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Big Data and Data Visualization

Big Data and Data Visualization Explained

Data Visualization: Turning Complex Data into Clear Insights Data visualization is the practice of converting information into visual formats, such as maps or graphs, to make data more accessible and understandable. The primary purpose of data visualization is to highlight patterns, trends, and outliers within large data sets, allowing users to quickly glean insights. The term is often used interchangeably with information graphics, information visualization, and statistical graphics. The Role of Data Visualization in Data Science Data visualization is a crucial step in the data science process. After data is collected, processed, and modeled, it must be visualized to draw meaningful conclusions. It’s also a key component of data presentation architecture, a discipline focused on efficiently identifying, manipulating, formatting, and delivering data. Importance Across Professions Data visualization is essential across various fields. Teachers use it to display student performance, computer scientists to explore AI advancements, and executives to communicate information to stakeholders. In big data projects, visualization tools are vital for quickly summarizing large datasets, helping businesses make informed decisions. In advanced analytics, visualization is equally important. Data scientists use it to monitor and ensure the accuracy of predictive models and machine learning algorithms. Visual representations of complex algorithms are often easier to interpret than numerical outputs. Historical Context of Data Visualization Data visualization has evolved significantly over the centuries, long before the advent of modern technology. Today, its importance is more pronounced, as it enables quick and effective communication of information in a universally understandable manner. Why Data Visualization Matters Data visualization provides a straightforward way to communicate information, regardless of the viewer’s expertise. This universality makes it easier for employees to make decisions based on visual insights. Visualization offers numerous benefits for businesses, including: Advantages of Data Visualization Key benefits include: Challenges and Disadvantages Despite its advantages, data visualization has some challenges: Data Visualization in the Era of Big Data With the rise of big data, visualization has become more critical. Companies leverage machine learning to analyze vast amounts of data, and visualization tools help present this data in a comprehensible way. Big data visualization often employs advanced techniques, such as heat maps and fever charts, beyond the standard pie charts and graphs. However, challenges remain, including: Examples of Data Visualization Techniques Early computer-based data visualizations often relied on Microsoft Excel to create tables, bar charts, or pie charts. Today, more advanced techniques include: Common Use Cases for Data Visualization Data visualization is widely used across various industries, including: The Science Behind Data Visualization The effectiveness of data visualization is rooted in how humans process information. Daniel Kahneman and Amos Tversky’s research identified two methods of information processing: Visualization Tools and Vendors Data visualization tools are widely used for business intelligence reporting. These tools generate interactive dashboards that track performance across key metrics. Users can manipulate these visualizations to explore data in greater depth, and indicators alert them to data updates or important events. Businesses might use visualization of data software to monitor marketing campaigns or track KPIs. As tools evolve, they increasingly serve as front ends for sophisticated big data environments, assisting data engineers and scientists in exploratory analysis. Popular data visualization tools include Domo, Klipfolio, Looker, Microsoft Power BI, Qlik Sense, Tableau, and Zoho Analytics. While Microsoft Excel remains widely used, newer tools offer more advanced capabilities. Data visualization is a vital subset of the broader field of data analytics, offering powerful tools for understanding and leveraging business data across all sectors. 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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Generative AI Regulations

Salesforce, Data Science, and Generative AI

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

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Leverage AI and Machine Learning in Your Data Warehouse

