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Create Delightful Experiences
13Sep

Create Delightful Experiences

Ever had one of those unexpected moments when you reach out to customer service to resolve an issue, and by the end of the conversation, you’ve ended up purchasing something new—and actually feel good about it? Salesforce can help you Create Delightful Experiences. It’s those delightful experiences—when a company truly understands you—that make all the difference. Yet, far too often, these moments are the exception rather than the rule. Why is that? Despite having access to mountains of data from every click, call, and transaction, many companies still fail to create the seamless, personalized experiences that customers expect. In fact, 80% of customers believe their experiences should be better, given the wealth of data available. However, many organizations remain trapped in silos, with marketing, sales, and service teams working in isolation. The data exists, but it’s not being utilized effectively. Siloed data, un-unified data, and restricted access data make your agents seem less emphathetic. Customers expect them to know everything about them there is to know. For CMOs, this presents both a challenge and an opportunity. Positioned at the intersection of every customer touchpoint, many find themselves navigating disjointed strategies from different departments. But what if we could turn the tide? What if every interaction across any channel—whether in marketing, sales, or service—felt like one continuous conversation? From Silos to Synergy: Maximizing Every Customer Interaction The reality is that customers don’t recognize the internal barriers we’ve erected. They don’t care about the silos of marketing, sales, and service; to them, it’s one relationship. What matters most to them is being understood and treated consistently, regardless of whom they are engaging with. Create Delightful Experiences This is where a more unified approach comes into play. It’s not about collecting more data—we already have plenty of that. Instead, it’s about piecing together a puzzle where each interaction reveals a bigger picture. By doing so, we can anticipate customer needs and respond in ways that feel personal and relevant. Consider Fisher & Paykel. By integrating data from their online stores and marketing efforts, they gain a clearer understanding of their customers’ buying habits. Whether someone is a one-time buyer or a frequent shopper, they can tailor the experience accordingly. For instance, if a customer purchases a new fridge, rather than suggesting another fridge during their next visit—as if they were unaware of the previous purchase—the system might recommend relevant accessories like water filters. Plus, with connected device data, they can send timely reminders when it’s time for a replacement part. Now, picture a customer calling in with a service issue. Instead of merely resolving the problem, the representative is empowered by AI to suggest the next best action—perhaps offering a discount on a recently viewed product or an option for self-service. By leveraging AI insights from browsing behavior and purchase history, service teams can present timely offers that build trust and drive future purchases. This transformation turns service interactions into opportunities for building loyalty and generating revenue while ensuring customers feel valued and understood. With customer acquisition costs rising by 60% over the last five years, strategies like upselling, cross-selling, and referral marketing can yield new revenue at a fraction of the cost of traditional channels. The Technology That Ties It All Together None of this is feasible without the right technology. To craft these interconnected experiences, we need systems that consolidate data from every corner of the business. Salesforce’s Data Cloud accomplishes this by centralizing customer data and layering Einstein AI on top to generate meaningful, actionable insights. If your marketing chops are your muscles, your Salesforce org is your tool box. Gone are the days of guessing what customers need—you’ll know exactly when and how to engage them, transforming transactional interactions into those delightful moments that keep customers coming back. Take Air India as an example. Faced with managing over 550,000 monthly service cases within a decentralized system, they utilized Salesforce’s Data Cloud to unify customer data from various sources, providing service teams with a 360-degree view of every passenger. With AI-driven recommendations from Einstein AI, Air India’s teams can offer personalized services, such as seat upgrades during delays or tailored travel deals based on past trips. This approach not only enhances customer satisfaction but also streamlines operations and fosters business growth. The Strategic Imperative for CMOs So, what’s the key takeaway for marketers? We must think beyond our traditional roles and collaborate across the entire customer journey. It’s crucial to advocate for breaking down silos, aligning teams, and integrating data throughout our organizations. However, let’s be realistic: this is easier said than done. Internal politics can complicate efforts to unify departments, with leaders often fixated on their own priorities. The key lies in fostering a spirit of collaboration, not competition—demonstrating to other leaders how a unified approach benefits everyone. By working closely with other departments, marketing can evolve from merely a function into a pivotal part of the broader business strategy, helping to drive consistent customer experiences, increased revenue, and long-term loyalty. The future of marketing isn’t about doing more; it’s about being smarter. It’s about crafting personalized, meaningful experiences that reach the right customers at precisely the right moment, transforming every touchpoint into an opportunity to build lasting relationships. Unified data is the cornerstone of achieving this goal. Ultimately, the companies that understand their customers best will thrive—and that journey begins with us. Create Delightful Experiences with technology and AI for your customers. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. 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September 13, 2024in Data, Salesforce, Salesforce Marketing Automation, Technology
E-Commerce Platform Improvement
13Sep

E-Commerce Platform Improvement

Section I: Problem Statement CVS Health is continuously exploring ways to improve its e-commerce platform, cvs.com. One potential enhancement is the implementation of a complementary product bundle recommendation feature on its product description pages (PDPs). For instance, when a customer browses for a toothbrush, they could also see recommendations for related products like toothpaste, dental floss, mouthwash, or teeth whitening kits. A basic version of this is already available on the site through the “Frequently Bought Together” (FBT) section. Traditionally, techniques such as association rule mining or market basket analysis have been used to identify frequently purchased products. While effective, CVS aims to go further by leveraging advanced recommendation system techniques, including Graph Neural Networks (GNN) and generative AI, to create more meaningful and synergistic product bundles. This exploration focuses on expanding the existing FBT feature into FBT Bundles. Unlike the regular FBT, FBT Bundles would offer smaller, highly complementary recommendations (a bundle includes the source product plus two other items). This system would algorithmically create high-quality bundles, such as: This strategy has the potential to enhance both sales and customer satisfaction, fostering greater loyalty. While CVS does not yet have the FBT Bundles feature in production, it is developing a Minimum Viable Product (MVP) to explore this concept. Section II: High-Level Approach The core of this solution is a Graph Neural Network (GNN) architecture. Based on the work of Yan et al. (2022), CVS adapted this GNN framework to its specific needs, incorporating several modifications. The implementation consists of three main components: Section III: In-Depth Methodology Part 1: Product Embeddings Module A: Discovering Product Segment Complementarity Relations Using GPT-4 Embedding plays a critical role in this approach, converting text (like product names) into numerical vectors to help machine learning models understand relationships. CVS uses a GNN to generate embeddings for each product, ensuring that relevant and complementary products are grouped closely in the embedding space. To train this GNN, a product-relation graph is needed. While some methods rely on user interaction data, CVS found that transaction data alone was not sufficient, as customers often purchase unrelated products in the same session. For example: Instead, CVS utilized GPT-4 to identify complementary products at a higher level in the product hierarchy, specifically at the segment level. With approximately 600 distinct product segments, GPT-4 was used to identify the top 10 most complementary segments, streamlining the process. Module B: Evaluating GPT-4 Output To ensure accuracy, CVS implemented a rigorous evaluation process: These results confirmed strong performance in identifying complementary relationships. Module C: Learning Product Embeddings With complementary relationships identified at the segment level, a product-relation graph was built at the SKU level. The GNN was trained to prioritize pairs of products with high co-purchase counts, sales volume, and low price, producing an embedding space where relevant products are closer together. This allowed for initial, non-personalized product recommendations. Part 2: User Embeddings To personalize recommendations, CVS developed user embeddings. The process involves: This framework is currently based on recent purchases, but future enhancements will include demographic and other factors. Part 3: Re-Ranking Scheme To personalize recommendations, CVS introduced a re-ranking step: Section IV: Evaluation of Recommender Output Given that CVS trained the model using unlabeled data, traditional metrics like accuracy were not feasible. Instead, GPT-4 was used to evaluate recommendation bundles, scoring them on: The results showed that the model effectively generated high-quality, complementary product bundles. Section V: Use Cases Section VI: Future Work Future plans include: Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 13, 2024in Generative AI
Challenges for Rural Healthcare Providers
12Sep

Challenges for Rural Healthcare Providers

Rural healthcare providers have long grappled with challenges due to their geographic isolation and limited financial resources. The advent of digital health transformation, however, has introduced a new set of IT-related obstacles for these providers. EHR Adoption and New IT Challenges While federal legislation has successfully promoted Electronic Health Record (EHR) adoption across both rural and urban healthcare organizations, implementing an EHR system is only one component of a comprehensive health IT strategy. Rural healthcare facilities encounter numerous IT barriers, including inadequate infrastructure, interoperability issues, constrained resources, workforce shortages, and data security concerns. Limited Broadband Access Broadband connectivity is essential for leveraging health IT effectively. However, there is a significant disparity in broadband access between rural and urban areas. According to a Federal Communications Commission (FCC) report, approximately 96% of the U.S. population had access to broadband at the FCC’s minimum speed benchmark in 2019, compared to just 73.6% of rural Americans. The lack of broadband infrastructure hampers rural organizations’ ability to utilize IT features that enhance care delivery, such as electronic health information exchange (HIE) and virtual care. Rural facilities, in particular, rely heavily on HIE and telehealth to bridge gaps in their services. For instance, HIE facilitates data sharing between smaller ambulatory centers and larger academic medical centers, while telehealth allows rural clinicians to consult with specialists in urban centers. Additionally, telehealth can help patients in rural areas avoid long travel distances for care. However, without adequate broadband access, these services remain impractical. Despite persistent disparities, the rural-urban broadband gap has narrowed in recent years. Data from the FCC indicates that since 2016, the number of people in rural areas without access to 25/3 Mbps service has decreased by more than 46%. Various programs, including the FCC’s Rural Health Care Program and USDA funding initiatives, aim to expand broadband access in rural regions. Interoperability Challenges While HIE adoption is rising nationally, rural healthcare organizations lag behind their urban counterparts in terms of interoperability capabilities, as noted in a 2023 GAO report. Data from a 2021 American Hospital Association survey revealed that rural hospitals are less likely to engage in national or regional HIE networks compared to medium and large hospitals. Rural providers often lack the economic and technological resources to participate in electronic HIE networks, leading them to rely on manual data exchange methods such as fax or mail. Additionally, rural providers are less likely to join EHR vendor networks for data exchange, partly due to the fact that they often use different systems from those in other local settings, complicating health data exchange. Federal initiatives like TEFCA aim to improve interoperability through a network of networks approach, allowing organizations to connect to multiple HIEs through a single connection. However, TEFCA’s voluntary participation model and persistent barriers such as IT staffing shortages and broadband gaps still pose challenges for rural providers. Financial Constraints Rural hospitals often operate with slim profit margins due to lower patient volumes and higher rates of uninsured or underinsured patients. The financial strain is exacerbated by declining Medicare and Medicaid reimbursements. According to KFF, the median operating margin for rural hospitals was 1.5% in 2019, compared to 5.2% for other hospitals. With limited budgets, rural healthcare organizations struggle to invest in advanced health IT systems and the necessary training and maintenance. Many small rural hospitals are turning to cloud-based EHR platforms as a cost-effective solution. Cloud-based EHRs reduce the need for substantial upfront hardware investments and offer monthly subscription fees, some as low as $100 per month. Workforce Challenges The healthcare sector is facing widespread staff shortages, including a lack of skilled health IT professionals. Rural areas are disproportionately affected by these shortages. An insufficient number of IT specialists can impede the adoption and effective use of health IT in these regions. To address workforce gaps, the ONC suggests strategies such as cross-training multiple staff members in health IT functions and offering additional training opportunities. Some networks, like OCHIN, have secured grants to develop workforce programs, but limited broadband access can hinder participation in virtual training programs, highlighting the need for expanded broadband infrastructure. Data Security Concerns Healthcare data breaches have surged, with a 256% increase in large breaches reported to the Office for Civil Rights (OCR) over the past five years. Rural healthcare organizations, often operating with constrained budgets, may lack the resources and staff to implement robust data security measures, leaving them vulnerable to cyber threats. A cyberattack on a rural healthcare organization can disrupt patient care, as patients may need to travel significant distances to reach alternative facilities. To address cybersecurity challenges, recent legislative efforts like the Rural Hospital Cybersecurity Enhancement Act aim to develop comprehensive strategies for rural hospital cybersecurity and provide educational resources for staff training. In the interim, rural healthcare organizations can use free resources such as the Health Industry Cybersecurity Practices (HICP) publication to guide their cybersecurity strategies, including recommendations for managing vulnerabilities and protecting email systems. Does your practice need help meeting these challenges? Contact Tectonic today. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. 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September 12, 2024in Salesforce Health and Life Sciences Solutions, Salesforce Managed Services, Salesforce Optimization
Salesforce and Tenyx
10Sep

