According to Gartner, Big Data will be the next “disruptive technology” and will transform customer relationship management industry.The possibilities that Big Data offers are endless but companies first need to invest in CRM software
steps included in the analytics process
why marketing analysis.
advantages of marketing analytics
the framework of marketing analytics
future of marketing analytics,
how analytics helped amazon small case study.
Ai in marketing: How Business & Marketing can benefit from adopting AI & MLleanstartuphh
How one company got 85% improvement in sales after adapting AI in their Marketing? This meetup is focused on real life cases and practical application of AI and Machine Learning in Marketing and Business. Main goal of this event is to give you understanding of what AI can do (and cannot) and to inspire you to start using AI in your company.
What Works in B2B Marketing. Difference Between B2B and B2C Marketing. IndiaMART.com
B2B Marketing : Difference Between B2B and B2C Marketing. And What Works in B2B Marketing. Presentation given by Arun Tyagi, GM, Marketing, IndiaMART at NIILM. 4 March 2011.
The document discusses consumer decision making. It outlines the consumer decision making process which includes need recognition, information search, evaluation of alternatives, purchase, and post-purchase evaluation. It also discusses the types of consumer decisions and factors that influence consumer decision making like marketing efforts, psychological factors, and socio-cultural environment. The goal is to understand the "how" and "why" of consumers' experiences with products and services.
Cara Pluff discusses how to build buyer personas and create a successful customer engagement lifecycle. She emphasizes the importance of buyer personas for targeted engagement. Pluff outlines the customer lifecycle stages of acquire, engage, convert, serve, grow, and retain. She discusses how to measure engagement through websites, email, mobile, social media, and direct channels. Pluff stresses knowing your customers, differentiating customer service, humanizing your brand, and being there when customers need help.
Digital Marketing Analytics Lecture 1 Loyola University ChicagoEric Bryn
Digital marketing analytics discussion focusing on core concepts and constructs of digital analytics. Includes discussion of Zero Moment of Trust (obviously related to Google's ZMOT).
The document provides an overview of marketing analytics, including defining marketing analytics, key elements and capabilities, impact, and getting started with analytics. Some key points:
- Marketing analytics is the process of identifying valid performance metrics, tracking them over time, and using the results to improve marketing. The goal is to measure progress toward objectives.
- Key elements include people, steps, tools/technology, inputs and outputs. Capabilities include understanding performance and reporting it externally.
- Impacts can include optimizing brand recognition, content, channels, customer understanding, and predictive intelligence.
- Getting started involves assessing readiness, reviewing objectives, and establishing metrics like website, social media, email, and digital advertising metrics.
The Roles of Analytics in Digital MarketingT.S. Lim
An Introduction to Machine Learning. Boca Raton, FL: CRC Press, 1984.
Hartman, Kevin. Digital Analytics for Marketing Professionals: Marketing Analytics in Theory.
Coursera, n.d. https://www.coursera.org/learn/marketing-analytics/home/info.
Hartman, Kevin. Digital Analytics for Marketing Professionals: Marketing Analytics in Practice.
Coursera, n.d. https://www.coursera.org/learn/digital-analytics/home/info.
Kotler, Philip, and Gary Armstrong. Principles of Marketing. 15th ed. Upper Saddle River, NJ:
Prent
The document discusses marketing information systems and the marketing research process. It provides details on:
1) The components of a marketing information system which gathers, analyzes, and distributes market data to aid decision making.
2) The steps in the marketing research process including defining problems, developing a research plan, implementing the plan by collecting and analyzing data, and reporting findings.
3) Common marketing research approaches like surveys, experiments, and observations and how they are used for descriptive, causal, and exploratory research.
Digital marketing for startups provides an overview of key digital marketing channels and tactics for startups. It discusses the importance of digital marketing in helping startups reach customers and convert them. The document outlines various digital marketing channels including display, search, social media, and analytics. It also covers topics such as developing a digital marketing strategy, identifying target audiences, and measuring success.
Marketing research involves the systematic collection, analysis, and reporting of data to help companies solve marketing problems and make better business decisions. It is a multi-step process that includes establishing research objectives, designing the research, collecting and analyzing data, and reporting findings. The goal is to gather accurate and up-to-date information to help with marketing effectively in areas like product development, pricing, advertising, and distribution in response to changes in customer needs and the business environment.
1) The document proposes using advanced data analytics to build knowledge of customer behavior, preferences, and aspirations in order to maximize revenue.
