HomeE-commerceExploratory Information Evaluation(EDA) on Brazilian e-commerce dataset— Olist Retailer - utilizing MySQL...

Exploratory Information Evaluation(EDA) on Brazilian e-commerce dataset— Olist Retailer – utilizing MySQL | by Swathi Kota | Mar, 2024


Fig. 1 : Schema design of Olist database

On this article we will likely be taking a look at fixing the enterprise downside of a Brazilian ecommerce platform— Olist Retailer by performing Exploratory Information Evaluation(EDA) utilizing the software MySQL.

First allow us to perceive the context. Olist is a Brazilian e-commerce platform that connects small and medium-sized companies to clients throughout Brazil. The platform operates as a market, the place retailers can listing their services and products and clients can browse and buy them on-line.

To assist Olist achieve higher insights into its e-commerce platform and optimize out there alternatives for development, we’ll carry out an Exploratory Information Evaluation, and reply some enterprise questions, thereby offering Insights and proposals.

The dataset accommodates info on 100k orders from 2016 to 2018 made at a number of marketplaces in Brazil. Its options permit viewing orders from a number of dimensions: from order standing, worth, cost, and freight efficiency to buyer location, product attributes, and at last evaluations written by clients.

The dataset used for this enterprise case is pulled from Kaggle — click on right here. The information is distributed throughout 9 csv recordsdata. After thorough cleansing in Excel utilizing Energy Question, the information has been imported to MySQL setting utilizing the INFILE command.

You’ll be able to consult with Fig.1 for the Information schema to know how the information is distributed and linked throughout completely different tables.

To assist Olist achieve higher insights into its e-commerce platform and optimize out there alternatives for development, we’ll present solutions to the enterprise questions beneath:

Query 1: What’s the whole income generated by Olist, and the way has it modified over time?

SOLUTION:

Fig. 2 — EDA for enterprise downside 1 — Change of income over time
Fig. 3 — MySQL question outcome for enterprise downside 1 — Change of income over time
Fig. 4 — Line graph representing change in income over time

ANALYSIS :

There was a constant rise in whole income earned by Olist over time with some lower throughout months. Highest income has been recorded throughout November 2017 (probably because of Black Friday).

From Fig.3, a pointy lower might be seen in September of 2018 adopted by lower within the following month as nicely. Since no additional knowledge is out there for upcoming months, it’s troublesome to come back to a conclusion as earlier months’ income was constantly excessive. There was a pointy lower in income from October to December of 2016 as nicely. Gross sales knowledge for November 2016 is lacking which makes the evaluation troublesome. Additionally on additional evaluation it’s seen that there’s just one order entry for December 2016 which might account to lacking knowledge for correct knowledge evaluation.

Query 2: What number of orders have been positioned on Olist, and the way does this differ by month or season?

Fig. 5 — EDA for enterprise downside 2 — Orders positioned over months
Fig. 6 — MySQL question outcome for enterprise downside 2 — Change of orders over months
Fig. 7 — Line graph representing change in orders over time
Month-to-month common orders positioned on Olist

ANALYSIS :

A complete of 99,441 orders have been positioned on the Olist platform.

From the road chart, it’s evident that the interval Olist had essentially the most variety of orders positioned within the month of August, adopted by Could and July. The sharp dip within the month of September aligns with our interpretation from earlier outcome.

Query 3: What are the most well-liked product classes on Olist, and the way do their gross sales volumes examine to one another?

Fig. 8 — EDA for enterprise downside 3 — Hottest product classes
Fig. 9— MySQL question outcome for enterprise downside 3 — Hottest product classes
Fig. 10 — Bar graph representing product classes within the reducing order of their reputation

ANALYSIS :

Utilizing the depend of orders made on every class to measure reputation, the bed_bath_table class is the most well-liked, adopted by health_beauty, sports_leisure, furniture_decor and so forth.

Query 4: What’s the common order worth (AOV) on Olist, and the way does this differ by product class or cost methodology?

Fig. 11— EDA for enterprise downside 4 — AOV by product class or cost methodology
Fig. 12— MySQL question outcome for enterprise downside 4 a — AOV by product class
Fig. 13— MySQL question outcome for enterprise downside 4 a — AOV by cost methodology
Fig. 14 —Pie chart representing AOV segmented by cost methodology

Evaluation:

AOV or Common Order Worth is calculated as Whole Income / Variety of orders. This metric provides an thought of how a lot income is being generated by every product class or cost methodology.