Salesforce and Tenyx

Salesforce has announced its acquisition of AI voice agent firm Tenyx, with the deal expected to close in the third quarter. While the financial terms have not been disclosed, Tenyx’s co-founders, CEO Itamar Arel and CTO Adam Earle, along with their team, will join Salesforce as part of the acquisition. This move comes after Salesforce, under pressure from activist investors, previously shifted away from acquisitions and increased its share buybacks following the dissolution of its mergers and acquisitions committee. However, the company is now pursuing strategic acquisitions to boost revenue growth. Conversational AI forthe Enterprise Tenyx Voice is an Interactive Virtual Agent (IVA) built from the ground up leveraging today’s modern AI stack. Built by a team with a proven track record in voice AI, and leveraging a unique core AI and voice platform, Tenyx promises to redefine customer interactions for the enterprise. Tenyx Voice is an Interactive Virtual Agent (IVA) built from the ground up leveraging today’s modern AI stack. Built by a team with a proven track record in voice AI, and leveraging a unique core AI and voice platform, Tenyx promises to redefine customer interactions for the enterprise. Industries and Use Cases If 2023 was the year of large language models (LLMs), 2024 is shaping up to be the year of voice agents. When ChatGPT made waves globally, startups, tech firms, and entrepreneurs rushed to discover business use cases for the new technology. The ideal applications targeted tasks that are costly, time-consuming, and hard to scale. Voice agents and automated customer service systems quickly emerged as one of the most promising solutions. However, many companies deploying these systems aren’t fully considering their impact on customers. That’s why Tenyx is launching its inaugural Voice AI Consumer Report. We surveyed hundreds of Americans across different age groups, races, geographies, and genders to better understand their preferences and experiences with AI-powered voice agents. Here are the key findings: What this means: Frustrating Calls Hurt Your Brand Imagine calling customer service for a quick solution, only to be met by an automated voice agent that can’t understand your request or handle complex issues. It’s a common and frustrating experience. Our data shows that nearly 7 in 10 people express frustration or annoyance with today’s automated voice agents—sentiments that can severely damage customer loyalty and business outcomes. “Our report highlights a major disconnect between consumer expectations and the performance of current automated voice agents,” says Itamar Arel, CEO of Tenyx. “While these systems promise efficiency and cost savings, they often fall short when it comes to addressing consumers’ nuanced needs.” Incomplete AI Systems Drive Customer Churn Subpar AI systems are driving customers away. Two-thirds of respondents said they wouldn’t return to a company after a negative experience with its AI voice agent. In fact, 67% still prefer interacting with human agents over automated ones. Why? Current AI voice agents struggle with complex issues and fail to provide the empathy and problem-solving skills that human agents, or more advanced AI systems, offer. Selective Deployment and Industry-Specific Agents Matter Our data shows that consumers are more accepting of voice agents in certain industries than others. Sectors like healthcare, restaurants, and telecoms saw the highest satisfaction with AI voice agents, while airlines, banking, and hotels ranked the lowest. This highlights the importance of selective deployment and tailoring voice agents for specific industries to better meet customer needs. Looking Ahead: The Promise of Perfect Automation Despite the skepticism, there’s hope. Two-thirds of respondents indicated they’d embrace automated voice agents if these systems could match the performance of human agents. This is exactly what we’re working on at Tenyx—building scalable, reliable AI agents that serve businesses and customers globally. “As leaders in voice AI technology, Tenyx is dedicated to closing the gap between consumer expectations and technological capabilities,” Arel says. “Our mission is to equip businesses with AI solutions that not only streamline operations but also boost customer satisfaction.” Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. 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September 10, 2024in Salesforce
Salesforce to Enhance AI-Powered Tools With Tenyx
07Sep

Salesforce to Enhance AI-Powered Tools With Tenyx

Salesforce to Acquire Tenyx, Enhancing AI-Powered Solutions Salesforce has announced its decision to acquire Tenyx, a California-based startup specializing in AI-driven voice agents. This acquisition aims to bolster Salesforce’s AI capabilities and further its commitment to enhancing customer service through innovative technology. The deal, set to close in the third quarter of 2024, will integrate Tenyx’s advanced voice AI solutions with Salesforce’s existing services. About Tenyx Founded in 2022, Tenyx has quickly established itself in various industries including e-commerce, healthcare, hospitality, and travel. The startup, led by CEO Itamar Arel and CTO Adam Earle, is renowned for developing AI voice agents that create natural and engaging conversational experiences. Salesforce’s Strategic Move This acquisition is part of Salesforce’s broader strategy to reinvigorate its growth and strengthen its AI capabilities. Following a year of focus on share buybacks and a reduction in acquisitions under pressure from activist investors, Salesforce is now pivoting to integrate cutting-edge technology. This move reflects a renewed emphasis on acquiring top-tier AI talent to drive innovation and maintain a competitive edge. Industry Context The acquisition aligns Salesforce with a growing trend in the tech industry, where major players like Microsoft and Amazon are also investing heavily in AI. Microsoft recently acquired talent from AI startup Inflection for $650 million, while Amazon brought in co-founders and employees from Adept. These strategic acquisitions highlight the escalating competition for AI expertise and tools. What This Means for Salesforce With Tenyx’s technology, Salesforce will enhance its AI-powered solutions, particularly within its Agentforce Service Agent platform. This integration aims to deliver more intuitive and seamless customer interactions, setting new standards in customer experience. Conclusion Salesforce’s acquisition of Tenyx is a strategic move to advance its AI-driven solutions and maintain its leadership in customer service technology. By integrating Tenyx’s innovative voice AI, Salesforce is positioned to redefine customer engagement and service standards. The deal is expected to close by the end of the third quarter of Salesforce’s fiscal year 2025, concluding on October 31, 2024, pending customary closing conditions. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 7, 2024in Salesforce, Technology
Salesforce Nonprofit Cloud Focuses Resources
04Sep

Salesforce Nonprofit Cloud Focuses Resources

The Pancreatic Cancer Action Network (PanCAN) is currently exploring how to harness the AI capabilities within Salesforce’s Nonprofit Cloud to enhance its mission-driven services. Salesforce Nonprofit Cloud Focuses Resources allowing PanCan to focus on cancer. Pancreatic cancer is among the most aggressive and deadly forms of cancer, with a five-year survival rate of just 12.8% in the U.S. Despite accounting for only 3.3% of new cancer cases, it is responsible for 8.5% of all cancer-related deaths, making it the third leading cause of cancer mortality after lung and colon cancer. Founded in 1999, PanCAN is dedicated to researching this devastating disease and advocating for patients nationwide. The organization also provides critical information and resources to help patients make informed decisions, thereby supporting a community of patients and their families. One of PanCAN’s key services is connecting patients with specialists in their area. Julie Fleshman, President, CEO, and PanCAN’s first employee, emphasizes the importance of this program: “Our patient service program, particularly our call center, is the cornerstone of what we offer. When someone is diagnosed, they are understandably scared. Our trained case managers provide support and recommend that each patient sees a specialist. We maintain a database of specialists across the U.S. and can provide a list of surgeons or oncologists based on how far patients are willing to travel.” This case management system allows patients and their families to work with the same case manager consistently, ensuring a seamless, free-of-charge service and simplifying the information-gathering process. Salesforce Nonprofit Cloud Focuses Resources PanCAN also helps patients find clinical trials in their area, an essential service given the current state of treatment for pancreatic cancer. Nonprofit Cloud focuses on technical resources allowing PanCAN to focus on patient services. Fleshman explains: “Clinical trials often offer the most cutting-edge treatments, which is why we recommend patients consider them. We maintain a database of trials and can quickly inform patients of their options during phone consultations. This allows them to discuss potential trials with their physician and determine the best course of action.” Evolving Technology to Better Serve Patients PanCAN’s initial case management system was developed in-house about a decade ago. As it neared the end of its life, searches could take up to two hours, prompting the organization to seek a more efficient solution. PanCAN enlisted a Salesforce consulting partner to evaluate options and develop a technology strategy aligned with its goals. In June, a new system based on Salesforce’s Nonprofit Cloud Person Accounts module was launched. The new system has significantly reduced the time required to search for information, enabling case managers to assist more patients daily—a crucial improvement given the projected 66,440 new pancreatic cancer diagnoses in the U.S. this year alone. Additionally, the system’s user-friendliness has led to higher job satisfaction among employees. Fleshman stresses the importance of involving a multifunctional team in the implementation process: “It’s essential to be clear about your objectives from the start, but it’s equally important to include the right people. If we had only involved the patient services team and not the tech team responsible for maintenance and security, or the finance team whose system needed to integrate with ours, the project would have been siloed and incomplete.” Salesforce Nonprofit Cloud Focuses Resources and Solutions for Nonprofits The updated system includes advanced features like the OmniStudio process automation tool, which has streamlined the patient questionnaire process, and an integrated data processing engine capable of saving multiple records simultaneously. Leveraging AI to Enhance Impact Looking ahead, PanCAN is assessing how to leverage the AI capabilities within Salesforce’s Nonprofit Cloud to further enhance its services. Fleshman outlines the next steps: “Providing information and resources to patients is crucial, but we also need to use data to optimize our programs and allocate our resources effectively. We hope AI will help us analyze data to better understand our impact and patient experiences. For instance, AI could reveal trends in how often we refer patients to specific doctors or studies, identify gaps in our services, or highlight areas where we should focus more of our efforts. Understanding these factors will help us allocate our time, energy, and resources more efficiently.” Despite the benefits, managing change has been key to addressing employee concerns about AI potentially threatening their jobs. Fleshman notes: “While there was excitement about getting a faster, more efficient tool, there was also anxiety about job security. Our focus was on demonstrating how AI could enhance our ability to provide better reports and insights rather than replacing jobs. We believe that even the best tools are useless if people aren’t trained to use them effectively.” A Vision for the Future PanCAN has developed a five-year technology roadmap that includes upgrading its grant management and financial systems, as well as introducing marketing applications to better understand its target audience, improve outreach, and personalize interactions. As Fleshman concludes: “Our executive team recognizes that without cutting-edge technology, we won’t achieve our ambitious goals. In our sector, technology often gets deprioritized, but updating systems allows us to deliver our mission more productively and efficiently, ultimately better serving those we’re here to help.” Our Take According to Salesforce’s sixth Nonprofit Trends Report, many charities view AI with a mix of optimism, curiosity, and caution. PanCAN’s approach to adopting AI—focusing on its potential to optimize resources and better support patients—demonstrates the organization’s forward-thinking and commitment to its mission. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and

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September 4, 2024in Salesforce for Nonprofits, Salesforce Nonprofit Cloud
AI and Big Data
31Aug

AI and Big Data

Over the past decade, enterprises have accumulated vast amounts of data, capturing everything from business processes to inventory statistics. This surge in data marked the onset of the big data revolution. However, merely storing and managing big data is no longer sufficient to extract its full value. As organizations become adept at handling big data, forward-thinking companies are now leveraging advanced analytics and the latest AI and machine learning techniques to unlock even greater insights. These technologies can identify patterns and provide cognitive capabilities across vast datasets, enabling organizations to elevate their data analytics to new levels. Additionally, the adoption of generative AI systems is on the rise, offering more conversational approaches to data analysis and enhancement. This allows organizations to extract significant insights from information that would otherwise remain untapped in data stores. How Are AI and Big Data Related? Applying machine learning algorithms to big data is a logical progression for companies aiming to maximize the potential of their data. Unlike traditional rules-based approaches that follow explicit instructions, machine learning systems use data-driven algorithms and statistical models to analyze and detect patterns in data. Big data serves as the raw material for these systems, which derive valuable insights from it. Organizations are increasingly recognizing the benefits of integrating big data with machine learning. However, to fully harness the power of both, it’s crucial to understand their individual capabilities. Understanding Big Data Big data involves extracting and analyzing information from large quantities of data, but volume is just one aspect. Other critical “Vs” of big data that enterprises must manage include velocity, variety, veracity, validity, visualization, and value. Understanding Machine Learning Machine learning, the backbone of modern AI, adds significant value to big data applications by deriving deeper insights. These systems learn and adapt over time without the need for explicit programming, using statistical models to analyze and infer patterns from data. Historically, companies relied on complex, rules-based systems for reporting, which often proved inflexible and unable to cope with constant changes. Today, machine learning and deep learning enable systems to learn from big data, enhancing decision-making, business intelligence, and predictive analysis. The strength of machine learning lies in its ability to discover patterns in data. The more data available, the more these algorithms can identify patterns and apply them to future data. Applications range from recommendation systems and anomaly detection to image recognition and natural language processing (NLP). Categories of Machine Learning Algorithms Machine learning algorithms generally fall into three categories: The most powerful large language models (LLMs), which underpin today’s widely used generative AI systems, utilize a combination of these methods, learning from massive datasets. Understanding Generative AI Generative AI models are among the most powerful and popular AI applications, creating new data based on patterns learned from extensive training datasets. These models, which interact with users through conversational interfaces, are trained on vast amounts of internet data, including conversations, interviews, and social media posts. With pre-trained LLMs, users can generate new text, images, audio, and other outputs using natural language prompts, without the need for coding or specialized models. How Does AI Benefit Big Data? AI, combined with big data, is transforming businesses across various sectors. Key benefits include: Big Data and Machine Learning: A Synergistic Relationship Big data and machine learning are not competing concepts; when combined, they deliver remarkable results. Emerging big data techniques offer powerful ways to manage and analyze data, while machine learning models extract valuable insights from it. Successfully handling the various “Vs” of big data enhances the accuracy and power of machine learning models, leading to better business outcomes. The volume of data is expected to grow exponentially, with predictions of over 660 zettabytes of data worldwide by 2030. As data continues to amass, machine learning will become increasingly reliant on big data, and companies that fail to leverage this combination will struggle to keep up. Examples of AI and Big Data in Action Many organizations are already harnessing the power of machine learning-enhanced big data analytics: Conclusion The integration of AI and big data is crucial for organizations seeking to drive digital transformation and gain a competitive edge. As companies continue to combine these technologies, they will unlock new opportunities for personalization, efficiency, and innovation, ensuring they remain at the forefront of their industries. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. 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August 31, 2024in Data, Enterprise, Generative AI, Google, Salesforce, Technology
Small Language Models
10Aug

