Strawberry Sequel: Modeling a What-If in the Fields
Mar 3, 2025
Val Poon
A seasonal tradition meets a quiet surplus, results in a new kind of offering.
It’s the last week of February.
You're biking toward the coast, board strapped on, chasing a morning swell.
As you pass the familiar stand tucked beside the slope, you see her — Sumi-san, waving from the entrance, flour still on her sleeves and a big smile on her face.
You stop. She hands you a small white box, warm and faintly sweet in your hands.
“Ichigo Daifuku,” she says. “A freshly made practice batch.
Thanks for your help last week. Enjoy my small token of appreciation.”
She taps the corner of the box.
“Girls’ Day is coming. Thought I’d make a few for the neighbors — like we used to.
When the little ones believed the berries were magic.”
She nods toward the crates of strawberries by the wall — still full.
“We always sell them as a single box or small pint.
But what if we tried something different this year?”
She pauses — a bit hesitant.
“Three boxes. For Girls’ Day.
A special set — for gifting. For sharing. Or for celebrating.”
That afternoon, you return to your homestay, taking a bite of the delicious, chewy, fresh strawberry daifuku.
You hop on your laptop — the SQL table still open in another tab.
Sumi-san's quiet concern lingers in your mind: the slightly furrowed brow, the growing pile of unsold crates, and the wide empty row once filled with Yamamoto-san’s name.
SQL Logic Time
What if the special set included daifuku, too?
What if the bundle celebrates the tradition with a new bundle?
You model the scenario:
Every customer receives a 3-box Girls’ Day set (or the daifuku gift set)
You simulate the revenue from that possibility
You model the scenario:
Every customer receives a 3-box Girls’ Day set, or a daifuku trio
You simulate the revenue from that possibility
You layer in SUM() on original price × 3.
Not to predict demand — but to measure a celebratory moment of opportunity.
The results appear — quiet rows, simple math.
But enough to clear the shelves.
Enough to turn extra into celebration.
In the first chapter, I used SQL to spot who hadn’t come — tracking a drop in visits.
This time, I’m using SQL to project what would happen if everyone did show up — and bought a seasonal bundle.
From Chapter 1: investigative querying → to this chapter: hypothetical modeling