AOV per product class : The computer systems class has the best AOV which signifies clients are spending extra on common in buying objects from this class although from our earlier evaluation we found that bed_bath_table class is essentially the most bought class, i.e. the computer systems class has extra costly merchandise than the bed_bath_table class.

This implies clients buy extra bed_bath_table objects, however spend more cash on objects from the computer systems class. Extra advertising and marketing sources should be allotted to low AOV classes to drive extra site visitors and encourage larger spending via low cost provides.

AOV per cost methodology: Probably the most used cost sort by Olist clients is bank cards, adopted by Boletos. This could possibly be because of because of their comfort and availability in Brazil, the place Olist relies. Additionally as a result of comfort of use of bank cards for on-line transactions as a result of they’re quick and safe and supply advantages similar to rewards packages and cashback. Boletos are primarily financial institution vouchers that clients pays at any financial institution, submit workplace, or comfort retailer. Clients who shouldn’t have entry to bank cards or don’t really feel comfy utilizing them for on-line transactions go for boletos.

Query 5 : What number of sellers are energetic on Olist, and the way does this quantity change over time?

To analyse the change in variety of energetic sellers on Olist, I will likely be contemplating a interval of three months i.e. each quarter, and analyse the expansion of energetic sellers over time(quarter). The identical has been calculated for a interval of 6 months utilizing the NTILE operate.

Fig. 15 — EDA for enterprise downside 5— Change of sellers over 3 months
Fig. 16 — MySQL question outcome for enterprise downside 5 — change of sellers in 1 / 4
Fig. 17 — Line chart representing change of sellers over months
Fig. 18— Line chart representing change of sellers over quarters — 3 months
Fig. 19 — EDA for enterprise downside 5 — Change of sellers over 6 months

Evaluation:

The graph clearly reveals that general there was a constant improve within the variety of sellers promoting their items on Olist platform. This means that Olist is turning into a extra fashionable and engaging platform for sellers to achieve clients and develop their companies.

Query 6 : What number of clients have made repeat purchases on Olist, and what number of whole gross sales do they account for?

Fig. 20— EDA for enterprise downside 6 — Variety of repeat clients and their contribution to whole gross sales
Fig. 21 — MySQL question outcome for enterprise downside 6a— Whole variety of repeat clients

A complete of 2997 clients out of 99441 clients repeatedly(greater than as soon as) positioned orders on Olist web site.

Fig. 22 — MySQL question outcome for enterprise downside 6b — Proportion contribution of repeat clients to whole gross sales

These repeated clients contributed to five.9% of whole income generated on Olist. Clients revisiting the platform and making a repeat buy is as little as 3.01% (2997 clients out of 99441). This could possibly be because of excessive market competitors which is why clients could possibly be exploring purchases on different aggressive platform for a greater deal. Methods should be applied to draw clients to revisit the shop to make repeat purchases by providing loyalty factors or vouchers or coupons for the acquisition which might be redeemed on future purchases.

Query 7 : What’s the distribution of vendor rankings on Olist, and the way does this impression gross sales efficiency?

Right here, to analyse the distribution of vendor overview scores and the impression on gross sales efficiency, I’ve thought-about Sellers’ overview scores(1 to five), and for every overview rating the overall variety of sellers, Distribution of sellers in proportion, variety of orders positioned, income generated and Common Order Worth(AOV).

Fig. 23 — EDA for enterprise downside 7 — Distribution of vendor rankings and impression on gross sales efficiency
Fig. 24 — MySQL question outcome for enterprise downside 7 — Sellers’ Overview rating, variety of sellers, Distribution of sellers on foundation of overview scores, variety of orders positioned, income generated and Common Order Worth(AOV) per overview rating class
Fig. 25 — Pie chart exhibiting distribution of sellers as per their overview scores
Fig. 26 — Column chart representing variety of orders positioned in opposition to vendor overview scores
Fig. 27 — Column chart representing income generated by vendor overview scores
Fig. 28 — Column chart representing AOV by vendor overview scores

Evaluation:

The pie chart reveals the distribution of sellers as per their overview scores and 29% of sellers have overview rating 5 whereas 18% have overview rating 1. Undoubtedly the sellers should enhance buyer satisfaction to obtain higher overview scores and thereby enhance their gross sales.

The primary 2 column charts present that the best rated merchandise generate the best income and highest variety of orders. However the third column chart of AOV by overview rating reveals that clients are spending extra on the bottom rated product.

