I recently completed the Google Analytics certification exam!
Here’s the Google Analytics Individual Qualification (GAIQ) certificate:
Paras Doshi – Blog
Top 2 Data Analytics Techniques:
StandardThere 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?
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:
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!
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:
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:
Business Intelligence system – Customer Complaints – B2B company:
Analyzing customer complaints in crucial for customer service & sales teams. It helps them increase customer loyalty and fix quality issues. To that end, here’s a mockup:
Note: Drill down reports are not shown, details are hidden to maintain confidentiality and numbers are made up.
Sales Bookings vs Quota Dashboard for a B2B company:
Business Goal:
Need a daily report delivered in sales team’s inbox that shows Sales Team’s Bookings vs Quota for current & next month.
Brief Description:
Ability to see Bookings vs Quota in near real-time is a key to effectively manage performance for any sales team. Before the project, analyst(s) would have to manually put together this report and since the report took more than a day to put together they couldn’t afford to run it daily and so they delivered this report bi-weekly/monthly basis to the sales team. After the project, the process was automated and the sales team received an email with a report on a daily basis and this helped them see Bookings vs Quota in near real-time. As a famous saying goes “if you can’t measure it, you can’t improve it” (by Peter Drucker) – in this case, the report helped them measure their actual numbers against their goals and helping them improve their sales numbers which directly hits their top-line!
Tools used: SharePoint report subscription, SQL server analysis services, SQL Server Integration services, SQL server reporting services & Excel.
Mockup:
Note: Drill down reports are not shown and the numbers are made up.
Cost Driver’s Dashboard for a Supply Chain Executive:
Summary:
Profitability equals revenue minus costs – To that end, A supply chain executive is mostly focused on optimizing cost elements to drive profitability. Here’s a mock up of a dashboard created for an executive to help him keep an eye on the overall health while making sure he gets alerted for key cost categories.
The Dashboard was created using profitability data-set & also had drill down capabilities to analyze numbers for cost buckets like Raw materials, manufacturing & logistics.
Mockup:
PASS September 2014 Outstanding Volunteer
StandardDan English and I got the “PASS Outstanding Award” for our work with Business Analytics Virtual Chapter. Thanks & Congrats Dan, It’s great to have you on the virtual chapter’s leadership team 🙂
Back to basics: Design your Business Intelligence system to have lowest level data even if it’s not asked!
StandardHere’s a scenario:
A Business Intelligence (BI) system for Sales is being developed at a company. Here are the events that occur:
1) Based on the requirements, It is documented that the Business needs to analyze Sales numbers by product, month, customer & employee
2) While designing the system IT learns that the data is stored at each Invoice Level but since the requirements document doesn’t say anything about having details down to invoice level, they decide to aggregate data before bringing in their system.
3) They develop the BI system within the time frame and sends it to business for data validation.
4) Business Analysts starts looking at the BI system and finds some numbers that don’t look right for a few products and need to see Invoices for those products to make sure that the data is right so they ask IT to give them invoice level data.
5) IT realizes that even though business had not requested Invoice Level data explicitly but they do NEED the lowest level data! They realize it’s crucial to pass data validation. Also, they talk with their business analysts and found out that they may sometimes need to drill down to lowest level data to find insights that may be hidden at the aggregate level.
6) so IT decides to re-work on their solution. This increases the timeline & budget set for the project. Not only that they have lost the opportunity to gain the confidence of business by missing the budget and timeline.
7) They learn to “Design BI system to have the lowest level data even if it’s not asked!” and decides to never make this mistake again in the future!
This concludes the post and it’s important to include lowest level data in your BI system even if it’s not explicitly requested – this will save you time & build your credibility as a Business Intelligence developer/architect.
Business Intelligence Dashboard for Inventory management for a manufacturing organization:
Mockup:
BI system allows the analysts & operational specialists to drill down to the lowest data available but here’s a dashboard for executives & Sr. managers:
