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Jazeera Airways

Building an advanced data analytics engine

TECH

used

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Python

our

impact

3 months

for data engine to go live

Jazeera Airways, operating from Kuwait, has established itself as a key player in the competitive airline industry in the region. The airline has successfully utilized tech platforms like Navitaire to streamline operations and improve booking experience. With time, it recognized a crucial need for deeper, context-specific data analysis capabilities.

The existing framework primarily focussed on operational metrics and destination-based analytics. In an industry where customer expectations and market dynamics shift rapidly, Jazeera Airways wanted to build a sophisticated data analysis engine to improve marketing campaigns and drive revenue.

This is where Cyces stepped in. Our team of a product manager, designer and a data engineer kicked things off with a discovery call. We worked closely with Jazeera Airways, understood their operations, tech stack and larger marketing campaigns.

In three months, we built an advanced data analytics engine that would enable the airline to get more from the rich data at its disposal. We wanted the engine to uncover insights that could inform more personalized, targeted marketing strategies.

Data collection and variable identification:

The initial step in enhancing Jazeera Airways' data analysis capabilities involved the extraction of passenger data from Navitaire's solutions. We built pipelines to capture a wide array of data points, including but not limited to passenger booking details, flight preferences, travel frequencies, and demographic information. About 15 data points were identified.

Once extracted, the data underwent a rigorous preparation phase.This preparation phase was critical to ensuring the accuracy and reliability of subsequent analyses, laying the groundwork for insightful, actionable findings.

Data Cleaning

Following extraction, our focus shifted to transformation, where we rigorously cleaned the dataset. This involved removing duplicate entries to prevent skewed analyses, handling missing data either by imputation or exclusion to maintain dataset integrity, and normalizing data formats to ensure consistency across variables.

Clustering via Different Algorithms and Presenting Data

We utilized K-Means clustering among other algorithms to segment Jazeera Airways' passenger data into meaningful clusters, aiming to unveil patterns and behaviors within the dataset. The application of the Elbow Method played a crucial role in determining the optimal number of clusters. By analyzing the within-cluster sum of squares (WCSS) against a range of cluster numbers, we identified a point where the decrease in WCSS began to diminish, indicating the most effective number of clusters for our analysis. This methodological approach not only optimized the clustering analysis but also ensured that the segmentation was both statistically significant and actionable for marketing strategies.

Identification of Differentiating Variables via Cluster Analysis

Our focus shifted to the identification of differentiating variables through cluster analysis. This step was pivotal in discerning the unique characteristics that defined each cluster, such as travel preferences, booking behaviors, and demographic factors. By focusing on the most impactful variables, we were able to sharpen our insights into passenger segments, enhancing the strategic value of our findings for Jazeera Airways.

Building Clusters in Decision Tree Format

We translated the identified clusters into a decision tree format, aiming to make the segmentation more intuitive and actionable for Jazeera Airways' marketing strategies. This approach enabled us to visually map out the paths leading to each cluster, based on key differentiating variables. Representing clusters in this manner facilitated a deeper understanding of the unique characteristics and behaviors within each segment, making it easier for the marketing team to devise targeted strategies.

Application to Marketing

The insights derived from our clustering and decision tree analysis were directly leveraged to refine Jazeera Airways' marketing initiatives. Recognizing the distinct needs and preferences of each segment, they developed tailored campaigns. These targeted approaches allowed Jazeera Airways to not only meet the specific needs of each segment but also enhance customer engagement and loyalty effectively.


FROM

THEM

The insights derived from our clustering and decision tree analysis were directly leveraged to refine Jazeera Airways' marketing initiatives. Recognizing the distinct needs and preferences of each segment, they developed tailored campaigns

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