2) A case study uses data from an online beauty/personal care subsidiary to demonstrate how clustering, classification, and regression analyses can provide insights.
3) The analyses identify customer subgroups, predict which customers will churn, and forecast spending amounts. This knowledge can then be used to target marketing and improve customer retention and spending.
Big data and AI will disrupt every aspect of digital marketing. Deep learning uses neural networks like the human brain to make predictions. AI can be used for tasks like predicting customer buying propensity, generating art, composing music, and personalizing ads and messages. By 2020, over 85% of customer interactions may be managed without humans as AI becomes more integrated into companies and used across the entire customer journey.
A/B testing involves comparing two versions of a web page (Version A and Version B) to determine which performs better. It directly compares a variation against the current experience to collect data on the impact of changes. A/B testing takes the guesswork out of optimization by enabling data-driven decisions. The process involves modifying a page to create a second version, then showing each version to half the traffic to measure which has a better conversion rate.
Social media analytics helps companies understand customer sentiment and trends from social media interactions to better meet customer needs. It allows companies to form, understand, and leverage online communities for business goals. Some key factors analyzed through social media analytics include engagement, reach, demographics, traffic sources, and interactions. The analytics process involves extracting relevant social media posts and profiles, participating to advance strategic goals, analyzing the data, and assimilating findings to identify best practices. While many companies don't fully understand how to effectively use social media analytics, areas like marketing, communications, and PR are mostly responsible for social media strategy development.
This document discusses marketing mix modeling and innovations in attribution modeling. It outlines challenges in modeling noisy marketing data and fitting statistical curves like s-curves. Various techniques are presented for transforming variables, detrending data, and fitting univariate and multivariate regression models. Vector auto-regression modeling is also discussed for measuring interdependencies between marketing drivers over time. The goal is to provide accurate estimates of the impact and effectiveness of different marketing investments to support strategic decision making.
I have made a slide for a marketing analytics course that I have recently completed on Coursera. The slide covers topics such as:
different types of marketing analytics and their uses,
brand value & brand architecture,
customer lifetime value and its applications,
before-after and full factorial marketing experiments
and regression analysis.
The document discusses using data mining techniques in e-commerce. It provides an introduction to data mining and e-commerce, describing common data mining tasks like classification, clustering, and association rule mining. The document outlines the basic data mining process and some popular data mining tools. It explains how data mining can be used in e-commerce for applications like customer profiling, personalization, basket analysis, sales forecasting, and market segmentation. The advantages of using data mining in e-commerce are also summarized.
This document discusses buyer behaviors, including:
1) The consumer decision-making process and factors that influence consumer attitudes and evaluations of alternatives.
2) Trends affecting consumer behavior and the different types of consumers in new product adoption.
3) Business-to-business (B2B) purchasing processes and the buying center of individuals involved.
4) Similarities and differences between consumer and B2B buying processes.
The document discusses the application of Web 2.0 technologies to market intelligence systems. It describes how a market intelligence system can collect data through RSS feeds, blogs, and web content. It then evaluates and organizes the data through tagging, rating, summarizing, and discussions. The system provides tactical reports, market summaries, strategy analyses and special topic reports to aid in strategic decision making.
This document provides an overview of a performance marketing strategy. It defines performance marketing and outlines how it can be used to identify and respond to customer moments to drive business outcomes. It presents a framework for performance marketing, including understanding customer intent, identifying where it fits, isolating desired actions, selecting channels, and tying results to business KPIs. The framework shows how different channels like paid search, social media, and display can be leveraged at different stages from awareness to conversion.
The document discusses internal record systems used by marketing managers to analyze orders, sales, costs, and inventory levels. It describes the order-to-payment cycle as the heart of these systems, where orders are dispatched, invoices prepared, and items shipped with accompanying documents. Modern systems allow for online order-payment through electronic data interface and give sales representatives mobile access to up-to-date customer and prospect information.
The document discusses the role of integrated marketing communications (IMC) in the marketing process. It covers topics like target marketing, segmentation, positioning, the 4 P's of marketing (product, price, place, promotion), and push vs. pull distribution strategies. The overall process involves analyzing markets, identifying customer segments, developing marketing strategies and programs for each segment, and promoting products to final buyers through various communication channels.