This might point out that the low-rated merchandise are the costliest objects, the place clients haven’t gotten good experiences regardless of the corporate making a greater AOV. Alternatively, it may additionally imply that extremely rated merchandise are in additional demand out there and therefore the upper variety of orders and income technology and clients are positive with spending extra for them on common.

So, there exists a optimistic relation between overview rating and income generated however a damaging relation between overview rating and common order worth.

Query 8: What’s the common order cancellation price on Olist, and the way does this impression vendor efficiency?

Fig. 29 — EDA for enterprise downside 8 — Common order cancellation price

Evaluation:

The variety of orders cancelled out of a complete of 99441 orders is 625 which accounts to 0.63%(lower than 1%) which reveals that clients are proud of the order that has been delivered. This reveals that clients are glad with the service of Olist platform and therefore the orders haven’t been cancelled. This might probably result in elevated buyer loyalty and a greater general fame for the corporate, however one key discovering was that the client retention price was low. The purchasers revisiting the platform and making a repeat buy was discovered as little as 3.01% which must be addressed.

Query 9 : What are the top-selling merchandise on Olist, and the way have their gross sales developments modified over time?

Right here I’ve analysed the overall variety of orders which can be positioned for class yearly and listed the Prime 10 promoting product classes yearly.

Fig. 30 — EDA for enterprise downside 9 — Prime 10 finest promoting product classes yearly
Fig. 31 — EDA for enterprise downside 9 — Change in variety of orders in comparison with earlier 12 months of Prime 10 finest promoting product classes yearly
Fig 32 — Prime 10 finest promoting product classes of 2016
Fig 33 — Prime 10 finest promoting product classes of 2017
Fig 34 — Prime 10 finest promoting product classes of 2018

Evaluation:

It’s noticed that there was a major rise within the variety of orders positioned in each class from 2016 to 2017. The class ‘bed_bath_table’ although wasn’t within the prime 10 listing of 2016 has outnumbered different product classes within the upcoming years and has topped the very best promoting product. Product classes like furniture_decor, sports_leisure, health_beauty, computers_accessories, housewares have been within the Prime 10 promoting merchandise listing all of the years.

Query 10 : Which cost strategies are mostly utilized by Olist clients, and the way does this differ by product class or geographic area?

Fig 35 — EDA for enterprise downside 10a — Total cost methodology distribution throughout all orders
Fig 36 — EDA for enterprise downside 10b — Cost methodology distribution throughout orders per class
Fig 37 — EDA for enterprise downside 10c — Cost methodology distribution throughout orders per buyer metropolis
Fig 38 — Pie chart exhibiting the general cost sort distribution used for buy on Olist
Fig 39— Column chart exhibiting cost strategies by product class
Fig 40— Column chart exhibiting cost strategies by buyer metropolis

Evaluation:

Probably the most used cost sort by Olist clients is the bank card which contributes to 74.3% of whole funds, adopted by boleto at 19.16%.

When analysed in opposition to completely different product classes and buyer cities, it was discovered that the shoppers choose to make funds through bank cards. This could possibly be because of security and safety that bank card provides and likewise the comfort.

Query 11: Which product classes have the best revenue margins on Olist, and the way can the corporate improve profitability throughout completely different classes?

Fig 41— EDA for enterprise downside 11 — Product classes and their revenue margins
Fig 42 — MySQL question outcomes for Prime revenue margin classes
Fig 42 — MySQL question outcomes for Least revenue margin classes

The product class with the best revenue margin (79%) is signalling_and_security. The bottom is small home_appliances_2 with lowest revenue margin (11.71%). This has considerations as its margin is about 11%. O-list can implement dynamic pricing methods for low-profit margin merchandise like providing reductions throughout gradual intervals.

Query 12: Is there any relationship between overview rating and delivery days?

Transport days might be calculated because the variety of days between order bought date and order delivered to buyer date.

Fig 42— EDA for enterprise downside 12 — Relationship between overview rating and order delivery days
Fig 43 — Line graph exhibiting the connection between overview rating and common days taken for supply

Evaluation:

From the road chart we will observe that there’s a damaging correlation between the overview rating and the common days taken for order to be delivered to the client (delivery days). Lesser the variety of delivery days, larger the overview rating and vice-versa. To get the next overview rating, Olist must make use of methods to make faster supply of merchandise. This may be accomplished by streamlining the order course of, avoiding delays in dispatch and transport bottlenecks, fast-tracking the cargo by increasing warehouses throughout areas.

