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D099 Module 5 Leave the first rating Students also studied Terms in this set (17) Save D099 Module 6 23 terms cvda00Preview Sales Management Module 6 & 7 58 terms jyasin89Preview D099 Module 9 24 terms cvda00Preview
D099 M
22 terms cvd How does sales analytics using CRM data contribute to a company's sales practices, strategies, and management decision-making?
Enhancing Sales Practices: By tracking performance, managing leads, and
identifying best practices.
Shaping Sales Strategies: Through customer segmentation, targeting, and
optimizing product/service promotions.
Informing Management Decision-Making: By providing accurate forecasting,
supporting strategic planning, and guiding resource allocation.
Improving Sales Efficiency: By optimizing sales processes, analyzing the sales
funnel, and tracking activities.
Enhancing Customer Relationship Management: By calculating CLV, analyzing
churn, and tailoring retention strategies.Define sales analytics and discuss the two significant roles sales analytics plays for a sales team.Sales Analytics involves analyzing sales data to gain actionable insights that can improve decision-making and optimize sales efforts.
Improving Sales Performance and Efficiency: By tracking performance metrics,
optimizing processes, and identifying training needs, sales analytics helps enhance the effectiveness and productivity of the sales team.
Guiding Strategic Decision-Making and Forecasting: By providing data-driven
insights for forecasting, strategic planning, and performance tracking, sales analytics supports informed decision-making and effective resource management.Describe the four steps in the sales analytics process or workflow.
1.) Data Collection 2.) Data Analysis 3.) Interpretation and Insight Generation 4.) Action and Implementation Describe the four levels of sales data analytics and explain how data analysis supports decision making efforts
Descriptive Analytics: Summarizes historical data to describe what happened.
Diagnostic Analytics: Examines why certain outcomes occurred by identifying
root causes and correlations.
Predictive Analytics: Forecasts future trends and outcomes based on historical
data.
Prescriptive Analytics: Provides actionable recommendations on how to achieve
desired outcomes and optimize performance.
Describe the steps in the workflow for prescriptive analytics.Define Objectives and Decision Criteria: Set clear goals and establish the criteria for decision-making.Gather and Prepare Data: Collect, clean, and prepare relevant data for analysis.
Develop Analytical Models and Scenarios: Create and test models to simulate
different scenarios and outcomes.
Generate Recommendations: Translate analysis results into actionable
recommendations and develop an action plan.
Implement and Monitor Actions: Execute the recommendations, monitor their
impact, and make adjustments as needed.List and describe the three Vs of Big Data.Volume: The vast amount of data being generated and stored, which requires scalable storage and processing solutions.
Velocity: The speed at which data is produced and needs to be processed,
necessitating real-time or near-real-time analytics.
Variety: The different types and formats of data, which require sophisticated
integration and analysis techniques to handle effectively.What is the internet of things (IoT) and how does it relate to big data and data analytics?
Data Generation: IoT devices contribute to the volume of data.
Real-Time Processing: The velocity of IoT data requires real-time or near-real-time processing.
Data Variety: IoT data includes diverse types and formats.
Advanced Analytics and Insights: IoT data enables predictive and prescriptive
analytics.
Enhanced Decision-Making and Automation: IoT data supports informed decision-
making and process automation.Describe at least three ways that Big Data can help organizations address business activities.
Enhanced Customer Insights and Personalization: By analyzing customer data,
organizations can segment customers, personalize recommendations, and predict future behavior, leading to improved customer engagement and satisfaction.Operational Efficiency and Process Optimization: Big Data allows organizations to identify inefficiencies, optimize resource allocation, and implement predictive maintenance, resulting in streamlined operations and cost savings.
Strategic Decision-Making and Risk Management: Data-driven insights support
strategic planning, risk identification, and performance monitoring, enabling organizations to make informed decisions and manage risks effectively.How can organizations utilize CRM data and analytics to segment their customer base and target specific customer profiles?
Collect and Integrate Customer Data: Gather and consolidate comprehensive
data from various sources to create a unified customer view.
Analyze Customer Data for Segmentation: Use descriptive and analytical
techniques to identify patterns and segment customers based on relevant criteria.
Develop Targeted Marketing Strategies: Create personalized marketing messages
and campaigns tailored to the needs and preferences of each segment.
Monitor and Optimize Segmentation Efforts: Track the performance of targeted
strategies, analyze feedback, and refine segmentation and marketing approaches based on results.What is a 360-degree view of the customer? Describe the two types of data used to create a 360-degree view of the customer.