SMO (social media optimization) is the process of driving website traffic through social media platforms like Facebook, Twitter, LinkedIn, and Pinterest. The key aspects of SMO include setting goals and strategies, researching trends, engaging customers across multiple platforms, and tracking results. Social media provides significant benefits to businesses, allowing them to build their brand, target ads, respond to customers, and increase sales. Social bookmarking sites are another tool that can be used to generate backlinks and boost website traffic.
Big data offers companies a big advantage if they can harness enormous data sets that were previously impossible to process. The document discusses how big data is transforming business models through creative destruction, as more data is created every day from various sources. It provides examples of how companies in various industries like retail, banking, and manufacturing are using big data for customer intimacy, product innovation, and improving operations. Specifically, companies are able to better customize products and services, improve supply chain management, and gain real-time insights from vast amounts of structured and unstructured data.
Leveraging big data to drive marketing innovationAndrew Leone
Summary of the book: "The Big Data-Driven Company." Contains insights into leveraging data to drive marketing innovation. To buy this book: http://amzn.to/1YTdtqY
The Roles of Analytics in Digital MarketingT.S. Lim
An Introduction to Machine Learning. Boca Raton, FL: CRC Press, 1984.
Hartman, Kevin. Digital Analytics for Marketing Professionals: Marketing Analytics in Theory.
Coursera, n.d. https://www.coursera.org/learn/marketing-analytics/home/info.
Hartman, Kevin. Digital Analytics for Marketing Professionals: Marketing Analytics in Practice.
Coursera, n.d. https://www.coursera.org/learn/digital-analytics/home/info.
Kotler, Philip, and Gary Armstrong. Principles of Marketing. 15th ed. Upper Saddle River, NJ:
Prent
The document discusses marketing information systems and the marketing research process. It provides details on:
1) The components of a marketing information system which gathers, analyzes, and distributes market data to aid decision making.
2) The steps in the marketing research process including defining problems, developing a research plan, implementing the plan by collecting and analyzing data, and reporting findings.
3) Common marketing research approaches like surveys, experiments, and observations and how they are used for descriptive, causal, and exploratory research.
Digital marketing for startups provides an overview of key digital marketing channels and tactics for startups. It discusses the importance of digital marketing in helping startups reach customers and convert them. The document outlines various digital marketing channels including display, search, social media, and analytics. It also covers topics such as developing a digital marketing strategy, identifying target audiences, and measuring success.
Marketing research involves the systematic collection, analysis, and reporting of data to help companies solve marketing problems and make better business decisions. It is a multi-step process that includes establishing research objectives, designing the research, collecting and analyzing data, and reporting findings. The goal is to gather accurate and up-to-date information to help with marketing effectively in areas like product development, pricing, advertising, and distribution in response to changes in customer needs and the business environment.
1) The document proposes using advanced data analytics to build knowledge of customer behavior, preferences, and aspirations in order to maximize revenue.
2) A case study uses data from an online beauty/personal care subsidiary to demonstrate how clustering, classification, and regression analyses can provide insights.
3) The analyses identify customer subgroups, predict which customers will churn, and forecast spending amounts. This knowledge can then be used to target marketing and improve customer retention and spending.
Big data and AI will disrupt every aspect of digital marketing. Deep learning uses neural networks like the human brain to make predictions. AI can be used for tasks like predicting customer buying propensity, generating art, composing music, and personalizing ads and messages. By 2020, over 85% of customer interactions may be managed without humans as AI becomes more integrated into companies and used across the entire customer journey.
A/B testing involves comparing two versions of a web page (Version A and Version B) to determine which performs better. It directly compares a variation against the current experience to collect data on the impact of changes. A/B testing takes the guesswork out of optimization by enabling data-driven decisions. The process involves modifying a page to create a second version, then showing each version to half the traffic to measure which has a better conversion rate.
Social media analytics helps companies understand customer sentiment and trends from social media interactions to better meet customer needs. It allows companies to form, understand, and leverage online communities for business goals. Some key factors analyzed through social media analytics include engagement, reach, demographics, traffic sources, and interactions. The analytics process involves extracting relevant social media posts and profiles, participating to advance strategic goals, analyzing the data, and assimilating findings to identify best practices. While many companies don't fully understand how to effectively use social media analytics, areas like marketing, communications, and PR are mostly responsible for social media strategy development.
This document discusses marketing mix modeling and innovations in attribution modeling. It outlines challenges in modeling noisy marketing data and fitting statistical curves like s-curves. Various techniques are presented for transforming variables, detrending data, and fitting univariate and multivariate regression models. Vector auto-regression modeling is also discussed for measuring interdependencies between marketing drivers over time. The goal is to provide accurate estimates of the impact and effectiveness of different marketing investments to support strategic decision making.
