13)Explain role of ML in Analytical CRM 14)Explain role of DL in Analytical CRM 15)Explain with diagram CRM Project design and

ain with diagram CRM Project design and 16)Explain steps of implementing Customer strategy
Machine Learning (ML) plays a pivotal role in Deep Learning (DL), a subset of Machine Learning, planning process Implementing a customer strategy involves a
Analytical Customer Relationship Management uses artificial neural networks to model complex The CRM project design and planning process involves structured approach to enhance customer value,
(CRM) by enabling systems to learn from historical patterns in large volumes of data. In Analytical CRM, structured steps to ensure successful implementation improve satisfaction, and align services with business
customer data and make intelligent, data-driven DL significantly enhances the ability to extract and alignment with business goals. objectives. The following are the key steps:
predictions and decisions. It enhances the analytical advanced insights from both structured and 1. Define Business Objectives 1. Define Customer-Centric Objectives
capabilities of CRM by automating complex processes unstructured customer data, leading to more Set clear CRM goals such as improving customer Establish clear, measurable goals focused on customer
and uncovering hidden patterns that traditional intelligent and proactive relationship management. retention, automating sales, or enhancing service. acquisition, retention, satisfaction, and lifetime value.
methods may overlook. Key Roles of DL in Analytical CRM: Align these with the organization’s overall strategy. 2. Customer Segmentation
Key Roles of ML in Analytical CRM: Sentiment Analysis 2. Requirement Analysis Analyze customer data to segment the market based
Customer Segmentation DL models such as Recurrent Neural Networks (RNNs) Identify specific functional and technical needs across on behavior, demographics, and value. This helps in
ML algorithms group customers based on behavior, and Transformers analyze customer reviews, emails, departments (sales, marketing, service) to determine personalizing offerings and resource allocation.
preferences, and interactions using clustering and social media to understand emotions and CRM system requirements. 3. Understand Customer Needs and Expectations
techniques for precise targeting. opinions, improving customer service strategies. 3. Stakeholder Involvement Use tools such as surveys, feedback, and data analytics
Churn Prediction Speech and Image Recognition Engage internal stakeholders and end users to gather to capture customer preferences, pain points, and
Predictive models identify customers at risk of leaving, Enables voice-based CRM interactions and visual feedback and promote system adoption across the expectations.
enabling timely retention strategies. content analysis, allowing for advanced customer organization. 4. Design Value Propositions
Sales Forecasting support solutions like voice assistants and automated 4. CRM Software Selection Develop tailored value propositions for each segment,
ML analyzes historical sales data to accurately forecast identity verification. Choose a CRM platform that fits business size, budget, ensuring they address specific needs and deliver
future trends, improving resource planning and Advanced Personalization and features. Decide between cloud or on-premise competitive advantage.
inventory management. DL systems analyze behavioral patterns to deliver deployment. 5. Align Internal Processes and Resources
Personalization hyper-personalized recommendations, offers, and 5. Data Collection and Integration Coordinate departments (sales, marketing, support) to
Recommender systems suggest products, services, or content in real time. Migrate customer data from legacy systems, ensuring deliver a consistent and seamless customer
content tailored to individual customer profiles in real- Customer Intent Prediction accuracy, completeness, and security compliance. experience. Ensure CRM systems and data
time. Deep models accurately predict future actions like 6. Customization and Development infrastructure support strategy execution.
Sentiment and Feedback Analysis purchasing or switching, enabling targeted Configure workflows, dashboards, automation rules, 6. Implementation and Communication
Natural Language Processing (NLP), a branch of ML, engagement strategies. and user permissions. Tailor reports and system Deploy the strategy across all customer-facing
interprets textual customer feedback to evaluate Anomaly Detection integrations. functions. Communicate roles, responsibilities, and
satisfaction and brand perception. Identifies unusual customer behavior or fraudulent 7. Testing and Training expected outcomes to all stakeholders.
Lead Scoring and Conversion Prediction activity, enhancing security and trust. Test the CRM for functionality and performance. Train 7. Monitor Performance and Optimize
ML models rank and prioritize leads based on their Conclusion: users for smooth adoption and maximum utility. Track KPIs such as Net Promoter Score (NPS), customer
likelihood to convert, enhancing sales efficiency. Deep Learning empowers CRM systems with 8. Implementation and Deployment retention, and satisfaction. Refine strategies based on
Conclusion: sophisticated, human-like analytical capabilities, Deploy CRM in phases or fully, monitoring performance data and customer feedback.
