Facilitator guide: Module 3, consumer and luxury economics
A teacher-facing companion to Module 3. The module is the full lesson; this guide adds the facilitation layer: a preparation checklist, timing cues, guidance for the discussion questions, and notes on what to emphasize and where students get stuck. Slides and a printable version are below.
View slides (PDF) Download slides (.pptx) View printable guide (PDF)
At a glance
- Length: 75 minutes.
- Level: undergraduate marketing and economics majors, or MBA students in a luxury or consumer goods concentration. An introductory microeconomics or consumer behavior course is the only prerequisite; no SQL or coding background is required.
- Access: the no-code sandbox at sandbox.altfndata.com only, using pre-written SQL you supply. No API key is needed and there is no code extension in this module.
- Goal of the session: students leave able to define and compute pricing power and sell-through for a brand, explain why unsold lots belong in the sell-through denominator, and articulate what revealed preference from auction data adds that a stated-preference survey does not.
Before class
- Register your own sandbox account and run the pricing power and sell-through queries end to end for Van Cleef and Arpels and for a second brand you plan to demo live, so you have known-good ratios on screen.
- Open the coverage browser once beforehand on the jewelry table and note the brand and vendor counts you will point to, plus a second brand with a reasonable row count to use as the comparison.
- Load the slides and confirm the Van Cleef and Arpels pricing power figure (near 1.36x) is ready as your anchor example.
- Load the pre-built charts tab and confirm you can quickly build a bar chart comparing two brands' pricing power live.
- Have students register their sandbox accounts before class if you can, since the session's 15 to 20 minute orientation block is short.
Timed agenda with cues
| Time | Segment | What to do | Watch for |
|---|---|---|---|
| 0 to 15 | Introduce pricing power and sell-through | Present both concepts using the Van Cleef and Arpels example (pricing power near 1.36x) as the anchor. | Students treating pricing power as a stated brand-strength score rather than a ratio computed from actual transactions. |
| 15 to 20 | Sandbox orientation | Walk students who have not used the sandbox through the SQL editor, coverage browser, and charts tab. | Students unsure where to find the designer filter or the data dictionary; keep this brisk since time is tight. |
| 20 to 40 | Guided demo: pricing power | Run the pricing power query for Van Cleef and Arpels, then change the designer filter to a second brand and compare. | Students reading the ratio backwards; confirm out loud that above 1.0 means buyers paid over the pre-sale estimate. |
| 40 to 55 | Guided demo: sell-through | Run the sell-through query for the same two brands, noting the WHERE clause does not filter on status. | Students who instinctively want to add status = 'sold' to this query, which would erase the metric's meaning. |
| 55 to 70 | Small-group discussion | Groups interpret what pricing power and sell-through together suggest about each brand's demand. | Groups collapsing the two metrics into one score instead of discussing where they agree or diverge. |
| 70 to 75 | Wrap-up and homework | Restate the takeaway and hand out the three-brand homework assignment. | Leave enough time to state the ranking-and-justification deliverable clearly. |
Guidance for the discussion questions
Talking points and what to listen for, not a graded key.
- What pricing power above 1.0 means economically. Listen for: buyers are paying more than the auction house's own forecast, which can reflect the house intentionally setting a conservative estimate to draw in competitive bidding as much as it reflects underlying brand strength.
- Why unsold lots must be included in sell-through. Listen for: dropping unsold lots leaves only successful sales in the denominator, which mathematically forces the clearance rate toward 100 percent regardless of how the brand is actually performing.
- Strong pricing power with weak sell-through. Listen for: yes, this combination is possible and suggests a narrow but highly committed collector base that pays well over estimate for the pieces it wants, while a broader set of offered lots fails to attract enough bidders to sell at all.
- Revealed versus stated preference, and why it matters for strategy. Listen for: revealed preference is what someone actually paid, a harder and costlier signal to fake than a survey response, which makes it more trustworthy input for decisions like where to invest marketing spend.
- Why pricing power might differ sharply between categories. Listen for: estimate-setting conventions, buyer pools, and collector behavior are not uniform across categories, so a brand's watches and its handbags can carry very different pricing power even under the same house name.
- What secondary market demand does and does not tell you. Listen for: it reveals collector willingness to pay in resale, but it says little directly about primary retail volume, new-customer acquisition, or overall revenue, since auction activity is a distinct and much smaller channel.
- Using both metrics to guide marketing spend. Listen for: high pricing power with weak sell-through points toward deepening engagement with an existing, high-conviction collector base, while strong sell-through with modest pricing power points toward broadening reach to a wider buyer pool.
- What other data would complete the demand picture. Listen for: retail sell-through, primary sales volume, brand-tracking or survey data, and social or search interest, each of which captures a piece of demand that auction records alone cannot.
Teaching notes
- Most common misconception: students filter the sell-through query to status = 'sold', which silently forces the clearance rate to 100 percent. Walk through the query's WHERE clause line by line and show what changes when that filter is added versus left out.
- Second misconception: treating one exceptional sale, a single record price, as evidence that a brand's overall demand has shifted. Reinforce that both metrics are built from medians and counts across many lots specifically to avoid being driven by one transaction.
- 50 minute variant: run only the pricing power demo live, assign the sell-through query as a paired sandbox exercise instead of a guided demo, and shorten small-group discussion to five minutes with two groups reporting back.
- 90 minute variant: after the small-group discussion, add the combined pricing-power-and-sell-through query from the module's Going deeper section and have groups apply it to a category outside jewelry to test whether the concepts hold up.
- If the sandbox is slow or blocked on the room network: fall back to the pre-run results and the bar chart you captured in preparation, and have students run both queries themselves as part of the homework instead of in class.
For the full lesson content, queries, and homework, see Module 3. Questions or a class API key: info@altfndata.com.