I have made a slide for a marketing analytics course that I have recently completed on Coursera. The slide covers topics such as:
different types of marketing analytics and their uses,
brand value & brand architecture,
customer lifetime value and its applications,
before-after and full factorial marketing experiments
and regression analysis.
The document discusses using data mining techniques in e-commerce. It provides an introduction to data mining and e-commerce, describing common data mining tasks like classification, clustering, and association rule mining. The document outlines the basic data mining process and some popular data mining tools. It explains how data mining can be used in e-commerce for applications like customer profiling, personalization, basket analysis, sales forecasting, and market segmentation. The advantages of using data mining in e-commerce are also summarized.
This document discusses buyer behaviors, including:
1) The consumer decision-making process and factors that influence consumer attitudes and evaluations of alternatives.
2) Trends affecting consumer behavior and the different types of consumers in new product adoption.
3) Business-to-business (B2B) purchasing processes and the buying center of individuals involved.
4) Similarities and differences between consumer and B2B buying processes.
The document discusses the application of Web 2.0 technologies to market intelligence systems. It describes how a market intelligence system can collect data through RSS feeds, blogs, and web content. It then evaluates and organizes the data through tagging, rating, summarizing, and discussions. The system provides tactical reports, market summaries, strategy analyses and special topic reports to aid in strategic decision making.
This document provides an overview of a performance marketing strategy. It defines performance marketing and outlines how it can be used to identify and respond to customer moments to drive business outcomes. It presents a framework for performance marketing, including understanding customer intent, identifying where it fits, isolating desired actions, selecting channels, and tying results to business KPIs. The framework shows how different channels like paid search, social media, and display can be leveraged at different stages from awareness to conversion.
The document discusses internal record systems used by marketing managers to analyze orders, sales, costs, and inventory levels. It describes the order-to-payment cycle as the heart of these systems, where orders are dispatched, invoices prepared, and items shipped with accompanying documents. Modern systems allow for online order-payment through electronic data interface and give sales representatives mobile access to up-to-date customer and prospect information.
The document discusses the role of integrated marketing communications (IMC) in the marketing process. It covers topics like target marketing, segmentation, positioning, the 4 P's of marketing (product, price, place, promotion), and push vs. pull distribution strategies. The overall process involves analyzing markets, identifying customer segments, developing marketing strategies and programs for each segment, and promoting products to final buyers through various communication channels.
SMO (social media optimization) is the process of driving website traffic through social media platforms like Facebook, Twitter, LinkedIn, and Pinterest. The key aspects of SMO include setting goals and strategies, researching trends, engaging customers across multiple platforms, and tracking results. Social media provides significant benefits to businesses, allowing them to build their brand, target ads, respond to customers, and increase sales. Social bookmarking sites are another tool that can be used to generate backlinks and boost website traffic.
Big data offers companies a big advantage if they can harness enormous data sets that were previously impossible to process. The document discusses how big data is transforming business models through creative destruction, as more data is created every day from various sources. It provides examples of how companies in various industries like retail, banking, and manufacturing are using big data for customer intimacy, product innovation, and improving operations. Specifically, companies are able to better customize products and services, improve supply chain management, and gain real-time insights from vast amounts of structured and unstructured data.
Leveraging big data to drive marketing innovationAndrew Leone
Summary of the book: "The Big Data-Driven Company." Contains insights into leveraging data to drive marketing innovation. To buy this book: http://amzn.to/1YTdtqY
Big data offers opportunities for companies to gain competitive advantages through improved customer intimacy, product innovation, and operations. The document discusses how various companies are leveraging big data across industries. It notes that 45% of companies have implemented big data initiatives in the past two years and over 90% of Fortune 500 companies will have initiatives underway soon. Harnessing big data's potential requires understanding where it can create value within a company and having the right organizational structure, technology investments, and plan to capture those benefits.
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
1) Big data is defined as large volumes of structured and unstructured data that is growing exponentially. It can be analyzed to provide more accurate insights and better decision making.
2) The key aspects of big data are volume, velocity, variety, and variability of data from multiple sources.
3) Companies that effectively analyze big data can improve marketing ROI by 15-20% and increase productivity and profits by 5-6% over peers.
