CS01 & CS02 | 1 Jan – 20 Jun 2018 (171 days) | Source: CS_Sales_Analysis.xlsx, CS01.xlsx, CS02.xlsx
What this dashboard shows: We looked at six months of sales for two coffee shop chains, CS01 (26 to 29 stores) and CS02 (5 stores). Sales per store held steady over this time for both chains, no clear rise or fall. What drives sales in both chains is simple: more customers coming in means more sales. Charging more per order, or customers spending more per visit, does not explain the ups and downs. Both chains also sell less on public holidays, and for CS02 the drop is dramatic, which likely means those stores are closed or open shorter hours on holidays.
CS01 Avg Weekly Sales per Store
₱5,377
Typically between ₱4,609 and ₱6,042
CS02 Avg Weekly Sales per Store
₱5,159
Typically between ₱2,719 and ₱7,068
CS01 Trend Over 6 Months
Flat
No real rise or fall in sales per store
CS02 Trend Over 6 Months
Flat
No real rise or fall in sales per store
CS01 Holiday Sales
9% lower
Small dip, could easily be normal variation
CS02 Holiday Sales
80% lower
Consistent, real drop every holiday
CS01 vs CS02 — Weekly Sales per Store
Both chains dipped the week of 26 March 2018, which covers the Easter holidays. CS02's line jumps around a lot more than CS01's because CS02 only has 5 stores, so a single unusual day has a much bigger effect on its weekly average.
CS01 opened new stores during this period, growing from 26 to 29 locations. However, sales at each existing store stayed about the same. There is no sign that opening new stores hurt or helped the older ones.
CS02 was constant at 5 stores during this period, and its weekly sales bounced up and down a lot more than CS01's. A slow day or a great day at just one CS02 store can swing the whole week's average noticeably.
Over the full six months, neither chain showed a real upward or downward trend. Sales stayed roughly stable throughout.
CS01 Weekly Sales per Store
Sales stayed roughly flat week to week, no consistent rise or fall.
CS02 Weekly Sales per Store
Sales bounce around a lot, but there's no consistent rise or fall over time.
Weekly Detail
Question: On days with more customers, do people also spend more per order, or less?
For CS01 busy days and quiet days shows the same average order size.
For CS02, there is a small pattern: on busier days, the average order is a little smaller likely due to stores being crowded and customers feeling rushed with smaller orders.
In both chains, foot traffic is the primary driver for total sales going up or down.
How to read this table: Pearson r and p-value, explained
Pearson r measures how closely two things move together, on a scale from -1 to +1. A number near 0 means no relationship. A number near +1 means they rise and fall together. A number near -1 means when one goes up, the other tends to go down. The numbers below are all close to 0, so the relationship between customer traffic and average order size is weak to non-existent in both chains.
p-value tells us how likely it is that a pattern we see is just random chance, rather than a real effect. A small p-value (under 0.05, marked "Yes" for significant) means we can be fairly confident the pattern is real. A larger p-value (marked "No") means the pattern could easily just be noise in the data, and we shouldn't read much into it.
Put together: for CS01, both the low r and the "No" mean there's genuinely nothing going on between customer traffic and order size. For CS02, the r is still fairly small but the "Yes" tells us this small effect is unlikely to be a fluke, it's a real, if modest, pattern.
Customer Traffic vs Average Order Size
CS01 sells about 9% less on holidays, but that small dip is within normal day-to-day variation.
An exception is Maundy Thursday and Good Friday, when sales dropped to just 15% to 20% of a typical day.
CS02 sells about 80% less on every single one of the 10 holidays observed, a big and consistent drop.
This pattern is strong and consistent that it's very unlikely to be a coincidence.
The most likely explanation is that CS02 stores are closed or open much shorter hours on public holidays.
Holiday vs Regular Day Sales per Store
Sales per Store on Each of the 10 Holidays
Holiday Detail
Store size matters when comparing two chains of very different scale, so we looked at sales per square meter of floor space, a way of asking "how well is each square foot of the store being used to generate sales"?
CS01 branch area size average 180 square metres. CS02 branches average 80 square metres.
Even though CS01 brings in more total sales per branch, CS02 generates more than double the sales for every square meter of space it has.
A smaller CS02 branch is working harder than a CS01 branch.
CS01 Store Size
180 m²
Average floor space per store
CS02 Store Size
80 m²
Average floor space per store
CS01 Sales per m² (Daily Avg)
₱30
Typically ₱5 to ₱55 a day
CS02 Sales per m² (Daily Avg)
₱64
More than double CS01, but swings widely day to day
Daily Sales per Square Meter — CS01 vs CS02
During this period, CS02's smaller branches consistently squeezed out more sales per square meter, though its daily figure jumps around much more since it's averaged across only 5 stores.
Monthly Average: Sales per Square Metre
CS02 consistently outperforms CS01 on a per-square-metre basis.
Which Store Uses Its Space Better?
Average sales per square metre across the full 6-month period.
What This Means
CS01's bigger branches bring in more total sales because they have more room and likely more seating, but a lot of that space is underperforming or maybe underutilised.
CS02's smaller branches make every square metre count, generating roughly twice the sales density.
This could mean CS02 has a more focused, faster-turnover format (grab-and-go, smaller footprint, less idle space), while **CS01 may be paying for more real estate than its sales actually need**.
**If CS01 were opening new branches, this points to a case for testing a smaller store format**. If CS02 were to expand, its space efficiency is a strong selling point for higher rent locations.
CS01 and CS02 are small, regional coffee chains. To understand how they stack up, we compared them against the well-known players in the Philippine coffee market: Starbucks, Bo's Coffee, and The Coffee Bean & Tea Leaf (CBTL).
There is no public data on sales per square metre for these named brands, so this is a comparison of scale and position, not a like-for-like numbers match.
Scale: CS01 and CS02 vs the Major Players
Chain
Approx. Store Count
Position in Market
Starbucks Philippines
500+
Market leader, national footprint, largest specialty coffee chain in the country
The Coffee Bean & Tea Leaf (CBTL)
~50 to 100 (est.)
Major national player, roughly 12% of Starbucks' Philippine revenue as of the last public estimate
Bo's Coffee
~50 to 90 (est.)
Established homegrown chain, strong regional presence outside Metro Manila
CS01
26 to 29
Small regional chain, expanding during the period studied
CS02
5
Very small, boutique-scale operation
What the Numbers Suggest
CS01 and CS02 are both far smaller than Starbucks, CBTL, and Bo's Coffee in terms of store count and are not competing at a national scale.
That said, the space-efficiency finding is a real strength: CS02's ability to generate roughly double the sales per square meter of CS01 suggests it operates more like a smaller-format, high-turnover coffee shop, a model similar to how some major chains use compact "grab and go" locations in busy areas to keep costs down while maximizing sales per square foot.
If CS01 or CS02 want to compete more directly with the bigger chains, the data here supports two different paths: CS01 has room to grow through new stores since it already has a working format, while CS02's tighter, more efficient format could be a template for opening more small, high-performing locations rather than large ones.
One caution: because there's no public per-square-meter data for Starbucks, CBTL, or Bo's Coffee, we can't say definitively whether CS02's ₱64 per square meter is good, average, or exceptional by industry standards.
This comparison should be treated as directional, not benchmarking, and would benefit from real competitor data if it becomes available.