Customer Lifetime Value Archives - gettectonic.com - Page 2
Salesforce Service Cloud

Differences Between Salesforce Sales Cloud and Salesforce Service Cloud Explained

Salesforce Sales Cloud focuses on the sales process, while Salesforce Service Cloud is dedicated to customer service and support. Sales Cloud is designed for managing leads, opportunities, and sales forecasts, whereas Service Cloud is tailored for handling customer inquiries and cases. Both Sales Cloud and Service Cloud share critical features as they are built on the core Salesforce Platform. If your business primarily emphasizes sales, Sales Cloud is the ideal choice. If your focus is more on customer service, then Service Cloud is the preferred option. For businesses involved in both sales and customer service, both Sales Cloud and Service Cloud may be the best Salesforce solution. Difference Between Sales Cloud and Service Cloud: Sales Cloud streamlines sales and marketing efforts, focusing on lead management and increasing sales. Service Cloud helps support agents provide excellent customer service, resolving issues proactively. Functions Included in Service Cloud but not in Sales Cloud: Service Cloud includes specialized functions for customer support, such as omnichannel case routing, Web-to-Case and Email-to-Case conversion, and configurable assignment rules to streamline support agents’ work. Sales Cloud Features: Service Cloud Features: Agent Productivity (Service Cloud): Call Center Management (Service Cloud): Live Chat (Service Cloud): Customer Portal (Service Cloud): Ticket Management (Service Cloud): Like2 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

Read More
Salesforce CDP Innovations

Salesforce CDP Innovations

New Salesforce CDP Innovations: Smarter, Faster, and More Personalized Customer Interactions Salesforce has launched new innovations for its Customer Data Platform (CDP), designed to help businesses leverage first-party data for more personalized customer experiences. Leading brands like Bank of Montreal and convenience store retailer Casey’s are already using Salesforce CDP to create a unified source of customer truth, streamlining interactions and providing frictionless customer experiences. The world is gradually recovering from the pandemic, and consumer behavior is shifting as shops, hotels, restaurants, and other establishments reopen. While customers are eager to engage in the experiences they’ve missed, companies recognize that digital innovations, such as curbside pickup and direct-to-consumer websites, which fueled pandemic-era growth, are here to stay. As expectations for personalized, connected experiences grow—with 70% of customers demanding this—many businesses struggle to unify customer data across systems, teams, and devices. This data fragmentation makes it difficult to create a single source of truth for customers. Salesforce CDP: Built on the World’s Leading CRM Salesforce CDP solves this challenge by capturing, unifying, and activating customer data across various touchpoints to drive more personalized experiences. Today’s new CDP features make data smarter, more connected, and easier to activate securely. Built on Salesforce’s #1 CRM platform, the CDP unifies data from sales, service, marketing, loyalty, and commerce systems, creating a comprehensive single source of truth. Businesses can then leverage this unified view for personalized marketing, advertising, analytics, and relationship-building strategies that increase customer loyalty and revenue. New Innovations in Salesforce CDP Include: How Businesses Are Using Salesforce CDP Availability of New Features: This insight helped you learn more about these innovations and how Salesforce CDP can enhance customer engagement from anywhere. 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

Read More
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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
  • 1
  • 2
gettectonic.com