data mining strategies and tools.


Please use the following to decode the data set:


Product customer bought our weedkiller product

1–2 times this year = 1
3–4 times this year = 2
5+ times this year = 3


Bought product primarily at:

Garden Specialty Store = 1
Mass Merchandiser = 2
Big Box = 3
Other = 4


Ready To Use = 1 (nets $1.00 profit each)
Concentrate = 2 (nets $2.00 profit each)


21–30  = 1
31–40  = 2
41–50  = 3
51–60  = 4
61–70  = 5
71–over = 6


Male = 0
Female = 1

Marital Status:

Single/Divorced/Widowed = 0
Married = 1

Household Income:

$0–50,000 = 1
$50,001–75,000 = 2
$75,001–100,000 = 3
$100,001–125,000 = 4
$125,001–150,000 = 5
$150,001+ = 6

Purchases within last year:

1 = yes
0 = no

Car                Home                Boat


Home under 1 acre = 1
Home over 1 acre = 2

Activities engaged in within last year:

1 = yes
0 = no

Gardening    Participant Sports    Spectator Sports    Nature    Home DIY

Profile Areas: 

1 = yes
0 = no

Smoker         Pet Lover         Health Conscious         Gourmet Cook

Throughout the course, you have examined and applied data mining strategies and tools. Now, imagine you are the Vice President of Marketing for a consumer weed killer brand. The raw data in the Analyzing the Case Assessment Data Set (linked in the Resources, under the Required Resources heading) has been gathered by your marketing research staff.

For this assessment, integrate your learning into an analysis of this product by completing the following:

  • Examine the Analyzing the Case Assessment Data Set linked in the Resources. This data set represents a sample of 100 customers of a weed killer product.
    • Note: The definitions of the columns in the file are those contained in Analyzing the Case Assessment Exhibit 1, also linked in the Resources under the Required Resources heading.
  • Develop a pivot table that summarizes customer profitability along other meaningful dimensions.
    • Explain how you are defining profitability.
    • What does this pivot table tell you?
  • Use regression analysis to see whether any attributes can be used to predict customer account profitability.
    • What is the best model you can develop?
    • What does it tell you in regard to identifying profitable customers?
  • What additional data would you want, in order to complete your analysis of customer profitability before any marketing? Explain why that data would be useful.
    • Identify additional data needed to support global management decisions.
    • Make a recommendation supported by information from your analyses and any other arguments you can logically support.
      • Who is the top target?
      • What type of retail stores would you expand?
      • How would you develop a communications plan?

Additional Requirements

Your report should contain the following elements:

  • Proper memorandum format.
    • Note: Several appropriate templates are provided in Microsoft Word.
  • Short introductory paragraph stating the purpose of the memo.
  • Brief concluding paragraph.
  • The following subdivisions (at minimum):
    • A profitability analysis, explaining how you define profitability and the dimensions by which you categorize customer groups. Use statistical analysis software to develop a pivot table for meaningful data analysis. Include your pivot table in your report as an appendix.
    • Your regression analysis results and interpretation. (Use statistical analysis software to develop a representative regression model.)
    • A discussion of additional data needed, and your rationale for using that data. (Assess the content and usefulness of internal data stores. Analyze data sources in internal organizational databases and identify additional data needed to support global management decisions.)
    • Present a business decision recommendation based on the results of statistical analysis.
    • Present results of analysis in a concise and compelling manner using effective communication strategies and appropriate business terms.