Small Language Models

Large language models (LLMs) like OpenAI’s GPT-4 have gained acclaim for their versatility across various tasks, but they come with significant resource demands. In response, the AI industry is shifting focus towards smaller, task-specific models designed to be more efficient. Microsoft, alongside other tech giants, is investing in these smaller models. Science often involves breaking complex systems down into their simplest forms to understand their behavior. This reductionist approach is now being applied to AI, with the goal of creating smaller models tailored for specific functions. Sébastien Bubeck, Microsoft’s VP of generative AI, highlights this trend: “You have this miraculous object, but what exactly was needed for this miracle to happen; what are the basic ingredients that are necessary?” In recent years, the proliferation of LLMs like ChatGPT, Gemini, and Claude has been remarkable. However, smaller language models (SLMs) are gaining traction as a more resource-efficient alternative. Despite their smaller size, SLMs promise substantial benefits to businesses. Microsoft introduced Phi-1 in June last year, a smaller model aimed at aiding Python coding. This was followed by Phi-2 and Phi-3, which, though larger than Phi-1, are still much smaller than leading LLMs. For comparison, Phi-3-medium has 14 billion parameters, while GPT-4 is estimated to have 1.76 trillion parameters—about 125 times more. Microsoft touts the Phi-3 models as “the most capable and cost-effective small language models available.” Microsoft’s shift towards SLMs reflects a belief that the dominance of a few large models will give way to a more diverse ecosystem of smaller, specialized models. For instance, an SLM designed specifically for analyzing consumer behavior might be more effective for targeted advertising than a broad, general-purpose model trained on the entire internet. SLMs excel in their focused training on specific domains. “The whole fine-tuning process … is highly specialized for specific use-cases,” explains Silvio Savarese, Chief Scientist at Salesforce, another company advancing SLMs. To illustrate, using a specialized screwdriver for a home repair project is more practical than a multifunction tool that’s more expensive and less focused. This trend towards SLMs reflects a broader shift in the AI industry from hype to practical application. As Brian Yamada of VLM notes, “As we move into the operationalization phase of this AI era, small will be the new big.” Smaller, specialized models or combinations of models will address specific needs, saving time and resources. Some voices express concern over the dominance of a few large models, with figures like Jack Dorsey advocating for a diverse marketplace of algorithms. Philippe Krakowski of IPG also worries that relying on the same models might stifle creativity. SLMs offer the advantage of lower costs, both in development and operation. Microsoft’s Bubeck emphasizes that SLMs are “several orders of magnitude cheaper” than larger models. Typically, SLMs operate with around three to four billion parameters, making them feasible for deployment on devices like smartphones. However, smaller models come with trade-offs. Fewer parameters mean reduced capabilities. “You have to find the right balance between the intelligence that you need versus the cost,” Bubeck acknowledges. Salesforce’s Savarese views SLMs as a step towards a new form of AI, characterized by “agents” capable of performing specific tasks and executing plans autonomously. This vision of AI agents goes beyond today’s chatbots, which can generate travel itineraries but not take action on your behalf. Salesforce recently introduced a 1 billion-parameter SLM that reportedly outperforms some LLMs on targeted tasks. Salesforce CEO Mark Benioff celebrated this advancement, proclaiming, “On-device agentic AI is here!” Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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August 10, 2024in Generative AI
Impact of Generative AI on Workforce
28Jul

