granularity of authorization (e.g., row-based security) has been moved to the enterprise reporting capability. Salesforce (Tableau) Tableau, a Salesforce company, is a Leader in this Magic Quadrant. Its products are mainly focused on visual-based exploration that enables business users to access, prepare, analyze and present findings in their data. CRM Analytics, formerly Tableau CRM, provides augmented analytics capabilities for analysts and citizen data scientists. Tableau has global operations and serves clients of all sizes. In 2022, Tableau reinforced its augmented consumer vision to provide contextualized insights with deeper integration with Salesforce Data Cloud. IT also improved decision intelligence by bringing domain-aware insights into action with Revenue Intelligence and other Salesforce-native apps. The extensible design and x-platform integrations (Salesforce Flow, MuleSoft, UiPath and Looker) further enable composable analytics to bring insights into workflow with agility. Strengths Cautions 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

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Einstein Discovery

Einstein Discovery Analysis

Elevate Your Business Outcomes with Einstein Discovery Analysis Einstein Discovery revolutionizes your approach to predictive analytics, allowing you to effortlessly build reliable machine learning models without any coding. Reduce reliance on data science teams with an intuitive model-building wizard and streamlined monitoring process. Transition swiftly from data to actionable insights, ensuring every decision is guided by intelligence. Enhance Your Business Intelligence with Einstein Discovery Incorporate statistical modeling and machine learning into your business intelligence with Einstein Discovery. Seamlessly integrated into your Salesforce environment, operationalize data analysis, predictions, and enhancements with clicks, not code. Developers can utilize the Einstein Prediction Service to access predictions programmatically, while data specialists can predict outcomes within recipes and dataflows. Tableau users can also leverage Einstein Discovery predictions and improvements directly within Tableau. Advanced Analytics Made Simple with Einstein Discovery Einstein Discovery offers a comprehensive suite of business analytics tailored to your specific data needs. Licensing and Permission Requirements for Einstein Discovery To utilize Einstein Discovery, your organization needs the appropriate license, with user accounts assigned relevant permissions. Supported Use Cases and Implementation Tasks Einstein Discovery solutions effectively address common business use cases, typically involving a series of defined implementation tasks. Key Differentiation: Einstein Analytics vs. Einstein Discovery While Einstein Analytics integrates predictive and analytical capabilities within Sales, Service, and Marketing clouds, Einstein Discovery is specifically focused on providing actionable insights and data-driven stories. Key Benefits of Einstein Discovery Supported Data Integration and Functionality Einstein Discovery enables direct integration and import of data from external sources like Hadoop, Oracle, and Microsoft SQL Server. It extracts data from diverse sources, leveraging AI, ML, and statistical intelligence to identify patterns and generate informed predictions. Enhanced Features Einstein Discovery seamlessly integrates insights into Tableau workflows, unlocks insights from unstructured data, fine-tunes prediction accuracy with trending data, handles missing values in datasets, accelerates prediction processing with high-volume writeback, and offers enhanced settings panels for efficient prediction management. Partner with Tectonic for Expert Guidance Collaborate with experienced Salesforce services providers like Tectonic to maximize the benefits of Einstein Discovery, ensuring a seamless implementation process and ongoing support. Empower Your Business with Einstein Discovery Einstein Discovery delivers automated data analysis, interactive visualizations, and predictive insights to elevate decision-making and optimize business operations. Unlock the power of AI-driven analytics within your Salesforce ecosystem to accelerate growth and gain a competitive edge. 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

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Opportunity Scoring with Einstein

Opportunity Scoring with Einstein

Salesforce has placed significant emphasis on developing their new Artificial Intelligence engine, Einstein, in recent years. One standout feature that has garnered attention is Einstein Opportunity Scoring, which will become available for free following the Summer ’20 release. This feature is offered to all Salesforce customers. So, what exactly is Einstein Opportunity Scoring? It’s a system that leverages data science and machine learning to score sales opportunities, enabling users to prioritize their actions effectively. Here’s how it works: The advantages of opportunity scoring are manifold: So, how does Einstein Opportunity Scoring operate? To implement Einstein Opportunity Scoring, certain prerequisites must be met: Building the prediction set involves defining positive and negative examples based on various criteria such as past outcomes, opportunity progress, and account activity. Implementation entails setting up the prediction using the Einstein Prediction Builder, defining the segment, specifying prediction outcomes, and selecting relevant fields. After deployment, users can monitor the prediction set status and review the predictions to ensure they align with business objectives. Over time, real-life data can be analyzed to assess the accuracy and effectiveness of the predictions. To enhance opportunity scores, users are advised to maintain accurate data, progress opportunities through stages promptly, and ensure completeness of opportunity records. Access to Einstein Opportunity Scoring is included in the Sales Cloud Einstein product suite at no additional cost, accessible through the Sales Cloud Einstein For Everyone permission set. By leveraging Einstein Opportunity Scoring, Salesforce customers can optimize their sales processes, improve efficiency, and make more informed decisions to drive business success. 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