A 360-degree view of the customer is achieved by integrating:
Transactional Data: Information about customer purchases and transaction history.
Interaction Data: Records of customer interactions and engagement across
various channels.Combining these data types provides a comprehensive understanding of the customer, enabling personalized marketing, improved customer service, and enhanced decision-making.Top of FormBottom of Form
Explain how sales data analysis is used to inform sales
practices in each of these areas: 1) Lead generation; 2)
Lead scoring; 3) Customer Lifetime Value (CLV); and 4) Forecasting and planning
Lead Generation: Identifies effective lead sources, targets high-potential
audiences, and evaluates campaign performance to optimize lead acquisition strategies.Lead Scoring: Helps prioritize leads based on their likelihood to convert by using historical data to refine scoring models and focus on high-value prospects.
Customer Lifetime Value (CLV): Calculates and segments customers based on
their long-term value, guiding acquisition, retention strategies, and resource allocation.Forecasting and Planning: Provides insights for predicting future sales, setting targets, and allocating resources effectively by analyzing historical trends and predictive models.Explain the concept of integrating a CRM system with other organizational systems and processes. How does this integration foster efficiency, collaboration, and better customer experiences?Integrating a CRM system with other organizational systems and processes
enhances efficiency, collaboration, and customer experience by:
Fostering Efficiency: Automating data syncing, streamlining processes, and
providing unified data access to reduce manual work and errors.
Enhancing Collaboration: Sharing customer data, aligning workflows, and
improving communication across departments.
Improving Customer Experience: Offering personalized interactions, ensuring
consistent service, and enabling proactive engagement through comprehensive and integrated customer information.What is a CRM dashboard? How does it impact the behavioral expectations of salespeople?A CRM dashboard is a powerful tool that impacts the behavioral expectations of
salespeople in several ways:
Increased Accountability: Provides visibility into individual performance, fostering a sense of responsibility and motivating salespeople to meet their targets.
Enhanced Motivation: Uses visual indicators of progress and achievement to
encourage salespeople and drive higher performance.
Improved Focus and Prioritization: Helps salespeople manage their time and
prioritize tasks effectively by highlighting key activities and metrics.
Data-Driven Decision Making: Empowers salespeople to make informed decisions
and adjust their strategies based on real-time data and insights.What are predictive sales analytics? Describe the four tools supplied to sales managers by predictive analytics to help them set objectives and priorities.Predictive sales analytics involves using historical data, statistical algorithms, and machine learning techniques to forecast future sales performance and outcomes.Lead Scoring Models: Helps prioritize leads based on their likelihood to convert, enabling focused efforts on high-potential prospects.
Sales Forecasting: Provides accurate projections of future sales performance,
aiding in setting realistic targets and planning resources.
Customer Segmentation: Analyzes and forecasts customer behaviors to tailor
strategies and allocate resources effectively.
Sales Performance Insights: Identifies patterns and factors influencing sales
performance, guiding optimization and goal setting.Explain how sales data analytics influences sales management decision-making in each of the following
areas: 1) Predictive sales; 2) Salesperson/Sales team
performance; 3) Conversion ratio, win-loss analysis, and activity goals; and 4) Key performance indicator Predictive Sales: Forecasts future sales performance and opportunities, enabling proactive planning and strategy adjustments.
Salesperson/Sales Team Performance: Provides insights into individual and team
performance, guiding evaluations, goal setting, and resource allocation.
Conversion Ratio, Win-Loss Analysis, and Activity Goals: Analyzes conversion
rates, reasons for wins and losses, and activity metrics to refine sales strategies and set effective goals.
Key Performance Indicators (KPIs): Monitors and evaluates performance against
key metrics, supporting strategic adjustments and benchmarking efforts.
Explain the concept of conversion ratios.Conversion ratios are critical metrics used to measure the effectiveness of sales and marketing efforts in turning prospects into customers or achieving other desired outcomes. By analyzing various types of conversion ratios, such as lead- to-customer, opportunity-to-deal, and click-to-conversion ratios, businesses can gain insights into their sales performance, optimize strategies, and make informed decisions to improve overall conversion rates.List and describe the sales data most used as Key Performance Indicators (KPIs).
Sales Revenue: Total money generated from sales.
Conversion Rate: Percentage of leads converted into customers.
Average Deal Size: Average value of closed deals.
Sales Cycle Length: Average time to close a deal.
Lead-to-Customer Ratio: Ratio of leads to customers acquired.
Customer Acquisition Cost (CAC): Cost to acquire a new customer.
Customer Lifetime Value (CLV): Total revenue from a customer over their lifetime.
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