| Course | DAA-7030 Data Analytics and Visualization with Capstone Project |
|---|---|
| Module | Module 6 |
| Paper type | Capstone project: data analytics and comprehensive marketing plan |
| Length | About 1,469 words, 8 pages |
| Format | APA 7 student paper |
| School | Thomas Edison State University |
| Program | MBA in Data Analytics |
| Updated | October 2026 |
Free sample paper for DAA 7030 Module 6
Where to Grow and How to Win: A Data-Driven Marketing Plan for a Seventh Studio
Student Name
MBA in Data Analytics, Thomas Edison State University
DAA-7030: Data Analytics and Visualization with Capstone Project
Mentor Name
Month Day, Year
Where to Grow and How to Win: A Data-Driven Marketing Plan for a Seventh Studio
Executive Summary
The chain should open its seventh studio in a younger, affluent and underserved corridor along Route 17, identified by clustering public Census data and confirmed by the chain's own member profile and a scan of competitors. A gravity model estimates that a studio at the recommended site could attract about 520 members within eighteen months. The marketing plan positions the studio on small classes and instructor coaching against two nearby competitors, prices founding memberships to build a base before opening and sets measures for the first year. The launch budget is $86,000.
Market Intelligence
From Public Data to Candidate Areas
The midterm joined American Community Survey and County Business Patterns data for 70 ZIP codes and grouped them into four clusters with k-means, following the cautions in Jain (2010) about choosing the number of clusters. Cluster 2, eleven ZIP codes with average median household income of about $142,000, a 31 percent share of residents aged 25 to 44 and 1.6 fitness centers per 10,000 residents, stood out as underserved.
Testing Against Our Members
Public data describe areas, not customers. To check that Cluster 2 residents resemble our members, I matched the home ZIP codes of our 2,900 active members to the clusters. Members came disproportionately from ZIP codes with high incomes and large shares of residents aged 25 to 44; 71 percent lived in Cluster 1 or Cluster 2 profiles, although only 36 percent of the region's population does. Members from Cluster 2 ZIP codes already travel an average of 14 minutes to our nearest studio, longer than the chain's average of 8 minutes, which suggests unmet demand close to home.
Wedel and Kannan (2016) argue that marketing analytics creates value when it joins data sources to answer specific decisions, and this step shows why: the public clusters suggested an opportunity, and our own data confirmed that the people in those areas look like our customers.
Estimating Demand by Site
Three available retail spaces in Cluster 2 were compared. Huff (1964) proposed a probabilistic model of retail trade areas in which the probability that a consumer chooses a store rises with the store's attractiveness and falls with travel time, relative to all competing options. I adapted the model with attractiveness measured by studio size and number of weekly classes, travel time from the center of each ZIP code and all existing studios and gyms in the area as competitors.
At an average of $135 a month, 520 members would produce about $842,000 in annual revenue, well above the chain's break-even of about 330 members for a studio of this size. Paramus is recommended despite its higher rent.
| Candidate site | Estimated members at 18 months | Nearest boutique competitor | Rent per year |
|---|---|---|---|
| Paramus, Route 17 corridor | 520 | 2.4 miles | $96,000 |
| Rochelle Park | 410 | 1.1 miles | $78,000 |
| Hasbrouck Heights | 360 | 3.0 miles | $72,000 |
Competitive Intelligence
A scan of competitors within ten minutes of the Paramus site, using their public schedules, class prices and online reviews, found two boutique studios and four large gyms.
The gap is clear: no nearby option offers small strength and conditioning classes with individual coaching at 6 a.m., our chain's most popular format and time.
| Competitor | Format | Price | Strength | Weakness in reviews |
|---|---|---|---|---|
| Studio A (cycling) | 45-minute cycling | $32 per class; $189 unlimited | Strong brand | Crowded classes; little coaching |
| Studio B (pilates) | Reformer pilates | $38 per class | High-end equipment | Limited early morning times |
| Large gyms (four) | Open gym, some classes | $25 to $60 a month | Price | Impersonal; inconsistent instruction |
Marketing Strategy
Segments and Targeting
Member data and the cluster profile point to two target segments: working professionals aged 28 to 44 who train before work and value coaching, and parents aged 35 to 50 who want midmorning classes after school drop-off. Together they account for 68 percent of current members.
