当前位置:网站首页>Reading notes of growth hacker
Reading notes of growth hacker
2022-07-05 11:44:00 【Yuanhao】
In the past year, I have been doing a project from 0 Starting projects , The number of users seems to have increased a lot , But there are also many problems . Sometimes there are vague guesses about the cause of the problem , Will also think about how to solve , But most of them are not systematic and have no chance to verify .
I feel very kind when reading this book , Because I wrote about many problems I have encountered ; At the same time, I learned a lot , Because it is not only systematic , It also extends to some Psychology 、 Behavioural things to explain the reasons behind the phenomenon . It's like a mirror , You know what you look like without a mirror , But you can still find many problems by looking in the mirror . This article records some gains and thoughts after reading .
Data driven
This book emphasizes ab Experiment and deep data , These two concepts have been everywhere in the past few years , There is even a feeling of being held to the standard . I saw a statement before , as long as ab It's fast enough , There is no need for a product manager . But recently I have seen a lot of reflection on data-driven , After personal experience, I also realized that we should do well , Especially in the growth period of products ( initial stage ) It's hard to be data-driven . Not only are there few samples 、 More variables , Many teams are even decent ab No tools .
When doing data analysis, I think there are two big gap, One is subjective expectation and actual user behavior gap, The other is the current user group and the target user group gap. The former one looks like a cold with obvious symptoms , Relatively easy to solve . The second is like a chronic disease that is difficult to find , It's hard to do . Because the second one gap The existence of , Data driven seems to cater to users , The product driver is looking for “ Right taste ” user . Data driven can lead to short-term success , But the long-term result may be a mediocre product without soul . And once personalized products find users who can resonate , It should produce closer links . In the final analysis, this is a question of choice , There is no such thing as silver bullet.
Personally, I prefer to use data as a tool , Not the driving force . Or a different angle , Two mediocre schemes ab, It won't let you choose an awesome plan . There must be a driving force independent of results to continuously improve the product , The team must send itself ab Responsible for the quality of the scheme .
Aha moment
“ Aha moment ” This is the moment when the product shines in front of the user , It is the user who really finds the core value of the product —— Why does the product exist 、 Why they need it and what they can get from it —— The moment of
Find or define the aha moment of the product , Then guiding users to aha moment should be one of the most important goals in this book . I think this is a very effective thinking model , But it seems that it is not well implemented in our products .
Because fighting on multiple lines 、 The language 、 Lack of user research team , We have never fully understood the feedback of products in the market . A few days ago, I asked my colleagues what is our aha moment , The direction of our answers is similar , But there is no complete consensus .
And we don't work hard enough to guide users to aha moment . For example, the location of users is very important to us , But the current design will only be in ta for the first time onboard Ask once when , Then I let it go . And I don't think the first inquiry of the previous version is very good .
Here's a problem I haven't figured out : Users from different channels have great differences in the proportion of locations . Although the product colleagues have given an explanation before , But I don't quite understand . Empathy is really an important and rare ability for product managers , Guess different channels according to products 、 The loss rate of different steps may be a good way to investigate .
Store value
In general , The personal information or property that users put into products is called stored value , There are many forms of stored value , For example, users write notes in impression notes , Contact on wechat , Buy on Amazon prime Annual membership fee, etc . Stored value can effectively increase the stickiness of users .
Unfortunately, our products have almost no way to store value ( It seems that there is only one favorite ), Like I said before , Our inquiries about user information are very conservative ; Users can't spend money to get value-added experience ; There are no rewards and rewards designed in the product . When the user has passed the honeymoon , Basically, we can leave our products without any burden .
To build a stored value system is actually quite difficult for our current products , But this is really important , After all, we have nothing irreplaceable , We need more channels to establish material and emotional connections with users .
Product carving
This topic is scattered in many parts of this book , for example
Find the matching point of the language market , Optimize advertising language According to three fundamental tasks : Communicate relevance , Show the value of the product and provide a clear call to action to design landing page. Less is not always more , Cheap prices don't always bring more users , Because people may regard price as a signal of quality according to “ relativity ” To design smoke bomb packages to optimize pricing , for example 《 The economist 》 The annual subscription price of online magazine is 59 dollar , Paper magazines are 125 dollar ( Smoke bombs ), The combined price of paper version and online version is also 125 dollar . This setting can greatly reduce the user's choice 59 Increase the proportion of users to buy the third package .
I read this book on wechat reading , I have to say that it's really great to provide these books for free on wechat , I'm going to read the book again later 《 Predictably Irrational 》 and 《 Design psychology 》, After reading it, I will communicate with you .
Wechat reading shows me 3 Hours 22 After reading this book , It feels shorter than the time I actually spend . This way of exaggerating the revenue per user unit time may be a growth strategy ?
边栏推荐
- Dynamic SQL of ibatis
- Mongodb replica set
- 1个插件搞定网页中的广告
- Guys, I tested three threads to write to three MySQL tables at the same time. Each thread writes 100000 pieces of data respectively, using F
- 【无标题】
- 以交互方式安装ESXi 6.0
- Crawler (9) - scrape framework (1) | scrape asynchronous web crawler framework
- SET XACT_ABORT ON
- Web API配置自定义路由
- Project summary notes series wstax kt session2 code analysis
猜你喜欢
12. (map data) cesium city building map
COMSOL -- establishment of geometric model -- establishment of two-dimensional graphics
12.(地图数据篇)cesium城市建筑物贴图
Splunk configuration 163 mailbox alarm
How did the situation that NFT trading market mainly uses eth standard for trading come into being?
【云原生 | Kubernetes篇】Ingress案例实战(十三)
Advanced technology management - what is the physical, mental and mental strength of managers
Harbor image warehouse construction
liunx禁ping 详解traceroute的不同用法
COMSOL--三维图形的建立
随机推荐
Ffmpeg calls avformat_ open_ Error -22 returned during input (invalid argument)
[leetcode] wild card matching
Solve the problem of slow access to foreign public static resources
Is it difficult to apply for a job after graduation? "Hundreds of days and tens of millions" online recruitment activities to solve your problems
go语言学习笔记-初识Go语言
redis主从中的Master自动选举之Sentinel哨兵机制
What about SSL certificate errors? Solutions to common SSL certificate errors in browsers
【上采样方式-OpenCV插值】
全网最全的新型数据库、多维表格平台盘点 Notion、FlowUs、Airtable、SeaTable、维格表 Vika、飞书多维表格、黑帕云、织信 Informat、语雀
AutoCAD -- mask command, how to use CAD to locally enlarge drawings
871. Minimum Number of Refueling Stops
一次生产环境redis内存占用居高不下问题排查
Pytorch weight decay and dropout
CDGA|数据治理不得不坚持的六个原则
COMSOL -- establishment of geometric model -- establishment of two-dimensional graphics
[upsampling method opencv interpolation]
Redis集群(主从)脑裂及解决方案
POJ 3176-Cow Bowling(DP||记忆化搜索)
【主流Nivida显卡深度学习/强化学习/AI算力汇总】
[calculation of loss in yolov3]