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AI-driven propensity scores

AI-Driven Propensity Scores

AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables through machine learning, without explicit programming. This insight has gone through numerous updates as the information and use of AI-driven propensity scores evolved. In many cases, writers give a brief overview of the what of a tool. Today, we are going way beyond “what the sausage tastes like” to “how the sausage is made” Tectonic hopes you will enjoy learning how propensity models and AI driven propensity scores improve your data. Propensity Model in Artificial Intelligence: Propensity modeling generates a propensity score, representing the probability that a visitor, lead, or customer will take a specific action. For instance, a propensity model, using data science or machine learning, can help predict the likelihood of a lead converting to a customer. AI-driven propensity scores take an existing data model and improve its predictions, speed, and analysis with AI. Propensity Score in CRM: In CRM, a propensity score is the model’s probabilistic estimate of a customer performing a specific action. Grouping customers by score ranges allows for effective comparison and analysis within each bucket. Enhancing Propensity Modeling with AI: Traditional statistical propensity models might lack accuracy, but integrating machine learning technologies, as demonstrated by Alphonso, can significantly optimize ad spend and increase prediction accuracy from 8% to 80%. That’s a whopping 72% improvement. Propensity Modeling Overview: Propensity modeling involves predictive models analyzing past behaviors to forecast the future actions of a target audience. It identifies the likelihood of specific actions, aiding in personalized marketing. Role of Machine Learning in Propensity Models: Propensity models rely on machine learning algorithms, acting as binary classifiers predicting whether a certain event or behavior will occur. Logistic regression and Classification and Regression Tree Analysis are common methods for calculating propensity scores. Characteristics of Effective Propensity Models: For robust predictions, propensity models should be dynamic, scalable, and adaptive. Dynamic models adapt to trends, scalable for diverse predictions, and adaptive with regular data updates. Propensity Modeling Applications: Propensity models find applications in predicting customer behavior, such as purchasing, converting, churning, or engaging. Real-time predictions, data analysis, and AI integration contribute to successful implementations. AI-driven propensity scores are extremely useful in that they can be coupled with many other models to give additional insights to your data. Types of Propensity Score Models: Various models include propensity to purchase/convert, customer lifetime value (CLV), propensity to churn, and propensity to engage. Combining models can enhance the effectiveness of marketing campaigns. When to Use Propensity Scores: Propensity scores are beneficial when random assignment of treatments is impractical. They help estimate treatment effects in observational studies, providing an alternative to traditional model-building methods. Limitations of Propensity Score Methods: While propensity scores help achieve exchangeability between exposed and unexposed groups, they do not claim to eliminate confounding due to unmeasured covariates. Findings from observational studies must be interpreted cautiously due to potential residual confounding. Content updated October 2021. Content updated February 2024. Like3 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

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Salesforce Service Cloud

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, and Salesforce’s CRM platform is no exception. Just a week after introducing a predictive decision-making tool in Marketing Cloud, Salesforce has unveiled a new Intelligence Engine for Service Cloud, designed to optimize customer interactions for service teams. One of the standout features is Intelligent Business Processes, which enables companies to assign cases dynamically based on agent expertise, case history, availability, or the channel through which the request originated. The system can also automate workflow triggers—escalating cases beyond support to sales or marketing. For example, high-value leads can be routed directly to top-performing sales teams for upsell opportunities. Intelligent Workload Management further enhances Service Cloud’s capabilities by automating case distribution across agents based on workload and skill set. Instead of manually assigning cases, the system ensures that tasks are balanced in real time. If an agent is engaged in a video consultation, for instance, new cases will be temporarily paused from reaching their queue until they become available. To improve customer experience, Seamless Omni-Channel Customer View ensures continuity across channels. If a customer initially reaches out via email but later requests a video chat, they’ll be routed to the same agent—eliminating the need to repeat information or restart the conversation. These innovations do more than just optimize service operations; they help businesses tailor customer interactions in a meaningful way, says Denis Pombriant, managing principal at Beagle Research Group. “Businesses often prioritize transactions, but customers need process—because process informs them about their needs and vendor capabilities,” Pombriant explains. “Using marketing analytics to identify customer needs from multiple data sources allows vendors to better understand their audience and provide the right solutions.” Many automated systems neglect this process-driven approach, instead focusing solely on transactions. Salesforce’s Intelligence Engine aims to bridge that gap. Service Cloud is priced at $135 per user, per month, while the Intelligence Engine will be generally available later this year—with pricing yet to be announced. Businesses interested in early access can contact their Salesforce account executive to join the pilot program. 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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