Miami Investigations, Inc. Is Considering Expansion To Browa ✓ Solved
Miami Investigations, Inc. is considering expansion to Browa
Miami Investigations, Inc. is considering expansion to Broward County. Using the following provided data—5‑year expansion forecast (income statement), service price list, CRM sample customer records, and city median household incomes for Broward and Miami‑Dade counties—conduct these analyses and recommend whether the company should expand to Broward County. Methods of Analysis: 1. Using the Net Revenues in the income statement, graph a trend analysis (line graph) of the forecasted revenue (loss) and label the graph. 2. Using the CRM customer data and the price list: a) create a pivot table summarizing frequency of services by customer type and the average revenue for each service by customer type; b) compute descriptive statistics (mean, median, standard deviation) on the prices each customer paid (all customers together); c) using the pivot table in (a), create two labeled graphs—one of revenue and one of customer counts. 3. Conduct a hypothesis test comparing the mean median household incomes of Broward County and Miami‑Dade County (use z and α=0.05). 4. Conduct a one‑way ANOVA comparing average amount spent between business and consumer customers for services purchased. Use the results to decide if Miami Investigations, Inc. should expand to Broward County and fully explain your decision.
Paper For Above Instructions
Executive summary
Using the provided 5‑year forecast, price list, CRM sample (30 customer records), and city median household incomes, I performed trend analysis, pivot and descriptive statistics, a z-test comparing county median incomes, and a one-way ANOVA comparing average spend by business vs. consumer customers. Key quantitative findings: (1) forecasted total revenues rise from $1.0M in Year 1 to $6.5M in Year 5 (after an initial baseline figure in the dataset), showing strong growth potential; (2) sample customer data mean price paid = $691.67, median = $500, sample standard deviation ≈ $635; (3) z-test comparing Broward and Miami‑Dade city median incomes yields z ≈ 0.06 (p ≈ 0.95) — no significant difference; (4) one-way ANOVA (business vs. consumer average spend) yields F ≈ 1.56 (p > 0.05) — no statistically significant difference at α = 0.05, although sample business mean ($855.77) > consumer mean ($566.18). The recommendation: proceed with a phased expansion into Broward County, targeting business clients first and running a six‑month pilot to validate demand and marketing channels.
1. Trend analysis of forecasted revenue
Using the income-statement forecast totals provided, total revenues by period (interpreting the sequence in the dataset) are: baseline 10,000,000 (prior period), Year 1 = $1,000,000, Year 2 = $2,000,000, Year 3 = $3,500,000, Year 4 = $5,000,000, Year 5 = $6,500,000. The line below visualizes the upward trend showing steady year‑over‑year growth after Year 1.
2. Pivot table, descriptive statistics, and charts from CRM sample
Data mapping: the sample contained 30 customer-service records. Service prices used: BACKGRD $75; COMPF $1,300; CORP1 $2,500; FRAUD2 $500; PERS1 $350; WKRCOMP $800. Records were categorized by Cust_Type = Business or Consumer.
| Service | Business: Count | Business: Avg Revenue | Consumer: Count | Consumer: Avg Revenue |
|---|---|---|---|---|
| BACKGRD | 3 | $75 | 3 | $75 |
| COMPF | 2 | $1,300 | 3 | $1,300 |
| CORP1 | 2 | $2,500 | 0 | — |
| FRAUD2 | 5 | $500 | 2 | $500 |
| PERS1 | 0 | — | 6 | $350 |
| WKRCOMP | 1 | $800 | 3 | $800 |
| Total | 13 | 17 |
Descriptive statistics (all 30 customer transactions): mean = $691.67, median = $500, sample standard deviation ≈ $635 (n=30). The price distribution is multimodal (common modes at $75, $350, $500, $1,300, $2,500) reflecting discrete service pricing.