Leverage AI and Machine Learning in Your Data Warehouse

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

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Salesforce Field Service

Understanding Salesforce Field Service

Salesforce Field Service is an expansive addition to Salesforce Service Cloud, offering a 360 degree perspective on workforce management, especially in scenarios where services extend beyond the office or work site into the field. This typically involves mobile employees such as service technicians, alongside service agents, dispatchers, and service managers. Let’s dive into the nuances of Salesforce Field Service, addressing key aspects like its definition, user roles, reporting, scheduling, optimization, and more. Salesforce Field Service is also a popular tool in public sector solutioning. Understanding Salesforce Field Service will help to decide if it is right for your service teams. Understanding Salesforce Field Service: Salesforce Field Service, formerly known as Field Service Lightning (FSL), is an extension of Service Cloud designed to provide a comprehensive workforce management view. It seamlessly manages services delivered in the field, such as a electrical installer arriving to fulfill a customer’s order. Field Service Lightning evolves with each Salesforce release, continually enhancing functionality. Salesforce Field Service enhances customer engagement with real-time personalization, optimization of field teams with a scheduling engine, and access to offline data. Components of Salesforce Field Service: Field Service comprises various components, including Service Appointments, Salesforce Service Cloud integration, Territory and Scheduling functionalities, the Field Service Dispatcher Console, and the Field Service Mobile App for Android and iOS with offline capabilities. A detailed data model diagram illustrates the relationship between Service Cloud and Field Service. Users of Salesforce Field Service: Salesforce Field Service is particularly valuable for organizations with mobile field technicians supporting customers. It excels in scenarios with intricate service delivery involving multiple individuals like dispatchers, field technicians, service agents, and service managers. Each user persona interacts with Salesforce Field Service in distinct ways, utilizing tools like the Dispatcher Console or Field Service Mobile App tailored to their roles. Use Cases for Salesforce Field Service: Field Service proves beneficial in diverse service scenarios, including repairs, maintenance, sales visits, and healthcare care visits. It facilitates efficient coordination among dispatchers, field technicians, service agents, and service managers, streamlining processes and enhancing customer satisfaction. Key Terms in Salesforce Field Service: Understanding key terms such as Work Orders, Service Appointments, Contracts, Entitlements, Work Types, Service Territories, Operating Hours, Resources, and Scheduling Policies is essential. These terms collectively form the foundation of Salesforce Field Service’s robust data model and optimized usability. Scheduling and Optimization: Scheduling in Salesforce Field Service is managed through the Dispatcher Console, empowering dispatchers to make informed decisions. Various methods, from manual to fully automated, enable the creation of service appointments, optimizing resource allocation and ensuring efficient service delivery. Forecasting and Planning: Use real-time data to quickly assess the impact of global or in-day optimization on travel time and resource utilization. Refine scheduling policies, get an instant view of optimization results, and KPI changes. Asset Service Management: Move from reactive to proactive service with real-time asset tracking. Monitor service outcomes and create preventive maintenance plans based on asset use, condition, age, and specific criteria. Service Territory Design: Service Territories, categorized as Primary, Secondary, and Relocation Territories, define where teams can perform work. Well-defined territories are crucial for efficient dispatcher satisfaction, requiring thoughtful initial design and ongoing maintenance. Field Service Mobile App: The Salesforce Field Service Mobile app is essential for on-the-move workforces, offering technicians essential information to optimize job execution. Offline capabilities ensure uninterrupted work, and the Visual Remote Assistant feature enables two-way video and audio communication between agents, technicians, and customers. Slack for Field Service: Empower your mobile workforce with seamless connectivity and timely assistance whenever it is needed. Mobilize for service appoints and tap into the expertise of colleagues throughout the organization with the user-friendly Slack interface. Reporting in Salesforce Field Service: Monitoring service efficiency and success involves leveraging Salesforce reports, list views, and other platform analytics. Work Order List Views and Field Service Report Types provide insights into service call-outs and overall performance. Enabling Field Service Lightning: Setting up Salesforce Field Service involves installing it as a Managed Package, assigning licenses to users, and configuring standard objects like Products, Assets, Service Contracts, and Entitlements. Creating records for Service Territories, Service Resources, and Operating Hours establishes the framework for the service model. Field Service Deployments: Deployments in Salesforce Field Service can be time-consuming due to its reliance on object data. Considerations for complex relational data movement between environments and potential risks, such as resistance to automation, require careful attention. Tectonic, as a Salesforce Consulting Partner, can assist your company in deploying Salesforce Field Service. Salesforce Field Service projects, while intricate, can be highly valuable when executed correctly providing a large ROI. Adequate planning, phased implementations, and a long-term perspective contribute to the success of organizations leveraging the capabilities of Salesforce Field Service. If you are looking for a more robust solution, consider Field Service Plus combining dispatch and technician functionality along with Service Cloud and Sales Cloud. Tectonic looks forward to helping your field service team utilize this Salesforce tool. Tectonic is please to announce Salesforce Service Cloud Implementation Solutions. 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

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Salesforce Einstein and Einstein Automate

