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D099 Sales Managementhttps://www.stuvia.com/user/filwe1099salmin
Study Guide Module 5 Insights and Analyticshttps://www.stuvia.com/user
D099- Sales Management
Study Guide
Module 5
1. How does sales analytics using CRM data contribute to a company's sales
practices, strategies, and management decision-making?
a. Purpose: Analyzes past sales activities to identify trends, set objectives,
and forecast outcomes.
b. Usage: Sales analytics helps relate sales activities to revenue, segment
customer bases, prioritize leads, and improve customer retention
strategies.
2. Define sales analytics and discuss the two significant roles sales analytics plays
for a sales team.
a. Definition: Extracts insights from sales data to model, understand, and
predict sales trends.
b. Roles:
i. Internal Perspective – Segment and prioritize customer bases for
efficient targeting.
ii. External Perspective – Validate company value to customers by
aligning services with customer needs.
3. Describe the four steps in the sales analytics process or workflow.
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D099 Sales Managementhttps://www.stuvia.com/user/filwe1099salmin
Study Guide Module 5 Insights and Analyticshttps://www.stuvia.com/user
a. Collection – Gather relevant data (e.g., surveys, online forms).
b. Processing – Organize data into spreadsheets or models for analysis.
c. Analysis – Use charts and graphs to visualize patterns.
d. Interpretation – Apply insights to guide business decisions.
4. Describe the four levels of sales data analytics and explain how data analysis
supports decision making efforts
a. Descriptive – Answers "What happened?" by reviewing historical data.
b. Diagnostic – Answers "Why did it happen?" by comparing historical data.
c. Predictive – Uses past patterns to forecast future trends.
d. Prescriptive – Suggests actions to achieve desired outcomes based on
data analysis.
5. Describe the steps in the workflow for prescriptive analytics.
a. Data Acquisition – Gather and process data.
b. Hypothesis Formation – Analyze data to form hypotheses.
c. Initial Actions – Test hypotheses with pilot actions.
d. Action Refinement – Adjust actions based on results.
6. List and describe the three Vs of Big Data.
a. Volume – Large amounts of data from various sources.
b. Velocity – Speed at which data is generated and collected.
c. Variety – Different forms of data (structured, unstructured, multimedia).
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https://www.stuvia.com/user/filwe1099salminhttps://www.stuvia.com/user/filwe1099salmin
D099 Sales Managementhttps://www.stuvia.com/user/filwe1099salmin
Study Guide Module 5 Insights and Analyticshttps://www.stuvia.com/user
D099- Sales Management
Study Guide
Module 5
1. How does sales analytics using CRM data contribute to a company's sales
practices, strategies, and management decision-making?
a. Purpose: Analyzes past sales activities to identify trends, set objectives,
and forecast outcomes.
b. Usage: Sales analytics helps relate sales activities to revenue, segment
customer bases, prioritize leads, and improve customer retention
strategies.
2. Define sales analytics and discuss the two significant roles sales analytics plays
for a sales team.
a. Definition: Extracts insights from sales data to model, understand, and
predict sales trends.
b. Roles:
i. Internal Perspective – Segment and prioritize customer bases for
efficient targeting.
ii. External Perspective – Validate company value to customers by
aligning services with customer needs.
3. Describe the four steps in the sales analytics process or workflow.
https://www.stuvia.com/user/filwe1099salmin
https://www.stuvia.com/user/filwe1099salmin
Downloaded https://www.stuvia.com/user/filwe1099salmin
by madiba South Africa stuvia ()
, lOMoARcPSD|44532475
https://www.stuvia.com/user/filwe1099salminhttps://www.stuvia.com/user/filwe1099salmin
D099 Sales Managementhttps://www.stuvia.com/user/filwe1099salmin
Study Guide Module 5 Insights and Analyticshttps://www.stuvia.com/user
a. Collection – Gather relevant data (e.g., surveys, online forms).
b. Processing – Organize data into spreadsheets or models for analysis.
c. Analysis – Use charts and graphs to visualize patterns.
d. Interpretation – Apply insights to guide business decisions.
4. Describe the four levels of sales data analytics and explain how data analysis
supports decision making efforts
a. Descriptive – Answers "What happened?" by reviewing historical data.
b. Diagnostic – Answers "Why did it happen?" by comparing historical data.
c. Predictive – Uses past patterns to forecast future trends.
d. Prescriptive – Suggests actions to achieve desired outcomes based on
data analysis.
5. Describe the steps in the workflow for prescriptive analytics.
a. Data Acquisition – Gather and process data.
b. Hypothesis Formation – Analyze data to form hypotheses.
c. Initial Actions – Test hypotheses with pilot actions.
d. Action Refinement – Adjust actions based on results.
6. List and describe the three Vs of Big Data.
a. Volume – Large amounts of data from various sources.
b. Velocity – Speed at which data is generated and collected.
c. Variety – Different forms of data (structured, unstructured, multimedia).
https://www.stuvia.com/user/filwe1099salmin
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