Introduction to Big Data Analytics Unit 1 .pdfMadhumithaN28
This presentation provides a comprehensive exploration of Big Data and its impact across industries. Key topics include:
Big Data Characteristics: Detailed insights into the essential characteristics of Big Data, including Variety, Velocity, Volume, and Veracity, showcasing how these traits shape data management.
Challenges in Big Data: An overview of the significant challenges encountered when handling Big Data, such as data integration, storage, security, and processing issues.
Big Data Analytics Explained: A deep dive into Big Data Analytics, highlighting its value, processes, and how it transforms raw data into actionable insights for businesses.
Applications of Big Data Analytics: Real-world applications across sectors like healthcare, finance, retail, and social media, demonstrating how analytics drives strategic decisions.
Types of Big Data Analysis: A breakdown of various Big Data analysis types, such as Descriptive, Diagnostic, Predictive, and Prescriptive analytics, with explanations of how each approach supports different organizational goals.
Enterprise Value of Big Data Analytics: Insight into the competitive advantages of Big Data, including enhanced decision-making, improved customer insights, and operational efficiencies.
Big Data Analytics vs. Big Data Analysis: Clarification of the core differences between these terms, helping readers understand their unique roles in data processing.
If you’re interested in creating a similar presentation or want additional insights, feel free to reach out via email at [email protected]. For students seeking guidance in undergraduate studies, I’m available for mentorship and support.
1) The document discusses how big data can be turned into smart data through thorough analytics that provide context and address limitations like bias and incompleteness.
2) It recommends capturing big data from various sources, processing and analyzing it using advanced techniques, and integrating reference data to understand context and create maximum value for clients.
3) An example shows combining behavioral internet data with survey data to map consumer purchase journeys in travel and optimize marketing spend.
In this white paper, we’ll spread the light on such issues as:
- What big data is
- How data science creates a real value in retail
- 5 big data use-cases revealing how retail companies can turn their customers’ data in action
D2 d turning information into a competive asset - 23 jan 2014Henk van Roekel
Understanding the evolution of Business Intelligence and Analytics and the challenges and opportunities that come with it. Exploring CGI's Data2Diamonds™ approach ensuring financial sound, technical viable and socially desirable Big Data initiatives.
How to Enable Personalized Marketing Even Before 'Big Data'DocuStar
This document discusses how companies can enable personalized marketing even without extensive big data capabilities. It defines big data and outlines the challenges it poses for marketing departments in terms of volume, velocity, and variety of data as well as costs, skills, and cross-functional collaboration needed. The document then introduces the MarketHUB+ solution as an alternative that allows personalized marketing through efficient use of field sales knowledge without the challenges of big data. It claims MarketHUB+ provides quick implementation and ROI as well as marketing services and workflow automation.
This document summarizes a presentation about using data-driven marketing approaches. It discusses trends like treating customers like royalty through personalized experiences, using big data and predictive analytics to gain insights about customers. It also covers challenges of data silos and lack of contextual data. The presentation advocates for using multi-dimensional customer data management, predictive analytics, streaming analytics and bi-directional digital platforms to better understand and interact with customers in real-time.
The Future Of Big Data In Business – 4 Emerging Trends In 2022.pptxArpitGautam20
Here are some exciting trends that will dictate the future of Big Data in Business in 2022. Read on to know about these exciting developments. https://arsr.tech/the-future-of-big-data-in-business-4-emerging-trends-in-2022/
The document discusses alternative data and its importance. It defines alternative data as data derived from non-traditional sources like mobile devices, websites, and sensors. This data can provide insights that complement traditional sources and help with decision-making. The document outlines 8 types of alternative data and 3 ways to access it, including hiring a data scientist, partnering with a third party, or using web scraping software. It provides examples of alternative data's applications in advertising, tracking corporate revenues, risk assessment, and more. Overall, the document promotes alternative data as a valuable new resource for businesses seeking a competitive edge.
Overview: Big Data Use Cases in Telecom, Retail, Insurance, Automotive, Media & Banking & Finances Industry Segments. How can we map these business challenges to Solutions on AWS Cloud? Let's Find Out!
Big Data is Growing Bigger & Bigger with a prediction of 40 Zeta Bytes of Data by 2020.
> What are the 4 Vs of Big Data?
> Big Data Industry Use Cases:
- Telecommunications
- Retail
- Insurance
- Automotive
- Media
- Banking
Which AWS Components can be mapped to each stage of the Big Data Life Cycle:
AWS S3, AWS EC2, AWS EMR, AWS Redshift, Data Pipelines & many more.