AI Fundamentals

The concept of Artificial Intelligence (AI) has long been a fascination for storytellers and sci-fi enthusiasts. However, for a considerable period, most individuals didn’t give AI much serious consideration, perceiving it as a distant futuristic sci-fi possibility. Nevertheless, researchers and computer scientists have been actively working to transform the dream of AI into a tangible reality, leading some to insist that we have already entered the Age of AI. The AI Fundamentals explained. While the extent of AI’s integration into our daily lives remains uncertain, it is evident that meaningful conversations about AI require a shared vocabulary and a solid foundation of core concepts. Presently, asking ten people to define artificial intelligence is likely to yield ten different answers. This insight attempts to establish a common understanding by exploring AI’s current capabilities and digging into the methodologies employed by computer scientists in creating remarkable AI systems. AI Fundamentals Defining AI proves challenging due to distorted perceptions influenced by science fiction narratives portraying AI as a potentially malevolent force. Additionally, our tendency to benchmark AI against human intelligence contributes to this challenge. And I don’t want AI to be able to write a blog post as well as me! Acknowledging the vast spectrum of intelligence in the animal kingdom, as well as the diversity in human intelligence, prompts a need to view artificial intelligence through a similar lens. As humans we may think we are a lot smarter than a bird. But I don’t know how to fly, do you? AI Capabilities Recognizing specific AI capabilities tailored to distinct tasks is crucial, dispelling the notion of a universally proficient AI, known as general AI, which remains a distant goal. AI currently exists in specialized forms, each excelling at particular jobs. Key AI capabilities fall into several categories: It’s safe to say, artificial intelligence encompasses computer abilities associated with human intuition, inference, and reasoning. Presently, AI skills are highly specialized, covering categories like numeric predictions and language processing. The evolving landscape of AI offers a glimpse into the transformative potential of this technology, emphasizing its current application in specific domains. 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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Cloud Analytics

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 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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Flexpricer and Optional Salesforce Line Items

Flexpricer and Optional Salesforce Line Items

If sales users are wasting time with the standard product selector while adding line items on Quotes and Opportunities in Salesforce, but treating them as optional, use Flexpricer’s Optional Line Items interface instead. Flexpricer and Optional Salesforce Line Items let users designate any line item for an opportunity or quote as optional. Flexpricer Benefits Flexpricer and Optional Salesforce Line Items What are Optional Line Items and Why are They Useful? In Sales Cloud, adding products to a Quote or Opportunity using the basic interface can be time-consuming and limited. Sales Cloud doesn’t offer functionality to easily mark products as additional or alternate. Flexpricer enhances Sales Cloud with Optional Line Items for Opportunities and Quotes. Sales Users can: Create Accurate Quotes in Salesforce Without specialized tools, creating quotes with optional line items is challenging and prone to pricing errors. Flexpricer’s Optional Line Items provides: Optional Line Items as Alternates Using the standard product selector in Sales Cloud, users struggle with adding line items for products that can be swapped out with others. With Flexpricer, selecting line items as alternates for upsell or replacements is easy: Standard Sales Cloud Objects Flexpricer uses standard Sales Cloud objects. Your Optional Line Items aren’t locked in custom objects requiring additional licenses. Optional Line Items are commonly used by companies to present alternate or additional line items on Quotes in Salesforce without contributing to the total amount. 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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Salesforce Einstein

Customize Forecast Insights

Tailor your forecasts page to align with your specific business requirements by leveraging Lightning App Builder. Craft bespoke forecasts pages that integrate all necessary information crucial for your sales leaders to generate precise forecasts tailored to your unique business demands. Construct pages comprising both standard and custom components meticulously tailored to complement your distinct sales processes. Customize Forecast Insights in Salesforce. For precise forecasting based on the Opportunity, Opportunity Product, or Line Item Schedule objects, utilize a custom number field as a measure. The forecasts grid provides insights into forecasts for individual months or quarters and offers flexibility in defining the range of months or quarters, catering to your preferences. While a default one-month forecast period is established, it can be adjusted as needed to suit your requirements. Forecast categories categorize opportunities within the sales cycle based on their respective opportunity stages. Standard forecast categories such as Pipeline, Best Case, Commit, Omitted (excluded from forecasts), and Closed are available. Salesforce administrators can further enhance forecasting capabilities by introducing a Most Likely category in Lightning Experience and renaming categories for both single category rollups and cumulative category rollup scenarios. Customize Forecast Insights to see the information most important. Different users can have different views. To modify forecast categories in Salesforce: Customize the presentation of standard and custom forecast columns on the forecasts page, allowing for tailored visualization. For instance, arrange a custom Most Likely column to be positioned alongside the Open Pipeline column. For adjusting forecasts in Salesforce: 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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Shifting KPIs With Real-Time Intelligence

