Cohort Analysis: What is it and why use it?

In this post, you’ll learn definition and benefits of Cohort Analysis. Let’s get started!

Cohort Analysis: What is it?

Cohort analysis is a data analysis technique used to compare similar groups over time.

Cohort Analysis: Why use it?

Here’s the basic idea: Businesses are dynamic and thus are continuously evolving. A customer who joined previous year might get a different experience compared to customer who joined this year. This is especially true if it’s a startup or tech company where the business models change (or Pivot!) often. You might miss crucial insights if you ignore the dynamic nature of businesses in your data analysis. To see if the business models are evolving in right direction, you need to to use cohort analysis to analyze similar groups over time – Let’s see an example to make it a little bit more clear for you.

You decide to analyze “Average Revenue per Customer” by Fiscal Year and came up with following report:

Simple Data Analysis Averages Hide Interesting Trends

It seems that your “Average revenue per customer” is dropping and you worry that your investors might freak out and you won’t secure new investments. That’s sad! But hold on – Let’s put some cohort analysis technique to use and look at the same data-set from a different angle.

In this case, you decide to create cohorts of customers based on their joining year and then plot “Average Revenue Per Customer” by their year from joining date. Same data-set but it might give you different view. See here:

Cohort Analysis Customer Revenue and Year Joined

It seems you’re doing a good job! your latest cohort is performing better than previous cohorts since it has a higher average revenue per customer. This is a great sign – and you don’t need to worry about your investors pulling out either and well, start preparations to attract new investors – all because of cohort analysis! :) WIN-WIN!

Conclusion:

As you saw, cohort analysis is a very powerful technique which can help you uncover trends that you wouldn’t otherwise find by traditional data analysis techniques.

You might also like: Top 2 techniques to analyze data

Author: Paras Doshi

Every Data Analyst Needs to check out this FREE excel add-in: Power Query!

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.

Power Query Refresh

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 Pivot or Unpivot? Done! Check this out: Unpivot excel data using 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.

Action-Item!

If you haven’t already then check out Power Query! it’s free and works with Excel 2010 and above.

Author: Paras Doshi

Achievement Unlocked! Passed Google Analytics certification Exam.

I recently completed the Google Analytics certification exam!

Here’s the Google Analytics Individual Qualification (GAIQ) certificate:

Google Analytics Certification Exam

Top two key techniques to analyze data:

There are many techniques to analyze data. In this post, we’re going to talk about two techniques that are critical for good data analysis! They are called “Benchmarking” and “Segmentation” techniques – Let’s talk a bit more about them:

1. Benchmarking

It means that when you analyze your numbers, you compare it against some point of reference. This would help you quickly add context to your analysis and help you assess if the number if good or bad. This is super important! it adds meaning to you data!

Let’s look at an example. CEO wants to see Revenue numbers for 2014 and an analyst is tasked to create this report. If you were the analyst, which report would you think resonated more w/ the CEO? Left or Right?

Benchmarking data providing context in analysis

I hope the above example helped you understand the importance of providing context w/ your data.

Now, let’s briefly talk about where do you get the data for benchmark?

There are two main sources: 1) Internal & 2) External

The example that you saw above was using an Internal source as a benchmark.

An example of an external benchmark could be subscribing to Industry news/data so that you understand how your business is running compared to similar other businesses. If your business sees a huge spike in sales, you need to know if it’s just your business or if it’s an Industry wide phenomenon. For instance, in Q4 most e-commerce sites would see spike in their sales – they would be able to understand what’s driving it only if they analyze by looking at Industry data and realizing that it’s shopping season!

Now, let’s shift gears and talk about technique #2: Segmentation.

2. Segmentation

Segmentation means that you break your data into categories (a.k.a segments) for analysis. So why do want to do that? Looking at the data at aggregated level is certainly helpful and helps you figure out the direction for your analysis. The real magic & powerful insights are usually derived by analyzing the segments (or sub sets of data)

Let’s a look at an example.

Let’s say CEO of a company looks at profitability numbers. He sees $6.5M and it’s $1M greater than last years – so that’s great news, right? But does that mean everything is fine and there’s no scope of optimization? Well – that could only be found out if you segment your data. So he asks his analyst to look at the data for him. So analyst goes back and after some experimentation & interviews w/ business leaders, he find an interesting insight by segmenting data by customers & sales channel! He finds that even though the company is profitable – there is a huge opportunity to optimize profitability for customer segment #1 across all sales channel (especially channel #1 where there’s a $2M+ loss!) Here’s a visual:

segmentation data Improve profitability low margin service offerings customers

I hope that helps to show that segmentation is a very important technique in data analysis!

Conclusion:

In this post, we saw segmentation & benchmark techniques that you can apply in your daily data analysis tasks!

Answering a question using data: Are marketers around the globe shifting their dollars to digital ads?

Answering a question using data: Are marketers around the globe shifting their dollars to digital ads?

According to the data shared by emarketer, we can clearly see that the Traditional Ad market is reaching a saturation state in 5 major economies (US, China, UK, Japan, Germany) while the digital ad market will see steady growth in some economies & explosive growth in US & China…but the market size of traditional ads will still certainly remain much bigger in US while market size of digital ads in china will overtake the traditional ads in 2017.

So to answer the question: Marketers are not decreasing their existing budgets for traditional ad channels but the increased marketing budget dollars seems to be directed to digital ads market.

Very interesting data-set, I encourage you to play with it!

Thanks Avinash Kaushik for sharing this interesting tool.

I was playing with the data using Excel & Tableau, here’s a public workbook if you’re interested: https://public.tableausoftware.com/profile/paras.doshi#!/vizhome/WorldWideAdSpend/Dashboard-DigitalAdSpendvsTraditionalAdSpend

YoY growth - Digital Ad Spends vs Traditional Ad Spend

Now, it’s your turn! What insights do you get from this data?