Big data comes from a variety of sources and in different formats. It is characterized by its volume, velocity, and variety. Organizations are using big data to gain business insights through analytics. This allows them to increase revenue, reduce costs, optimize processes, and manage risks. Examples of big data uses include marketing campaign analysis, customer segmentation, and fraud detection. Companies must overcome technological and organizational challenges to successfully leverage big data.
A marketers guide to data analytics marketing finder webinar 17 july 2013marketingfinder.co.uk
The document discusses using data analytics to gain insights for marketing. It describes how predictive analytics can provide a competitive advantage by analyzing customer, product, transaction, and other data to predict customer behavior and optimize marketing strategies. The presentation outlines building a complete view of the customer to personalize interactions through segmentation, predictive modeling, and integrating insights into marketing decisions and interactions with customers.
This document discusses key technology trends impacting the retail industry in 2016, as identified by IBM. It covers four main dynamics of transformation: analytics, cloud computing, mobile and social engagement, and security. Analytics and cognitive computing allow retailers to gain insights from big data to personalize customer experiences. Cloud computing enables speed, agility and flexible infrastructure upgrades. Mobile and social technologies connect retailers with customers in real-time and on-the-go. Security is a growing concern as data volumes increase and attack sophistication rises. The document provides an overview of IBM solutions that address these trends, such as analytics platforms, cloud services, and security offerings to help retailers adapt to ongoing disruption and digital transformation in retail.
Big data refers to extremely large data sets that are too large to be processed using traditional data processing applications. It is characterized by high volume, variety, and velocity. Examples of big data sources include social media, jet engines, stock exchanges, and more. Big data can be structured, unstructured, or semi-structured. Key characteristics include volume, variety, velocity, and variability. Analyzing big data can provide benefits like improved customer service, better operational efficiency, and more informed decision making for organizations in various industries.
CRM focuses on managing customer relationships and interactions through databases and personalized experiences, while CXM attempts to understand customers and influence their perceptions at various touchpoints. Key differences are that CRM is more quantitative and helps derive insights, while CXM is qualitative and focuses on customer experiences. Integrating CRM with CXM software allows companies to track customer experiences for retaining customers and gaining a competitive advantage. The document promotes RT CXM as a premier CXM solution that seamlessly integrates with SugarCRM.
What are the 3 key functions of a CRM?
1. Sales Force Automation
2. Marketing Automation
3. Customer Service Automation
How will CRM functionalities benefit your business?
What is Salesforce?
Why Choose Salesforce?
Top 10 Reasons to Choose Salesforce
1. Easy to Use
2. Easy to Customize
3. Cloud-Based
4. Efficient Reporting
5. Multitenant Platform
6. Integration Options
7. AppExchange Ecosystem
8. Mobile CRM
9. Customer Success Platform
10. Diverse Community
1. Growth of eCommerce in Retail Business.
2. Key Issues in eCommerce and Retail Business.
3. Why CRM is the right solution for eCommerce and Retail Business problems?
4. Some Case Studies: so you can hit the ground running!
What is Data Mining?
The process of determining useful patterns and relationships in big data through algorithms, to extract knowledge from data warehouses.
Role of Data Mining in CRM:
1. Get a holistic view of customer life-cycle
2. More data will result in accurate models
3. Leverage forecasting and descriptive modeling techniques
4. Have a proactive approach with predictive analytics
How will Data Mining Benefit my Business?
1. Conduct Basket Analysis for better stocking, store layout, and promotion strategies
2. Sales Forecasting to optimize your sales, supply chain and financial management
3. Predictive Lifecycle Management for each customer to segment them accordingly
4. Optimal Allocation of company’s resources for enhanced productivity and better ROI
5. Product Customization by predicting features that meet the customer’s demands
6. Database Marketing for a defined target market, personalized campaigns, and promotional offers
7. Predict Warranty Claims by the customers and their average cost to have efficient management of funds
8. Fraud detection by analyzing past fraudulent transactions and taking corrective measures
Techniques of Data Mining in CRM
1. Clustering – Identify similar data sets and understand both similarities and differences in data to increase conversion rates
