The modern marketer is often faced with uncertainty.
Malls are closing, brick-and-mortar stores are written off as a thing of the past, and online shopping is the only way. At the same time, new, immersive ways of in-store shopping are propping up. So, should you completely give up on your offline marketing campaigns?
One way to eliminate guesswork and decide the next-best-action is by analyzing customer data.
Do you know how many times does a person visit your website before purchasing something? And if they visit more than the average customer, are they more likely to convert? What age groups prefer online shopping? Once you have these insights, you can tweak your marketing campaigns to attract them, and boost sales.
While immensely helpful, data analysis can be challenging. You need to write endless queries, figure out numerical functions, and wrestle with large datasets.
But not with Gigasheet!
Phew! That is quite a number of columns.
Let us see how this data looks when imported in Gigasheet. To open a file, just click on the ‘New’ button and select your file. In a few seconds, you can view all the 2,240 rows of this dataset.
Customer Behavior Data in Gigasheet
In this dataset, you can view individual customers and the data about their monthly website visits.
Let us go back to the questions we mused about right at the beginning. Let us say, a marketer wants to understand how many times on an average a person visits their website.
You can use Gigasheet’s ‘Average’ function to find this out.
Simply click on the button below the column NumWebVisitsMonth, and a menu will pop up, listing a number of functions:
Select ‘Average’ and Gigasheet will instantly show you the average value for that column. In this case, we get the value 5.317.
So, on an average, a customer visits the website around 5 times a month.
In the real world, things are hardly this simple. This information is probably insufficient for a refined, personalized campaign. A marketer may want to know more. For example, how many times do millennials visit the website, on an average?
Here is where Gigasheet’s filters can help you extract relevant data with powerful queries, without actually writing code.
Let us define millennials as those born between 1981 and 1996 (both limits included). Here is how we will design a filter for this condition:
We are now left with 385 rows. If we calculate the Average of NumWebVisitsMonth for this subset of data, the value is 5.43, slightly higher than before.
Now, suppose you are working on a campaign for millennials who buy your products from a brick-and-mortar store.
We have two columns to consider here:
It’s time for some math now. Navigate to Tools -> Arithmetic
Let us find the difference between the number of purchases made in store and the number of purchases made online:
This operation yields a new column: NumStorePurchases-NumWebPurchases.
Now, to find out how many millennials prefer to purchase from the store, let us filter out those rows where the values for NumStorePurchases-NumWebPurchases are - you guessed it - greater than zero.
Gigasheet allows you to apply filters on values you calculate from existing columns. How cool is that!
This is how it looks in action. Notice how this new column name is automatically populated in the drop-down list in the filter option.
After applying this condition, we are left with 286 out of the previously fetched 385 rows.
The average value for the number of monthly website visits has also gone down a little, from 5.438 to 4.913.
Let us further analyze this data. Is there a connection between online shopping and parenthood?
It makes sense that millennials with small children are busy (and exhausted.) They may prefer shopping online, and not in store. Childless millennials may enjoy an in-store shopping experience more. But, let us not make mere guesses.
Instead, let us group this data by the field KidHome, which indicates how many children each customer has.
Here we have some concrete, interesting results!
Using the same Average function for the column NumWebVisitsMonth, we can see that for childless millennials who prefer in-store shopping, the average number of website visits a month has dropped to 3.283.
And that’s it. That is how easy it is to analyze complex datasets with Gigasheet without writing a single line of code!
Like this blog? Why not upload your own dataset on Gigasheet and have some analytical fun? Or, you can also check out our free data community.
Gigasheet supports large data files in a variety of formats, from CSV to XLSX to JSON. You can upload the data from your device or a cloud platform like Google Drive, Dropbox and more.