#sqlpass webinar: “Data Analytics Explained for Business Leaders” on 1/15

A quick blog post to let you know about a #sqlpass webinar on 1/15.

Data Analytics Explained for Business Leaders

Thu, Jan 15 2015 12:00 (UTC-05:00) Eastern Time (US & Canada)

RSVP: http://bit.ly/PASSBAVC011515


Abstract:

Description: The world is becoming more efficient. Today, seventy percent of the companies that graced the Fortune 1000 list a mere decade ago have vanished. Agility and survival are function of innovation, culture, and the ability to predict the future. To that end, data analytics offers a lifeline, a means of survival that will drive productivity and continue to disrupt and redefine business. However, the resources available to today’s business leaders sit on two vastly different ends of the spectrum. On the one hand, highly technical academic resources and on the other largely fluffy overviews of value propositions and potentials. The state of the industry shouldn’t be surprising. The same dynamics played out in early years of the internet. Software providers, technical leaders, and consulting firms greatly benefit from mystifying the world of data analytics into something that is incomprehensible. That lack of conceptual understanding is incredibly risky and propels the cost of analytics initiatives upwards. This webcast aims to bridge that gap between the technical data scientists and business leaders. Ultimately, this understanding will help to: – Connect the strategic goals of business leaders with the capabilities of technical advisers – Focus investments and initiatives within analytics and technology – Distill immensely complex subject matter into comprehensible examples – Accelerate the path to value and increase the ROI of analytics initiatives


Speaker Bio

Alex is a Predictive Analytics Architect in the Oil and Gas industry with a passion for distilling complexity into insights and evangelizing data science. His work has been featured on KDNuggets and he was recognized by DataScienceCentral as a top 180 blogger in 2014.

RSVP: http://bit.ly/PASSBAVC011515

I hope to see you there!

Five actions that you can take if you measure your analytics/business-intelligence solution usage:

Summary:

In this post, I am going to share five actions that you can take you if measure your analytics/business-intelligence solution usage:

Five actions!

I’ll highly encourage business stakeholders & IT managers to consider measuring the usage of their analytics/business-intelligence solutions. From a technical standpoint, it shouldn’t be a difficult problem since most of the analytics & business intelligence tools will give you user activity logs. So, what’s the benefit of measuring usage? Well, in short, it’s like “eating at your restaurant” – if you’re trying to spread culture of data driven decision-making in your organization, you need to lead by example! And one way you can achieve that is by building a tiny Business Intelligence solution that measures user activity on top of your analytics/business-intelligence solution. if you decide to build that then here are five actions that you can take based on your usage activity:

Let’s broadly classify them in two main categories: Pro-active & Reactive actions.

A. Pro-active actions:

1. Identify “Top” users and get qualitative feedback from them. Understand why they find it valuable & find a way to spread their story to others in the organization

2. Reach out to users who were once active users but lately haven’t logged into the system. Figure out why they stopped using the system.

3. Reach out to inactive users who have never used the system. it’s easy to find inactive users by comparing your user-list with the usage activity logs. Once you have done that, Figure out the root-cause – a. Lack of Training/Documentation b. unfriendly/hard-to-use system c. difficult to navigate; And once you have identified the root-cause, fix it!

B. Reactive actions:

4. If the usage trend if going down then alert your business stakeholders about it and find the root-cause to fix it?

Possible root causes:

– IT System Failure? Fix: make sure that problem in the system never happens again!

– Lack of documentation/Training? Fix: Increase # of training session & documentation

downward trend line chart

5. It’s a great way to prove ROI of an analytics/business-intelligence solution and it can help you secure sponsorship for your future projects!

Conclusion:

In this post, you saw five actions that you can take if you measure your usage activity of your analytics/business-intelligene solution.

I hope this was helpful! I had mentioned user training in this article and so if you want to learn a little bit more about it, here are a couple of my posts:

1. http://parasdoshi.com/2014/05/05/presented-at-sqlsat-305-dallas-ba-edition/

2. http://parasdoshi.com/2014/05/07/how-to-train-your-users-to-create-their-own-business-intelligence-reports-5-of-5-post-training/

Example of using segmentation to identify low-margin service offerings:

Example of using segmentation to identify low-margin service offerings:

Problem:

Need advanced data analytics techniques to analyze profitability data

Solution:

Here’s an example of how customer segmentation helped identify some low margin service offerings:

Improve profitability low margin service offerings customers

Live tweeted #sqlpass’s Business Analytics VC webinar: 13 Excel Tips!

I was live tweeting during our monthly PASS Business Analytics VC meeting, Here are the tweets to learn about 13 Excel Tips!

Thanks everyone who attended, I hope it was helpful!

Here are some ways to follow the Virtual Chapter:
Website: http://bavc.sqlpass.org/
Youtube: https://www.youtube.com/channel/UCOiRAA4gBxEeVxwmEZ1qy1w
Twitter: https://twitter.com/passbavc
LinkedIn: https://www.linkedin.com/groups/PASS-Business-Analytics-Virtual-Chapter-6701113

Dashboard – Asset management & planning for a global crisis response team:

Dashboard – Asset management & planning for a global crisis response team:

Problem:

Asset (Volunteers, Field offices & Equipments) management & planning for a global crisis response team.

Solution:

Working in a team, we created statistical surveys for field works to collect data about current state & estimated future needs. We also helped them with data gathering & cleaning tasks. After that, we helped them analyze & visualize the data to find actions for executives leading the global crisis response team.

Here’s a mockup of one of the ten data visualization created for them:

Asset Management Global crisis response