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 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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Cloud First

Advances in Generative AI

What is generative AI?  Generative AI focuses on creating new and original content, chat responses, designs, synthetic data or even deepfakes.  While predictive AI worked on predefined, human supplied rules, generative AI functions somewhat autonomously. Advances in Generative AI have been groundbreaking. Advances in generative AI represent a significant advancement beyond established technologies like predictive AI, and business leaders are eagerly embracing its potential. A remarkable 91% recognize generative AI as a major advantage, driven by its diverse applications, from content creation to software development. Despite its novelty, generative AI is rapidly progressing, causing over three-quarters of business leaders to express concerns about potentially missing out on its benefits. In particular, marketing leaders are apprehensive about not fully leveraging generative AI in their workflows, with 88% worried that their companies are lagging behind. Insight Generation and Decision-Making: Going beyond traditional data analysis, generative AI excels by not only analyzing existing data but also generating potential scenarios. This predictive modeling empowers businesses to anticipate market shifts, understand consumer preferences, and identify potential risks, fostering proactive strategies over reactive ones. Generative AI’s Global Impact: Generative AI has captivated global attention, with ChatGPT becoming the fastest-growing software program in history, reaching a hundred million users within two months of its public debut. This surge has sparked an arms race among tech giants like Microsoft and Google, and AI chip maker Nvidia has witnessed increased business. Unlike previous AI programs that provided numeric scores, generative AI, including programs like Stability AI’s Stable Diffusion and OpenAI’s DALL-E, reproduces elements of the real world. Amazon announced in 2023 that its voice assistant Alexa now comes with generative AI capabilities. Apple is developing a large array of features that use generative AI, including a new version of Siri expected to launch in 2024. Mixed Modality in AI: The concept of mixed modality or “multi-modality” is taking center stage, enabling programs to fuse text, images, physical space representations, sounds, video, and entire computer functions as smart applications. This approach enhances program capabilities and contributes to continuous learning, potentially advancing the goal of “embodied AI” and robotics. Evolution of Generative AI: Generative AI will continue evolving, contributing to advancements in translation, drug discovery, anomaly detection, and the generation of new content, spanning text, video, fashion design, and music. A generative AI chatbot, for example, is a type of conversational AI system that uses deep learning and natural language processing techniques to generate human-like text responses in real-time. These chatbots can hold text-based conversations with users, understand user input, and generate contextually relevant responses. Transformative Trends in Marketing and Sales Operations: Generative AI is reshaping marketing and sales operations with key trends, including hyper-quick sales and marketing content creation, automation of repetitive tasks (e.g., keyword research, administrative work, content formatting, and data analysis), and the facilitation of sales enablement and custom materials. What is the Main Goal of Generative AI? The answer likely would vary depending on who you ask, but commonly we expect generative AI tools to change the calculus of knowledge work automation. Generative AI isn’t going to eliminate the need for human workers, but it will assist them with the ability to produce human-like writing, images, audio, or video in response to plain-English text prompts. The potential to collaborate with human partners to generate contact that represents practical work is exciting. Curious how generative AI could help your business? Contact Tectonic today to learn more. 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 Big Data and Travel and Hospitality

Utilizing Generative AI

Taking advantage of generative AI requires complete, unified, and accurate data, according to over half of IT leaders. Yet roadblocks remain. A recent survey found most IT leaders don’t have a unified data strategy and can’t integrate generative AI into their current tech stack. Technical requirements aside, generative AI also surfaces serious ethical considerations. Utilizing Generative AI. Nearly three-quarters of IT leaders are wary of biased or inaccurate results, yet fewer than a third consider ethical use guidelines critical. As an IT practitioner, it’s your job to understand the tech landscape and educate your organization about the power and risks of certain technology solutions, regardless of their application. Generative AI is no different. At this stage, you should be thinking about generative AI from a board-level perspective, looking beyond the near term and well into the future. What are all the risks and rewards? What are the ways your organization might win or lose? How will your people react or respond? How might generative AI make your organization more competitive and effective? Don’t assume anyone within your organization is thinking strategically about how or where generative AI should be applied. Developing a proprietary generative AI solution will take months to deliver (if not longer), but if done correctly, the resulting model would be highly secure and likely very impactful for your specific organization. Most organizations will lean toward buying or leasing a base model and fine-tuning as needed. This approach would still consume time and resources but be optimized for use cases and maintain a level of security. Generative AI focuses on creating new and original content, chat responses, designs, synthetic data or even images. It’s particularly valuable in creative fields and for novel problem-solving, as it can autonomously generate many types of new outputs. How can we use generative AI? Generative AI models can create graphs that show new chemical compounds and molecules that aid in drug discovery, create realistic images for virtual or augmented reality, produce 3D models for video games, design logos, enhance or edit existing images, and more. Which tasks uses generative AI? Generative AI or generative artificial intelligence refers to the use of AI to create new content, like text, images, music, audio, and videos. Generative AI is powered by foundation models (large AI models) that can multi-task and perform out-of-the-box tasks, including summarization, Q&A, classification, and more. 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 Loyalty Management

