The Entertainment Value Curve - Awesome post by @ravi_mehta (Former CPO Tinder, FB, TripAdvisor) on product strategy in the social space and why TikTok is on 🔥 and Quibi is 📉
1/ Social content products are driven by entertainment value. Entertainment value is a combo of:
a: Social Value = the personal connection the viewer has with the content.
b: Production Value = quality of content relative to genre.
This creates The Entertainment Value Curve
2/ Different products live along this curve.
Snapchat on one end of the spectrum. Production value is low, but social value is very high with personal connection to everyone making the content.
Netflix on the other end. Production value high, social value low.
3/ You can measure social value with creation participation rate among users. 60% of users on Snapchat create per day. 2.5% of users on Youtube have every created....
4/ You can measure production value with a content distribution curve. High production value have views concentrated on a smaller number of pieces of content. Vice versa for low production value.
5/ "The problem with Quibi is the product isn't optimized for the Entertainment Value Curve. Quibi added format, length, viewing constraints that make it hard to equal production value of Netflix content, but did not supplement those constraints with increased social value."
6/ "The solitary nature of Quibi’s experience is reflected in the app’s design. There are no signals that anyone else in the world is watching. Twitch, YouTube, Instagram, and TikTok are brimming with activity. Reactions, comments, and messages make those apps feel alive."
7/ The TikTok product is specifically built for sharing:
a. The infinite loop facilitates passing the phone around and sharing while still playing in the background.
b. The default experience (right hand rail) is dedicated to social...
8/
c. They break the wall down through its algorithm to reward creation increasing creation participation rate.
d. They encourage mimicry increasing creation participation rate even further.
9/ All of this leads to nailing the social content engagement loop of Creation feeding Consumption feeding Conversation feeding Creation.
I've rarely found a company that doesn't think their marketing tech stack is a mess. Austin Hay of Ramp has published a few amazing artifacts to help ↴
Austin Hay is Head of Marketing Technology at Ramp and has advised companies like Notion, Warby Parker, Krisp, and more.
He's published a few amazing Artifacts on @reforge . You can also check out his new Marketing Technology course. 🔗 Links in the thread.
🗺️ Marketing Tech Architecture Diagram 🗺️
Austin says "You really need to offload your mental model of the [Martech] system on paper so other people can see what you’re seeing and get on the same page with you in order to do work."
There are four parts you need to illustrate:
1️⃣ The system's data inputs
2️⃣ Data storage
3️⃣ Data capabilities
4️⃣ Data federation
🔭 Technical Strategy Planning Doc 🔭
Martech is often one of this things that is important to everyone, but owned by no one. You need to get buy-in among execs and a lot of teams which can be complicated.
Austin has made his strategy planning doc that he used to pave a roadmap of the martech stack. He says there are four levers you need to plan for:
➿ Good Redundancy vs Bad Redundancy
🪢 Loose Coupling vs Tight Coupling
🔀 Interoperability vs Incompatibility
⚡ Focused vs Unfocused
💵 Ad Platform Conversion Optimization 💵
Building, assessing and improving a Martech stack is not enough. It’s critical that Martech is adding value to it’s cross-functional stakeholders for it to continue to receive investment.
Austin walks through a project to implement some advanced infrastructure to improve efficiency of paid acquisition across multiple channels.
In this project they were able to implement infrastructure that allowed them to focus their paid programs around bottom of funnel performance across six different channels.
@onecaseman and @far33d previously wrote that all Product Management work is not created equally. There are distinct types requiring different problem solving approaches, skills, and more:
😃 I'm excited to announce @reforge has raised $21M from @andrewchen and @illscience at @a16z along with a group of product, eng, marketing, data, and design leaders...
🏫 Traditional education institutions have tried copying and pasting their university online for professionals and it hasn't worked. For the past 4 years, Reforge has been rebuilding education for professionals from the ground up ...
💪 Our participants have grown into executive leaders at companies like Roblox, Plaid, Github, Adobe, Dropbox, Shopify, Credit Karma, Google, and more and we are excited to be building the place where top tech talent comes to scale. Some history...
1/ Career decisions about your next role/opp are the most impactful decisions you might make in your career. As an operator, all of your eggs are in one basket at a time, and we get a limited number of swings at the plate. So getting good at these decisions is important...
2/ At FB, Bangaly managed Rotational PM's. At the end of a rotation, he'd have the same convo: How are you going to choose your next rotation/role?
As a result, Bangaly got a lot of reps guiding this type of decision and created a framework to help with it...
This is not an exhaustive list by any means, but these are 6 mistakes I see in defining metrics over and over....
1 - Metrics before Strategy
Your metrics are a reflection of your strategy. They help answer, is the strategy working? Metrics without strategy is looking at a bunch of random numbers. Define the strategy before you define your metrics.
2a - Definition Is More Important Than A Dashboard
People focus on "building a dashboard." 10X more important is choosing which metrics are important and defining those metrics well. Defining is more complicated than people think...
Almost every team I talk to says "Our data is mess!"
@crystalwidjaja (Fmr SVP BI & Growth at Gojek and @reforge Partner) wrote an excellent piece on the real root causes of analytics failures, and a step by step process on how she thinks about it: reforge.com/blog/why-most-…
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First, if you don't know @crystalwidjaja and the story of Gojek, it's impressive. Crystal joined at 30 people and helped them scale to complete more daily food orders than Grubhub, Uber Eats, and DoorDash combined, and more trips than Lyft per day 🤯
There are a lot of symptoms of bad analytics/data:
1. Lack of shared language 2. Slow transfer of knowledge 3. Lack of trust 4. Inability to act on data quickly