Data Driven Decision Making Templatestudent Nameid Numberdateprompt ✓ Solved

Data Driven Decision Making Template Student name: ID number: Date: PROMPT RESPONSE B. Describe a real-world business situation that could be addressed by collecting and analyzing a set of data. B1. Summarize one question or decision relevant to the real-world business situation you will answer by collecting and analyzing a set of data. B2.

Explain why the situation or question would benefit from a data analysis. B3. Identify data you will need to collect that is relevant to the situation or question. Note: A sample size of 30 or more is suggested to provide a statistically reliable finding. B4.

Describe the data gathering methodology you will use to collect data. B5. Identify the appropriate data analysis technique you will use to analyze this data (e.g., linear programming, crossover analysis, t-test, regression). B5a. Explain why the data analysis technique you chose is an appropriate technique to analyze the data collected.

C. Sources Used (if applicable)

Paper for above instructions

Data Driven Decision Making Template

Student Name: [Your Name]
ID Number: [Your ID Number]
Date: [Today's Date]

B. Business Situation and Decision-Making

B.1. Summarize One Question or Decision:
A mid-sized retail company wants to understand the impact of its promotional campaigns on sales revenue. The key question to be answered is: "What is the effect of different promotional strategies (e.g., discounts, buy-one-get-one-free offers, loyalty points) on the sales of our best-selling products over the past year?"
B.2. Explain why the situation or question would benefit from data analysis:
In an increasingly competitive retail environment, understanding the effectiveness of promotional strategies is essential for maximizing sales revenue and gaining a competitive edge. Past experiences have shown that not all promotions yield the anticipated increase in sales. By analyzing data on sales figures in conjunction with various promotions, the company can pinpoint which strategies are most effective in driving sales. This can lead to more informed decision-making regarding future promotions, ultimately resulting in cost savings, increased customer loyalty, and higher profit margins (Sharma & Gupta, 2018).
B.3. Identify Data to Collect:
To address the promotional impact question, the following data will be collected:
1. Sales Data: Daily sales figures for the best-selling products over the past year (sample size = 365 days).
2. Promotion Type: Categorization of promotional strategies used (discount, buy-one-get-one-free, loyalty points).
3. Promotion Dates: Start and end dates for each promotional campaign.
4. Customer Footfall: Daily foot traffic into the store over the promotional period to establish context regarding sales.
5. Competitor Promotions: Information about competitor promotions during the same period for benchmarking.
6. Customer Demographics: Data on customer profiles who made purchases during promotion periods, if available.
This data collection will create a comprehensive dataset to analyze patterns and correlations.
B.4. Describe the Data Gathering Methodology:
Data will be gathered through the following methodologies:
1. Sales Records Extraction: Sales data will be sourced from the company’s point-of-sale (POS) systems to extract daily transaction records. This includes total sales, items sold, and customer identifiers.
2. Promotion Logging: Marketing records will log details about promotional campaigns, including their nature, duration, and goals.
3. Foot Traffic Monitoring: Store foot traffic will be tracked through an automated system using sensors or manual logging by store employees to understand customer behavior during promotions.
4. Competitor Analysis: Competitor promotions will be tracked through direct observation, websites, local advertisements, and customer surveys.
5. Surveys for Demographics: A survey will be administered to customers at check-out to gather demographic data, incentivizing participation with discounts on future purchases.
The data collection process will take place over a span of three weeks, ensuring a comprehensive capture of promotional activities and sales data.
B.5. Identify the Appropriate Data Analysis Technique:
The primary data analysis technique suitable for this situation will be Multiple Regression Analysis.
B.5.a. Explanation of Chosen Data Analysis Technique:
Multiple regression analysis is appropriate because it allows the assessment of the relationship between a dependent variable (i.e., sales revenue) and multiple independent variables (i.e., type of promotion, foot traffic, and demographics). This technique will enable the quantification of the impact of each promotional strategy on sales while controlling for other influencing factors (Hood, 2020). By applying this technique, the retail company can find a clear understanding of how different promotions contribute to sales, ultimately driving more effective marketing strategies.

C. Sources Used

1. Hood, D. (2020). Understanding Multiple Regression: A Comprehensive Guide. New York: statistical Publishing.
2. Sharma, A., & Gupta, P. (2018). Data-driven decision making: A systematic approach to better marketing strategies. Journal of Marketing Management, 34(9-10), 845-866.
3. Anderson, E. (2019). Retail Analytics: Transforming Data into Insights and Action. Retail Management Review.
4. Kotler, P., & Keller, K. L. (2016). Marketing Management. Pearson Education.
5. Lewis, M. (2021). Exploring the impact of promotional strategies on customer behavior. International Journal of Retail and Distribution Management, 49(5), 568-583.
6. Fader, P. S., & Hardie, B. G. S. (2021). Customer-base analysis: A marketing approach to understanding relationships and profitability. Journal of Marketing Research, 58(3), 415-434.
7. Jones, C., & Chacko, R. (2020). Data analytics in retail: Lessons learned from successful companies. Journal of Business Research, 118(2), 20-35.
8. Zeng, C., & Goh, M. (2019). The role of data analytics in retail supply chain management. Supply Chain Management: An International Journal, 24(1), 46-58.
9. Monroe, K. B. (2019). Pricing: Making profitable decisions. Business Strategy Review, 23(4), 20-35.
10. Sweeney, J. C., & Soutar, G. N. (2020). Consumer perceived value: The development of a multiple item scale. Journal of Retailing, 76(2), 203-220.
This comprehensive approach to data collection and analysis will empower the retail company to enhance its promotional strategies effectively, ensuring long-term profitability and market relevance.