All articles in 'Analytics' Category

Do you want to simulate multiple cash-flow scenarios and calculate the rate of return? Then this article is for you. In this page, learn how to,

• Introduction to IRR & XIRR functions
• Calculate rate of return from a set of cash-flows with XIRR
• Simulating purchase or terminal value changes with data tables
• Apply conditional formatting to visualize the outputs
• Common issues and challenges faced when using XIRR

Can we predict cricket match score in Excel? Using machine learning, ensemble modeling, multiple regression and Excel formulas we can. This tutorial explains how.

We had to switch power providers soon, so I started reviewing the options. There are heaps of providers in New Zealand and each offer a ton of different plans. Some offer welcome bonus or credit worth up to $ 200. Other offer straight forward rates. Some others offer discount if you sign up for both electricity and gas with them. So how do you decide which one is better for you?

Using Excel of course.

The result is awesome. I ended up saving more than $1000 with a simple model. Puzzled? Curious? Check out this short but powerful video tut.

Imagine you run an office furniture company. You want to stop reordering two brands of furniture – Relaxer (a type of chair) and Boca Top (a type of table). You currently have 20,000 Relaxer chairs and 5,000 Boca Tops. These are valued at $200,000 and $100,000 respectively. When sold, they will yield $100,000 and $25,000 gross profit. You are hoping to sell them off in 2 or 3 years. You forecast that we can sell off these as per some yearly schedule.

You need to analyze this and prepare a cash flow model.

Let’s learn how to answer such open ended questions using various analysis techniques in Excel.

Last week, I asked you to share an analysis problem that you couldn’t solve in Excel. We got quite a few very interesting problems in comments and email. In this post, let me explain how to solve Sara’s copy shop problem using Excel.

What is Sara’s copy shop problem?
Thanks to Caroline who posted this problem.

Sara wants to open a copy shop. Each copier costs $5,000 per year to lease. The rent & other fixed costs per month are $300. There is a $0.02 variable cost per copy. Each copier can print up to 100,000 copies per year. She plans to charge $0.11 per copy from her customers. Sara estimates that the demand can be any of the 4 values – 500, 1000, 1500 or 2000 copies per day.

1. Build a model to estimate profit per given number of copiers & demand values
2. Find the mix of copiers & demand values that can make maximum profit for Sara (copiers – 1 to 6, demand – 500 to 2000)

Time for a quick show & tell.

Tell me about an analysis problem that you couldn’t solve with Excel?

It can be because you didn’t know how to solve the problem or Excel isn’t the tool for it or any other reason.

Go ahead and speak up. Post your tricky analysis problems in the comments section.

Every week, we read news about failed analysis projects. If you listen carefully, you can hear the grunts, screams and curses of thousands of analysts all over the world about their analysis nightmares.

At Chandoo.org, we talk a lot about best practices for data analytics. So today, let’s peek in to the dark side and understand the mistakes that can turn your analysis project into a nightmare.

There are 3 parts in any analysis project

To understand these worst practices in analysis world, first let’s break analysis projects in to 3 parts.

• Requirements
• Data Structure
• Tools & Construction

Let’s deep dive in to each area of the analysis projects to see what can go wrong.

Around 2 months back, I asked you to visualize multiple variable data for 4 companies using Excel.  30 of you responded to the challenge with several interesting and awesome charts, dashboards and reports to visualize the financial metric data. Today, let’s take a look at the contest entries and learn from them.

First a quick note:

I am really sorry for the delay in compiling the results for this contest. Originally I planned to announce them during last week of July. But my move to New Zealand disrupted the workflow. I know the contestants have poured in a lot of time & effort in creating these fabulous workbook and it is unfair on my part. I am sorry and I will manage future contests better.

Over the weekend, I got an email from Mr. E, one of my students. Mr. E works at a police department in California and as part of his work, he was looking at calls received by police. Whenever police get a call for help, multiple teams can respond to the call and go to the location. All of these dispatches are recorded. So a single call can have several such dispatches. And Mr. E wanted to findout which team responded the first. The problem?

Finding the first responded team is tricky.

Today let’s take up this problem as a case study and understand various methods to solve it.  We are going to learn about writing better lookups, pivot tables, power pivot and optimization. Put on your helmets, cause this is going to be mind blowingly awesome.

How would you analyze data when you have lots of it? That is the inspiration for this series.

Let’s meet our data – Finance Industry Consumer Complaints

As part of open data initiatives, US government & Consumer Financial Protection Bureau maintain a list of all consumer complaints made against financial institutions (banks, credit unions etc.) You can download this data from the catalog page here. I have obtained the data on 1st of February, 2016. The download has 513,824 records. Each row contains one complaint.

In this and next two parts of the series, we are going to analyze these half a million complaints to find insights.

Here is a tricky problem often faced by accountants and finance professionals: Let’s say you have 5 customers. Each of them need to pay you some money. Instead of paying the total amount in one go, they paid you in 30 small transactions. The total amount of these transactions matches how much they need to pay you. But you don’t know which customer paid which amounts. How would you reconcile the books?

If you match the transactions manually, it can take an eternity – after all there are more than 931 zillion combinations (5^30).

This is where solver can be handy. Solver can find optimal solution for problems like this before you finish your first cup of coffee.

Here is a situation all too familiar.

You are looking at a spreadsheet full of data. You need to analyze and tell a story about it. You have little time. You don’t know where to start.

Today let me share 15 quick, simple & very powerful ways to analyze business data. Ready? Let’s get started.

Podcast: Play in new window | Download

Subscribe: Apple Podcasts | Spotify | RSS

In the 30th session of Chandoo.org podcast, let’s learn how to uncover fraud in data.

What is in this session?

In the wake of hedge fund scams, accounting frauds and globalization, We, analysts are constantly second guessing every source of data. So how do you answer a simple question like, “am I being lied to?” while looking at a set of numbers your supplier has sent you.

That is our topic for this podcast session.

In this podcast, you will learn

• Quick announcements about 50 ways & 200k BRM
• Introduction to fraud detection
• 5 techniques for detecting fraud
  • Benford’s law
  • Auto correlation
  • Discontinuity at zero
  • Analysis of distribution
  • Learning systems & decision trees
• Implementing these techniques in Excel
• A word of caution

Who is the most consistent of all?

Imagine you are a category manager at a large e-commerce company. Your site offers various products, but you don’t really make these products. You list products made by other vendors on your site. Every day, these vendors would send you invoices for the amount of product they have sold. Above is a snapshot of such invoices.

Looking at this list, you have a few questions.

1. Who is the best seller?
2. Who is the most active seller?
3. Who is the most consistent seller?
4. Which seller has fewest invoices?

Let’s go ahead and answer these using Excel. Shall we?

Situation: Our commissions are growing way faster than revenues
Let’s say you are looking revenues & sales commissions of your company for last few years. The data looks like this:

And you want to highlight the fact that commissions are growing faster than revenues.

So you plot YoY growth rates for revenues & commissions.

Problem: The chart of YoY growth rates is not convincing

Take a look at the chart. It doesn’t convey the message that we want. At best it says “revenue growth is less than commission growth”

How to convey the message “Commission growth is a problem for us”?