ML transforms analytical CRM by delivering deeper helping organizations achieve deeper customer performance closely during rollout. Conclusion:
insights, proactive engagement, and continuous insights, smarter automation, and improved customer 9. Continuous Optimization A well-implemented customer strategy strengthens
learning, ultimately enhancing customer satisfaction experiences. Regularly monitor CRM KPIs and improve the system loyalty, improves service quality, and drives
and business outcomes. based on feedback and evolving business needs. sustainable growth.

17)Explain how foundations of CRM project can be 18)Write short note on partner selection 19)Explain the process of project implementation
build Partner selection is a critical process in strategic Project implementation is the phase where strategic
Building the foundation of a CRM (Customer customer relationship management and business plans are executed, turning ideas into operational
Relationship Management) project requires a alliances, where organizations evaluate and choose reality. In the context of CRM or business
strategic, structured approach to ensure alignment external entities—such as vendors, service providers, transformation projects, this phase ensures that
with business goals and customer needs. The following or channel partners—that will support and enhance resources, timelines, and deliverables align to achieve
steps form the foundational base: their CRM objectives or overall business strategy. targeted outcomes.
1. Establish Clear Business Objectives Key Considerations in Partner Selection: Key Steps in the Project Implementation Process:
Define what the organization aims to achieve through Strategic Alignment Project Planning Finalization
CRM—such as improving customer satisfaction, The partner’s vision, goals, and values must align with Confirm scope, objectives, resource allocation,
increasing sales, or enhancing service efficiency. the organization’s strategic objectives, especially in timelines, and roles. Develop a detailed project plan
2. Secure Executive Sponsorship areas such as customer service, technology, and long- with defined milestones.
Obtain strong leadership support to ensure alignment term growth. Team Mobilization
of vision, funding, and organizational commitment Capability and Expertise Assemble cross-functional teams, assign
across all departments. Assess the technical and operational capabilities of the responsibilities, and ensure all stakeholders are
3. Conduct Stakeholder Analysis partner, including industry experience, product aligned with the project’s goals.
Identify all internal and external stakeholders. Engage knowledge, and innovation potential. Infrastructure and Tool Setup
them early to gather insights, encourage buy-in, and Reputation and Reliability Implement technical environments (e.g., CRM
reduce resistance to change. Evaluate the partner’s market credibility, client software, data systems), ensuring hardware and
4. Understand Customer Needs and Behavior references, and history of delivering consistent, high- software readiness.
Analyze existing customer data to understand quality services. System Configuration and Customization
preferences, expectations, and pain points. This helps Cultural Fit and Communication Configure workflows, user roles, dashboards, and
in designing customer-centric CRM processes. A shared work culture and transparent communication automation based on business requirements.
5. Define Processes and Workflows practices contribute to smoother collaboration and Customize functionalities as per department needs.
Map existing customer-facing processes and identify problem-solving. Data Migration and Integration
areas for improvement. Establish standardized Cost and Contractual Terms Transfer and integrate legacy data into the new
workflows for sales, marketing, and support functions. Consider pricing models, service-level agreements system, ensuring data quality, accuracy, and
6. Choose the Right Technology (SLAs), risk-sharing mechanisms, and contractual compliance.
Select CRM software that matches the business size, flexibility. Testing and Quality Assurance
industry requirements, and scalability needs. Conclusion: Conduct rigorous system testing (unit, integration, and
7. Prepare Data Strategy Effective partner selection ensures long-term user acceptance) to verify functionality and
Ensure data quality, integration, and security across all collaboration, operational efficiency, and enhanced performance.
sources. Data is the backbone of any CRM system. value delivery to customers, forming a strong Training and Change Management
Conclusion: foundation for CRM success and sustainable growth. Provide user training and support change
Laying these foundations ensures a stable, scalable, management initiatives to ensure user adoption.
and successful CRM project aligned with long-term Go-Live and Monitoring
business value. Launch the solution, monitor initial performance, and
address issues in real time.
Conclusion:
A structured implementation process ensures smooth
execution, risk mitigation, and sustainable project
success.
