business intelligence Archives - gettectonic.com

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 You may still come across EA in outdated documentation.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 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

July 15, 2023in Salesforce

data cloud and marketing cloud personalization

11Jul

What is the Difference in a Data Lake and a Data Warehouse

Is a Data Lake Necessary? Difference in a Data Lake and a Data Warehouse? Do I need both? Both Data Lakes and Data Warehouses play crucial roles in the data processing and reporting infrastructure. They are complementary approaches rather than substitutes. Relevance of Data Lakes: Data lakes are losing popularity compared to their previous standing. Advanced storage solutions like data warehouses are progressively taking their place. Can Data Lakes Replace Data Warehouses? Data lakes do not directly replace data warehouses; they serve as supplementary technologies catering to different use cases with some overlap. Organizations typically have both a data lake and a data warehouse. Distinguishing Between Data Lakes and Data Warehouses: Data lakes and data warehouses serve as storage systems for big data, utilized by data scientists, data engineers, and business analysts. Despite some similarities, their differences are more significant than their commonalities, and understanding these distinctions is vital for aspiring data professionals. Data Lake vs. Data Warehouse: Key Differences: Data lakes aggregate structured and unstructured data from multiple sources, resembling real lakes with diverse inflows. Data warehouses, on the other hand, are repositories for pre-structured data intended for specific queries and analyses. Exploring Data Lakes: A data lake is a storage repository designed to capture and store large amounts of raw data, whether structured, semi-structured, or unstructured. This data, once in the lake, can be utilized for machine learning or AI algorithms and later transferred to a data warehouse. Data Lake Examples: Data lakes find applications in various sectors, such as marketing, education, and transportation, addressing business problems by collecting and analyzing data from diverse sources. Understanding Data Warehouses: A data warehouse is a centralized repository and information system designed for business intelligence. It processes and organizes data into categories called data marts, allowing for structured data storage from multiple sources. Data Warehouse Examples: Data warehouses support structured systems and technology for diverse industries, including finance, banking, and food and beverage, facilitating secure and accurate report generation. Data Warehouses compared to Data Lakes: Data warehouses contain processed and sanitized structured data, focusing on business intelligence, while data lakes store vast pools of unstructured, raw data, providing flexibility for future analysis. Key Differences Between Warehouses and Lakes: Intended purpose, audience, data structure, access and update cost, access model, and storage and computing are crucial factors distinguishing data warehouses and data lakes. Choosing Between Data Warehouse and Data Lake: The decision depends on organizational needs, value extracted from data analysis, and infrastructure costs. Organizations may opt for agility with a data lake, a data warehouse for larger data quantities, or a combination for maximum flexibility. A data lake stores raw, unstructured data indefinitely, providing cost-effective storage, while a data warehouse contains cleaned, processed, and structured data, optimized for strategic analysis based on predefined business needs. Data Warehouse, Data Lake, and Data Hub Differences: Data warehouses and data lakes primarily support analytic workloads, whereas data hubs focus on data integration, sharing, and governance, serving different purposes in the data landscape. Salesforce Data Cloud is a powerful data warehouse solution that allows companies to effectively manage and analyze their data. It provides users with the ability to stream input data from Salesforce and other sources, making it a comprehensive platform for data integration. Content updated February 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 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

July 11, 2023in Data, Salesforce, Salesforce Data Cloud, Technology

30May

Cloud Analytics Explained

Understanding Cloud Analytics Cloud analytics refers to leveraging cloud computing resources to conduct data analysis more efficiently. It involves using advanced analytical tools to extract insights from vast datasets, presenting information in a user-friendly format accessible via web browsers. Core Concepts of Cloud Analytics Explained Cloud analytics shifts traditional data analytics operations, such as processing and storage, to public or private cloud environments. Similar to on-premises analytics, cloud solutions facilitate pattern identification, predictive modeling, and business intelligence (BI) insights. They leverage cloud technologies and algorithms, notably artificial intelligence (AI), including machine learning (ML) and deep learning (DL). Operational Framework of Cloud-Based Analytics Cloud analytics platforms offer capabilities to build, deploy, scale, and manage data analytics solutions in a cloud-based infrastructure. Examples include cloud enterprise data warehouses, data lakes, and on-demand BI and marketing analytics. Users can subscribe to services under flexible pricing models, alleviating concerns about scalability, performance, and maintenance. Types of Cloud Analytics Cloud-based analytics solutions vary by deployment model: Key Features and Benefits Cloud analytics offers several advantages: Applications and Use Cases Cloud analytics supports diverse applications, including: Comparing Cloud Analytics with Traditional Data Analytics Cloud analytics leverages cloud infrastructure for scalable and flexible data processing, contrasting with traditional analytics tools deployed on-premises. This shift enhances agility and accessibility while reducing operational complexities and costs. Why Cloud Analytics Matters Cloud analytics empowers organizations to harness actionable insights efficiently, driving informed decision-making and competitive advantage. It streamlines operations, fosters collaboration, and enhances data reliability and strategic planning capabilities. Adopting cloud-based analytics enables businesses to transform data into valuable intelligence, fueling innovation and growth. By leveraging cloud-based resources, organizations can achieve operational excellence, secure data-driven insights, and maintain a competitive edge in today’s dynamic business landscape. 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

