Introduction

365 Experience is an important component of Live Concept stores. It emphasizes the concept of Community by gathering people together for sports-related activities. Live stores in Beijing and Shanghai have held many events to serve our members. The purpose of this study is to help teams to have a better understanding of how the events influence our business.

This study focuses on two main aspects:

  • Live Sports KPI
  • 365 Experience Stats

Data

1. Event Data 1

Event data includes the date, event ID, event name, and the information of people who registered. The time period is from 03/01/2021 to 08/31/2021.

2. Member Data 2

Member data includes information about the member who registered for the events and members who made inline purchases in Live Concept stores, including gender, UPMID, and so on.

3. Beijing and Shanghai Live inline Transaction Data 3

All transactions occurred in Beijing and Shanghai Live since opening.

4. Online Transaction Data 4

Online transaction data used includes all online transactions from all sources, including Nike.com, App, TMALL, etc. The time period is from 03/01/2021 to 09/19/2021.

5. Scalper Data 5

Scalpers were selected based on Yunhai’s Scalper Detection Machine. Scalper data includes scalpers’ UPMID.

6. Shipping Address Data 6

All geospatial relationships between our customer and Live stores use online shipping address. Shipping addresses are geo-coded, and distances between store and customers are calculated.

Live Sports KPI

Purpose

  • Post Event First Time Purchase Ratio: Test the effectiveness of our event to turn non-buyer into first-time purchase for live
  • Post Event DPM: Test the effectiveness of our event on consumer stickiness and loyalty
  • Post Event First Time Women with Sports & Fitness Purchase: Test the effectiveness of our event to turn the target group from non-buyer to first-time buyer

Interpretation

  • Signed-up Members: Nike members who registered for the 365 experience events
  • Showed-up Members: Nike members who registered for the 365 experience Events, and fulfilled onsite check-in

According to the Event Data from March to August, we calculated the KPIs based on the transaction of the showed-up members:

Store Post Event First Time Purchase Ratio Post Event DPM Post Event First Time Women with Sports & Fitness Purchase
SH 3.78% 78.9 1.03%
BJ 3.85% 170.7 1.92%

Taking Shanghai Live as an example.

  • 3.78% of the showed-up members who are non-buyers (never spent in the Shanghai Live), turned into first-time buyers (made the first purchase in Live within 7 days after participating in an event).
  • Within 30 days after participating in an event, the showed-up members spent $78.9 through all platforms on average.
  • 1.03% of the showed-up members who are non-buyers turned into first-time buyers, and they are female and they bought Sports & Fitness products.

365 Experience Stats

1. Number of Events

Event Type Mar Apr May Jun Jul Aug Total
SH-NRC 4 0 4 7 3 4 22
SH-Yoga 3 0 0 7 3 3 16
SH-Special 1 0 2 7 10 3 23
BJ-NRC 1 0 0 3 3 0 7
BJ-Yoga 1 0 2 2 1 1 7
BJ-Special 0 0 1 0 2 0 3

The above table shows the number of events held in Beijing and Shanghai. Totally, Beijing has held 17 events, Shanghai has held 61 events. Due to the COVID-19 or other reasons, events were suspended on a large scale in April. During the first three months, the events were held without regularity. Like in May, there wasn’t any yoga event in Shanghai, consumers who are interested in yoga events may lose stickiness. If the events can be held continuously, it may have better results. Not considering uncontrollable factors, better cooperation with vendors may be the key for the successful and continuous holding of events.


2. Participation Rate

The overall participation rate of BJ events is 61%, of SH events is 56%. These numbers can guide the setting of the maximum number of applicants. For example, if we prepare to hold an event with 100 showed-up members, we may allow 120 members to directly register, as we know some of them may not show up.


3. Number of Members

Store Signed-up Member Scalper Employee Female Male
SH 679 13 0 303 311
BJ 348 3 0 152 180

With 61 events held in Shanghai and 17 in Beijing, Shanghai has 679 distinct members who registered for the events, Beijing has 348. Shanghai has a higher proportion of female members, close to 49%, 46% in Beijing. We may consider setting some events specially designed for females to have more target consumers.

We were surprised to find that scalpers 7 also participated in the events.

Based on the purchase record in Live Concept Stores, we classify the showed-up members into four groups.