1)Explain the process of building customer related 2)Explain some common types of data stored in CRM 3)Explain structured and unstructured data with 4)Write short note on data integration
database. systems examples Data Integration is the process of combining data from
A customer-related database is a centralized system CRM (Customer Relationship Management) systems In CRM and data management, information is broadly multiple sources into a unified view to provide
for storing and managing customer information used store a wide range of customer-related data to facilitate classified into structured and unstructured data, each consistent, accurate, and real-time information for
in marketing, sales, and service activities. Building efficient relationship management, targeted marketing, serving distinct analytical purposes. decision-making. In CRM systems, it plays a critical role
such a database involves several key steps: and informed decision-making. The key types of data Structured Data: in delivering a 360-degree view of the customer by
Define Objectives and Data Requirements typically stored include: Structured data is organized and stored in predefined consolidating data from various departments such as
Identify business goals (e.g., lead generation, Demographic Data formats such as rows and columns in databases. It is sales, marketing, customer service, and finance.
retention) and determine necessary data types— Includes customer name, age, gender, occupation, easily searchable and analyzable by standard software Key Objectives:
demographic, transactional, behavioral, and feedback- income level, and location. This helps in segmenting the tools. Eliminate data silos across systems
related. customer base for personalized communication. Examples: Ensure data consistency and accuracy
Data Collection Contact Information Customer names and contact details Enable seamless customer experience and
Gather data from sources like websites, CRM tools, Stores phone numbers, email addresses, social media Purchase history and transaction records personalization
surveys, email campaigns, and physical interactions. handles, and mailing addresses to maintain direct Order dates, payment status, product IDs Common Sources for Integration:
Ensure compliance with privacy regulations (e.g., communication channels. This type of data fits into relational databases and is CRM platforms
GDPR). Transactional Data essential for reporting, segmentation, and forecasting. ERP systems
Data Integration Records details of purchases, order history, billing Unstructured Data: Marketing automation tools
Consolidate data from multiple systems into a unified information, and payment methods to track buying Unstructured data is not organized in a fixed schema E-commerce websites
format using CRM platforms or data warehouses, behavior and trends. and is often more complex to process. It includes data Customer support platforms
eliminating redundancies. Behavioral Data from varied formats and sources that don’t fit neatly Techniques Used:
Database Design Captures customer interactions across channels— into tables. ETL (Extract, Transform, Load)
Structure the database logically—organize fields such website visits, email opens, click-through rates, and Examples: API-based integrations
as customer ID, purchase history, preferences, and social media engagement. Customer reviews and feedback comments Middleware platforms (e.g., Zapier, Mulesoft)
communication logs. Service and Support History Emails and chat transcripts Benefits:
Data Cleansing and Validation Maintains records of past service requests, complaints, Social media posts, images, and audio recordings Enhances decision-making through a single source of
Remove outdated or duplicate data to maintain resolutions, and feedback to ensure consistent and Unstructured data provides deep customer insights truth
accuracy and reliability. improved customer service. but requires advanced tools like natural language Improves operational efficiency and customer
Security and Compliance Account and Relationship Data processing (NLP) and machine learning to analyze responsiveness
Implement data protection measures such as Stores company names, account numbers, relationship effectively. Enables real-time analytics and reporting
encryption, access controls, and audits to safeguard status, key contacts, and contract details for B2B Conclusion: Conclusion:
sensitive information. relationships. Understanding both structured and unstructured data Data integration ensures that all customer-related
Maintenance and Updates Preferences and Interests is crucial for leveraging CRM systems. While structured information is synchronized and accessible across the
Regularly update customer records and monitor Includes data on preferred products, communication data supports operational decisions, unstructured data organization, enabling businesses to make informed,
database performance for accuracy and usability. frequency, and shopping habits to tailor offerings. reveals customer sentiment and behavior patterns, timely, and customer-focused decisions.
Conclusion: Conclusion: driving strategic improvements.
A well-maintained customer database supports CRM systems consolidate diverse data types to deliver 8)Explain some common techniques used in Big data
personalized marketing, efficient service, and data- personalized experiences, streamline customer 7)Define data analytics and explain its key analytics
driven decision-making, contributing to improved interactions, and drive business growth. components Big Data Analytics refers to the process of examining
customer relationships and business performance. Data Analytics refers to the systematic process of large, complex, and varied datasets to uncover hidden
6)Explain some key areas where analytics is applied in
examining data to discover useful insights, draw patterns, correlations, and trends that support
5)Define Data Warehousing and explain its basic CRM Strategy
conclusions, and support decision-making. It involves the strategic decision-making. It involves specialized
Analytics plays a critical role in shaping and optimizing
characteristics collection, transformation, and interpretation of raw techniques and technologies designed to handle high
Customer Relationship Management (CRM)
Data Warehousing refers to the process of collecting, data into meaningful information to drive business volume, velocity, and variety of data.