Positioning
Against the cycling studio's energy and the gyms' low prices, the new studio will be positioned on coaching: classes capped at 14, an instructor who knows every member's name and a progress check every six weeks. The message is "small classes, real coaching, before your day starts."
Product and Schedule
The schedule will lead with 6 a.m. and 7 a.m. strength classes, add 9:30 a.m. classes for parents and offer fewer evening classes than the chain's other studios, reflecting the gap competitors leave.
Price
A founding membership at $119 a month for the first year, $16 below the regular rate, will be sold before opening, capped at 250 members to build a base and word of mouth. Class packs and a $35 drop-in rate match the market.
Promotion
Current members who live in Cluster 2 ZIP codes, about 410 people, will be invited first, since they already know the brand and may transfer or refer others.
| Phase | Timing | Activities | Budget |
|---|---|---|---|
| Pre-opening | 3 months before | Geotargeted social ads to Cluster 2 ZIP codes, founding member offer, free outdoor classes in a local park | $38,000 |
| Opening | First month | Opening week of free classes, referral credit for current members in nearby ZIP codes | $22,000 |
| Growth | Months 2 to 6 | Partnerships with two employers and a pediatric practice, member referral program | $26,000 |
Measures and Controls
If members at six months fall below 300, the plan calls for a review of the schedule and price, informed by the cancellation model built in Module 2.
| Measure | Target |
|---|---|
| Founding memberships sold before opening | 250 |
| Members at 6 months | 380 |
| Members at 18 months | 520 |
| Monthly cancellation rate | Under 4 percent |
| Average class fill at 6 a.m. | 85 percent |
The Future of BI for the Chain
Module 6 also looks ahead. The dashboard built for this project can expand to monitor all seven studios, add the cancellation risk model and, later, incorporate wearable and app data members choose to share, a step Chen et al. (2012) describe as the third generation of BI and analytics. Each addition should follow the same discipline as this plan: a decision first, then the data and analysis to support it.
Risks to the Plan
Three things could go wrong. A competitor could open a similar coaching studio nearby before we do; the founding membership campaign is designed to build a base quickly so the studio opens with members. Instructor supply could limit the 6 a.m. schedule; two current instructors have agreed to split time between their studio and the new one for the first six months while new instructors are trained. And members transferring from our existing studios could cannibalize their attendance; the gravity model suggests that about 60 of the 520 projected members would come from existing studios, a small share that would free space in classes that now often fill.
Organization and Responsibilities
The general manager will own the opening, the marketing analyst will run the pre-opening campaign and the dashboard, and the head instructor will build the schedule and hire. Weekly opening meetings in the three months before launch will review founding membership sales against the target, so the exit clause can be used early if demand falls short.
Data Refresh and Monitoring After Opening
After opening, the dashboard will track the new studio's members by home ZIP code each month, compared with the gravity model's forecast. If members come from farther away than predicted, the model's travel time assumptions will be revised, improving the next site decision. Class fill rates by hour will show whether the early morning positioning is working, and a short survey at three months will ask new members why they joined and what nearly stopped them.
The dashboard will also show the founding membership count each week before opening, the earliest test of the plan's assumptions, and it will flag any week in which sales fall more than 20 percent behind the path needed to reach 250 by opening day.
Limitations
The gravity model's estimates depend on assumed attractiveness weights and travel times, and Census data lag by one to two years. Competitor information comes from public sources and may miss promotions or plans. The founding membership target is an assumption that the pre-opening campaign will test before the lease is signed, with an exit clause negotiated if fewer than 150 founding memberships are sold.
Appendix: Backend Files
The R scripts that reproduce the analysis and visualizations are attached in a folder with a readme. Script 1 downloads and cleans the Census tables and writes the joined csv file. Script 2 standardizes the variables, runs k-means for three to six clusters and saves the chosen solution. Script 3 matches member ZIP codes to clusters. Script 4 computes the gravity model estimates for the three sites. Script 5 draws the cluster map, the scatterplot and the site comparison chart. Each script begins with checks for missing values and duplicates and can be rerun with updated data.