May 30, 2023in Salesforce

25Apr

Shifting KPIs With Real-Time Intelligence

Marketing without metrics is akin to driving blindfolded. To gauge the effectiveness of their efforts, marketers are investing in analytics capabilities to gain a precise understanding of how their messages, campaigns, and marketing expenditures impact their objectives. The ability to swiftly unlock these insights empowers marketers to promptly address customer needs and make well-informed decisions to propel business growth. This is bringing about Shifting KPIs With Real-Time Intelligence In contrast to 61% of underperforming marketers, a significant 72% of high-performing marketers can analyze marketing performance in real time. This real-time analysis provides them with a distinct advantage in responding to and optimizing campaign performance. However, a notable 33% of marketers still rely on manual processes for marketing attribution, a figure that has seen marginal improvement from 34% in 2020. In 2022, 68% of marketers claim they can analyze marketing performance in real time. For organizations aiming to enhance efficiency and maximize value, identifying the right metrics to track is imperative. As marketing budgets face rigorous scrutiny, analytics offer leaders the insights needed to optimize spending and reduce acquisition costs, reinforcing the value of marketing efforts. Shifting KPIs With Real-Time Intelligence The landscape of analytics has expanded, with marketers now monitoring a comprehensive set of metrics, including year-over-year revenue and customer satisfaction. Personalization and customer touchpoints have gained prominence, leading to increased tracking of web/mobile analytics, content engagement, and customer lifetime value. Marketers are now monitoring an extensive array of key performance indicators (KPIs), encompassing revenue, customer satisfaction metrics (CSAT), web and mobile analytics, customer acquisition costs, B2B sales funnel statistics, content engagement, customer retention rates, customer referral rates, and customer lifetime value. Despite the growing sophistication in tracking various metrics, marketers highlight measuring marketing ROI/attribution as their second most significant challenge. This indicates a continued need for streamlining the reporting process to enhance efficiency and effectiveness. 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

April 25, 2023in Salesforce Einstein

30Mar

Data Cloud and Marketing Cloud Personalization

Choosing the correct Customer Data Platform (CDP) for your organization is crucial for adapting to challenges and capitalizing on opportunities in the evolving marketing technology landscape. While AI, behavioral patterns, and infrastructure play pivotal roles in this decision-making process, it’s essential to understand the landscape. However, the same factors, including AI, behavioral habits, and infrastructure, can influence this decision. Data Cloud and Marketing Cloud Personalization together capture and utilize customer data. Selecting the right tools makes it easier to know and cater to your prospects and customers. Without them, you are firing into the darkness. You must understand the necessary infrastructure for a marketing technology team to meet challenges and leverage new opportunities. It integrates four essential AdTech (Advertising Technology) principles applicable to MarTech in the evolving landscape. The external market poses challenges, notably the discontinuation of third-party cookies by major browsers like Google. This shift impacts prospecting and underscores the significance of first-party data. The rise of AI, exemplified by technologies like ChatGPT and integrated into platforms like Salesforce’s Einstein, further complicates the landscape. The AI era raises concerns about data usage and collection, employment risks, and the ethical consideratins. Organizations rush to incorporate AI, with Salesforce introducing Einstein GPT shortly after the emergence of ChatGPT. In this dynamic environment, organizations grapple with managing diverse data sources, implementing AI/ML, and ensuring privacy. AdTech principles become imperative in MarTech for effective targeting, personalization, and measurement. The focus shifts to the role of a Customer Data Platform (CDP) within the MarTech stack. Distinguishing between Data Management Platforms (DMPs), CDPs, Data Warehouses, and Data Lakes sets the stage. The article explores three CDP types: Enterprise, Event-Based, and Real-Time Personalization. The significance of a Customer Data Platform (CDP) like Salesforce’s Data Cloud cannot be stressed enough. Bear in mind there are differences between DMPs, CDPs, Data Warehouses, and Data Lakes, each with their own use cases. And for your situation a DMP, Data Warehouse, or Data Lake might be required. Salesforce’s CDP platform undergoes scrutiny, aligning its features with AdTech principles. Read more about Tectonic’s thoughts on Data Cloud here. The CDP’s contribution to targeting, personalization, and both deterministic and probabilistic measurement is detailed. Salesforce’s Data Cloud and Marketing Cloud Personalization (Interaction Studio) emerge as solutions catering to distinct needs. In conclusion we must underscore the criticality of choosing the right CDP for organizational resilience, superior customer experiences, and addressing privacy concerns. A robust infrastructure facilitates efficient data management, collaboration, and scalability, empowering organizations to make informed decisions with AI/ML and business intelligence. #data-cloud-and-marketing-cloud-personalization 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 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