strategies. By leveraging data-driven insights,
storing, and managing large volumes of data from organizations can enhance customer engagement, strategies and improve performance. Common Techniques Used:
different sources in a centralized repository. This data improve service delivery, and drive profitability. Key Key Components of Data Analytics: Data Mining
is structured and optimized for querying and analysis areas of application include: Data Collection Extracts useful patterns and relationships from large
to support strategic decision-making in an 1. Customer Segmentation Involves gathering data from various internal and datasets.
organization. Analytics helps classify customers into meaningful external sources such as CRM systems, websites, social Techniques include classification, clustering, and
A Data Warehouse enables organizations to groups based on demographics, behavior, and purchase media, and IoT devices. association rule mining.
consolidate historical and current data for business history. Ensures relevant, accurate, and real-time data is Machine Learning (ML)
intelligence (BI), reporting, and forecasting. Enables targeted marketing and personalized available for analysis. Enables systems to learn from data and make
Basic Characteristics: communication strategies. Data Processing predictions or decisions without explicit
Subject-Oriented 2. Customer Lifetime Value (CLV) Analysis Transforms raw data into a usable format by cleaning, programming.
Organized around key business subjects such as Predicts the total value a customer brings over time. organizing, and standardizing it. Common algorithms: decision trees, neural networks,
customers, sales, or products, rather than specific Helps prioritize high-value customers for retention and Eliminates inconsistencies and prepares data for regression models.
processes. loyalty programs.
analysis. Natural Language Processing (NLP)
Integrated 3. Predictive Analytics
Data Analysis Analyzes unstructured text data such as reviews,
Forecasts future customer behavior such as purchase
Consolidates data from various sources (e.g., CRM, Uses statistical methods, algorithms, and tools to social media posts, and customer feedback.
intent, churn probability, or response to campaigns.
ERP, spreadsheets) into a consistent format using identify patterns, trends, and correlations. Used for sentiment analysis and chatbots.
Supports proactive engagement strategies.
standard naming conventions and data types. Includes techniques like descriptive, diagnostic,
4. Sales Forecasting Predictive Analytics
Time-Variant Uses historical sales data and trends to predict future predictive, and prescriptive analytics. Uses statistical models and historical data to forecast
Stores historical data to allow for trend analysis and sales performance. Data Visualization future outcomes.
long-term decision-making. Data is typically Aids in inventory planning, resource allocation, and Presents analytical results using charts, graphs, Common in demand forecasting and customer churn
maintained with time stamps. revenue management. dashboards, and reports. prediction.
Non-Volatile 5. Campaign Effectiveness Analysis Enhances understanding and aids quick decision-making. Stream Analytics
Once data is entered into the warehouse, it is not Measures the ROI and impact of marketing campaigns. Data Interpretation and Action Processes real-time data from sources like IoT devices
updated or deleted frequently, ensuring stability for Identifies what works and informs future campaign Draws meaningful insights from the analysis and and live transactions.
reporting and analytics. planning. recommends strategic actions. Supports instant decision-making.
Optimized for Analysis 6. Customer Satisfaction and Feedback Analysis Supports informed business decisions and continuous Data Visualization
Structured to support complex queries and data Analyzes survey data, reviews, and support interactions improvement. Translates complex analytics into interactive visuals
mining, not day-to-day transaction processing. to gauge customer sentiment. Conclusion: for better interpretation and communication.
Conclusion: Drives improvements in products and services.
Data analytics empowers organizations to make Conclusion:
Data warehousing is essential for organizations seeking Conclusion:
evidence-based decisions, uncover opportunities, and These techniques enable organizations to gain
CRM analytics transforms raw customer data into
to leverage data for strategic insights, performance maintain a competitive edge in a data-driven market. actionable insights from massive datasets, driving
actionable insights, enabling smarter decisions and
tracking, and competitive advantage. innovation, efficiency, and competitive advantage.
stronger customer relationships.
9)What are different ways of generating Analytical
11)Define data mining and explain its procedure Insights?