2. Classification – Gather all information about a data set and classify it into proper categories.
3. Anomaly Detection – Information that does not match the expected behavior or projected pattern, providing actionable information
4. Association Rule Learning – Uncover hidden patterns from the data to better understand your customer’s habits and predict their decisions
5. Regression – Find dependency between different data items and map out the effect of variables on each other. It helps in determining customer satisfaction levels and its impact on customer loyalty
Read our entire blog here: https://www.rolustech.com/blog/data-mining-crm
This document discusses key CRM features for the financial industry. It outlines 9 features: 1) 360 Degree View of Financials to provide a centralized customer database, 2) Contact Management with a searchable database of customer information, 3) Lead Management tools to route and track leads, 4) Customer Engagement tools to improve interactions, 5) Marketing Management for campaigns, 6) Sales Automation to support sales teams, 7) Integration Capabilities to sync data across platforms, 8) Workflow Automation to standardize processes, and 9) Reports and Analytics to evaluate performance. The document promotes contacting the company to customize a CRM for an organization's needs.
The document discusses how the Internet of Things (IoT) will impact customer relationship management (CRM) software. As more everyday objects become connected to the internet, companies will be able to gather vast amounts of consumer data from 50 billion connected devices by 2020. This data can provide real-time insights into customer behavior and preferences. CRM software will help companies leverage this IoT data to improve customer retention, offer customized marketing, adjust prices in real-time based on demand, and provide complaint-free customer service. The integration of IoT and CRM represents a major shift that businesses need to adopt in order to effectively engage digitally savvy customers.
Surprisingly, many Higher Education Institutions still do not use CRM software because they lack adequate knowledge about such systems. Read our blog to learn the benefits of CRM for Higher Education Institutions
According to research, entrepreneurs of high-growth companies attribute their success to CRM but those with low-growth don’t understand its real value. In fact, the real pain-point is that companies don’t get their CRM implementation right and 63% of them fail as a result. Therefore, we have compiled here all you need to know and plan before implementing a CRM
The document discusses RTGSYNC, which enables synchronization of calendar entries, contacts, emails, and documents between Google and SugarCRM. It synchronizes data in real-time so information is instantly updated in both interfaces. This provides a single point of access to all activities and enhances visibility. RTGSYNC features include calendar and contact syncing, email archiving, and Google Drive syncing. It received an award from Sugar Outfitters for its integration capabilities.
Scaling GraphRAG: Efficient Knowledge Retrieval for Enterprise AIdanshalev
If we were building a GenAI stack today, we'd start with one question: Can your retrieval system handle multi-hop logic?
Trick question, b/c most can’t. They treat retrieval as nearest-neighbor search.
Today, we discussed scaling #GraphRAG at AWS DevOps Day, and the takeaway is clear: VectorRAG is naive, lacks domain awareness, and can’t handle full dataset retrieval.
GraphRAG builds a knowledge graph from source documents, allowing for a deeper understanding of the data + higher accuracy.
Interactive Odoo Dashboard for various business needs can provide users with dynamic, visually appealing dashboards tailored to their specific requirements. such a module that could support multiple dashboards for different aspects of a business
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Explaining GitHub Actions Failures with Large Language Models Challenges, In...ssuserb14185
GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers’ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise.
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Adobe After Effects is a software application used for creating motion graphics, special effects, and video compositing. It's widely used in TV and film post-production, as well as for creating visuals for online content, presentations, and more. While it can be used to create basic animations and designs, its primary strength lies in adding visual effects and motion to videos and graphics after they have been edited.
Here's a more detailed breakdown:
Motion Graphics:
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Visual Effects:
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After Effects is primarily used in the post-production phase, meaning it's used to enhance the visuals after the initial editing of footage has been completed.
How Valletta helped healthcare SaaS to transform QA and compliance to grow wi...Egor Kaleynik
This case study explores how we partnered with a mid-sized U.S. healthcare SaaS provider to help them scale from a successful pilot phase to supporting over 10,000 users—while meeting strict HIPAA compliance requirements.
Faced with slow, manual testing cycles, frequent regression bugs, and looming audit risks, their growth was at risk. Their existing QA processes couldn’t keep up with the complexity of real-time biometric data handling, and earlier automation attempts had failed due to unreliable tools and fragmented workflows.
We stepped in to deliver a full QA and DevOps transformation. Our team replaced their fragile legacy tests with Testim’s self-healing automation, integrated Postman and OWASP ZAP into Jenkins pipelines for continuous API and security validation, and leveraged AWS Device Farm for real-device, region-specific compliance testing. Custom deployment scripts gave them control over rollouts without relying on heavy CI/CD infrastructure.