March 30, 2023in Data, Enterprise, Google, Salesforce, Salesforce Data Cloud, Salesforce Marketing Cloud, Technology

18Mar

Marketing Cloud Intelligence For Data Integration

What exactly is Salesforce Datorama, now referred to as Marketing Cloud Intelligence? It is a versatile, cloud-based marketing data platform offering a suite of solutions aimed at enhancing data integration, reporting, analysis, and optimization. Marketing Cloud Intelligence For Data Integration. However, the question arises: Does Marketing Cloud Intelligence truly deliver the cloud-based Marketing Intelligence as Salesforce touts? Let’s dive into what this platform offers and dissect its capabilities without the fluff. Understanding the Platform: Salesforce’s Marketing Cloud Intelligence, formerly known as Datorama, serves as an analytics tool meticulously designed to integrate and visualize various forms of marketing performance data. It strikes a balance, catering to both analytically inclined marketers and seasoned analysts seeking to bridge data with conventional BI tools like Tableau. Flexible SaaS with Tailored Customization: Despite its Software-as-a-Service (SaaS) nature, Datorama surprises with its flexibility. It can function autonomously, handling data storage, modeling, ETL, and visualization, or seamlessly integrate with other platforms like Azure Databricks or Looker. While it accommodates numerous data use cases, its primary focus remains on Performance Marketing. Marketing Cloud Intelligence often gets misclassified as a traditional Business Intelligence or Analytics platform, but it truly excels in data management. For those contemplating its adoption, familiarity with its functionalities through resources like “Getting to Know Marketing Cloud Intelligence” or video walkthroughs is encouraged. Transition to Marketing Cloud Intelligence: The rebranding from Datorama to Marketing Cloud Intelligence was proposed in early 2022, gaining momentum recently. Despite the name change, the platform’s features and capabilities remain intact. Origins and Evolution: Originating from an Israeli-based technology firm in 2012, Datorama swiftly gained traction under the stewardship of its founders Ran Sarig, Efi Cohen, and Katrin Ribant. In 2018, Salesforce acquired Datorama, integrating it into the Marketing Cloud suite alongside Account, Engagement, Personalization, and Data Cloud platforms. However, as of February 2, 2023, the original founders and core engineering teams have moved on, possibly signaling a shift in the platform’s trajectory. Functionalities and Capabilities: Marketing Cloud Intelligence boasts robust data onboarding and connectivity features, with a rich assortment of connectors and retrieval mechanisms supporting popular data management platforms like SAP Hana, AWS, Oracle, Vertica, and SQL Server. It excels in ingesting and managing aggregated marketing performance data, with the capacity to handle event-level data as well. Pricing and Competitors: While its pricing model revolves around data row consumption and user seats, the platform may become cost-prohibitive at higher volumes. However, recent enhancements like Data Lake offer expanded row count flexibility without escalating costs. Its primary competitors include Domo, Adverity, NinjaCat, Improvado, Looker, PowerBI, and Google Data Studio. Use Cases and Industries: Marketing Cloud Intelligence serves marketers and advertisers across various industries, including communications, media, technology, healthcare, finance, manufacturing, automotive, retail, and publishing. Its versatility lies in supporting six specific marketing data use cases, ranging from building a single source of data to producing informative dashboards. Continuous Evolution: With frequent product releases, Marketing Cloud Intelligence remains dynamic, adapting to evolving market needs and technological advancements. Its commitment to enhancing analytics, visualization, connectivity, and marketplace apps ensures its relevance in the ever-changing landscape of marketing data management. Future Outlook: As Salesforce navigates the competitive analytics space, the future of Marketing Cloud Intelligence remains intriguing. While challenges like pricing pressures persist, the platform’s integration within the Marketing Cloud ecosystem and ongoing enhancements hint at a promising trajectory. Whether it evolves into a fully integrated analytics solution or retains its standalone utility, only time will tell. But one thing is certain: Salesforce’s promotion of Marketing Cloud Intelligence will continue to shape its evolution and market positioning moving forward. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

