Projects
Data-Driven Predictive Modelling for Product Success
Uncovering Market Potential for Wearables
To be able to foresee the success of a product before its official release is vital in a rapidly changing market. The primary objective of this project was to assess the feasibility of early forecasting of a product’s success and to determine a product's market fit ability through the utilization of machine learning, by integrating historical data. The chosen industry for this research was wearable technology, which comprised smartwatches and fitness trackers, both of which are currently flourishing in the healthcare industry.The existing literature review focused on increased customer adoption and acceptance of technology; however, these studies exhibited bias. This project included the application of predictive machine learning modelling, along with thorough data pre-processing techniques, to forecast product success by incorporating technical specifications, customer reviews, ratings, and pricing features of the smart devices and trackers. Additionally, with new datasets, assuming the availability of data before the official launch, the model was able to provide highly accurate results regarding the product's likelihood of success. Key insights and recommendations have been extracted from these data analytics findings.This project held importance as it helped mitigate financial losses for businesses that introduced unsuccessful products that did not meet consumer or market needs. Thus, this model optimized the success prospects of wearable technology products in a highly competitive and rapidly evolving market. Moreover, these models were scalable and could be applied to any product or industry, demonstrating their angiographic scalability. Keywords: predictive analytics, machine learning, wearable technology, smartwatches, fitness trackers, product innovation, new product development.
Code file available on request
Data Visualisation using Tableau
Dashboards
Product Market fit Demand analysis
Insights from Public Opinion and Entrepreneurial PerspectivesI conducted an insightful survey a 'Product Market Fit Demand Analysis,' focusing on gaining valuable insights from both the general public and entrepreneurs. This comprehensive research endeavor aimed to shed light on the critical intersection of public opinion and entrepreneurial perspectives. Through a well-structured methodology and thoughtful data analysis, I uncovered significant findings that illuminate the challenges and opportunities in achieving product-market fit. This survey underscores my commitment to bridging the gap between consumer preferences and entrepreneurial strategies, offering a fresh perspective on the dynamic relationship between products and the markets they serve.
Results from the survey:
Customer Segmentation Analysis
Identifying High-Value Customers and Opportunities for Personalised marketing strategies
This project involved conducting a customer segmentation analysis for a national convenience store. The objective was to gain insights into the customer base and identify distinct segments using clustering methodology. By analyzing transactional data, valuable information was obtained regarding purchasing behavior and characteristics of different customer groups.
The analysis began by defining various customer variables, including the number of baskets, average quantity of items, total and average spends per visit, product categories, purchase time, amount spent on baskets, product IDs, and purchase quantities. The data was then cleaned to handle missing values, duplicates, irrelevant information, and outliers.
Next, the RFM analysis (Recency, Frequency, Monetary) was used to engineer features that facilitated the segmentation of customers based on their buying patterns. The most relevant features were selected through the application of PCA and RFM variables, focusing on key aspects of customer behavior.
Utilizing the K-means algorithm, customers were clustered into different segments. The optimal number of clusters was determined using the elbow method and silhouette score, allowing for grouping based on similarities in purchasing behavior.
The identified segments were then analyzed to identify unique features that differentiated each group. This analysis contributed to the creation of detailed customer profiles for each segment, combining insights from clustering and feature engineering.
Finally, recommendations were provided on which customer segments the company should prioritize. These suggestions were based on both quantitative and qualitative criteria, considering potential profitability and alignment with the company's overall strategy. The analysis offered actionable insights to develop targeted and effective marketing strategies, supporting business performance improvement.
Code file available on request
Brand Analysis
An Exploratory Analysis of Twitter User's Discussion on Tesla: A Snapshot Analysis
This project aims to gain valuable insights into the brand perception of Tesla by analyzing Twitter discussions. Through the examination of a snapshot of tweets, this project seeks to uncover patterns, trends, and insights regarding how Twitter users perceive Tesla and its products. The objective is to understand the public's opinions, identify potential issues or concerns, and provide information that can assist Tesla in refining its marketing and communication strategies. By leveraging the findings from this analysis, Tesla can enhance their brand image, improve customer engagement, and incorporate valuable feedback to enhance their overall products and services.
This project analyzed over 3000 tweets related to Tesla, keywords and hashtags in the year 2023. The analysis highlights a strong and positive brand perception of Tesla on Twitter, with a notable level of engagement from its customers and followers. The sentiment analysis indicates that more than 50% of the Tesla-related tweets expressed a positive sentiment, while only 12% reflected a negative sentiment.
Furthermore, Tesla's brand is strongly associated with keywords such as electric cars, eco-friendliness, and pricing, illustrating the company's leadership position in the electric vehicle industry.
Based on these findings, it is recommended to engage micro-influencers to further promote Tesla's products. The report also identifies specific areas where Tesla's brand could be improved based on public views and offers potential recommendations.
Therefore, this analysis demonstrated that Tesla enjoys a robust and positive brand perception on Twitter, accompanied by high levels of customer engagement. Leveraging these insights can significantly enhance customer engagement and refine marketing strategies for Tesla.