Q10: Explain Key Aspects of Analytics of Structured Analytical insights are data-driven observations and 12)Explain role of AI in Analytical CRM
Data Mining is the process of discovering patterns,
trends, correlations, or useful information from large Data conclusions that support strategic business decisions. Artificial Intelligence (AI) plays a transformative role in
datasets using statistical, mathematical, and machine Structured data refers to highly organized information Organizations can generate such insights using various Analytical Customer Relationship Management (CRM)
learning techniques. It plays a key role in Analytical that is easily stored, accessed, and analyzed using approaches, depending on data availability, business by enhancing data interpretation, prediction accuracy,
CRM by enabling organizations to make informed relational databases and spreadsheets. It typically objectives, and analytical capabilities. and personalized customer engagement. AI enables
decisions based on customer behavior and preferences. exists in rows and columns and is ideal for analytical 1. Descriptive Analytics businesses to extract deeper insights from large and
Procedure of Data Mining: tasks in CRM, finance, and operations. Analyzes historical data to understand past behavior complex datasets, facilitating smarter and faster
Data Collection Key Aspects of Structured Data Analytics: and performance. decision-making.
Relevant data is gathered from various sources such as Data Organization Example: Sales reports, customer demographics, and Key Roles of AI in Analytical CRM:
CRM systems, transactional databases, and web Structured data is stored in predefined formats, such as website traffic trends. Customer Behavior Prediction
analytics. tables with fields like customer ID, purchase date, or 2. Diagnostic Analytics AI algorithms analyze past interactions and transaction
Data Cleaning transaction amount. Explores the root causes of past outcomes using data
history to predict future behavior, such as likelihood to
Removes errors, duplicates, and inconsistencies to correlations and comparisons.
It facilitates efficient querying using SQL and similar purchase or churn.
ensure data quality and reliability for analysis. Example: Identifying why customer churn increased
tools. Personalization at Scale
Data Integration during a specific quarter.
Data Quality and Consistency 3. Predictive Analytics AI customizes communication, product
Combines data from different sources into a unified
Structured formats ensure high data accuracy, Uses statistical models and machine learning to recommendations, and offers based on individual
dataset, often using data warehousing techniques.
consistency, and integrity, which are critical for reliable forecast future trends and behaviors. customer preferences and behavior in real time.
Data Selection and Transformation
Filters and converts data into the appropriate format analysis. Example: Predicting which customers are most likely Sentiment Analysis
required for mining. This includes normalization and Statistical and Trend Analysis to respond to a campaign. Natural Language Processing (NLP), a branch of AI,
feature selection. Enables use of descriptive and inferential statistics to 4. Prescriptive Analytics interprets customer sentiment from reviews, feedback,
Pattern Discovery identify trends, averages, and anomalies. Suggests optimal actions by simulating different and social media to gauge satisfaction and reputation.
Utilizes algorithms such as classification, clustering, Helps in making data-driven forecasts and decisions. scenarios and outcomes. Sales Forecasting
association rules, and regression to identify meaningful Segmentation and Classification Example: Recommending pricing strategies based on Machine learning models identify sales trends and
patterns. Allows easy grouping of customers or transactions predicted demand. seasonality, helping forecast demand more accurately.
Evaluation based on parameters like demographics, frequency, or 5. Real-Time Analytics Customer Segmentation
Assesses the discovered patterns to ensure they are value. Involves instant analysis of live data streams for AI dynamically segments customers into meaningful
valid, novel, and useful for decision-making. Performance Monitoring immediate decision-making. groups using clustering and classification techniques,
Deployment Structured data is essential for creating dashboards, Example: Monitoring website activity to trigger beyond traditional demographic filters.
Insights are applied to CRM strategies, such as personalized offers.
KPIs, and performance reports. Automated Reporting and Insights
customer segmentation, predictive modeling, or 6. Text and Sentiment Analysis
Integration with BI Tools AI tools generate dashboards and reports, highlighting
campaign optimization. Extracts insights from unstructured data such as
Seamlessly integrates with business intelligence and key insights and anomalies without manual
Conclusion: reviews, surveys, and social media.
reporting tools like Power BI, Tableau, and Excel. Example: Understanding customer sentiment towards a intervention.
Data mining empowers businesses to unlock actionable
Conclusion: new product launch. Conclusion:
insights, improve customer targeting, and enhance
Structured data analytics is fundamental for Conclusion: AI enhances the analytical capability of CRM systems,
overall CRM effectiveness.
operational efficiency, strategic planning, and accurate Using a combination of these techniques enables enabling deeper customer understanding, proactive
decision-making in data-driven enterprises. businesses to make informed, timely, and strategic engagement, and data-driven strategy formulation.
decisions based on robust analytical insights.