March 18, 2023in Google Analytics, Salesforce Communications Cloud, Salesforce Marketing Cloud

18Feb

Case Study: Salesforce Innovation for Hospitality

Major hospitality management firm, moves to the cloud and adopts Google Cloud and Salesforce to improve operational insights and decision-making. Tectonic assisted them to move to the cloud and obtatin quicker, actionable insights with business intelligence. Salesforce Innovation for Hospitality.

February 18, 2023in Google Analytics, Salesforce, Salesforce Development Services, Salesforce Hospitality Travel and Transportation, Salesforce Implementation Services, Salesforce Optimization, Salesforce Success Stories

15Jul

Healthcare Technology Solutions

Healthcare institutions have the golden opportunity to enhance the technology systems implemented over the past few pandemic years, particularly in the realm of virtual rounding—where clinicians utilize collaboration technologies to interact with patients remotely. This approach gained significant traction during the pandemic and is now poised for further growth and refinement. Healthcare Technology Solutions. Healthcare technology solutions refer to the use of technology to improve both healthcare delivery and outcomes. These solutions encompass a wide range of technologies from electronic health records to wearable devices to mobile applications. Many believe the solution to better healthcare is data and collaboration. The right software can help. Healthcare Technology Solutions Here are ways to maximize the impact of virtual rounding specifically in your healthcare organization: Devices: Collaboration Software: Telehealth Peripherals: Electronic Medical Records: Patient Experience APIs: Asset Tracking: Business Intelligence: Mobility Management: Whether your organization requires healthcare technology hardware, software, or cloud-based solutions, Tectonic is well-equipped to expedite the implementation process with numerous healthcare implementations under our belt. Five Exciting and emerging healthcare technology solutions coming in 2023 and beyond: 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

July 15, 2022in Salesforce Development Services, Salesforce Government Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Implementation Services, Salesforce Public Sector Solutions

02Jun

What is the Difference Between Datorama and Tableau?

In the current business environment, customer and prospect data serve as the driving force in most everything we do, emphasizing the importance of analyzing, understanding, and acting on accurate data for informed decision-making. Business Intelligence (BI) tools like Datorama and Tableau play an important role in facilitating these processes. This insight conducts a comparative analysis of Datorama vs. Tableau, covering features, capabilities, pricing, support, and training options. Tectonic’s goal is to assist businesses in making informed decisions aligned with their specific needs. If you are choosing between these two tools, contact Tectonic for assistance. Overview of Datorama: Datorama, a cloud-based BI platform, specializes in providing insights into data across a variety of marketing channels. Offering real-time analytics and pre-built connectors for various marketing platforms, Datorama serves as a comprehensive tool for marketing analytics. Its dashboard provides a centralized view of marketing data, automates real-time processing, and incorporates AI-powered insights generated by Salesforce Einstein. Overview of Tableau: Tableau, a widely used BI platform, facilitates easy data connection and visualization. With a user-friendly interface, it allows users to build interactive dashboards and visualizations without coding expertise. Tableau’s adaptability enables it to connect to various data sources, create interactive visualizations, offer data blending, and include forecasting capabilities. Key Features of Datorama and Tableau: Datorama Features: Tableau Features: Pricing Models: Datorama: Custom plans with varying costs based on specific business needs, starting at $3,000 USD per month. Tableau: Tiered plans with pricing ranging from $12 to $70 per user per month. Support and Training: Datorama: Knowledge base, community forums, training courses, and a certification program. Tableau: 24/7 support, online courses, and live training sessions. Choosing the Right BI Solution: Datorama: Suited for businesses with complex data integration needs, ideal for multi-channel marketing analytics and forecasting, offers advanced AI-powered insights. Tableau: Suited for businesses with data visualization and reporting needs, ideal for ad-hoc data analysis and dashboarding, offers powerful visualization capabilities. Benefits of Integration: Final Thoughts: Both Datorama and Tableau excel as BI tools, offering unique strengths. Datorama is tailored for marketing analytics with real-time insights, while Tableau provides versatility in connecting and visualizing data from various sources. Choosing the right solution depends on specific business needs, goals, and budget considerations. Contact Tectonic today for assistance. 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 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