Impact of Generative AI on Workforce

The Impact of Generative AI on the Future of Work Automation has long been a source of concern and hope for the future of work. Now, generative AI is the latest technology fueling both fear and optimism. AI’s Role in Job Augmentation and Replacement While AI is expected to enhance many jobs, there’s a growing argument that job augmentation for some might lead to job replacement for others. For instance, if AI makes a worker’s tasks ten times easier, the roles created to support that job could become redundant. A June 2023 McKinsey report highlighted that generative AI (GenAI) could automate 60% to 70% of employee workloads. In fact, AI has already begun replacing jobs, contributing to nearly 4,000 job cuts in May 2023 alone, according to Challenger, Gray & Christmas Inc. OpenAI, the creator of ChatGPT, estimates that 80% of the U.S. workforce could see at least 10% of their jobs impacted by large language models (LLMs). Examples of AI Job Replacement One notable example involves a writer at a tech startup who was let go without explanation, only to later discover references to her as “Olivia/ChatGPT” in internal communications. Managers had discussed how ChatGPT was a cheaper alternative to employing a writer. This scenario, while not officially confirmed, strongly suggested that AI had replaced her role. The Writers Guild of America also went on strike, seeking not only higher wages and more residuals from streaming platforms but also more regulation of AI. Research from the Frank Hawkins Kenan Institute of Private Enterprise indicates that GenAI might disproportionately affect women, with 79% of working women holding positions susceptible to automation compared to 58% of working men. Unlike past automation that typically targeted repetitive tasks, GenAI is different—it automates creative work such as writing, coding, and even music production. For example, Paul McCartney used AI to partially generate his late bandmate John Lennon’s voice to create a posthumous Beatles song. In this case, AI enhanced creativity, but the broader implications could be more complex. Other Impacts of AI on Jobs AI’s impact on jobs goes beyond replacement. Human-machine collaboration presents a more positive angle, where AI helps improve the work experience by automating repetitive tasks. This could lead to a rise in AI-related jobs and a growing demand for AI skills. AI systems require significant human feedback, particularly in training processes like reinforcement learning, where models are fine-tuned based on human input. A May 2023 paper also warned about the risk of “model collapse,” where LLMs deteriorate without continuous human data. However, there’s also the risk that AI collaboration could hinder productivity. For example, generative AI might produce an overabundance of low-quality content, forcing editors to spend more time refining it, which could deprioritize more original work. Jobs Most Affected by AI AI Legislation and Regulation Despite the rapid advancement of AI, comprehensive federal regulation in the U.S. remains elusive. However, several states have introduced or passed AI-focused laws, and New York City has enacted regulations for AI in recruitment. On the global stage, the European Union has introduced the AI Act, setting a common legal framework for AI. Meanwhile, U.S. leaders, including Senate Majority Leader Chuck Schumer, have begun outlining plans for AI regulation, emphasizing the need to protect workers, national security, and intellectual property. In October 2023, President Joe Biden signed an executive order on AI, aiming to protect consumer privacy, support workers, and advance equity and civil rights in the justice system. AI regulation is becoming increasingly urgent, and it’s a question of when, not if, comprehensive laws will be enacted. As AI continues to evolve, its impact on the workforce will be profound and multifaceted, requiring careful consideration and regulation to ensure it benefits society as a whole. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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July 28, 2024in Generative AI
Technology Cancels Your Flight
23Jul

Technology Cancels Your Flight

What to Do If Technology Cancels Your Flight – the Recent Crowdstrike Microsoft Outage The recent Crowdstrike Microsoft outage caused widespread disruption beyond just computers, stranding thousands of air travelers. When Technology Cancels Your Flight, here’s what you can do next: The Impact of the Outage Air travelers posted pictures on social media of crowded airports in Europe and the United States due to the technology outage on Friday. In the U.S., major airlines like American, Delta, United, Spirit, and Allegiant had all their flights grounded for varying lengths of time. The outage affected crucial systems, including those for checking in passengers, calculating aircraft weight, and communicating with crews. Travelers began to panic. By early evening on the East Coast, nearly 2,800 U.S. flights had been canceled and almost 10,000 delayed, according to FlightAware. Worldwide, about 4,400 flights were canceled. Delta and its regional affiliates canceled 1,300 flights, United and United Express canceled more than 550 flights, and American Airlines canceled more than 450 flights. Airports became crowded zoos of passengers milling around waiting for answers. The outage, blamed on a software update from cybersecurity firm CrowdStrike, affected Microsoft’s computers used by many airlines. Despite CrowdStrike identifying and fixing the issue, the damage was done, leaving hundreds of thousands of travelers stranded. What to Do Next Contact Your Airline Check Other Airlines and Airports Weekend Flights Air Traffic Control Refunds and Reimbursements Transportation Secretary Pete Buttigieg emphasized the need for airlines to take care of passengers experiencing major delays. Airlines affected by the outage are offering rebooking, vouchers, refunds, and other assistance. The Transportation Department fined Southwest $35 million last year as part of a $140 million settlement for nearly 17,000 canceled flights in December 2022. The department maintains a “dashboard” showing what each airline promises to cover during travel disruptions. By taking proactive steps and utilizing available resources, travelers can navigate the challenges posed by this unexpected technology outage and find alternative solutions to reach their destinations. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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July 23, 2024in Salesforce
Leverage AI and Machine Learning in Your Data Warehouse
12Jul

