There are lot of ways to apply a CLV (customer lifetime value) model. But I hadn’t seen a single document that would summarize all of them — Until I saw this: http://srepho.github.io/CLV/CLV
If you are building a CLV model, one of first things that you might want to figure out is whether you have a contractual model or non-contractual model. And then figure out which methodology would work best for you. Here are 8 methods that were summarized in the link that I shared with you:
- Recency Frequency Monetary (RFM) Summaries
- Markov Chains
- Hazard Functions
- Survival Regression
- Supervised Machine Learning using Random Forest
- Management Heuristics
- Distribution Based Approaches
Hope that helps!
This question was posted on Springboard forum.
Here’s my answer:
It depends on your target industry & where they are in their life-cycle.
It has four stages: Startup, Growth, Maturity, Decline.
Generalization is great in earlier stages. If you are targeting jobs at startups; generalize. You should know enough about lot of things.
T-shaped professionals are great for Growth stage. They specialize in something but still know enough about lot of things. E.g. Sr Growth/Marketing Analyst. Know enough about analytics & data science to be dangerous but specializes in marketing.
Specialization is great for mature industries. They know a lot about few things. E.g. Statisticians in an Insurance industry. They have made careers out of building risk models.
Find a mentor, where do I. Hmmmmmm….
There are few options. 1) Paid online courses with Mentoring 2) Free Options
#1, Paid online courses with mentoring.
I am a mentor for an ed-tech startup Springboard – Learn Data Science & UX Design online — it’s similar to what you are asking for. If you see value in that, you should check it out.
#2. Free options:
a. Quora: You could ask questions here and A2A — Build a network and someone may offer to mentor you offline
b. Mooc: You could join courses on MOOC’s like coursera and udacity — they have good forum support so you could use it for getting your questions answered
c. Cold email: There are lot of analytics/data-science professionals active in the community (linkedin groups, blogs, etc) and if you cold email them, you might find one!
d. local meetups: go to local meetups, meet people and find your mentor.
Stepping back, having a mentor helps and accelerates your progress – but not having one, shouldn’t stop you from achieving what you want.
VIEW QUESTION ON QUORA
Someone asked on Quora: What analytics data gives you the most actionable advice to improve your blog? so here’s my answer:
I have been blogging about Analytics for past few years and this question is at the intersection of both so let me give it a shot:
It depends on two things: 1) Your goal for running the blog 2) Age of the blog
#1: Your goal
First let’s talk about your goal for running the blog. It’s important to define this as this would help set the metrics that you will monitor and take actions to improve it.
Let’s say that the goal of your blog is to earn is to monetize using ads. So your key performance indicator (KPI) will be monthly ad revenue. In that case you can improve by one of the three things: Number of People visiting the blog x % of visitors clicking on ads x average revenue per ad click. You can work on marketing your blog to increase number of people visiting the blog. Then you can work on ad placement on your blog to increase % of visitors clicking the ad and then you can work on trying different ad networks to see which one pays you the most per click.
let’s take one more example. Like me if your goal is to use your blog for “exposure” which helps me build credibility in the field that I work in. In this case, the KPI i look at is Monthly New Visitors. I drill down further to see which marketing channels are driving that change. That helps me identify channels that I can double down on and reduce investments in other areas. For example: I found that Social is not performing that great but Search has been working great — I started investing time in following SEO principles and spent less time on posting on social.
So first step: Define your goal and your KPI needs to align with that.
#2: Age of your blog:
- Early: Now at this stage, you will need to explore whether you can achieve what you set out to using blogging. So let’s say you wanted to earn money online. In first few weeks/months, you need to figure out if it’s possible. Can you get enough traffic to earn what you wanted? yes? Great! If not, blogging might not be the answer and eventually all your energy is being wasted. Figure this out sooner rather than later — and take first few weeks/months to make sure blogging helps you achieve your goal.
- Mid: By this stage, you should know how blogging is helping you achieve your goal. So it’s time to pick one metric that matters! So if your goal was to earn money using ads then go for Monthly ad revenue and set up systems to track this. Google Analytics will be a great starting point. Also, at this stage, you should be asking for qualitative feedback. Ask your friends, ask on social, get comments, do guest blogging on popular platforms and see if you get engagement — basically focus on qualitative feedback since you won’t have enough visitors that you can analyze quantitative data.
- Late: In this stage, you have the data and the blog is starting to get momentum. Don’t stop qualitative feedback loops but now start looking at quantitative data too. Figure out the underlying driving forces that move the needle on your KPI. Focus on improving those!
TL;DR: Define your “why” and then pick a metric— then use combination of qualitative and quantitative data to improve the underlying driving factors to improve the metric.
VIEW ON QUORA
It’s a three-step process:
- Figure out where (location) you want to work and who (company) you want to work for.
- Note the “skills” required in job Descriptions at companies in your desired location(s) > find common themes from job descriptions > Pick up those skills if you don’t have them already!
- Start Applying!
- Getting a job is a function of Number of Job Applications and your conversion rate (Offers Received/#of Job Applications). Optimizing # of Job Applications is easy — you just need to apply to as many jobs as you could. To improve conversion rate, you would need to do number of things: clear HR/Culture-fit rounds, clear TECH rounds, create a portfolio of projects to talk about, etc.
- You could also consider applying for internships to get experience. This should help you land full-time roles.
Related Answer: Paras Doshi’s answer to How do I prepare myself to be a data analyst?
VIEW QUESTION ON QUORA
What is the title these days for a person that assures data quality?
(I need to hire a person to make sure my data is as good as it can be. They need to inspect the data for issues, create logic for how it can be found and fixed, and finally, court the project through application development for a robust solution to stop it from occurring in the first place.)
Quality of the data shouldnt be a responsibility of just one person — ideally, you want all members of the team (and broader business community) to care and own some part of it. But i like the idea of one person owning the “co-ordination” of how this gets done. It might not be a full time gig in a small org but can see this as a full time role in bigger orgs and enterprises. Some titles:
- data co-ordinator
- Data quality analyst (or just data analyst)
- Data steward
- Master data management analyst
- Data quality engineer (or just data engineer)
- Project manager (data quality)
- Manager, data quality and master data management
Read the original question on Quora
Originally published on Quora: How do I prepare myself to be a Data Analyst?
Based on how you are framing your question, it seems that you currently don’t have “Data Analysis” Background but want to build a career in this field. Here are three things you could do:
- Learn Tech Skills: You will need technical knowledge to be successful at analyzing data. SQL and Excel are a good starting point. You could do a lot with these tools — then depending on the bandwidth that you might have you could explore R. How do you learn this? Here’s a learning pathway: Learn #Data Analysis online – free curriculum ; Also search for free courses on Coursera or other platforms.
- Learn Soft/Business Skills: This is as important as tech skills (if not more!) when it comes to Data Analysis. Finding Insights from your data is half the battle, you will need to put the insights in a context/story and influence business decisions and sometimes influence business change. we know change is always hard! So your soft/business skills will be very important. Also, you will benefit a lot from learning about how to break down problems, communicate your solution by using “business” language vs tech-speak.
- Apply them (and keep improving): Now that you have picked up some tech and soft/biz skills, apply them! Get an internship, Help out a non-profit in your free time (Data Kind, Statistics Without borders, Volunteer Match are good resources to find a non-profit) and start applying your skills! It would also help you get some “Real” world experience and applying what you have learned while “learning-on-the-job” is arguably the BEST way to pick something up!
Hope that helps!