Salesforce Loyalty Program Explained

Loyalty Management enables you to customize programs that precisely align with the unique needs of your business and customer base. Salesforce, equipped with a flexible platform, aids businesses in reimagining their loyalty strategies. The Salesforce Loyalty Cloud empowers businesses across various industries to effortlessly build intelligent loyalty programs without the need for coding. These programs incentivize customers to engage more with the brand, fostering repeat business. Salesforce Loyalty Program Explained. Unleash the full potential of your Customer Loyalty platform through impactful features: The centralized program management feature allows you to configure loyalty programs tailored to your needs, activate segments across channels quickly, and iterate based on results. Improved customer engagement uses behavior analysis to optimize experiences across web and mobile, tailoring them to individual users. Cross-industry partnerships provide customers with more options, and fast time-to-market for referral promotions is facilitated through a guided setup wizard. Identifying brand advocates and implementing AI-powered referral programs with predictive dashboards enhance program impact. Personalizing promotions based on member data and utilizing predictive performance insights maximize engagement and ROI. A single customer view connects loyalty across the customer experience, triggering marketing journeys and providing insights for enhanced interactions. Elevate your loyalty initiatives, engage customers effectively, and drive business growth with the robust capabilities of the Customer Loyalty platform and the expert guidance from Tectonic. 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 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

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Improving Customer Experiences with Salesforce

Improving Customer Experiences with Salesforce

Sustaining competitiveness continues to demand businesses prioritize personalization and agility in their customer experience strategy in 2023. Improved Customer Experiences with Salesforce are attainable. Read on. “You are caller number 10; please stay on the line.” A few years ago, a phone message like this from a company was a standard part of the customer experience. But today, customers expect more. They expect callback options. They expect to open a chat window on your website, send a text message or a tweet, and get a near-instant reply. They expect that you already know who they are when they call, that you’re aware of their most recent order, and that you understand what they need next. They don’t want to repeat their information. And they definitely don’t want to wait. Improved Customer Experiences with Salesforce Here are ways to enhance your customer experience program: Enhancing Customer Experience for Brand Loyalty: Advance Personalized Experiences: By concentrating on these core areas, organizations can navigate the evolving CX landscape, delivering exceptional customer experiences. Tectonic is poised to collaborate, customizing Salesforce tools to craft remarkable customer experiences. CX-Customer experience, gets thrown around a lot. But how is it defined, and what does it mean? “Customer experience and service have converged, it’s more than call centers and successful responses to problems. It is service opportunities in sales, support, and marketing. Delivering great customer experiences now means providing amazing, almost magical service at every opportunity.” Peter Schwartz, Salesforce senior vice president of strategic planning What Does Customer Experience Mean? Customer experience encompasses every interaction a prospect or customer has with a business, spanning various channels and touchpoints. Here’s how to enhance customer experience with Salesforce: Customers have always wanted a consistently good buying experience and the best value they can get. If they’re not happy buying expensive razor blades, they can sign up for inexpensive monthly delivery. If they’re fed up with mediocre hotel chains that don’t offer value, why not try Airbnb? The Good News The good news? Customers are willing to pay a premium for great experiences, and not just great products and services. This means businesses have an opportunity to increase revenue by delighting their customers in a way that no one else can. What are the 3Rs of customer experience? Science shows that customers are looking for 3 things in every interaction. These can be summarized in the 3 service values: be reliable, be responsive and build relationships. What are the six pillars of customer experience excellence? Developed by KPMG Nunwood of the UK following research involving more than one million customers across three continents, the system spotlights six key principles as the building blocks for customer experience success: personalization, integrity, expectations, resolution, time and effort, and empathy. 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

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