The result? Test cycle times were reduced from 3 days to just 8 hours, regression bugs dropped by 40%, and they passed their first HIPAA audit without issue—unlocking faster contract signings and enabling them to expand confidently. More than just a technical upgrade, this project embedded compliance into every phase of development, proving that SaaS providers in regulated industries can scale fast and stay secure.
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FL Studio is a Digital Audio Workstation (DAW) software used for music production. It's developed by the Belgian company Image-Line. FL Studio allows users to create and edit music using a graphical user interface with a pattern-based music sequencer.
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Meet the Agents: How AI Is Learning to Think, Plan, and CollaborateMaxim Salnikov
Imagine if apps could think, plan, and team up like humans. Welcome to the world of AI agents and agentic user interfaces (UI)! In this session, we'll explore how AI agents make decisions, collaborate with each other, and create more natural and powerful experiences for users.
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"YouTube by Click" likely refers to the ByClick Downloader software, a video downloading and conversion tool, specifically designed to download content from YouTube and other video platforms. It allows users to download YouTube videos for offline viewing and to convert them to different formats.
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Adobe Master Collection CC (Creative Cloud) is a comprehensive subscription-based package that bundles virtually all of Adobe's creative software applications. It provides access to a wide range of tools for graphic design, video editing, web development, photography, and more. Essentially, it's a one-stop-shop for creatives needing a broad set of professional tools.
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Adobe Photoshop: For image editing and manipulation.
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Adobe InDesign: For page layout and desktop publishing.
Adobe Premiere Pro: For video editing and post-production.
Adobe After Effects: For visual effects and motion graphics.
Adobe Audition: For audio editing and mixing.
Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak
As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi
A secure test infrastructure ensures that the testing process doesn’t become a gateway for vulnerabilities. By protecting test environments, data, and access points, organizations can confidently develop and deploy software without compromising user privacy or system integrity.
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2. The Importance of
Big Data
“There were 5 exabytes (1 Exabyte= 1 billion
gigabytes) of information created between the dawn
of civilization through 2003, but that much
information is now created every 2 days.” – Eric
Schmidt, of Google, said in 2010.
“How you gather, manage and use information will
determine whether you win or lose.”-Bill Gates
3. What is this Big Data everyone is
talking about these days?
• Big Data can be defined as the huge volumes of
data being generated everyday that can be
mined for information.
• The data can be structured or unstructured and
is often characterized by the 3 Vs:
i)extreme volume of data
ii)wide variety of data types
iii)velocity at which the data must be processed.
4. Where does this data come from?
• The data is generated from multiple sources such
as social media platforms, server logs, web
clickstream, mobile apps, database stores, business
records etc.
• Interestingly, limitless data will be generated with
real-time sensors embedded in devices that will be
connected to the internet in what is know as
“Internet of Things”.
• But this data is insignificant without the tools that
can process and analyze it.
5. How can CRM leverage Big Data
• The possibilities that Big Data offers are endless but
you need to have the right tools to derive detailed
insights from heaps of data.
• If companies want to reap the full benefits of big
data, they first need to invest in Customer
Relationship Management software.
• A CRM can keep track of the data trail that
customers leave on various online platforms and
present the data in a rational and coherent way.
7. • The tastes and preferences of today’s digitally empowered customers
are constantly evolving.
• Big Data empowers businesses to predict how customers are going to
respond in the future based on their past buying behavior.
• The better the recommendations, the higher the conversion rate.
1.Predictive Modeling
8. 2.Market Analysis
• Every brand has a story to tell and a story to sell.
• The need to know your customer base for targeted marketing has
become more important than ever.
• With CRM & Big Data, customer trends can be mined and used to
predict needs, directing product development and promotional
efforts.
9. 3.Superior Customer Understanding
• By integrating big data with CRM, companies can now easily find out
how their product or brand is perceived by the customers online.
• They can use the data to identify their weaknesses and revise their
marketing and sales strategies.
• This will both improve customer retention and enrich customer
experience
10. 4.Drive Revenue & Growth
• Big Data provides companies with accurate performance metrics,
helping them to make more informed decisions.
• Sales team can generate leads and close deals faster knowing their
target audience.
• Moreover your customer service staff will have more information at
their disposal when serving customers.
11. • According to Gartner, Big Data will be the
next “disruptive technology” and will
transform customer relationship
management industry.
• By integrating Big Data and CRM, businesses
can revolutionize the way they market their
products and serve their customers.
Conclusion