Apache Iceberg

Why Traditional Data Architecture Falls Short—and How Apache Iceberg Can Help Traditional data architecture patterns come with significant limitations. These outdated methods often require Extract, Transform, Load (ETL) processes to move data into each tool, a costly and cumbersome approach that leads to data silos and drift. Moreover, this practice locks your data into specific proprietary tools and formats. Fortunately, there’s a better way, and this book will show you how. Apache Iceberg offers a modern solution that delivers the capabilities, performance, scalability, and cost-efficiency needed for an open data lakehouse. By applying the concepts in this book, you’ll be able to handle interactive, batch, machine learning, and streaming analytics without the need to duplicate data across various proprietary systems and formats. What is Apache Iceberg? Apache Iceberg is a high-performance table format designed for massive analytic tables. It brings the reliability and simplicity of SQL tables to big data, allowing engines like Spark, Trino, Flink, Presto, Hive, and Impala to work with the same tables concurrently and safely. Here’s what makes Iceberg stand out: Comparing Apache Iceberg with Other Technologies What Problems Does Apache Iceberg Solve? Apache Iceberg simplifies building data lakes and performing data operations for anyone familiar with SQL. It ensures data consistency, meaning that any user accessing the data will see a unified view. Is Apache Iceberg a Lakehouse? Yes, Apache Iceberg is the open table format at the heart of the data lakehouse architecture. Its detailed metadata files and analytics-optimized design enhance query engine efficiency. Iceberg and Snowflake Iceberg Tables combine the performance and familiar query capabilities of Snowflake tables with customer-managed cloud storage. This integration helps Snowflake users overcome common barriers and unlock the full value of their data. Can Databricks Read Iceberg? Yes, Databricks can read Iceberg tables through the Delta Universal Format (UniForm), provided you are using Databricks Runtime 14.3 LTS or later. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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July 12, 2024in Data, Snowflake
AI Center New Era of Cross Industry Collaboration
20Jun

AI Center New Era of Cross Industry Collaboration

On Tuesday, Salesforce celebrated the launch of its new AI center in London with a complimentary training event that was attended by over a hundred software developers and administrators. This event was part of the center’s “AI Now Tour,” aimed at equipping developers from various industries with the skills to build the next generation of AI applications. AI Center New Era of Cross Industry Collaboration is an exciting new stage of artificial intelligence. A lot of “artificial intelligence” captures human creativity that is original. Regulators need to insist on training data transparency. No doubt the center will stand up plenty of conversations around regulations. This initiative underscores Salesforce’s commitment to training 100,000 developers worldwide. Attendees represented diverse sectors including automotive, financial services, retail, consumer goods, travel, hospitality, health and life sciences, and insurance. Needless to say, everyone has an appetite for AI. Reasons People Have Appetite for AI and Reasons They Do Not According to some polls, people are more concerned than excited about artificial intelligence (AI): Paul O’Sullivan, the new head of the Salesforce AI center and the UK&I CTO, discussed the center’s event and content strategy with publication TechInformed. He emphasized a plan to host “deep industry events” with a sector-specific focus, as well as cross-industry events to explore new business models. “Cross-industry opportunities are crucial in the AI landscape,” O’Sullivan noted. “For example, the future of self-driving cars impacts not just the automotive and OEM sectors but also insurance—who will be insured in the future? The driver, the car manufacturer, or the algorithm engineer?” He added that the center will foster thought leadership and cross-industry collaboration, offering new business models for customers. Despite the developer training courses being fully booked until the end of September, O’Sullivan highlighted that the 14,000 sq. ft. center, located in the Blue Fin building near Waterloo, is open to all businesses interested in exploring AI, not just Salesforce customers. In partnership with business growth agencies like London & Partners, O’Sullivan aims to extend training opportunities to local universities and apprenticeship program supporters. “We want to leverage our relationships to support the next generation, including students in art, design, and creative fields,” he said. AI Center New Era of Cross Industry Collaboration The new AI center, part of Salesforce’s $4 billion investment in the UK over the next five years, is located near iconic London landmarks such as the Tate Modern and Shakespeare’s Globe Theatre. The opening event was attended by notable UK business and industry figures, including Howard Dawber, the Deputy Mayor of London for Business and Growth, and Janet Coyle CBE, Managing Director of Grow London at London & Partners. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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June 20, 2024in Salesforce
Hybrid Worker Satisfaction
17Jun

Hybrid Worker Satisfaction

Hybrid Worker Satisfaction One of the most debated topics of the post-pandemic era is whether hybrid work represents the best or worst of both worlds. Hybrid Worker Satisfaction can be built at just about any company. A study published in Nature found that hybrid work significantly boosts job satisfaction, with negligible productivity loss. It also promotes employee well-being and benefits businesses such as cafes and gyms. Proponents of hybrid work argue that it enhances work-life balance and engagement. Studies have shown it has increased women’s participation in the labor force. However, employers often worry that it may reduce employee productivity and collaboration. A new study published in Nature might settle the debate. It suggests that productivity concerns are negligible and that hybrid work could indeed be the optimal setup for workers. The study tracked over 1,600 employees of Trip.com, a Chinese travel agency, for two years, dividing them into two groups: one working in the office five days a week, and the other working three days in the office and two days at home. The hybrid group exhibited higher job satisfaction and a one-third reduction in quit rates, particularly among non-managers, women, and those with long commutes. There were no significant downsides. The study found no measurable impact on performance or productivity. Managers also became more supportive of hybrid work after participating in the study. We might have not had the light bulb experience, were it not for the pandemic. But we did. The stufdy concluded that hybrid work can increase company profits by reducing quit rates, which are “estimated to cost about 50% of an individual’s annual salary.” It also provides substantial societal benefits by offering a valuable perk to employees, reducing commuting, and easing child-care challenges. Additionally, hybrid work can boost earnings for other local businesses like cafes, bars, gyms, and beauty salons, all of which have seen increased revenue due to the work-from-home economy. If you are struggling with making your company more hybrid-work friendly, talk to Tectonic. Our cloud-based solutions help everyone work smarter, right where they are. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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June 17, 2024in Salesforce
gettectonic.com