Carrying Capacity - 이승건 대표의 PO 세션 영상

https://youtu.be/tcrr2QiXt9M 

Carrying Capacity

Q1.

You notice that your power users all have taken some action (e.g. filled out their profile) so you try to encourage all users to fill out their profile to get them more hooked on your product. Does this actually help?

 

-> Without this model its easy to mislead yourself into thinking it helps. You will probably be able to increase the metric but it may just weaken the correlation with power users. However, with this model you just watch your loss % each day and if it doesn't change then you haven't had any net impact on retention.

 

Q2.

You have 24 hours of downtime, the next day you come back up your traffic is down. Will this have a long-term effect you need to worry about?

 

-> Even if your traffic is lowered for a few days, all you have to do is measure your new visitors per day & lost customers per day and you'll see if your carrying capacity has changed. If it has not changed then you don't have to worry, you know your traffic will rise back up to your carrying capacity.

 

Q3. 

You have 100K uniques per day and so does your competitor, but are these 100K people who come back everyday or 700K people who each come once per week? Does it matter?

 

-> If you're incorrectly caught up in # of unique visitors per day then this does seem like an important thing. And in fact, if you realize your visitors are not returning as often as your competitors you may even be tempted to spam them regularly because this will increase # of unique visitors each day and can give you the illusion of improving things. In reality a move like this would probably increase your % lost each day and hurt your carrying capacity but you wouldn't notice this for a while because the increased # of uniques each day would mask the harm.

 

However, with the model your emphasis on # of customers not # of visitors. You'll quickly realize that you don't care how frequently people visit as a primary factor; if it doesn't impact # of new customers per day or % of lost per day then you haven't actually helped your product.

 

Q4.

You turn on a new advertising campaign and see your # of unique visitors per day start to increase, you assume that this will continue increasing so long as you keep the ads running, right?

 

-> Nope, you'll level off once you've reached your new carrying capacity.

 

Q5.

You start having email deliverability problems (or Facebook turns off notifications) so you can't notify users of new activity on the site. The # of unique visitors decreases slightly but you're not too worried, shoud you be?

 

-> This is similar to question 3, it may or may not be important but you'll quickly be able to tell by focusing on the core numbers. Increasing # of unique visitors per day does not necessarily lead to more total customers.

데이터 그로스 모델링

1. Total Customer는 New Customer Today와 Lost Customers Today, 단 두 가지 요소만 영향을 미친다.

2. Customer에 대한 정의

    A. active를 어떻게 정의하나.

        i. 95% 이상의 Visitor가 꼭 하게 되는 활동

        ii. Page by Page, Repetable하고 Meaningful한 Action인가?

 

    B. Churn은 어떻게 정의하나?
        i. 얼마를 안써야 안오는 거라고 정의할까? 1일? 4일?

        ii. 상식적으로 이 정도를 안썼으면 Loss 될 것 같다를 정한다 (나중에 바꾸면 안됨)

            - ex. 샤잠 : 한 달에 한 번 쓰는 앱. 3개월을 Churn으로 정의

            - 토스 송금은? 30%가 이전달에 온 적이 없는 유저

Carrying Capacity = # Of New Daily Customers / % Customers You Lost Each Day

 

제품의 본질적인 체력(마케팅, 광고가 제외된)

Q1 : 현재 MAU 10만, Carrying Capacity 75만, 광고를 해야할까?

Q2 : 현재 MAU 70만, Carrying Capacity 75만, 광고를 해야할까?

Q3 : 현재 MAU 100만, Carrying Capcity 75만, MAU는 떨어질까?

마케팅 활동을 통해 일시적으로 Inflow Boosting은 가능하지만, 결국 광고를 끄면 그대로 다시 주저앉게 됩니다. Carrying Capacity가 변하지 않았기 때문이죠.

 

결국 근본적인 Carrying Capacity의 향상은 제품 개선을 통한 Inflow와 Retention의 향상, Churn 감소 외에는 방법이 없고, 이것은 마케팅 활동으로는 바꿀 수 없습니다.

 

Carrying Capacity는 '내 서비스가 도달할 최종적인 유저수'라고 할 수 있습니다. 이 값은 서비스를 운영하고 3~6개월 안에 알 수 있기 때문에, 제품의 성장을 미리 예측할 수 있다는 점에서 매우 중요합니다.

 

결국 C.C에 영향을 주는 것은 Inflow(=New User + Ressurrection)과 Churn Rate 두 가지에 의해서 결정된다는 것이죠.

 

여기서 또 고려해야 할 점은

- 서비스의 Organic Inflow는 서비스의 특성에 따라 결정되며 일정한 숫자로 유지된다.

- Churn Rate (1 - Retention Rate)은 전체 유저에 대해 일정한 비율로 유지된다.