Asking effective questions

 Asking Effective Questions:

We all learn the tools like SQL or Python to analyze dataset but one thing that is more important before we start coding is what is it that we want to accomplish with the analysis.

That is where asking effective questions come into play. In real life too, we are used to ask questions for everything- some are good questions that can bring meaningful answers which we can use for decision making and some are just questions without giving us any valuable direction.

For example, if you go to ice-cream shop and ask if they have any tropical flavor ice cream, the answer would be yes or no. That may not necessarily gives the exact ice cream that you want.

Or someone going through a depression asking why are they feeling depressed, may not give them the actual useful answer that they are looking for to come out of depression.

A better question may be, which are the tasks that I do after which I feel depressed or is there a certain time of the day when I feel depressed more than any other time or am I feeling more depressed in some weather condition than others or are there any activities that make me feel happy so I can do them more often?

In data analytics once you understand your dataset, then you will come out with all such questions. A good practice would be to list all questions that come to your mind and then later on decide which ones are useful for your analysis.

In the dataset given below, what potential questions that you can ask that will help you with analysis.

This is a data of online sales orders placed by customers through app for a grocery company.

Column fields:

user_id  customer id

order number indicates what is the number of this order that user_id has placed

order_dow is which day of the week order is placed

order_hour_of_day is what hour of the day order is placed 

days_since_prior_order is days since last order was placed

Questions:

• Which customer has placed maximum no of orders
• Which is the busiest day of the week when max orders are placed
• Which is the busiest hour of the day when max orders are placed
• List customers who place orders at least once a week
• List customers who did not order for 30 days or more
• List top 10 customers
• How many orders are received every day of the week

These are very basic questions but may help the company with following tasks

Insights:

• Who are our most precious customers so we can run loyalty program for them
• Is there a specific day when we need to keep max staffing?
• Is there a period of the day when we need to keep max staffing
• Can we look at app load during the day so it runs without any failures
• Are there customers who order once and then did not order so we can target marketing campaign for them?
• For warehouse and packaging capacity how much daily volume we need to prepare for
• Who are our most regular/loyal customers so we can run targeted marketing campaign for them
• Are there customers who like to place orders only during certain time of the day?
• Are there customers who order only during weekends?

In case, if you thin more questions can be asked from this dataset, please feel free to list them and I will add them here for the benefit of everyone.

As you can see from this example that from what looks like a mundane data otherwise, now companies can use it to make decisions to improve operational efficiency and customer satisfaction using the historical data

Now if we can ask the company to provide more data regarding what products are bought in each orders at what price and some customer data about location, age, gender, family status etc. we can use these combinations and derive more insights which can help the company with specific targeted list of buying behavior of different groups of customers. 

This can be very valuable as instead of generic marketing, now using these insights, companies can get more response from their marketing effort. And that is why data analysts are important resource for companies.