| Course | MGT 215 Customer Relationship Management |
|---|---|
| Module | Module 3 |
| Paper type | Customer analytics paper |
| Length | About 1,058 words, 6 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | Business Administration |
| Updated | October 2026 |
Free sample paper for MGT 215 Module 3
Reading the Order History: Recency, Frequency and Spending Analysis for an Online Tea Retailer
Student Name
Business Administration Program, Aspen University
MGT 215: Customer Relationship Management
Instructor Name
Month Day, Year
Reading the Order History: Recency, Frequency and Spending Analysis for an Online Tea Retailer
Blue Ridge Leaf Tea Company, a composite business in Asheville, North Carolina, sells loose-leaf teas, teaware and gift sets online across the United States. Over three years it has served about 48,000 customers and recorded every order. Yet its relationship marketing consists of one weekly newsletter sent to everyone, from a customer who orders monthly to one who bought a single gift tin in 2023. Sales have flattened at about $3.4 million a year. The owners want to know what their order history can tell them. This paper applies recency, frequency and monetary analysis to the company's customer base and recommends actions for each group.
The Data Available
Each order record includes the customer's email address, shipping address, date, items and amount. The retailer also knows which newsletter links each customer clicks. It does not know why customers buy, whether a purchase was a gift, or what customers think of the products, except through occasional reviews. The data are good for describing behavior and weak for explaining it. About 6% of customers appear under two email addresses, which the analyst merged by shipping address before segmenting.
Why Recency, Frequency and Spending
Fader et al. (2005) connected the long-used RFM approach to customer lifetime value. Their analysis of a retailer's customers showed that recency and frequency interact: a customer who bought many times but not for a long while is likely to have stopped, so frequency without recency overstates value. Spending matters too but was largely independent of the other two. They concluded that the three measures together summarize a customer's past behavior well enough to forecast future purchasing, which makes RFM a sound starting point for a firm with limited analytic resources.
The Segments
Each customer was scored on days since last order, number of orders in three years and average order value. Five segments emerged.
| Segment | Customers | Share of three-year revenue | Pattern |
|---|---|---|---|
| Devoted buyers | 3,100 | 31% | Ordered within 90 days, six or more orders a year |
| Steady customers | 9,800 | 34% | Ordered within six months, two to five orders a year |
| Lapsing regulars | 4,200 | 9% | Once ordered often; nothing for nine months or more |
| Recent first-timers | 11,500 | 12% | One order within the past year |
| Dormant one-time buyers | 19,400 | 14% | One order, more than a year ago |
What the Results Reveal
Two findings stand out. First, value is concentrated: 13,000 devoted and steady customers, about 27% of the base, produced 65% of revenue. Second, the 4,200 lapsing regulars averaged five orders a year before they stopped. Fader and colleagues' work suggests their chance of returning falls the longer they stay away, so the window to win them back is closing. Analysis of their last orders showed that many stopped after a price increase on two popular blends in early 2025, a clue the retailer can test.
Testing the Price Clue
The link between the 2025 price increase and the lapsing group is a hypothesis, not a finding. Customers may also have left because a competitor opened, because shipping times grew during the holidays or simply because tastes change. The retailer can test the price explanation directly. Half of the lapsing regulars, chosen at random, will receive an offer that restores their favorite blend's old price for three orders, while the other half receive a friendly note with no discount. If the discount group returns at a clearly higher rate, price was likely the cause and the company can decide whether a loyalty price for frequent buyers makes sense. If both groups return at similar rates, the note itself matters more than the money, which would be cheaper to scale.
Building the Capability
Davenport (2006) described companies that compete on analytics as treating data analysis as a core capability rather than an occasional project. They have leaders who use evidence in decisions, staff with analytic skills and data systems that make information easy to use. Blue Ridge, with 22 employees, cannot build a large analytics team, but it can apply the principle on a small scale: a monthly segment refresh, a part-time analyst and a habit of testing changes on a sample before rolling them out.
Cleaning the Data Regularly
Analysis is only as good as the records behind it. The duplicate email addresses found during this project suggest other problems, such as customers who moved and gift purchases recorded under the buyer's name rather than the recipient's. The retailer will add a checkbox at checkout asking whether an order is a gift, so that gift buyers are not mistaken for tea drinkers who stopped buying. Duplicate records will be merged monthly. These steps cost little but keep segments from drifting away from reality as the customer base grows.
Privacy and Respect
Wedel and Kannan (2016) reviewed how firms use customer data and noted growing customer concern about privacy, warning that personalization perceived as intrusive can damage trust. For Blue Ridge, that means using data customers would expect a tea shop to use, such as what they bought, and avoiding inferences that feel personal, such as guessing life events from purchase patterns. Customers will be able to set how often they hear from the company.
