Introduction

Nike By You allows consumers to customize Nike shoes. The purpose of this study is to help teams explore the characteristics of the Nike By You buyers, and analyze how Nike By You influence the customer behaviors.

This study focuses on five main aspects:

  1. The general exploration on NBY member and order percentage
  2. The analysis and comparison of NBY members and non-NBY members
  3. New NBY abtesting (before and after 12-10)
  4. HOI NBY modeling
  5. Online NBY modeling

Data

1. Inline Transaction Data 1

ALL transactions occurred in China. Time period is from 01/01/2021 to 12/30/2021.

2. Member Data 2

Member data includes information about the members who made inline purchases in stores, including register_date, UPMID, and so on.

3. Member Segment 3

Membership segments were created by the member science team to better understand our existing members and to drive direct engagement of members through targeting.

4. Scalper Data 4

Scalper data was based on Yunhai’s Scalper Detection Machine, including scalpers’ UPMID, and so on.

The general exploration on NBY member and order percentage

1. The general analysis

NBY order percentage
Type NBY_order_quantity Total_order_quantity NBY_order_.
HOI 2887 210738 1.37%
Online 265962 5611055 4.74%
Period: 2022-01-01 ~ 2022-12-30

The online NBY order rate is 4.74%, which is 346% higher than the HBI NBY order rate, 1.37%. We can tell that consumers are more likely to buy NBY products online.

NBY member and non-NBY member quantity comparison
Type NBY_member non_NBY_member Percentage
HOI 2637 147568 2%
Online 237185 1998271 12%
Period: 2022-01-01 ~ 2022-12-30

Likewise, the online platform shows a much higher NBY member percentage than that of HOI, which also proves that online consumers have more interests in NBY products.

The NBY category distribution
Period: 2022-01-01 ~ 2022-12-30

As for NBY member segments, there’re more than 50% of the NBY members are classified as Lifestyle. So it’s speculated that lifestyle products are most popular among NBY products.

The new member percentage
Period: 2022-01-01 ~ 2022-12-30

It is found that NBY plays an important row to acquire new customers. About 22.37% of the NBY members are new registered members, which means they started to register Nike memberships when they purchase NBY.

2. The analysis and comparison of NBY members and non-NBY members

HOI NBY member and non-NBY member gender distribution comparison

Period: 2022-01-01 ~ 2022-12-30

From the bar chart above, there’s no significant discrepancy on gender distribution between NBY members and non-NBY members for HOI.

Online NBY member and non-NBY member gender distribution comparison
Period: 2022-01-01 ~ 2022-12-30

As for the online gender distribution comparison, the percentage of female NBY members is 62% larger than that of the non-NBY members, which is different from the HOI gender distribution.

Inline NBY member and non-NBY member age distribution comparison
Period: 2022-01-01 ~ 2022-12-30

From the HOI age distribution, NBY members show the younger age trend comparing with non-NBY members.

Online NBY member and non-NBY member age distribution comparison
Period: 2022-01-01 ~ 2022-12-30

Similarly, from the online age distribution, NBY members also show the younger age trend comparing with non-NBY members.

Inline NBY member and non-NBY member segment distribution comparison
Period: 2022-01-01 ~ 2022-12-30

The NBY members show the much stronger interests in Lifestyle than the non-NBY members, which is aligned with the conclusion we got from member segments distribution that lifestyle products are most popular to NBY members. Besides, NBY members are less likely to be classified as Sneaker head.

Online NBY member and non-NBY member segment distribution comparison

Period: 2022-01-01 ~ 2022-12-30

For the online NBY member segments, “Family” takes the largest proportion and is much higher than that of the non-NBY members. It is speculated that it’s because the family buyers are more likely to shop online and buy Nike products for their family and friends, and the characteristic of customization of NBY is more likely to match their interests. Besides, The proportions of Performance and Sneaker head of NBY members are much lower than that of non-NBY members.

3. New NBY abtesting (before and after 12-10)

Post NBY Purchase AUR = Average Unit Revenue AFTER Their NBY Experience
Post NBY Purchase UPT = Average Unit Per Transaction AFTER Their NBY Experience.
Post NBY Purchase ADPT = Average Demand Per Transaction AFTER Their NBY Experience.Percent of transactions containing a mixture of FTW/APP/EQ AFTER NBY experiences. This helps us to see if NBY member purchase more head-to-toe combination than regular consumer.
Post NBY Purchase Product Design – Lifestyle = Percent of Lifestyle items purchased AFTER NBY experience
Repurchase within 30 days = Percent of NBY member that came back to HOI and made a purchase within 30 days AFTER the NBY experience.

From the Abtesting data, we can see that NBY Member’s tendency to purchase Lifestyle product is much higher than control group. After the new NBY, we see that NBY member’s tendency to repurchase is much higher than the control group. Besides, NBY member shows the lower UPT/ADPT values comparing with the control group before new NBY, but the UPT/ADPT value gaps are gone after the new NBY.

5. HOI NBY modeling

From the EDA part, we have conclusions as shown below:
1. NBY members show the younger age trend comparing with non-NBY members. NBY members have much higher percentage (8%) on age group, ‘0-17’, which is four times higher than that of non-NBY members.
2. The NBY members show the much stronger interests in Lifestyle and less interests in sneaker head than the non-NBY members.
Therefore, we include the terms ‘0-17’, ‘Lifestyle’, and ‘SNKRHEAD’ in the logistic model.

Variable Log_Odds.β.
Lifestyle 2.4903539
SNKRHEAD 0.7963516
age_0_17 4.8202517
age_18_24 1.4512485

ROC Plot:

The model accuracy on the test set (25% of the dataset) is 0.5955, and the AUC value is 0.6313.

5. Online Modeling

  1. Source: Nike Inline Order Line↩︎

  2. Source: Nike Membership Data↩︎

  3. Source: Nike member science team↩︎

  4. Based on the scalper detection machine built by Yunhai Zhang ()↩︎