  • First Time Buyer: The member who made the first purchase in Live within 7 days after participating in an event
  • Former Buyer: The member who’s already consumed in Live before participating in an event
  • Non 7Days Buyer: The member who made the first purchase in Live after 7 days after participating in an event
  • Non Buyer: The member who never consumed in Live

The following two pie charts show the reasons for the low KPIs.



Most of the showed-up members of the event don’t spend in the Live Concept Stores.


4. Monthly DPM

Even the showed-up members don’t spend much in the Live stores. We found the signed-up members are different from general members who consume in Live stores. This chart shows the Demand Per Member in the ecosystem.


After excluding the scalpers, we found that, for both Shanghai and Beijing, the signed-up members have a higher DPM in the ecosystem than general members. For Shanghai, 17% higher on average; for Beijing, 60% higher on average. Considering the whole business, the signed-up members spend more and have a bigger contribution than general members, and they shows high brand loyalty and stickiness.


5. Monthly Order Frequency

We can find a similar result for monthly order frequency. The signed-up members of Beijing events buy more frequently, 31% higher than the general members. This is another proof of their high brand loyalty and stickiness.

For Shanghai, the numbers are close.


6. Purchasing Behavior in the Ecosystem

Let’s have a closer look at the signed-up members’ purchasing behavior in the ecosystem within 30 days after signing up for any events. Scalpers’ data has been excluded.

Platform 30-day demand of BJ Live event signed-up members 30-day demand of SH Live event signed-up members Total % of ecosystem
SNKRSAPP 21671 29407 51078 46%
Retail Store 6849 17485 24335 22%
NIKEAPP 9072 10026 19098 17%
tmall 6058 8802 14860 13%
WECHAT 308 1033 1341 1%
NIKEWEB 78 520 598 1%

Besides Live Concept stores, the signed-up members tend to spend more through SNKRS APP(46%) and other retail stores(22%).

Top 5 Category 30-day demand of BJ Live event signed-up members 30-day demand of SH Live event signed-up members Total % of ecosystem
NSW BASKETBALL 13828 23088 36916 33%
JORDAN BRAND 14037 20639 34676 31%
NSW RUNNING 2776 4826 7602 7%
NIKE BASKETBALL 3991 3427 7419 7%
NSW OTHER SPORTS 2225 4540 6765 6%

Products of NSW Basketball and Jordan Brand are very popular, account for 64% of the total demand in the ecosystem.


Top 3 most popular STYLE in SH

The popular STYLEs in BJ and SH are similar. Most of them are high-heat items.

In summary, the signed-up members were looking for the Jordan and Basketball products which the Live stores don’t provide much. And more releases of high-heat sneakers could be a huge attraction to consumers.


Geospatial Analysis

Shanghai Live

Data Average Distance to Store Percent within 5KM
Shanghai Live Customers 11.3KM 26.6%
Event Participants 9.2KM 37.3%

Event Participants live/work much closer than general Live customers, and the percentage of participants within 5km of the store is much higher, indicating that our event is gathering customers from the neighborhood. Many office buildings and residential areas within walking distance of the store are top origins of our event participants.

Beijing Live

Data Average Distance to Store Percent within 5KM
Beijing Live Customers 13.3KM 26.6%
Event Participants 12.2KM 23%

Beijing Live is very different comparing to Shanghai Live. The distances between store and customers’ origin are much further, and there is almost no difference between the distance of event participants and general customers. Percent within 5KM is even lower for event participants comparing to store general. However, there is still opportunities from the office buildings near the store, as we see a number of origins are within walking distance.

Conclusion

Although 365 experience events didn’t directly generate much revenue for Live Concept Stores, the signed-up members tend to spend more than general members in the ecosystem. They are potential high-value members and in-store buyers with high brand loyalty and stickiness. With proper plans, we can definitely expect better KPIs.

Suggestions

  • Look for the targeted communication, design new events for attracting new targeted members.
  • Set some events specially designed for females.
  • Set special “next clicks” for the showed-up members to make them stay connected.

Acknowledgment

Thank Yunhai Zhang for the supervision and guidance.

  1. Source: recorded by event vendors, owned by Minhua Yu () and Vivi Wang ()↩︎

  2. Source: Nike Membership Data↩︎

  3. Source: Nike Inline Order Line↩︎

  4. Source: Nike Digital Order Line↩︎

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

  6. Source: Baozun shipping address from all online platforms.↩︎

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