ARTEA (A)
Alex Campbel, co-founder and CEO of Artea, an online retailer specialized in selling handmade clothing
and accessories, was reflecting on the results of an A/B test that the data team carried out over the last two
months. To improve conversation rates, she decided to test what would happen if they start sending coupons to
consumers. However, she wanted to target these coupons carefully, in order to prevent consumers from
becoming deal shoppers and to ensure higher margins. To decide whether those coupons would be beneficial
and which customers should be targeted, the team ran an A/B test with the 5,000 users who had visited the
website in the last two months but had not made a transaction since. The team randomly selected half of those
customers and sent them a coupon that offered a 20% discount on the next purchase. The coupon was nontransferable to other users and was valid for one month. One month later, the team collected the number of
transactions (and total expenditure) made by all of the relevant users to evaluate the results of the experiment.
Based on the experimental data, the team analyzed the impact of receiving a coupon on future revenues. In
their analysis, they included other marketing variables available in the company’s database. These were:
‘channel of acquisition’ (including Google, Instagram, Facebook, Referral and Other), date in which the
customer last visited the website, number of past purchases, amount spent (in USD) in the last purchase, time
spent browsing the website during the last visit, and whether the customer had left any product(s) in the
shopping cart.
The analysis revealed that, on average, receiving a coupon increased the number of transactions. However,
because the coupons offered a discount off the total purchase, the revenue generated was not higher than that
obtained from customers who did not receive the coupons. Interestingly, the team found out that customers who
had arrived to the website from Facebook or Instagram, and those that previously left products in shopping
carts were most responsive to the coupons, increasing not only the number of transactions, but also their
revenue. Specifically, the team predicted that if the coupon was sent to the 15% of customers with those
characteristics (i.e., customers who had left a product in the shopping cart and who had been acquired via
Facebook or Instagram), revenues would increase by almost 5%. Encouraged by these results, the team was
getting ready to run the next marketing campaign where they would send the coupon only to these types of
users.
Based on the analysis of A/B test the marketing team develops following targeting strategies and asks the data analytics
team to provide findings on expected transactions and revenues under each targeting strategy for 6,000 customers
participating in the new campaign based on their behaviours.

Option 1b: everyone gets the coupon

Option 1c: random targeting

Option 2a: target customers from Facebook (channel 2)

Option 2b: target customers from Instagram (channel 3)

Option 2c: target customers in channels 2 and 3

Option 3a: target customers with product(s) in the shopping cart

Option 3b: target customers with product(s) in the shopping cart and who came from channels 2
and 3

Option 3c: target customers with product(s) in the shopping cart or who came from channels 2
and 3

Option 4: target customers with no purchase history
The following figures show the expected number of transactions and the expected revenue under each of the
targeting policies.
Artea (B)
Campbel wanted to run more analyses before sending out coupons. Campbel asked the analytics team to
evaluate the demographic information for the 6,000 customers that visited Artea in the last two months.
Specifically, she asked the team to evaluate whether customers who identified themselves as non-majority
(minority and non-malea) customers were systematically different than the rest of the customers in terms of
their past behavior. The team evaluated the average transaction patterns across groups, as well as their channel
of acquisition (See Figures below).
The plots below compare demographic information among targeted users for most common policies. The
analytics team computed the number of customers identified as “Non-male” and “Minority” in each of the
targeting policies and compare it with the average in the data (represented by the vertical dashed line).
.
While Artea’s inclusive surveys allowed users to identify themselves as any race, ethnicity, or gender, and
also included also a “prefer not to answer”, and a self-specified response, and these data include higher-level
categorization of minority/majority and male/non-male
a
Note:
Each figure shows the proportion of customers of a specific demographic group who belong in each
category. Across all figures, the horizontal (dashed) line represents the average proportion of
customers of that specific demographic group in the whole customer base. For example, the figure in
row 2 column 1 shows the proportion of non-male customers in each channel of acquisition. The
horizontal like takes value 0.58 because the proportion of non-male customers in the customer base is
58%.
X-axis labels indicate: channel of acquisition (in column 1), as follows: 1=Google; 2=Facebook;
3=Instagram; 4=Referral; 5=Other; purchase indicator (0=customers without any previous purchases,
1= customers with at least one previous purchase); and shopping cart indicator (0=customers with no
abandoned shopping carts, 1=customers who abandoned their shopping cart previously).