Seven-Day Shopify Performance Scorecard: Before vs. After Single-Pack Removal

Welcome. This course is a short, practical sprint for increasing average order value without paying more to acquire traffic. The first module establishes whether removing the Throat Soothing Pops single pack created a real commercial improvement, rather than just making the dashboard’s AOV number look better.

By the end of this lesson, you will have a matched seven-day, before-and-after scorecard. It will show whether customers shifted from the two-pack toward the three-pack, whether conversion held up, and whether more orders crossed your automatic-discount thresholds. That last point matters: a higher basket can still be a weak outcome if it requires substantially more discounting.

Plan for roughly 35–40 minutes. Use two complete seven-day periods and keep the raw exports or screenshots so that the next lesson can turn the promising outcomes into a contribution-profitability check.

---

1. Establish a fair before-and-after comparison

The removal date is the dividing line, but a calendar day is not always a clean dividing line. If the single pack was removed at 3 p.m., that day contains both the old and new product architecture. Treat it as a transition day and exclude it.

Use this structure:

Period	Dates to use	Purpose
Before	The seven complete days immediately before the removal date	Baseline while the single pack was purchasable
Transition	The partial day on which the single pack was removed	Exclude
After	The first seven complete days after removal	Initial result under the new pack architecture

If the product change happened precisely at the start of a Shopify reporting day, you can include that day as the first post-change day. If you are uncertain, exclude it. A clean comparison is worth one fewer data point.

Using seven consecutive days on each side automatically gives each period the same mix of weekdays. That matters because wellness-product buying can vary by weekday, payday, weekend, or campaign schedule.

Use fixed ranges, not “last seven days.” A rolling range changes every day and can accidentally include today’s incomplete data.

Before pulling numbers, write this at the top of your sheet:

Change being evaluated: Single Throat Soothing Pops pack removed.
No intended price or automatic-discount change: Yes / No
Store reporting timezone: [enter timezone]
Before window: [dates]
Transition day excluded: [date, if applicable]
After window: [dates]

Also add a short “events log” with anything that could distort the result:

• A paid-media budget or targeting change
• A creator post, press mention, or unusually large campaign
• A stockout or low inventory for either pack
• A shipping-policy change
• A site outage or checkout problem
• A major product-page, cart, or bundle change

The scorecard is a strong rapid diagnostic, but it is not yet a controlled experiment. These notes stop you from wrongly crediting the pack change for a traffic or operations event.

---

2. Pull traffic and conversion data first

Start with Shopify’s Conversion rate over time report. It supplies the traffic denominator for the rest of the scorecard: sessions.

In the report:

1. Set the post-removal fixed seven-day range.
2. Set the pre-removal period as the custom comparison range.
3. Group by day as a quality check. Confirm that there are seven full rows per period and no partial current day.
4. Record the period totals for:
   • Sessions
   • Sessions that completed checkout
   • Conversion rate

A necessary distinction: orders are not always completed-checkout sessions

For this scorecard, retain both concepts:

• Sessions that completed checkout come from the Behavior report and support Shopify’s session conversion rate.
• Orders come from your valid Online Store order data and are used for AOV.

They can differ because a customer can place more than one order in a session. Do not replace Shopify’s conversion rate with orders divided by sessions and call it the same thing.

For each period, pull Online Store orders only from your Orders data or the relevant sales report. Use the same rules for both periods:

• Include valid placed orders from the Online Store sales channel.
• Exclude test and cancelled orders.
• Do not exclude unfulfilled orders merely because they have not shipped yet.
• Use the same reporting timezone as the Behavior report.
• Keep marketplace, retail/POS, wholesale, and other channels out of this comparison.

For revenue, use one consistent measure: net product revenue after discounts and before shipping and taxes. In many Shopify views this is labelled net sales. Record the precise Shopify label you use in your sheet. Consistency is more important than trying to reconcile several slightly different revenue definitions.

---

3. Build the scorecard from one clear measurement contract

Create a simple spreadsheet with a Before and After column. Do not average daily AOVs or daily conversion rates. Calculate each metric from the seven-day totals; this weights every order and session correctly.

Let:

• $S$ = Online Store sessions
• $C$ = sessions that completed checkout
• $O$ = valid Online Store orders
• $R$ = net product revenue after discounts, excluding shipping and taxes

Your core calculations are:

$$\text{Conversion rate}=\frac{C}{S}\times 100$$ $$\text{AOV}=\frac{R}{O}$$ $$\text{Revenue per session}=\frac{R}{S}$$

Revenue per session is the reality check for AOV. AOV can rise simply because smaller orders disappear. Revenue per session asks the more useful question: did each visit become more valuable overall?

Use this scorecard template.

Metric	Before: 7 full days	After: 7 full days	Change	Interpretation
Dates included			—	Record exact dates
Sessions, $S$				Traffic volume
Completed-checkout sessions, $C$				Shopify funnel outcome
Conversion rate			percentage points	Did purchase conversion hold?
Valid Online Store orders, $O$				AOV denominator
Net product revenue, $R$	₹	₹	₹	Revenue after discounts
AOV	₹	₹	₹ and percent	Basket value
Revenue per session	₹	₹	₹ and percent	Overall visitor value
Hero-product orders				Orders containing any Pops pack
Single-pack-only hero orders				Should be zero after removal
Two-pack-only hero orders				Pack-choice count
Three-pack-only hero orders				Pack-choice count
Mixed-pack hero orders				Orders containing both two- and three-pack variants
Two-pack share of clean choices			percentage points	Two versus three mix
Three-pack share of clean choices			percentage points	Two versus three mix
Orders with an automatic discount				Discount exposure
Automatic-discount order mix			percentage points	Share of all Online Store orders
Total automatic-discount value	₹	₹	₹	Useful input for next lesson

For counts and currency amounts, calculate both the absolute and relative movement:

$$\Delta x=x_{\mathrm{post}}-x_{\mathrm{pre}}$$ $$\text{Relative change}=\frac{x_{\mathrm{post}}-x_{\mathrm{pre}}}{x_{\mathrm{pre}}}\times 100$$

For conversion rate and order-mix percentages, use percentage points, not percent change. For example, moving from 3.0% to 3.4% is a gain of 0.4 percentage points. Describing it as a 13.3% increase can be mathematically correct but makes an operational dashboard harder to read.

Define pack mix at the order level

Your current question is not simply “Did revenue rise?” It is “When a shopper chooses a remaining Pops pack, are they choosing the three-pack more often?”

Export or extract line-item data for the two periods. Use the exact SKU or variant title for the Throat Soothing Pops two-pack and three-pack; titles are more reliable than general product-name searches if there are similarly named variants.

Classify each order containing Throat Soothing Pops into one mutually exclusive category:

Category	Rule
Single-pack only	Contains a single-pack variant and neither remaining pack variant
Two-pack only	Contains a two-pack variant and no three-pack variant
Three-pack only	Contains a three-pack variant and no two-pack variant
Mixed pack	Contains both two-pack and three-pack variants
Other hero-product order	Contains a relevant hero-product configuration that does not fit the categories above

An order can contain a Pops pack plus spray, syrup, or another product and still be classified as two-pack-only or three-pack-only. “Only” refers to the Pops pack variants, not to the entire order.

For the direct two-versus-three choice calculation, exclude mixed packs from the denominator and show them separately:

$$\text{Two pack share}=\frac{N_2}{N_2+N_3}\times 100$$ $$\text{Three pack share}=\frac{N_3}{N_2+N_3}\times 100$$

Here, $N_2$ is the count of two-pack-only hero orders and $N_3$ is the count of three-pack-only hero orders.

The single-pack row is still valuable in the baseline. It helps distinguish three possible outcomes after removal:

1. Former single-pack customers shifted to the two-pack.
2. Former single-pack customers shifted to the three-pack.
3. Some former single-pack customers stopped buying, which may show up as weaker conversion or fewer hero-product orders.

Define automatic-discount mix without guessing

Your automatic offers at ₹799 and ₹1,299 make discount mix central to the analysis. Record:

$$\text{Automatic discount mix}=\frac{\text{Orders with an automatic discount}}{O}\times 100$$

Count an order only when its discount application is explicitly identified as an automatic discount in your order-level data. If possible, break the count out by automatic discount name or threshold, such as the 5% and 15% offers.

Do not assume that a blank discount-code field means an automatic discount was applied. Blank can also mean no discount, a manual adjustment, or a reporting limitation. Verify the available discount fields against a few actual orders before classifying the full export.

Your revenue field should already reflect the discount. The separate automatic-discount rows answer a different question: how much of the apparent AOV improvement came from shoppers being pushed into discounted thresholds?

---

4. Read the scorecard as a decision tool, not a vanity report

At your weekly order volume, seven days should produce a useful directional read. Still, do not conclude that the pack removal alone “caused” the change unless the events log is quiet and the traffic mix is reasonably stable. The A/B test planned later in the course will provide a cleaner causal read.

For now, use these patterns.

What you see	What it probably means	Immediate implication
AOV rises, conversion is steady, revenue per session rises, and three-pack mix rises	Promising movement toward the larger pack without an obvious conversion cost	Proceed to profitability analysis
AOV rises but revenue per session falls	Larger baskets did not compensate for lost conversion	Do not promote the three-pack yet
Three-pack share rises, but automatic-discount mix rises sharply too	Shoppers may be crossing discount thresholds rather than accepting the larger pack at healthy economics	Check contribution carefully before scaling
AOV rises but two-pack share absorbs almost all former single-pack demand	The minimum pack may be doing the work; the three-pack is not yet proven as the preferred upgrade	Focus later merchandising on a clearer three-pack value proposition
Hero-product orders fall materially and conversion falls	Removing the entry option may have introduced too much purchase friction	Treat the change as a possible conversion trade-off
Revenue per session, conversion, and AOV all improve	Strongest commercial signal, subject to the next lesson’s profit gate	Candidate for a more deliberate three-pack merchandising test

Finish by saving:

• The two raw date ranges
• The Shopify report screenshots or exports
• The order/line-item export used for pack classifications
• The completed scorecard
• The short events log
• A note naming the exact revenue field and discount-identification field used

This makes the result auditable. It also prevents a common problem in fast ecommerce analysis: revisiting the dashboard later, seeing different rolling periods, and being unable to reconstruct the decision.

---

The central takeaway is that an AOV lift is only the first signal. A worthwhile result should show a defensible increase in revenue per session, an acceptable conversion outcome, a visible shift in the two-pack versus three-pack mix, and no hidden dependence on deeper automatic discounts.

Next, you will take the scorecard’s pack and discount data and calculate the incremental contribution of a three-pack order versus a two-pack order after product cost, fulfillment, shipping subsidy, payment fees, and discounts. That will establish the profitability gate before you give the three-pack more prominence on the product page.