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Einstein Relationship Insights

Einstein Relationship Insights

Setting Up Einstein Relationship Insights: Configure ERI Insights to empower your sales team in managing relationships among individuals, companies, and their interactions. Follow these steps for enabling and configuring: Enabling Einstein Relationship Insights: To enable ERI, follow these steps: Understanding Einstein Relationship Insights (ERI): ERI, serves as an AI-powered research assistant, enhancing sales processes. ERI operates as a desktop plugin with a browser extension, exploring diverse data sources to provide relevant insights for expediting deal closures. Key Features of ERI: Salesforce Einstein Relationship Insights Implementation: Sales representatives benefit from ERI’s relationship intelligence, providing: Accelerating Sales with ERI: Supercharge Sales with Intelligent Relationship Management: Salesforce ERI improves customer understanding. AI-driven relationship management aids in forming deeper connections. Salesforce offers various tools and capabilities to enhance sales productivity. Salesforce introduced Einstein Relationship Insights, a new AI-powered research agent that autonomously explores the internet and internal data sources to discover relationships between customers, prospects, and companies, assisting sales reps in closing deals faster. Einstein Relationship Insights functions as a virtual assistant, scanning the web, social media, collaboration apps, email, and other online sources to uncover and recommend related people and companies. Why it matters: Einstein Relationship Insights showcases how AI collaborates with, rather than replaces, salespeople to enhance deal closure and increase revenue. AI-powered tools are crucial for over-burdened reps, automating critical relationship research and network analysis around key decision-makers. For further details on how Salesforce solutions can address your business needs, consult the Tectonic team for comprehensive assistance Content updated February 2024. Like1 Related Posts 50 Advantages of Salesforce Sales Cloud According to the Salesforce 2017 State of Service report, 85% of executives with service oversight identify customer service as a Read more Salesforce Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains 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 Artificial Intelligence Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust Read more Salesforce’s Quest for AI for the Masses The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Sales Cloud Einstein Forecasting Salesforce, the global leader in CRM, recently unveiled the next generation of Sales Cloud Einstein, Sales Cloud Einstein Forecasting, incorporating Read more

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Sales Cloud Einstein Forecasting

Sales Cloud Einstein Forecasting

Salesforce, the global leader in CRM, recently unveiled the next generation of Sales Cloud Einstein, Sales Cloud Einstein Forecasting, incorporating AI into every phase of the sales cycle, from pipeline building and deal closing to business growth. The new Einstein Forecasting tool enhances visibility and intelligence, enabling sales leaders to more accurately predict sales revenue at each stage. Additionally, Einstein Opportunity Scoring prioritizes high-value opportunities, and Einstein Email Insights identifies critical emails, allowing sales reps to sell faster and smarter. Introducing Einstein Forecasting Einstein Forecasting revolutionizes business predictability, benefiting everyone from the head of sales to the CFO. Traditional forecasting methods, often reliant on incomplete spreadsheets and outdated systems, result in inaccurate projections, underperformance, and business disconnection. In fact, less than half of deals close as forecasted, leading to missed quotas and revenue shortfalls. Sales Cloud Einstein Forecasting Einstein Forecasting is a fully automated, out-of-the-box solution utilizing a company’s historical CRM data to eliminate guesswork. By combining data mining and machine learning, it analyzes factors like seasonality and historical performance to deliver highly accurate, individualized sales forecasts. Its self-learning algorithms assess individual and team forecasting behaviors, adjusting for optimism or pessimism to provide unbiased analysis. Moreover, Einstein translates forecast data into human language, helping sales leaders understand pipeline expectations and reasons. For example, a regional manager for a trucking company can use a dashboard to see if the team is on track or if any deals are in jeopardy of not closing or being lost. This foresight allows for timely intervention. Similarly, a CFO considering expansion can use Einstein Forecasting to predict funding availability for new regions. Enhancing Sales Reps’ Efficiency with AI In addition to Einstein Forecasting, Salesforce introduced Einstein Opportunity Scoring and Einstein Email Insights to keep sales reps focused on vital deals. Einstein Opportunity Scoring Einstein Opportunity Scoring identifies, surfaces, and prioritizes the most valuable deals within Sales Cloud, such as those with large deal sizes and significant executive engagement. It monitors deals in progress, flagging high-value deals at risk, allowing reps to concentrate on building the pipeline and closing deals efficiently. For instance, sales reps can focus on the most promising deals instead of spending hours sifting through opportunities. Einstein Email Insights Einstein Email Insights acts as a personal email assistant for sales reps, powered by natural language processing (NLP). It identifies crucial emails and recommends actions or responses, helping reps prioritize their inbox and quickly address customer needs. This proactive approach ensures that deals continue moving forward, from scheduling meetings to sending quotes. For example, a sales rep returning from a day of meetings can quickly find and address important emails without sifting through their inbox. Benefits of Einstein Forecasting Einstein Forecasting leverages AI technology to bring certainty and visibility to forecasts, enhancing accuracy, predicting outcomes, and tracking team performance. Salesforce Einstein Celebrates One Year of Innovation Since its launch in September 2016, Salesforce Einstein has brought AI capabilities to every business user, transforming customer experiences across the Customer Success Platform. Today, Einstein delivers over 475 million daily predictions, enabling companies like U.S. Bank, Room&Board, FareCompare, Silverline, and Black Diamond to operate smarter and more productively. Additionally, Einstein Platform Services empower developers to build AI-powered CRM apps using computer vision and NLP, with over 7,000 developers already creating Einstein-powered apps. Under Chief Scientist Dr. Richard Socher, Salesforce Research has published 10 academic papers, advancing deep learning technology for Salesforce customers. Einstein Predictions Enabling Einstein Forecasting displays the Einstein prediction column on the forecasts page, showing median predicted amounts for each manager’s team based on opportunities within the Best Case and Commit forecast categories. Predictions may not appear if there is insufficient historical data or a large prediction range. Predictions are typically in US dollars unless multiple currencies are used, in which case amounts are converted based on the static conversion rate set by the Salesforce admin. Selecting a prediction value reveals detailed information in the side panel, including the prediction range, a breakdown of wins from existing and new deals, and top factors contributing to the prediction. The Forecast Changes Chart offers a visualization of predicted closings within a forecast period, highlighting key performance indicators. New Salesforce AI Innovation Fund To foster next-generation AI solutions, Salesforce Ventures announced a $50 million Salesforce AI Innovation Fund. Fast-growing AI startups Highspot, Squirro, and TalkIQ are the first recipients, accelerating their development of transformative AI solutions on Salesforce. Additionally, Salesforce Ventures has invested in All Turtles, an AI startup studio partnering with founding teams to create AI-centric products. Salesforce will collaborate with All Turtles to co-create advanced AI solutions on the Salesforce platform. 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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Roles in AI

