This article discusses how simple analyses of large datasets can provide powerful insights for businesses. It describes an example where a company analyzed its entire customer base and found that 73% were from one geography and 27% from another. Analyzing just the top 10% of high-value customers, the ratio changed drastically to 94-6, and for the top 5% of customers it was 98-2. These simple summaries and visualizations from big data provided important strategic insights for how to focus marketing efforts. The article advocates exploring simple analyses before complex modeling so as not to miss opportunities for intuition from basic statistics.

1. Data Curry
Welcome to stories on Big Data and Data Science. This is perhaps the 100th article on Big Data you
might have come across this week. So, why do you need one more? Well, because:
Each story is a very simple one where I know for sure that decision makers gained
powerful insights
You can most probably read these in 5 minutes
You and your team can implement them with equal ease in your own business
I will also write them for the business users and do away with any
engineering/technology stuff
Really simple analysis for extremely powerful insightsReally simple analysis for extremely powerful insights
I was taught to always spend quality time with data; you know,
asking questions and exploring answers, prior to starting
complex modelling on the same. I was told, rightly, that this
exercise would give me the needed comfort and intuition with the
data.
In the initial days, I used to explore hundreds of records.
However, in the past few years, thanks to the unbelievable
computing power available, I am able to play with hundreds of
thousands of records even for exploration.
Now, what I find with customer after customer is that the simple
sums and averages I find from this big data itself are the intuition-
crushers that they are looking for.
Here is a customer whose customers predominantly come from
two geographies. When I looked at the entire customer base, I
found that approximately 73% of the customers are from one
geography and 27% from the other.
Now, I verified the distribution amongst high value customers
(top 10%) and found that the ratio changed drastically to 94-6 and
finally in the extremely high value customers (top 5%), it is 98-2.
The graphs provided below tells the story I am talking
about.
So, what could they do with this insight? They could
focus more on selling more to high value customers in
Region1 if they believed it has as much potential as
Region2 or spend more on Region2 as that seems to be
the low-hanging fruit. They may even want to see what
aspects of the product make it dear to Region2.
But, that is not the point. What I presented here is not
complex analytics. It is a simple graph that I plotted
probably in the first half hour but on really big data.
So, before you go for really complex million dollar
models, ensure that you milked all the powerful insights
that you can get from simple sums, averages, etc.
27%
73%
Entire Customer Base
0
20000
40000
60000
80000
Region 1 Region 2
6%
94%
Top 10% customers
0
5000
10000
Region 1 Region 2
98%
Top 5% customers
0
1000
2000
3000
4000
Region 1 Region 2
2%
5000
These stories are written by Dr. Dakshinamurthy V Kolluru, Chief Advisor – Data Science,
Usha Martin Education, President, International School of Engineering
(http://www.insofe.edu.in)
www.datacurry.com