Tailored Brands – Sales Performance Analysis

Created By: Artem Zadvoryanskiy
Date: 01/29/2022
Client: Tailored Brands

PROJECT OVERVIEW:

Tailored Brands is a known US men’s apparel retail store. The purpose of this project is to analyze their 2020-2021 transactions, assess overall sales performance and answer the following questions:

Measure core KPIs for 2021
Assess the overall YoY sales performance
Identify the best performing stores
Analyze customer profiles

DATA PRE-PROCESSING:

Before I started exploratory analysis, I pre-processed the data by solving hygiene issues: removed transactions with 0 and negative quantity (~3% of all data), imputed missing COGS values, and removed duplicates.

EXPLORATORY ANALYSIS:

Net Sales:

First, we want to understand the overall health of the business by plotting a monthly net sales chart. As we see, there is a positive trend that started after a significant drop in March, 2020. We can also observe two major spikes that happened in Jan, 2020 and May, 2021 due to a large number of discounts that company offered, which led to a massive increase in the number of transactions and overall net sales.

When looking at the total net sales YoY, there’s a 40% increase, explained by the growth in customer base and the number of transactions, which both increased by roughly 50%.

Sales margin, on the other hand, remained consistent YoY, despite the fact that average price of sold items was 18% higher in 2020, partially offset by higher discounts in 2020 (13% higher).

Average order size has decreased compared to 2020.

Stores’ Analytics:

According to the chart below, Friday – Sunday exhibit the highest net sales combined for both online and offline channels, Saturday being the best performer.

When looking at the left side of the bar chart below, we can see that TMW_3311 retail location offers more expensive products than other stores but the average product price is lower across all locations in 2021.

On the right side, we observe a significant increase in net sales YoY and that TMW_3314, the best performer of 2020, has lost its leadership in 2021.

CUSTOMER ANALYSIS (RFM MODEL):

To analyze customer behaviour and identify top customers, we are segmenting our user base by using an RFM model by following the next steps:

Rank customers in each of the following categories: recency, frequency, and monetary value
Normalize the above ranks to bring the data to the same scale
Compute the RFM score for each customer
Segment customers into “top customers” and ”other customers”

According to the chart below, an average “top customer” has a much higher frequency of purchases, better recency and bring the most monetary value in terms of net sales. In addition, “top customers” bring 25% of total net sales, while constituting only ~11% of the total user base.

FINAL RECOMMENDATIONS:

To analyze customer behaviour and identify top customers, we are segmenting our user base by using an RFM model by following the next steps:

Monitor important KPIs
Increase average order value by up-selling & cross-selling
Identify the reasons of TMW-3314 store poor performance and take actions based on that
Segment customers into more groups and analyze their purchasing patterns
Attract more “top” customers through marketing campaigns