Salesforce’s Quest for AI for the Masses

The software engine, Optimus Prime (not to be confused with the Autobot leader), originated in a basement beneath a West Elm furniture store on University Avenue in Palo Alto. A group of artificial intelligence enthusiasts within Salesforce, seeking to enhance the impact of machine learning models, embarked on this mission two years ago. While shoppers checked out furniture above, they developed a system to automate the creation of machine learning models. Thus Salesforce’s Quest for AI for the Masses started. Despite being initially named after the Transformers leader, the tie-in was abandoned, and Salesforce named its AI program Einstein. This move reflects the ambitious yet practical approach Salesforce takes in the AI domain. In March, a significant portion of Einstein became available to all Salesforce users, aligning with the company’s tradition of making advanced software accessible via the cloud. Salesforce, although now an industry giant, retains its scrappy upstart identity. When the AI trend gained momentum, the company aimed to create “AI for everyone,” focusing on making machine learning affordable and accessible to businesses. This populist mission emphasizes practical applications over revolutionary or apocalyptic visions. Einstein’s first widely available tool is the Einstein Intelligence module, designed to assist salespeople in managing leads effectively. It ranks opportunities based on factors like the likelihood to close, offering a practical application of artificial intelligence. While other tech giants boast significant research muscle, Salesforce focuses on providing immediate market advantages to its customers. Einstein Intelligence The Einstein Intelligence module employs machine learning to study historical data, identifying factors that predict future outcomes and adjusting its model over time. This dynamic approach allows for subtler and more powerful answers, making use of various data sources beyond basic Salesforce columns. Salesforce’s AI team strives to democratize AI by offering ready-made tools, ensuring businesses can benefit from machine learning without the need for extensive customization by data scientists. The company’s multi-tenant approach, serving 150,000 customers, keeps each company’s data separate and secure. Salesforce’s Quest for AI for the Masses To scale AI implementation across its vast customer base, Salesforce developed Optimus Prime. This system automates the creation of machine learning models for each customer, eliminating the need for extensive manual involvement. Optimus Prime, the AI that builds AIs, streamlines the process and accelerates model creation from weeks to just a couple of hours. Salesforce plans to expand Einstein’s capabilities, allowing users to apply it to more customized data and enabling non-programmers to build custom apps. The company’s long-term vision includes exposing more of its machine learning system to external developers, competing directly with AI heavyweights like Google and Microsoft in the business market. Originally published in WIRED magazine on August 2, 2017 and rewritten for this insight. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Salesforce CRM for AI driven transformation

Salesforce Artificial Intelligence

Is artificial intelligence integrated into Salesforce? Salesforce Einstein stands as an intelligent layer embedded within the Lightning Platform, bringing robust AI technologies directly into users’ workspaces. The Einstein Platform offers administrators and developers a comprehensive suite of platform services, empowering them to create smarter applications and tailor AI solutions for their enterprises. What is the designated name for Salesforce’s AI? Salesforce Einstein represents an integrated array of CRM AI technologies designed to facilitate personalized and predictive experiences, enhancing the professionalism and attractiveness of businesses. Since its introduction in 2016, it has consistently been a leading force in AI technology within the CRM realm. Is Salesforce Einstein a current feature? “Einstein is now every customer’s data scientist, simplifying the utilization of best-in-class AI capabilities within the context of their business.” Is Salesforce Einstein genuinely AI? Salesforce Einstein for Service functions as a generative AI tool, contributing to the enhancement of customer service and field service operations. Its capabilities extend to improving customer satisfaction, cost reduction, increased productivity, and informed decision-making. Salesforce Artificial Intelligence AI is just the starting point; real-time access to customer data, robust analytics, and business-wide automation are essential for AI effectiveness. Einstein serves as a comprehensive solution for businesses to initiate AI implementation with a trusted architecture that prioritizes data security. Einstein is constructed on an open platform, allowing the safe utilization of any large language model (LLM), whether developed by Salesforce Research or external sources. It offers flexibility in working with various models within a leading ecosystem of LLM platforms. Salesforce’s commitment to AI is evident through substantial investments in researching diverse AI areas, including Conversational AI, Natural Language Processing (NLP), Multimodal Data Intelligence and Generation, Time Series Intelligence, Software Intelligence, Fundamentals of Machine Learning, Science, Economics, and Environment. These endeavors aim to advance technology, improve productivity, and contribute to fields such as science, economics, and environmental sustainability. Content updated April 2023. 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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