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Quest to be Data-Driven

Quest to be Data-Driven

“Data-driven” is a business term that refers to the utilization of data to inform or enhance processes, decision making, and even the revenue model. The quest to be data-driven is afoot. In recent years, a data-driven business approach has gained a great deal of traction. It is true that every business deals with data — however, data-driven businesses systematically and methodically use data to power business decisions. Incorporating the notion of being a data-driven enterprise enriches the understanding of how data can profoundly impact business operations. Leveraging data not only offers valuable insights but also enhances adaptability, thereby sharpening the competitive edge of an organization. These insights serve as a foundation for making market predictions and adapting business strategies accordingly, often leading to revenue growth. While data may not provide solutions to all organizational challenges, embracing a data-driven approach lays a solid groundwork for achieving organizational goals. Data-driven contrasts with decision making that may be driven by emotions, external pressure, or instinct. So, what exactly constitutes a data-driven enterprise? It transcends mere number-crunching; it involves creating sustainable value for customers and innovating efficiently in the digital economy. Encouraging a data-driven approach across all facets of the business is paramount to success. Gaining data insights from data is invaluable. It allows organizations to reshape customer interactions, provided the data is accurate, accessible, and integrated into existing processes. However, many struggle to extract value from their data due to the complexity of transforming raw data into actionable insights. Understanding the hierarchy of data, information, and insights is crucial, as actionable insights drive data-driven success. Furthermore, adaptability emerges as a crucial factor in today’s rapidly evolving landscape. The ability to swiftly respond to changes and leverage data for informed decision-making is paramount. Data-driven insights serve as powerful tools for facilitating change and fostering agility, ensuring organizations remain competitive. Moreover, data serves as a catalyst for revenue generation through various business models such as Data as a Service (DaaS), Information as a Service (IaaS), and Answer as a Service (AaaS). By putting customer satisfaction at the forefront and leveraging data-driven insights, organizations can evolve their products proactively and drive growth. Building a data-driven enterprise involves a strategic approach encompassing nine key steps, including defining end goals, setting tangible KPIs, and fostering a data-driven culture across the organization. However, challenges such as deciding what to track, lack of tools or time for data collation, and turning data into meaningful insights may arise. Overcoming these challenges requires a cultural shift towards data-driven decision-making and the adoption of modern data architectures. Walking (or perhaps running) the data-driven journey with Tectonic involves connecting and integrating various data sources to ensure seamless data flow. By embracing a data-driven approach, organizations can unlock the full potential of their data, driving innovation, enhancing customer experiences, and achieving long-term success in today’s dynamic, rapidly evolving business landscape. Expanding upon this foundation, let’s go deeper into the transformative power of data-driven enterprises across various industry sectors. Consider, for instance, the retail industry, where data-driven insights revolutionize customer experiences and optimize operational efficiency. In the retail sector, understanding consumer behavior and preferences iscrucial to daily, quarterly, and annual success. By harnessing data analytics, retailers can analyze purchasing patterns, demographic information, and social media interactions to tailor marketing strategies and product offerings. For example, through personalized recommendations based on past purchases and browsing history, retailers can enhance customer engagement and drive sales. Moreover, data-driven insights enable retailers to optimize inventory management and supply chain operations. By analyzing historical sales data and demand forecasts, retailers can anticipate fluctuations in demand, minimize stockouts, and reduce excess inventory. This not only improves operational efficiency but also enhances customer satisfaction by ensuring products are readily available when needed. Furthermore, in the healthcare industry, data-driven approaches revolutionize patient care and treatment outcomes. Electronic health records (EHRs) and medical imaging technologies generate vast amounts of data, providing healthcare professionals with valuable insights into patient health and treatment efficacy. By leveraging predictive analytics and machine learning algorithms, healthcare providers can identify patients at risk of developing chronic conditions, enabling early intervention and preventive care. Additionally, data-driven approaches facilitate personalized treatment plans tailored to each patient’s unique medical history, genetic makeup, and lifestyle factors, improving treatment outcomes and patient satisfaction. In the manufacturing sector, data-driven strategies optimize production processes, enhance product quality, and reduce operational costs. By implementing Internet of Things (IoT) sensors and connected devices on the factory floor, manufacturers can collect real-time data on equipment performance, energy consumption, and production efficiency. Analyzing this data enables manufacturers to identify inefficiencies, minimize downtime, and proactively schedule maintenance to prevent costly equipment failures. Moreover, data-driven insights inform process improvements and product innovations, enabling manufacturers to stay competitive in an increasingly globalized market. The ultimately transformative impact of data-driven enterprises extends across various industry sectors, revolutionizing business operations, enhancing customer experiences, and driving innovation. By embracing a data-driven approach and leveraging advanced analytics technologies, organizations can unlock new opportunities for growth, efficiency, and competitive advantage in today’s data-loaded digital economy. Becoming data-driven requires harnessing the full potential of your data, transforming it into actionable insights, and iteratively refining your processes. Remember, data itself is not the ultimate goal but rather a powerful tool to drive informed decision-making and organizational growth. To establish a truly data-driven organization, consider the following nine steps: By following these steps, your organization can effectively harness the power of data to drive innovation, improve decision-making, and achieve sustainable growth in today’s data-driven landscape. Tectonic recognizes the challenges in the quest to be data-driven. We’ve launched a Data Cloud Salesforce Implementation Solution to help you. Content updated May 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 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,

