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:
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.
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.
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!
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.
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:
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!
When I hire for Data Analyst (Jr. or Intern) positions, I look for three things:
1) Analytical mindset:
I would do this by sharing a hypothetical case study and seeing how you go about solving this. I would look for things like: a) Approach: How do you break down the problem? b) Effectiveness: How effectively can go about solving the case. I am NOT looking for the “Right” answer but just want to see how you go about solving the case.
(Search for “Management consulting case studies” — I usually pick a simple case)
2) Communication skills:
This is pretty standard across many roles but it’s important for data analysts to be able to communicate their recommendations/findings to stakeholders.
3) Basic hard/tech skills + Willingness to learn new tech skills:
I would ask you basic tech questions around SQL, Excel OR other “tech skills” that you might have mentioned in your resume. I am not looking for expert-level knowledge but just want to make sure you know things that you have listed on your resume or things that you studied. Also, I would ask you questions that would help me figure out whether you are open to learning new tech skills.
So now that I have shared the framework with you, let me try and answer your question: How do I answer the most challenging data analysis project that I have done?
a. If you had a good approach for your project then It would mean that you know how to break down data analysis problems and solve them. So solving a basic case study shouldn’t be difficult for you and I could check box #1!
b. If you can communicate the “complexity” of the project effectively then I think I would check the box #2: communication skills!
c. Since you solved a challenging project, I assume that you picked up some tech skills (Bonus points if you picked up new tech skills while solving this problem). Just let me know what tech you used to solve the problem so that I can ask questions around that — if you are able to answer them then I would check box #3!
It’s NOT about the challenging project but your learning/takeaways from that project that will be help you the most!
Now, assuming that the interview team think you are a good “culture fit” plus you came out on top compared to other candidates then you will get an offer to join the team as a Data Analyst!
Power Query is amazing! It takes the data analysis capabilities of Excel to whole new level! In this post, I am going to share three reasons:
1. it enables repeatable mash-up of data!
Have you every had to do your data analysis tasks repeatedly on the data with same structure? Do you get “new” data every other week and need to go through the same data transformation workflow to get to the data that you need?
What’s the solution? Well, you can look at MACRO’s! Or you can request your IT department to create a Business Intelligence platform. However, what if you need to modify your data mashup workflow then these solutions don’t look great, do they now?
Don’t worry! Power Query is here!
It enables repeatable mashup of data like you might have never seen before! You need to try it to believe.
It’s very easy to input new data to Power Query and it enables you to retrieve final output based on new data using a “refresh” feature.
Each data-mashup is recorded as steps which you can go back and edit if you need to.
2. It’s super-flexible!
Any data mashup performed using Power Query is expressed using its formula language called “M”. You can edit the code if you need to and as you can imagine such a platform enables much-needed flexibility for the analyst’s.
3. It has awesome advance features!
Do you want to Merge data? How about Join? Are you tired with VLOOKUP’s! Don’t worry! it’s super easy with Power Query! Here’s a post: Join Excel Tables in Power Query
How about searching for online & open data sets? Done!
How about connecting to data sources that “Data” section of Excel doesn’t support yet? (Example: Facebook) – DONE! Power Query makes that happen for you.
And That’s not a complete list!
Plus you can unlock the “Power” (pun intended) of Power Query by using it with other tools in Power BI Stack. (Power Pivot, Power View, etc…) OR you can use the your final output from Power Query with other tools too! After all it’s an excel file.
If you haven’t already then check out Power Query! it’s free and works with Excel 2010 and above.