June 2, 2022in Data, Salesforce, Salesforce Einstein

01Jun

Salesforce Marketing Cloud Intelligence Explained

What is Marketing Cloud Intelligence? Salesforce’s Marketing Cloud Intelligence, formerly known as Datorama, is an analytics tool designed to integrate and visualize marketing performance data across various platforms. It caters to analytically driven marketers and seasoned analysts, providing an easy-to-use interface while offering connections to traditional BI tools like Tableau. Marketing efforts generate extensive data across multiple platforms, and Marketing Cloud Intelligence consolidates all these marketing tools into a centralized source. It serves as a comprehensive solution for reporting, measurement, and optimization. Market intelligence involves gathering real-time data from the market to understand customers, trends, behaviors, and more, enabling a company to stay competitive and meet market demands. By leveraging out-of-the-box connections, Marketing Cloud Intelligence seamlessly links platforms like Google, YouTube, Instagram, and others without the need for complex coding. The tool, now known as Marketing Cloud Intelligence, reveals trends, tracks progress against goals, and quantifies the ROI of marketing initiatives once connected. The system features a connected library of over 170 connectors for acquiring data from major advertising, commerce, CRM, and database vendors. The unique universal connector, powered by AI, allows effortless connection of any data stream within minutes, even from sources lacking an API connection. Marketing Cloud Intelligence addresses the challenge of data consistency by providing an out-of-the-box marketing data model. It helps organize data into a clear and consistent taxonomy, enriching it with naming conventions, data classification, and automated maintenance alerts for trustworthy decision-making. Beyond reporting and dashboards, Marketing Cloud Intelligence, with the assistance of Einstein, provides actionable insights. Marketers can select a KPI to improve and create a perpetual pipeline of AI insights, addressing overarching questions or specific areas like reducing spend or analyzing creative impacts. What can marketers do with Marketing Cloud Intelligence? Marketers can efficiently compile multiple sources of data in Marketing Cloud using various KPIs, creating at-a-glance and visually appealing dashboards and reports. Marketing Cloud Intelligence, powered by Datorama, facilitates the organization and analysis of diverse data within Marketing Cloud. What does Marketing Cloud Intelligence do? Marketing Cloud Intelligence integrates data from marketing and advertising platforms, web analytics, CRM, e-commerce, and more. It offers a unified view for optimizing campaign performance and real-time insights. The tool optimizes marketing spend and customer engagement with unified performance data, automated reporting, and AI-driven insights. Why is marketing intelligence important in Salesforce? Marketing intelligence tools help businesses gather and analyze market data. CRM and CDP tools, such as Salesforce Marketing Cloud Intelligence, unite data from disparate sources to provide a fuller picture of their customers and the marketplace. 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

