All these research studies!

Reading about claims/findings from many of these research studies often makes me shake my head in disbelief. The latest one I saw was titled "Scientists suggest beer after a workout". Below are some of the claims/statements from that article:

1. The study involved a group of students who were asked to work out until their body temperature reached 104 degrees. Researchers then gave beer to half of the students and water to the other half.

2. Professor Manuel Garzon announced the results at a press conference in Granada, saying the hydration effect in those who drank beer was “slightly better,”

3. And their conclusion? Researchers at Granada University in Spain have found that beer can help the body rehydrate better after a workout than water or Gatorade.

My questions:

1. How many students? How were they selected? How were they split in the two groups?

2. Slightly better? Really??

3. Wow, that was fast!!

You can read the full article here

Clustering vs. Segmentation vs. Profiling

You want more drama? Use less data!

Beware of making statements on averages or proportions based on small samples. Take the example of a 10% response rate for your customer base. Now consider a particular customer segment - let's say, customers who bought hiking boots. And you find out that the response rate within this group is 30%. Before you consider that as something to share with your clients or before assuming/concluding that this segment will be a significant driver in your response model - look at the size of this segment. Chances are, that segment will be too small to get any business interest or there is an interaction effect you should explore and identify (in your model).

In many instances, I have come across interesting findings and patterns that turn out be nothing but occurrences in extremely small samples.

The title of this post and the idea of writing something on this came from "Deceptive Data and Statistical Skullduggery" by Gordon H. Bell.

Sampling or Weights

Below is a small excerpt from a post on one of my favorite Analytics blogs - Data Miners Blog  

When we have a binary target variable and our goal is to predict the rare outcome - we either do oversampling or we use weights.

Oversampling is when we use all the rare outcomes and an equal-sized random sample of the common outcomes. This is most useful when there are a large number of cases, and reducing the number of rows makes the modeling tool run faster.

The second approach is weighting. Rare cases are given a weight of more than 1 and common cases are given a weight less than 1, so that the sum of the weights of the two groups is equal.

Assume that we have data that is 10% rare and 90% common, and we oversample so it is 50%-50%. If we are using weights, we will multiply each rare observation by 5 and each common observation by 5/9.

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Dear Readers,

In the past few months, my roles and responsibilities have changed and I will be spending lesser time on this blog. Instead of lengthy/detailed posts once in 2-3 months, I would like to do short posts more regularly. These posts will now be mostly tips, common mistakes, something new about an old familiar technique we all know, interesting insights, etc. And all these will be based on my own experiences or something I came across a book, or a website. I hope you will like the new format, and find these short posts useful and interesting.

Thanks,

DataLLigence

Discount/Variety stores productivity

I was looking at the "RetailSails 2011 Chain Store Productivity Report" available at retailsails. I do most of my "everyday needs" shopping at Discount/Variety stores and so that section was the most interesting to me.

The table has the following columns:
1. Sales
2. YoY change in Sales
3. # of Stores
4. Average Store Size
5. Sales per Store
6. YoY change in Sales/Store
7. Sales per sqft
8. YoY change in Sales/sqft

So, which companies are doing good?

Total Sales is very much dependent on the number of stores and the size of the stores. YoY change in Sales would have been useful if there was information on the change in the number of stores, YoY.

Sales per store is again strongly associated with size of the store. But does bigger store size always mean higher sales?

Walmart has the biggest stores, with an average size of about 162,000 sqft. But Costco, with an average store size of 145,000 sqft has an impressive sales per store - $58 million more than a Walmart store. A Costco store is about 0.9 times the size of a Walmart store but its sales is 1.9 times that of a Walmart store.

Another interesting comparison is Target and Sam's Club. Almost the same store size, but sales at a Sam's Club store is almost twice that of a Target store. 

You will see a very similar story if you use Sales per sqft instead of Sales per store. But what makes these analyses incomplete is the absence of average price/item. The comparison between Costco and Walmart is obvious - prices will be much lower at Walmart. But what about Target & Sam's Club? Prices at Sam's Club may still be lower but can that alone explain a Target store having twice the sales of a Sam's Club? Other factors that can be the reason behind a Target store's higher sales will be - optimized layouts (more efficient use of space, more items per unit area), higher traffic, larger proportion of high ticket items, etc..   

And how are these companies growing?

Looks good - almost all of these companies are seeing positive trends in both total sales and sales per sqft, YoY. What about Kmart? Sales per sqft is growing YoY but total sales YoY is down. The first thing that comes to my mind - Kmart must have closed some of its non-performing stores. Any other reasons you can think of?

How to make an impact with Analytics

From the sascom magazine blog - How to make an impact with Analytics by Bob Messier, Senior Director of Business Analytics, SAS.

Keep it simple

Is analytics all about actionable insights? Go and check out some of the websites of companies that offers analytics as a service. Chances are extremely high that you will come across either “actionable” or “actionable insights”.

But the truth is a lot of the work done at Analytics companies is not actionable at all. A lot of the daily or regular requirements will be the “good to know” numbers or information, or what many analysts will derogatorily refer to as Reporting work.

To be honest, when I just started my Analytics career I had the same biased or uninformed opinions. Predictive Analytics or Modeling was the only “cool” thing in Analytics. As they say, much water has flowed under the bridge and below are some of the things I have learned along the way, over the years.

All numbers and insights need not be actionable

When working with a new department or analyzing a new customer base, most of the analyses will start with understanding the business or the customers. The simple reports or the exploratory data analysis is going to be a very useful and important guide for future business plans and strategies.

If the results of your analysis can answer a business question, that’s good. And sometimes, it can be very good.

Averages are sometimes the best

I learned this all over again recently. The client wanted to see how different their 2 groups of customers were. As the initial discussion was focused on the purchase behavior or purchase life cycles of these two groups, I jumped into analyzing the customers’ monthly transactions since their acquisition dates - trying to see if these two groups have different buying patterns across their tenures.

When their overall sales didn’t throw up any surprises, I went into sales within specific product categories. By the end of the week, I found a few interesting patterns. But during the second meeting with the client next week, as I was going through the slides one by one (about 7-8 slides) – both my client and I realized that though the buying patterns were very similar, there was a big gap between the lines. Instead of analyzing how the behavior spiked or dipped or flattened out month on month, the biggest and most important analysis would have been a single slide on the averages of the two customer groups over their 1 year tenure. The differences in their average sales, basket size, number of trips, etc. was clearly seen – one single slide, one table – that was what I should have done when my hypothesis or what we all wanted to see was proved wrong by the data.

Understand the drivers – is modeling really required?

When clients say they want to understand the drivers of customer attrition or response, the first thing many analysts will do is to develop a model. But wait a minute, have some patience and ask a few more questions. A model has to be built if the client has a marketing plan or strategy because you will need to score customers for targeting.

What if all the client wants is to understand the drivers? Maybe all she needs is to identify and understand who these attriters or responders are. And to answer that, you don’t need to develop a model that will take a lot of time and money.

All you need is EDA. Means, frequencies and cross tabs based on the target variable (example, responded or not) will reveal things like – 70% of the responders visited the store in the last 3 months, 80% of the responders live within 5 miles of the store, 65% of the responders use a Credit Card, etc. And this will very much answer your client’s questions.

Signing off with:

Karma police, arrest this man
He talks in maths
He buzzes like a fridge
He's like a detuned radio
-- Karma Police by Radiohead