The experiments behind the choices.

Follow the money, from assumptions to result

Each round has 1,000 sample shoppers and eight preset conversion rates: one for each combination of three choices. Buyers equal the shopper count multiplied by the selected rate, rounded to a whole person. Each buyer purchases one item.

Money made after item costs equals buyers × (price after discount − item cost). For example, 36 × ($48 − $18) = $1,080. The 36 buyers come from an assumed 3.6% rate, not an observed campaign.

Both players use the same rates and calculation. myuser.ai starts with the highest-scoring combination in this exercise; choosing it ties the round. This game rule does not establish that AI always makes the best decision in a real store.

The totals exclude plan fees, sending fees, shipping, payment fees, taxes, returns and other costs. They also do not subtract purchases that would have happened without the email.

A discount has a calculable hurdle

In the $48 face-wash example, the item costs $18. A full-price sale makes $30 after that cost. With 10% off, the customer pays $43.20 and the same item makes $25.20.

Matching the full-price total requires $30 ÷ $25.20 = about 1.1905 times as many buyers: about 19.05% more. With 15% off, $40.80 − $18 leaves $22.80, requiring about 31.58% more buyers. These percentages are arithmetic, not predictions that a discount will attract those buyers.

Whole orders matter. To match 36 full-price buyers, the 10%-off offer needs at least 43 buyers: 43 × $25.20 = $1,083.60. This comparison holds the item cost fixed and excludes other costs.

What supports the choices?

Some choices follow explicit facts in the scenario: a guide was promised, a bottle is estimated to last 30 days, or an offer expires on Sunday. A 30-day supply minus five days of shipping gives a day-25 reminder, counting from delivery when this shopper starts using it. That assumes a prompt reorder; it does not prove how quickly an individual shopper uses the product.

For links, W3C guidance says their purpose should be understandable from the wording or context. “Back to my cart” and “Open my free guide” make the intended destination clear. That supports understandable links, not a particular conversion rate.

A recent product view is a useful clue, but it does not prove that a product-specific email always beats a broader message. The game’s preferred choices and sales differences teach the scenario’s logic; they are not independent experimental findings.

Source: W3C WAI — Understanding WCAG 2.4.4: Link Purpose (In Context)

38.3% higher purchase rate with a 24-hour cart reminder

The finding: at a Japanese fashion retailer, 8.3% of shoppers bought after a reminder sent at 24 hours, compared with 6.0% in the corresponding no-email control. Li, Luo, Lu and Moriguchi measured purchases over the following month. Their email experiment included 33,234 shoppers across all timing groups.

That is +2.3 percentage points (p < .01). myuser.ai’s relative-lift calculation is (8.3 ÷ 6.0 − 1) × 100 = 38.3%. The reminder repeated information about the abandoned product; it did not offer a discount. See Figure 2A and page 8 of the author copy.

Timing mattered in both directions: at one hour, 13.4% bought with a reminder versus 16.7% without. Each timing group has its own no-email control, so those rates are not a direct comparison of sending at one hour versus 24 hours.

Our application: the game illustrates waiting a day because this study gives a concrete reason to test reminder timing. It does not establish tomorrow as the best choice for every store. The external 38.3% result supplies none of the game’s preset conversion rates or simulated dollar advantage.

Sources: Li et al. — The Double-Edged Effects of E-Commerce Cart Retargeting (Journal of Marketing) · Li et al. — Author-uploaded article (PDF), Figure 2A and page 8 · The authors’ free explanation — American Marketing Association

How to measure a real improvement

Randomly assign comparable eligible shoppers to versions of the same message. Change the feature you want to test, such as the destination or send time. Keep the other conditions comparable. Count recipients and purchases over the same period for each group.

To estimate additional sales caused by sending, include an appropriate group that does not receive that message. An order attributed to an email may still have happened without it. Include discounts, item costs and the other costs relevant to your store.

Report the sample size, absolute rates, time window and uncertainty with any percentage increase. A game result or a published result from another retailer does not substitute for that measurement.

A name in the subject: about 20% more opens

The finding: Sahni, Wheeler and Chintagunta (Marketing Science, 2018) added the recipient’s name to an email subject and compared it with a subject without the name. In their main randomized experiment, the open rate rose from 9.05% to 10.80%. The authors report an approximately 20% relative increase; the displayed, rounded rates give 19.3%. The absolute difference is 1.75 percentage points.

Our application: use a specific, tested example to explain what personalization can mean. The welcome round includes Maya’s name in both subject choices. Its score then compares the promise, timing and destination—different choices from the one this experiment tested.

The reported lift concerns opens, not 20% more purchases. It does not establish a general benefit from AI writing or every personalized message, and it does not supply the game’s conversion rates. This is myuser.ai’s analysis of the external finding, not another experiment.

Sources: Sahni, Wheeler & Chintagunta (2018) — Personalization in Email Marketing, Stanford research record · Personalization in Email Marketing — published paper, Marketing Science