Let’s talk about Analytics: My real-world project experience
By: Deepak Jain
Tags: Analytics, Data Science, Data Analysis, Retail Industry

Let’s talk about Analytics: My real-world project experience

An encounter with real-world project experience

After publishing a series of (technical) articles where we talked mostly about fundamental concepts in Data Science and Machine learning, today, I want to talk about my experience at work.

Photo by Clark Street Mercantile on Unsplash

For starters, I work with a client who is considered as one of the biggest retail giants in my country (India). They operate a chain of more than 180 stores pan India and have 27 brands registered under their name. Now you might wonder why am I talking about my client? I was supposed to talk about analytics, and about the work I do. The reason I am giving this background is because I want you to imagine the amount of data that gets generated every day. Just to give you a flavor, the different types of data points captured are the number of walk-ins, sale in terms of revenue as well as product quantity, goods returned, loyalty programs, etc. These and many other data points for all 180+ stores. Just imagine the volume of data that gets generated every single day.

SO WE HAVE DATA, NOW WHAT?

I work in the field of retail analytics. The scope of performing analysis is tremendous in retail space. Be it marketing, logistics, loyalty programs, customer segmentation, store segmentation, etc. For any analytics project, it is very important to define the objective and scope of the project. Defining the objective and scope creates the base level framework that we need to execute. It also helps us understand the kind of data points that we would need to gather to perform our analysis. Gathering data is simpler if we know what we are looking for and where to find it? In my case, I have to deal with multiple sources of data. I work with MySQL database, PostgreSQL, and even csv files for some static data. Once you gather the data points you need, the next step is to clean that data. I have come across so many articles where people share their experience wherein they talk about spending 60–70% of their time in gathering and cleaning data. This is 100% true in my case. I spend a considerable amount of time writing SQL queries to get the right data points with the right calculations.

Personal experience highlight: The data that you pull from the database to perform your analysis and the data that the management team refers to using various reports might differ. In my case, the data presented to the management team by various sources of reporting contained a lot of internal filters which I was totally unaware of. I learned it the hard way. However, I personally feel that things like this happen in an organization and that becomes a learning curve for future projects.

SO, YOU GOT THE REQUIRED DATA POINTS AND YOU EVEN CLEANED IT, BUT WHAT THE HELL DO YOU DO WITH THAT DATA?

Alright, I’ll tell you what I do. But before that, I need to explain to you one term. One term, which describes my entire project. Its called Sales Per Square Foot aka SPSF. Some even refer to it as SPF.

You might have visited some or the other retail outlets of a specific brand at some point in time. When you enter the store, you fall in awe of just how huge they are in terms of space. There are many which are having multiple floors. Now, this huge space has its own set of pros and cons.

Pros:

Have sufficient space to display their options

Doesn’t feel overcrowded (especially during rush hours)

Customers can have their own space while shopping (improves customer shopping experience)

Cons:

Maintenance of huge stores becomes difficult

In the case of multilevel stores, many times the customer doesn’t even go to the above floors

Increases operational cost

So, when you visit these stores, you`ll come across specific brands being placed at a specific place. Why do you think they are where they are? A lot of research and analysis of data takes place to come up with the brand location within a store. There’s also an entire area of visual merchandising connected to it.

ALL THIS SOUNDS INTERESTING BUT YOU STILL HAVEN’T COVERED ABOUT YOUR ROLE?

Be patient. Everything is coming together.

In the retail industry, it is very important to measure the performance of each and individual store. This helps the company understand which of their stores are performing best and which are not. Based on this they can make critical decisions like store expansion or shutting down a specific store. There are various metrics to measure store performance. One such measure is SPSF.

Let’s dissect the term: Sale per square foot (SPSF). Commonsensically, I do what the term says. I measure the SPSF of stores and analyze the data to find patterns and come up with suggestions on how can we improve it. Let's take an example:

Say store A generates a revenue of Rs. 1,00,00,000 for a fiscal year 2018–2019. The total carpet area of the store is, say 20,000 sq. ft.

So, the SPSF of store A =1,00,00,000 / 20,000

Thus, the SPSF of store A = 500 Rs/sq. ft

To interpret the above result in simple language, we can say, store A generated Rs.500 for every square foot of area for the fiscal year 2018–2019. What an interesting idea to measure store performance.

However, there is one critical piece of thought that I want to highlight. So, when you visit a store, a typical store consists of cash counters, changing rooms (in case of apparels outlet), escalators, elevators, walking area, displaying options, storage room, etc. Now, one would argue that the space occupied by escalators or elevators isn’t the place where we keep our products to purchase for our customers, and hence that space doesn’t contribute to the overall revenue of the store. Let's call this space as “non-selling” space. So now we have two types of spaces in the same store. One where we have the actual products displayed called “selling space” and other where we don’t have any products on display (like cash counters, elevators, escalators, storage room) called “non-selling space”. Let me surprise you with another fact based on my experience.

This “non-selling space” contributes to average 35–40% of the total store area.

It means out of 20,000 sq. ft area, we use 13,000 sq. ft (65%) of the area. The rest 7000 sq. ft (35%) is occupied under “non-selling space”.

Now, if we modify the value in our above calculation and consider only “selling space” then we get the SPSF of store A = 769.23 Rs/ sq.ft. Now, the next question arises, which one is correct amongst the two? Because even though the “non-selling space” doesn’t generate income, it is part of the overall store and hence should be included in the calculations. This is the topic of debate. Some brands consider the “selling space” as the area whereas some others consider “total carpet area”. For now, I`ll follow what my client wants me to follow (after all “client is the king”). We use only the “selling space” area while performing the calculations. That’s how we calculate the SPSF.

Conclusion

The objective of this article was to give my readers a brief idea about retail analytics and analytics in general. The discussion in this article is just the tip of the ice-berg. A more interesting story is the part where we perform analysis of data from various viewpoints and gather patterns to understand customer behavior, brand behavior, store layout, assortments, etc. and how we can leverage that information to increase SPSF. But that’s the topic for another article.

Please feel free to respond in the comments section. Your response inspires me to write more.

Happy learning!