Code file available on request
Big Data Analytics Pitch for Beauty Bay
A pitch was created to provide a solution for BeautyBay.com, addressing issues of low brand awareness and customer dissatisfaction.
The pitch proposed utilizing network analysis for influencers and text analysis for customer satisfaction. By collecting and analyzing data from social media platforms and customer feedback sources, the pitch aimed to identify influential users, enhance brand awareness, improve the shopping experience, and offer targeted marketing campaigns, customer retention strategies, and social media engagement improvement recommendations.
The pitch emphasized an ethical approach, compliance with data privacy regulations, and implementation through an Agile project management methodology.
Customer Churn Prediction Model
A Machine Learning Approach
The objective of this project was to develop a machine learning model to predict customer churn for FoodCorp, a food retail company. To achieve this, the dataset was obtained from the source and processed to develop relevant features for the prediction model. The dataset was cleaned, and feature engineering was performed, followed by feature selection.
The K-fold validation method was used to determine an appropriate value for churn, which was determined to be 75 days. Three machine learning algorithms were implemented and evaluated using cross-validation. The logistic regression model achieved the highest accuracy score and was selected for the final implementation due to its high accuracy score, robustness, and interoperability.
The model had an average accuracy score of 0.89 and was robust to changes in the data. Although not as interpretable as some of the other models, it provided some level of interpretability through its feature importance scores. The churn prediction model for FoodCorp is expected to help the company improve customer retention by identifying customers who are at risk of churning and developing appropriate retention strategies.
Code file available on request
Predicting Marketing Success
A Comprehensive Analysis for Banking Deposit Product
The project focuses on developing a predictive model to identify individuals who are likely to be good prospects for N/LAB Enterprises' telemarketers to promote their financial product, the "N/LAB Platinum Deposit."
By utilizing a carefully curated dataset, this analysis aims to provide insights into the target audience that would yield the highest success rate for marketing campaigns. The project also includes recommendations on how N/LAB Enterprises can optimize their marketing efforts based on the analysis results. The project deliverables comprise a comprehensive business analysis report that outlines the findings and a Python3 implementation of the predictive model. These outcomes will empower N/LAB Enterprises to make informed decisions, enhance their marketing strategies, and maximize the effectiveness of their promotional activities.
Code file available on request
CashBot
CashBot (Smart eye Wallet) is a hardware cum software project based on Internet of Things that helps the visually challenged in identifying counterfeit notes and handling their currency.
This has been one of the top 10 projects selected for the Student Innovator award and is also recognized by the ICT Academy, Chennai.
Digital Marketing Audit & Strategic Plan
Maximizing Etsy's Digital Potential
This project involves a critical management audit of Etsy.com, an e-commerce platform that primarily focuses on selling handmade and vintage items, as well as craft supplies. The study applies the 7Ps framework to analyze Etsy's digital marketing mix and evaluates the platform's main competitors. The study also looks at the macro-environmental elements that might have an impact on Etsy over the coming year and includes a customer journey map with important touchpoints for consumers. A strategic web development recommendation is also included in the study, emphasizing the need to enhance Etsy's overall UX performance through mobile-friendly design, a speedy and user-friendly website, and better accessibility for consumers.
The work also assesses Etsy's social media strategy, and it concludes by providing key performance indicators and specific conversions that might be utilized to track the customer journey and measure and enhance business performance.
Note: Audit file available on request
Effectiveness of Entrepreneurship Policies
A case study in comparison with the USA
This case study explores the effectiveness of digital entrepreneurship policies in the United States and examines various initiatives and programs implemented to support digital entrepreneurship. The goal is to assess the effectiveness of these policies in achieving their intended objectives and address the challenges faced by digital entrepreneurs.
The study discusses different policies that aim to support digital entrepreneurs, including access to finance, education and training, favorable regulatory environments, tax policies, innovation policies, intellectual property policies, and access to markets. It provides examples of specific programs and initiatives in each area, such as the Small Business Innovation Research (SBIR) program, Small Business Investment Company (SBIC) program, and various tax credits for business location and research and development.
Also examines the potential benefits and drawbacks of these policies. It acknowledges that while these policies provide support and resources for digital entrepreneurs, there are limitations and challenges that need to be addressed. These include access to finance, navigating complex regulations, inclusivity, and tailoring policies to the specific needs of small businesses.
Recommendations are provided to improve the policies and make them more effective. These recommendations include enhancing data protection, improving connectivity and infrastructure, streamlining data privacy regulations, adopting flexible workforce policies, and promoting artificial intelligence (AI) adoption. The case study also highlights the importance of addressing barriers faced by underrepresented groups in digital entrepreneurship and suggests peer learning, skill development, and networking as potential solutions.
The study concludes by emphasizing the need for continuous evaluation and improvement of these policies to better support the growth and development of digital entrepreneurship in the United States.
Note: Case study file available on request
Freelancing Gig
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