Actions by Segment
Devoted buyers will receive early access to new harvests and a note from the tea buyer. Steady customers will get reminders timed to their usual reorder interval. Lapsing regulars will receive a personal email from the owner asking what changed, with an offer on their favorite blend. Recent first-timers will get a guide to brewing what they bought and a sampler suggestion. Dormant buyers will receive email only quarterly, cutting cost and complaints.
Measuring Results
The retailer will compare each segment's order rate with a randomly held-out group that receives the old newsletter. For the lapsing regulars, the measure is the share who order again within 90 days; for devoted buyers, it is retention over twelve months; for dormant buyers, it is unsubscribe rates as email frequency drops.
Conclusion
Blue Ridge's order history holds far more than a sales total. RFM analysis, supported by Fader, Hardie and Lee's findings, reveals concentrated value and a lapsing group of former regulars that sales reports hide. Building a modest analytics habit, using data in ways customers accept and treating each segment differently should turn a single weekly newsletter into relationship management.
References
Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98-107.
Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430. https://doi.org/10.1509/jmkr.2005.42.4.415
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
What the MGT 215 Module 3 instructions ask for
Expect a third-module paper in Aspen's MGT 215 to ask how a company collects and analyzes customer information to strengthen relationships. What your classroom's Module 3 directions request governs; this paper analyzes one retailer's order history. Describe the data the company holds and its quality. Explain an analytic method, such as recency, frequency and monetary segmentation, with research support. Apply it and present the segments with figures. Interpret what the results reveal, especially patterns that simple sales reports hide. Recommend specific actions for each segment. Discuss privacy and the limits of what customers will accept, and close with how the company will measure whether the actions work.
How the MGT 215 Module 3 example is put together
The paper begins with Blue Ridge Leaf Tea Company, whose 48,000 customers all receive the same weekly newsletter. Fader, Hardie and Lee's Journal of Marketing Research article found that how lately and how often customers order jointly forecast their future buying, and that a frequent buyer who has not ordered recently is less valuable than raw frequency suggests. A table divides customers into five segments, from 3,100 devoted buyers producing 31% of three-year revenue to 19,400 single-purchase customers who have been inactive for a year. The key finding is a lapsing group of 4,200 former regulars. Davenport's Harvard Business Review article describes the data, skills and leadership commitment analytics require. Wedel and Kannan's Journal of Marketing review frames privacy concerns. Actions include a personal win-back offer for lapsing regulars and fewer emails to dormant customers.
Where the marks sit in the MGT 215 Module 3 rubric
A customer analytics paper earns credit for describing the data honestly, applying a sound method, reading the results with judgment and turning them into actions. This example states what the retailer records and what it lacks before segmenting. Fader, Hardie and Lee's Journal of Marketing Research findings justify using recency and frequency together, which is why the lapsing group stands out. The segment table gives counts and revenue shares, so the reader can see where value sits. Davenport's article moves the discussion from one analysis to the capability needed to repeat it. Wedel and Kannan's review supports a section on privacy that sets boundaries rather than ignoring them. Measures tie each action to a result.
MGT 215 Module 3 help: mistakes that cost marks
Analytics papers often present a segmentation and stop, leaving the reader to guess what to do with it. Explain what each segment means and what the company should do differently for it. Another weakness is relying on total spending alone; recency and frequency change the picture, as the lapsing group in this example shows. Describe your data and its gaps before analyzing it. Support your method with research. Keep tables readable, with counts and shares. Address privacy, because customers react badly to personalization that feels intrusive. Avoid promising that more data automatically means better relationships. Finally, propose measures for each action so the analysis leads to learning.
Write yours, or have the desk draft it
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MGT 215 Module 3 questions, answered
What does MGT 215 Module 3 usually ask for?
Aspen's MGT 215 turns to customer data and analytics in this module, so a paper showing how a company can analyze customer information to manage relationships is typical. Read your classroom prompt.
What is RFM analysis?
A method that groups customers by how recently they bought, how often they buy and how much they spend, which together predict future purchasing.
Why does recency matter as much as frequency?
Fader, Hardie and Lee showed that a customer who bought often but not recently is less likely to buy again than frequency alone suggests.
Where can I find a free MGT 215 Module 3 sample paper?
The example above segments an online tea retailer's 48,000 customers by recency, frequency and spending and recommends an action for each group.
How should companies handle customer privacy in analytics?
Wedel and Kannan note that customers resist intrusive personalization, so companies should use data customers expect them to use and explain how it benefits them.