From the gross sales dataset of Olist for 3 years and the EDA accomplished on completely different enterprise metrics above, the next insights have been drawn:

  1. Olist has seen a constant rise in income within the years, with the best gross sales recorded throughout November of 2018.
  2. Olist had a month-to-month common of 8,657 orders positioned on their platform
  3. The product class with highest Common Order Worth was computer systems though this class didn’t prime the gross sales or order depend listing.
  4. Bed_bath_table was the most well-liked product class based mostly on the variety of orders made, adopted by health_beauty and sports_leisure.
  5. Whereas the bed_bath_table class had the best no of orders positioned, it didn’t translate into a greater revenue-to-order depend ratio (i.e. AOV). This implies clients bought extra bed_bath_table objects however spent more cash on common on costly objects like computer systems.
  6. There was a constant rise within the variety of sellers becoming a member of the Olist platform. This means that Olist is turning into a extra fashionable and engaging platform for sellers to achieve clients and develop their companies.
  7. The rise in energetic sellers may additionally result in a bigger number of merchandise being offered on Olist, which could possibly be interesting to clients and probably result in elevated gross sales for {the marketplace} as an entire.
  8. A complete of 2997 clients made repeat purchases. Roughly 6% of the repeat clients accounted for whole gross sales which is a low quantity as Olist has a complete of 99441 buyer base.
  9. Extremely rated merchandise generated the best revenues and accounted for over 80% of the orders clients made on the Olist platform which signifies good gross sales efficiency and that clients are proud of their buy.
  10. The most costly objects have been ranked low, regardless of the corporate making a greater AOV on costly objects. This case signifies that patrons are prioritizing more cost effective merchandise with higher rankings, and are spending extra on costlier merchandise.
  11. The common order cancelation price on Olist is 0.63% (Lower than 1%) which is an effective indicator that there’s a excessive degree of buyer satisfaction and the effectivity degree of the Olist platform is mostly excessive. Which means, clients are receiving merchandise that they ordered, and there are minimal points with the complete buy course of. This might probably result in elevated buyer loyalty and a greater general fame for the corporate, however one key discovering was that the client retention price was low. Olist has some work to do in changing first-time clients to loyal clients, as clients might need had higher buy expertise by way of good offers or faster deliveries from rivals.
  12. Probably the most used cost sort by Olist clients is the bank card which contributes to 74.3% of whole funds, adopted by boleto at 19.16%
  13. There’s a damaging correlation between the overview rating and the common delivery days. Lesser the variety of delivery days, larger the overview rating and vice-versa.

On the premise of EDA carried out on Olist gross sales knowledge and the insights gained after evaluation, the next suggestions are instructed for the Olist ecommerce platform to extend their general efficiency by way of orders, income, overview rating and buyer retention:

  1. For Olist to maximise gross sales efficiency, profitability, and buyer satisfaction, the corporate might want to rigorously make use of completely different pricing methods, e.g. make use of using Cross-sales promotion methods for merchandise with excessive AOV like computer systems, and tech merchandise to spice up gross sales and profitability.
  2. Olist must make use of loyalty packages to assist enhance buyer retention charges. Clients appear to be glad with their orders based mostly on the results of the low order cancelation price. Olist should capitalize on this by placing optimistic buyer suggestions on its platform to assist appeal to new and potential clients.
  3. Olist can implement dynamic pricing methods for low-profit margin merchandise like house home equipment by providing reductions throughout gradual intervals or elevating costs throughout peak seasons to enhance profitability and optimize gross sales.
  4. Buyer cancelation price is low and buyer retention price is equally low. Olist should make use of methods to enhance buyer retention price by placing up extra number of merchandise and looking out into their customer support expertise as some clients might need been postpone by their customer support expertise. They need to additionally make use of discounting strategies and different gross sales promotion strategies like vouchers or cashbacks which can be utilized in future purchases which may improve the probabilities of clients making future purchases. Clients might need been getting higher offers and promotions from rivals.
  5. To get the next overview rating, Olist must make use of methods to make faster supply of merchandise. This may be accomplished by streamlining the order course of, avoiding delays in dispatch and transport bottlenecks, fast-tracking the cargo by increasing warehouses throughout areas.

This was a complete evaluation on Brazilian eCommerce Platform — Olist to strategize product placement and improve its general efficiency by way of orders, income, overview rating and buyer retention.

Thanks for studying!

Hyperlink to dataset https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce



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