3. Hypothesis test: Broward vs. Miami‑Dade median household incomes
Using the provided city median household incomes, I grouped cities by county (n=23 city medians per county), computed sample means: Broward mean ≈ $56,700.13, Miami‑Dade mean ≈ $56,271.96. Sample standard deviations: Broward s ≈ $22,635; Miami‑Dade s ≈ $27,673. Two‑sample z statistic (using large-sample normal approximation as requested): z = (56,700.13 − 56,271.96) / sqrt(s1^2/n1 + s2^2/n2) ≈ 0.06 (p ≈ 0.95). Conclusion: fail to reject H0 — there is no statistically significant difference in mean city median incomes between the two counties at α = 0.05. Therefore, household income levels alone do not distinguish Broward as higher- or lower-potential market relative to Miami‑Dade.
4. One‑way ANOVA: business vs. consumer average spend
Sample group means: Business mean = $855.77 (n=13); Consumer mean = $566.18 (n=17); grand mean = $691.67. One‑way ANOVA computed SSB ≈ 617,737, SSW ≈ 11,058,890, MSB = 617,737, MSW ≈ 395,674, yielding F ≈ 1.56. With df(1,28) the F critical ≈ 4.20 at α = 0.05; p > 0.05. Result: fail to reject H0 — the difference in average spend observed in the sample is not statistically significant at the 5% level (though point estimates show businesses spend more). Practically, businesses purchase higher-priced services (corporate investigations, computer forensics) more often than consumers (personal investigations), suggesting B2B focus can yield higher average transaction value even if not statistically significant in this small sample.
Recommendation and implementation plan
Synthesis of results supports a controlled, phased expansion into Broward County, justified by: (a) strong projected revenue growth in the forecast; (b) similar income characteristics to Miami‑Dade (no statistical barrier); (c) evidence that business customers pay more per transaction (point estimate), and several services (CORP1, COMPF) are business-focused and high‑value. Recommended actions:
- Phase 1 (pilot, 6 months): open a small Broward commercial sales team focused on corporate, computer‑forensics, and fraud investigations. Track conversion rate, average revenue per client, and CAC (customer acquisition cost).
- Marketing: target B2B segments (insurance carriers, corporations, law firms) emphasizing COMPF and CORP1 capabilities; use local partnerships and direct sales in Broward.
- Operations: keep IT/DBMS integration consistent with the Miami office (MS SQL Server, Salesforce) and enable remote staffing where feasible to control costs.
- Decision gate: after 6–12 months evaluate actual revenue, customer mix, and ROI against forecast. If business pipeline and revenue per client meet targets, scale staffing and invest in local marketing.
Risk considerations: the sample CRM dataset is small and may not represent full demand; city median incomes are proxies for market capacity and do not measure business concentration or sector demand. Therefore the pilot approach mitigates risk while enabling revenue validation.
References
- U.S. Census Bureau. (2016). American Community Survey: Median household income data. https://www.census.gov
- Field, A. (2013). Discovering Statistics Using IBM SPSS Statistics. Sage. (Reference for ANOVA and descriptive statistics)
- Wonnacott, T., & Wonnacott, R. (1990). Introductory Statistics (5th ed.). Wiley. (Hypothesis testing and z-test reference)
- Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2018). Multivariate Data Analysis. Cengage. (Market analysis and segmentation)
- Kotler, P., & Keller, K.L. (2016). Marketing Management (15th ed.). Pearson. (Market-entry strategy)
- Agresti, A., & Franklin, C. (2017). Statistics: The Art and Science of Learning from Data. Pearson. (Descriptive/inferential methods)
- Salesforce. (n.d.). CRM best practices for service businesses. https://www.salesforce.com
- U.S. Bureau of Labor Statistics. (n.d.). Local area unemployment statistics. https://www.bls.gov
- Gallo, A. (2014). A Refresher on Regression and Correlation. Harvard Business Review. (Interpreting test results and practical significance)
- Burns, A., & Bush, R. (2010). Marketing Research (6th ed.). Prentice Hall. (Market testing and pilot design)