June 1, 2022in Data, Google, Salesforce, Salesforce Marketing Cloud, Technology

11Apr

Drive Sales and Service With Real Time Data

Sales and Service Personalization: drive sales with real time data Enhance customer and prospect interactions by integrating real-time engagement data directly into your sales and service consoles. Provide service teams with the ability to proactively address queries and deliver effective case resolutions by swiftly accessing a customer’s recent interactions across diverse business touchpoints. Empower your sales teams with deeper insights into an account or prospect’s purchase journey, enabling more relevant conversations and offers based on real-time knowledge of consumed content, viewed products, or time spent on your site. Know your buyers. Attain a comprehensive view of your buyers by combining 1st and 3rd party data with the robust capabilities of Salesforce’s industry-leading Customer Data Platform (CDP). Imagine closing prospects instantly. With real-time data, it’s possible! Real-time sales data enables you to identify recent changes, such as mergers, acquisitions, new job openings, or promotions. Equip your sales team with a competitive advantage, allowing them to promptly contact potential customers and initiate sales activities. So, what is real-time data, how does it work, and how can you implement it without a complex data infrastructure? This article explores all these aspects and highlights the benefits of utilizing accurate B2B data for real-time sales. What is real-time data? Real-time data refers to immediate and continuous access to information about sales activities, customer interactions, and market trends. For your sales and marketing teams, this means capturing, analyzing, and utilizing up-to-date data to make informed decisions, enhance sales processes, create personalized experiences, and strengthen customer relationships. Real-time data is crucial because it offers numerous benefits for B2B businesses. This insight will explore some tangible benefits that real-time data can provide for your company: Access up-to-the-minute information on customer behaviors, preferences, and buying patterns, allowing your B2B sales team to engage with prospects immediately. Real-time insights into events like funding, promotions, or team expansions can trigger timely sales activities, such as emails, LinkedIn messages, or call invitations. Immediate updates from real-time sales insights enable businesses to adjust pricing based on market fluctuations or competitive moves. Real-time data collection helps track competitor pricing, customer demand, and inventory levels, allowing for optimized pricing strategies and instant adjustments with minimal effort from your sales team. Incorporate robust key management for data security to safeguard sensitive information and avoid additional risks. When a prospect expresses interest or takes specific actions, such as visiting a website or filling out a form, you can immediately engage with them. Define sales triggers and actions, such as emailing to schedule a demo after a prospect visits your pricing page. Real-time data processing allows for automated nurturing of prospects, eliminating the need for manual tracking and outreach. Gain real-time actionable insights into sales performance, leading to accurate sales forecasting. Sales managers can monitor sales data in real time, track progress against targets, adjust strategies, and manage pipeline visibility for more precise financial projections aligned with future financial planning. Instant data offers the opportunity to personalize customer interactions more effectively. Access real-time data analytics on customer preferences, purchase history, and previous interactions to tailor relevant recommendations and provide a personalized customer experience. Real-time data analysis provides instant visibility into sales performance metrics. Sales representatives can monitor their performance, including call activity, conversion rates, and revenue generated, in real time. Immediate feedback enables reps to course-correct, improve sales techniques, and achieve better results. By monitoring real-time market trends, competitor activities, and customer feedback, sales managers can make data-driven decisions, adjust sales strategies, and seize emerging opportunities. Business intelligence tools offering real-time data services help sales teams promptly address customer issues or concerns. By tracking customer behavior, feedback, complaints, and inquiries in real time, sales reps can proactively contact customers and help resolve issues. How does real-time data work? Real-time data involves capturing specific actions on the go, such as customers’ activities on your website or offsite, like visiting sales pages, checking your company’s LinkedIn profile, or exploring similar sites. Events are collected before storing any information, allowing for instant management of sales data and predictive analytics. Marketing and Sales Use of Real-Time Data: Updating lead records in real time results in better sales performance and cost savings across the entire business. Real-time big data can be used in various ways for better business decisions, such as: Examples of Real-Time Data: Real-time intent data helps identify potential customers actively researching or showing interest in products like you are selling. This data can be gathered from various sources, including website tracking, social media monitoring, and content consumption patterns. Ultima used a real-time data solution to access intent data and direct dials, resulting in ROI in just 8 weeks. Real-time data is a valuable asset for B2B businesses, offering timely opportunities, dynamic pricing, immediate lead engagement, accurate forecasting, personalized customer interactions, instant sales performance insights, agile sales strategies, and prompt issue resolution. Understanding how real-time data works and leveraging it effectively can significantly enhance the performance and efficiency of your sales and marketing teams. How do you use data to drive sales? What is an example of a data-driven sales? A B2B company that manufactures and sells industrial equipment can use a data-driven approach that involves analyzing purchasing data from their CRM, tracking industry trends, and using customer feedback surveys to understand what customers truly value. To drive sales with real time data, you need a tool like Salesforce and Salesforce Data Cloud. A real-time data sales strategy is a strategy that focuses on delivering immediate responses from customers. The methodology of real time selling is a way for brands to interact with their customers using stuff that’s actually happening at that time. The real time sales are based on insights into a customer’s online actions. The insights are analyzed and utilized quickly with AI. Drive sales real time data. 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

April 11, 2022in Einstein Insights, Salesforce, Salesforce Commerce Cloud, Salesforce Sales Cloud, Salesforce Service Cloud

10Jun

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

June 10, 2021in Artificial Intelligence, Data, Enterprise, Google, Mulesoft, Salesforce, Salesforce Data Cloud, Salesforce Einstein, Salesforce Flow, Salesforce Managed Services, Technology

21Apr

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

April 21, 2021in Data, Google, Salesforce, Salesforce Einstein, Unified Knowledge

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22Mar

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 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

March 22, 2017in Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce, Salesforce Einstein, Technology

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