Problem

You work for Airbnb. You are the New Operational Manager in New York for 1 year. You are then responsible of the good development of the Airbnb service on renting in this town.

• In 1 week, the Directory Manager comes to New York in order to: 1. Make a review of your operational KPI on your perimeter (New York) 2. Work with you to improve the quality of service of the plateform, by keeping only the best and profitable renters

• Until now, the reports have been made in Excel / Power Point and Airbnb aims to digitalize its reporting with Power BI in order to: 1/ Make them more interactive; 2/ Make them more industrialized ; 3/ Access to anytime to the dashboard on PC, smartphone, etc.

• You decided with your team that it would be a great opportunity to make a dashboard in Power BI

Objectives

1. Make review of your activities and present them to the Directory Manager

2. Prepare the analysis to align to the next year target operating model of Airbnb: - Identify good and bad practionner - Evaluate the number of bad practionner and the reasons

Solution

Please find detailed dashboard by clicking the image

We need to create an analytical dashboard for Airbnb New York Directory Manager by

analyzing the dataset of properties located in New York.

We have the following data fields in our dataset:

Host Id - The unique ID value of host

Host Since- The date year the host joined in Airbnb

Name - The name of the property

Neighbourhood - The details of borough where each property located

Property Type - The type of property

Review Scores Rating (bin) - The scaled value of review rating

Room Type - Type of room like whether its shared or private

Zip Code - Property zipcode

Beds - The number of beds for each property

Number of Records - Number of records of property

Number Of Reviews- the total reviews per property

Price - The price listed for property in Airbnb

Review Scores Rating - The actual consolidated review score rating.

Operation Analysis

We could see that the properties in New York have some negative reviews and our concept is to

choose the review rating score as a metric of profitability. We are identifying the properties

which have no reviews for a long period and bad reviews which affects the reputation of Airbnb.

We are also doing the customer segmentation based on the property type to identify the best

places available in each locality. After doing the customer segmentation we are finding the best

reviewed places by creating new popularity score by considering the number of reviews and

review rating. For the best popularity score we are considering the number of reviews > 10 and

the average review score rating > 75. In the design of the worst popularity score we are creating

the measure where the average rating score < 50 and at least number of reviews > 3.

Methodology

1. Data Cleaning and Data Manipulation - We found some irregularities in the dataset and

removed and created extra columns.

I. Removed the number of records columns since every row has only the value as 1.

II. Removed the rows in host id which give spam values by comparing the other host

ids.

III. Changed the data type : Date(Locale), Zip Code(Text), the host Id (Text)

IV. Extracted year as new column.

V. Created a new column for price range and review range.

2. Create measures for the analysis of Dataset- We created certain measures like popularity

score, count blank rating and Review Remarks for simplicity and analyse the properties

based on the reviews.

3. Data visualization - We divided the data visualization based on our story

I. Identify the general statistics details from the dataset

II. Identify the bad practitioners and the properties which have no reviews for sustained

years

III. Identify the Customer segmentation and find the best practitioners. We used the

property type and number of beds columns to be considered for the customer

segmentation.

"I am able to master the basics of Power BI (connect, pre-process, visualize)"

Airbnb NY Data Analysis Boosts Revenue & Satisfaction