References
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503
Huff, D. L. (1964). Defining and estimating a trading area. Journal of Marketing, 28(3), 34-38. https://doi.org/10.1177/002224296402800307
Jain, A. K. (2010). Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8), 651-666. https://doi.org/10.1016/j.patrec.2009.09.011
Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121. https://doi.org/10.1509/jm.15.0413
Reading the DAA 7030 Module 6 assignment instructions
The DAA-7030 capstone, worth 30 percent, asks you to create a data analysis and a 7- to 10-page APA report: a comprehensive marketing plan based on the data collected for the midterm, aimed at market intelligence, competitive intelligence and associated competitive marketing strategies. Include a copy of the backend code or the file used to produce your visualizations so they can be rerun. Module 6 covers the capstone and the future of BI and data mining. Build on your midterm data, add the information the decision needs, connect each part of the plan to the analysis and give measures for success. Attach or describe your scripts so a reader could rerun them.
How this DAA 7030 Module 6 example is built
An opening summary gives the site, expected members, positioning and budget on one page. Market intelligence extends the midterm clusters with the chain's own member data, citing Wedel and Kannan on joining sources, and Huff's gravity model compares three sites in a table. A margin note explains the triangulation. Competitive intelligence profiles nearby studios and gyms from public information and finds a gap in early morning coaching. The strategy covers segments, positioning, schedule, founding membership pricing and a three-phase promotion budget. Measures, a look at the future of BI, limitations and an appendix describing five rerunnable R scripts close the report. Sections on risks and responsibilities show how the plan would be managed. Monitoring after opening feeds results back into the next site decision.
Where the marks sit in the DAA 7030 Module 6 rubric
Mentors grade the capstone on the quality of the data analysis, the link between analysis and marketing decisions, coverage of market intelligence, competitive intelligence and competitive strategy, inclusion of rerunnable code or files and professional APA writing. Reports that triangulate a decision with several data sources show analytical maturity. Competitive intelligence drawn from verifiable public sources and tied to a positioning choice shows strategic thinking. A plan with prices, budget and measures shows that analytics has been turned into action. Clear limitations and an exit condition show sound judgment. Naming risks like stealing members from sister studios, and an owner for each launch task, adds credit.
DAA 7030 Module 6 help from the desk
Capstones lose points when the analysis and the marketing plan read as separate documents, when competitive intelligence is a list of competitor names or when no code or files are attached. Others propose strategies with no budget or measures. Start from the midterm data, add what the decision needs, let each strategy element follow from a finding, compare competitors on the dimensions customers care about, attach rerunnable scripts and set targets. A free first draft of your DAA-7030 capstone can be built on your midterm data. Assign owners to each part of the plan and say how early warning signs will be caught.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Thomas Edison State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
More DAA 7030 and MBA in Data Analytics sample papers
- DAA 7030 Module 1: Discussion: What Business Intelligence Means for a Fitness Studio Chain
- DAA 7030 Module 2: Written Assignment: A Data Mining Model and Process Fix for Member Cancellations
- DAA 7030 Module 3: Midterm Paper: Collecting, Mining and Presenting Public Data on Where to Open the Next Studio
- DAA 7030 Module 4: Written Assignment: Choosing Analytics Software for a Small Chain's First BI Project
- DAA 7030 Module 5: Discussion: One BI Platform for Classes, Staff and Money at a Fitness Chain
- DAM 7020 Module 4: Discussion: R for a Forecasting Pilot, and What a Small Analytics Team Needs From Software
- MST 7000 Module 2: Written Assignment: Damage, Carriers and Bayes' Theorem
- BFO 7010 Module 1: Discussion: What Can and Cannot Be Forecast
DAA 7030 Module 6 questions, answered
Where can I find a free DAA 7030 Module 6 sample paper?
The capstone above turns Census clusters, member data and a gravity model into a marketing plan for a new fitness studio.
What is the DAA-7030 capstone project?
A data analysis and 7- to 10-page APA marketing plan built on the midterm data, with backend code or files attached so visuals can be rerun.
What is the Huff gravity model?
A model in which the probability of choosing a store rises with its attractiveness and falls with travel time relative to competitors.
What is competitive intelligence in a marketing plan?
Systematic information on competitors' offers, prices, strengths and weaknesses used to choose a position.
How much is the DAA-7030 capstone worth?
Thirty percent, the largest single piece of the grade.