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Financial Services Sector

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 Services Cloud with Einstein Analytics. This amalgamation, known as Einstein Analytics for Financial Services, harnesses Salesforce’s robust query engine and interpretation layers, fueled by the enterprise data analytics prowess acquired through BeyondCore in 2016. Salesforce Unites Einstein Analytics with Financial CRM This integrated platform – Salesforce Unites Einstein Analytics with Financial CRM – offers two prebuilt analytical models, meticulously designed to gauge client churn (identifying clients at risk of leaving) and the potential for clients to bring additional assets to a firm. These models, while prepackaged, can be tailored to specific needs, providing insights into future scenarios within the firm. Advisors can leverage these models to assess client characteristics against firm-wide benchmarks and receive actionable suggestions to enhance client retention. Home office professionals and data scientists have the option to delve into the underlying mathematical frameworks of these models, allowing for customization if required. While the tool offers enterprise-level benchmarking, firms can incorporate their own industry-specific data to run the models, ensuring tailored insights. This initiative builds upon previous endeavors integrating machine learning into Financial Services Cloud, which aimed to identify crucial life events and offer actionable recommendations. The decision to develop a more holistic solution stemmed from observing customer behavior and the growing trend of custom dashboard creation. By streamlining and prepackaging these insights, Salesforce aims to accelerate adoption and empower users to focus on their core tasks. Although customization remains a key feature, the platform aims to simplify adoption by providing templated solutions. However, the efficacy of insights depends on the quality of the ingested data, emphasizing the importance of data aggregation and normalization. Future updates are expected to introduce additional machine learning models focused on reducing heldaway assets and increasing assets under management. Developed in collaboration with diverse stakeholders, ranging from enterprise financial advisors to firms of varying sizes, the service is priced at $150 per user per month. It’s not a standalone product and requires integration with Financial Services Cloud or Einstein Analytics Plus. 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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Data Collection

Collecting Customer Data and Acting On It

Salesforce Data Collection and Customer Journey Mapping for Better Outcomes At the forefront of customer engagement, sales revenue leaders have a pivotal role in shaping a company’s overarching strategy and enhancing customer experience (CX). Through the extraction of valuable insights from Salesforce data collection, they illuminate areas for improvement, fostering long-term revenue growth.  This is all driven by customer data. Customer Journey Mapping A powerful method to gain a comprehensive understanding of the customer journey involves creating a customer journey map. This is based on your Salesforce data collection. This map traces customers’ experiences, unveiling pain points and moments of truth within the sales process. Utilizing this information, companies can enhance CX, predict revenue more accurately, and make data-driven decisions. For instance, if sales revenue executives observe that only a limited number of customers are transitioning from a free service to a paid one, they can experiment with innovative approaches to prompt purchases. Employing digital nudges, such as reminding customers of the limited time remaining to avail the free service, revenue leaders can iterate and refine their strategies until they resonate with customers. Salesforce Next Best Action can notify sales representatives of customers most likely to be ready to convert. Salesforce automations can move likely to convert customers to next best action campaigns and make intuitive decisions based upon predetermined criteria. Thanks to technological advancements in Salesforce, tracking and analyzing customer behavior is now more accessible than ever. Leveraging data analytics, AI, and machine learning, companies can delve deeper into every digital touchpoint, assessing its impact on CX. This empowers revenue leaders to evaluate the success of diverse initiatives, compare the effectiveness of multiple communication channels, and make decisions grounded in data. Decision Based on Salesforce Data Collection One consequential decision involves identifying high-value customers in the sales pipeline. Through data analytics, revenue leaders can ascertain which customers are most likely to complete a purchase, allowing for resource allocation optimization. This approach prevents the squandering of time and resources on low-value prospects and facilitates an accurate prediction of future revenue. Sales revenue leaders emerge as key drivers of growth and CX enhancement. By harnessing technology and data-driven insights, they can make informed decisions, fine-tune customer journeys, and ultimately propel revenue growth. Let Tectonic craft a tailored program for data collection and customer journey mapping today. 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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Consent Management Analytics and Data Quality

Consent Management Analytics and Data Quality

Understanding Data Analytics Consent and Consent Management Why Consent Management is Crucial Consent Management Analytics and Data Quality. With laws like GDPR and CCPA imposing stringent data processing requirements, an effective consent management strategy is essential for compliance and building user trust. A Consent Management Platform (CMP) simplifies this process, offering comprehensive solutions for managing consent and user preferences. Emerging trends such as AI, blockchain, and browser-based preference signals like Global Privacy Control (GPC) are shaping the future of consent management, pushing for more dynamic, user-centric models that prioritize control and privacy. What is Data Analytics Consent? Data analytics consent involves obtaining and managing individuals’ permission to collect, store, and use their personal data for specific purposes. With an entire generation accustomed to constant data collection and analysis, consent management has become a vital component of digital operations. This discipline focuses on the practices and processes organizations use to handle and record user consent for data collection and processing. The Role of a Consent Management Platform (CMP) A CMP is a software tool designed to help websites comply with cookie regulations. It plays a crucial role by: How is Consent Managed in Data Privacy? Consent management in data privacy is a procedural approach ensuring compliance by informing users about data collection and usage practices. An effective process logs and tracks consent, enhancing data quality and relevance. Users who actively consent to data use are more likely to provide accurate and relevant information, which is vital for informed business decisions and personalized services. Why Consent Management is Essential for Data Protection Compliance Compliance with data protection regulations requires explicit consent for data processing, especially for sensitive data. Consent management: Informed Consent and Data Strategy Informed consent is crucial for ensuring participants voluntarily engage in data collection and understand its purpose. Implementing a consent management system involves: Explicit vs. Implicit Consent The Future of Consent Management Emerging trends like AI, blockchain, and GPC are influencing the evolution of consent management, necessitating more dynamic, user-centric models that prioritize user control and privacy. Businesses must refine their data strategies for increased transparency and prepare for a future without third-party cookies, ensuring that user consent is respected in all marketing activities. Content updated February 2024. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more 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 Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to Read more

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