---- ### 1. What is the cold start problem? >What came first? the chicken or the egg? You need to understand that users bring more users but how do you get your first set of users? That's the cold start problem to solve. Social products are hard to emulate because with every new user you add, the potential network links in the network increases quadratically. How? Every new user you add, they can potentially interact with every new user in the original set. So, potential network links scale by O(N²) while your users are scaling by O(N) ![[Pasted image 20241221171839.png]] ### 2. The Three Problems? There are three questions you need to solve for: 1. What do users do on the platform? 2. Why will users invited other users on the platform? 3. What value do users get ultimately? It usually helps by constraining the problem. Think like this, social networks are like living organisms and you can't expect to end up with a self sustaining network right away. You need to solve for it, in the following order. #### 2.1 Content The platform needs to have a library of content even before the first user lands on your app. There's a reason why artists who achieve long term success are the ones who have built a library of content before they go mainstream. This allows new fans to consume more content. Imagine, being a one hit wonder and having nothing else in your catalogue. Depending on your app, a user needs to have at least a week's worth content to consume or else. #### 2.2 Engagement Engagement is the unit that you need to survive. You need to innovate on some interaction unit that gives users short bursts of delight. For many dating apps, it is the novelty of finding someone new with every swipe while for e-commerce apps, it is the moment when they receive their first order. Mathematically, the interaction unit you design has to: > $maximize$, $Z = β(D) - α(E)$ $where,$ - $Z$ = Overall engagement score to be maximized - $E$ = User effort function - $D$ = User delight function - $α$ = Perceived effort weight coefficient $(0 < α ≤ 1)$ - $β$ = Perceived delight weight coefficient $(0 < β ≤ 1)$ User effort function is defined as: > $E = Σ(w_i × t_i + c_i)$ Where: - $w_i$ = Cognitive load weight for task $i$ - $t_i$ = Time required for task $i$ - $c_i$ = Number of clicks/interactions required for task $i$ User delight function is defined as: > $D = Σ(r_i × v_i × p_i)$ Where: - $r_i$ = Relevance score of the interaction unit $i$ , note that, $r_i∈[0,1]$ - $v_i$ = Value delivered by interaction $i$ - $p_i$ = Personalization factor for user segment $i$ In some ways, you can make appropriate trade offs and maximize $Z$. #### 2.3 Go To Market This is the hardest thing to solve for. No two apps do it the same way and here your ingenuity will help you a lot. Also, this answer depends heavily on the target demographic and where are they present physically and digitally. Here's my advice: ==Do things that don't scale.== Here's Paul Graham's essay >> ["Do Things that don't scale"](https://paulgraham.com/ds.html) They generally tend to be hard and you learn so much about the search space that the information gain is actually an advantage. Plus, it helps you validate and invalidate ideas very fast.