Tracking the Online Buyer’s Journey: A Behavioural Study Using Google Analytics on Raymond’s Platform

Sakshi Atkalikar, Post Graduate Academic Scholar, Department of Fashion Management Studies, National Institute of Fashion Technology, Ministry of Textiles, Govt of India, Daman Campus.

Dr. Rahul Kushwaha, Assistant Professor, Department of Fashion Management Studies, National Institute of Fashion Technology, Ministry of Textiles, Govt of India, Daman Campus.

 Abstract

This study discusses how consumers engage with Raymond's direct-to-consumer (D2C) e-commerce website through what has been learned using Google Analytics. With increasingly more people doing their shopping online, it can make a huge difference to improve customer experience and business outcomes if one knows how users behave on a website. The research targets two overall aims: first, to identify patterns in user behavior like browsing behavior, navigation flow, and exit points and second, to recommend real-world approaches for optimizing the user journey and increasing conversion rates. The study is based on practical experience during my internship at Raymond's E-commerce team, where I dealt with actual-time analytics data. I studied how visitors interacted with various pages, which products were most in demand, and at what point users would abandon the site. Important metrics such as bounce rate, session duration, traffic sources, and funnel drop-offs were scrutinized thoroughly. The findings revealed helpful insights about user preferences and possible friction points on the site. Based on this, I’ve proposed several ideas to improve the website such as making navigation smoother, highlighting popular products more clearly, and simplifying the checkout process. The study shows how using tools like Google Analytics can give businesses a clearer picture of their online audience and help shape smarter, more effective digital strategies.

Introduction

The way people shop has changed dramatically over the past few years. With online shopping becoming more convenient and accessible, many consumers now prefer browsing products from their phones or laptops rather than visiting physical stores. This shift has pushed brands to rethink how they connect with customers in the digital space. E-commerce is no longer just another sales channel it’s now a crucial part of a brand’s identity and a key way to understand what customers want and how they behave online. Raymond, one of India’s most trusted and well-known menswear brands, has embraced this shift by building its own direct-to-consumer (D2C) e-commerce platform. The idea is to create a smooth and enjoyable shopping experience that matches the brand’s reputation for quality and style. But just having a website isn’t enough it's important to understand how customers are actually using it. Are they finding what they need easily? Which products are they spending time on? Where do they tend to drop off? These questions are important for improving both customer satisfaction and business performance. To answer them, tools like Google Analytics are incredibly useful. They track how people navigate the website, where they come from, how long they stay, and what actions they take. This kind of data can help companies figure out what’s working and what needs fixing whether it is improving site speed, redesigning a page layout, or making the checkout process more user-friendly. In today’s competitive digital market, using website analytics to make informed decisions is not just smart it’s essential. This research highlights how a well-known brand like Raymond can use data to stay ahead of the curve and build a stronger connection with its online customers.

Literature Review

Kotler & Keller (2012) describe the consumer purchase decision process as a five-stage problem-solving approach: problem recognition, information search, evaluation of alternatives, purchase decision, and post-purchase behavior. Understanding these stages is key to analysing how consumers behave when making buying decisions.

Dholakia et al. (2004), social identity significantly influences consumer participation in online communities. They found that a sense of belonging motivates users to engage in collective activities, known as “we-intention.” This includes shared commitment, frequency of interaction, and voluntary participation. Social identity also contributes to emotional support, especially during depressive states.

(Karthik & Swathi, 2013) Web mining applications in e-commerce have been studied extensively, covering aspects such as session identification, behavior analysis, and algorithm optimization. (Jiménez, & Martín, 2017) Google Analytics has become a powerful tool for analysing user behavior, tracking navigation paths, and improving conversion metrics

(Korsching & El-Ghamrini, 2003) Consumer behavior models highlight how users evaluate service quality before making online purchase decisions. (Kotler & Keller, 2012) The five-stage decision-making model remains fundamental to understanding digital buyer behavior. (Dholakia et al., 2004) Social identity plays a vital role in influencing online consumer participation and engagement

Research Methodology

This research adopts a quantitative research method to examine the city-wise performance of Raymond's e-commerce site between January and March 2025. The research design revolves mainly around measuring user interaction, buying behavior, and conversion rate in five major cities: Mumbai, Bengaluru, Delhi, Patna, and Pune. A quantitative method was adopted to spot trends and patterns from numerical data, allowing for a more accurate measurement of the performance of the e-commerce site.

The information for this study was gathered from Raymond's analytics dashboard from January to March 2025. The following are the major metrics to get data.

• Total Users: The number of unique visitors to the website across the five cities.
• Purchase Revenue: The total sales revenue generated from each city.
• Purchase Quantity: The number of units sold in each city.
• Conversion Rate (CVR%): The percentage of website visitors who made a purchase.

The information was mostly gathered using website analysis tools offered by Raymond, which recorded and monitored user activities, purchase behavior, and transaction metrics in real-time. The system automatically logged this data, providing an accurate and complete data set for analysis. The data collection process was purely secondary, based on prior existing user behavior and sales data that could be accessed from the e-commerce site. There were no further surveys, interviews, or direct observations involved in the process since the emphasis was on existing metrics and performance indicators.

 Data Analysis

The data gathered was processed with descriptive statistics to capture overall trends and patterns in users' behavior, buying activities, and conversion rates. In particular, Excel was employed to derive monthly totals, detect patterns of growth, and examine the correlation between users' engagement and buying behavior among the five cities. The analysis further entailed computing percentage differences in revenue, quantity purchased, and conversion rates to determine the improvement and decrease over the period of three months.

Data Analysis & Interpretation

Monthly city wise data

A closer look into Raymond's city-by-city e-commerce performance during the period from January to March brings forth the significant trends among larger metros and Tier-2 cities. The five cities that were analyzed Mumbai, Bengaluru, Delhi, Patna, and Pune exhibited different patterns of user engagement, purchase behavior, and conversion rate. In all, there was a declining trend in the total number of users in all the cities during the three-month period. For example, Mumbai recorded a drastic decline in overall users from 58,106 in January to 28,272 in March, while Bengaluru dropped from 44,349 to 33,943 and Pune from 31,712 to 22,319. While the decline in user traffic was seen in all cities, purchase revenue stabilized in most of them, and in a few, even showed improvement. Observationally, Patna saw a great boost in earnings, from ₹8.5 lakh in January to ₹12.2 lakh in March, showing increased activity of consumers at Tier-2 markets. Like Pune, while its user base decreased, there were steady figures for purchases made, reflecting perhaps a loyal customer base or customers with high intentions.

Purchase quantity figures also indicated an increasing trend in new cities. Patna showed a steep increase in purchases 519 in January and 783 in March while Pune's purchase volume went up from 365 to 428 units over the same timeframe. This increase in order quantities was supported by a steady rise in conversion rates (CVR %). Mumbai’s CVR rose from 1.3% to 1.7%, Patna’s from 1.6% to 2.1%, and Pune’s from 1.2% to 1.9%, reflecting improved user experience, targeted promotions, or higher product relevance. These conversion gains, despite lower website traffic, signal that returning users or well-targeted campaigns played a major role in influencing purchase behavior.

The overall results point to increasing potential in Tier-2 cities, where digital adoption and online shopping habits are gaining strength. While metros such as Mumbai and Bengaluru remain at the top in revenue volume, the performance in cities such as Patna and Pune shows great scope for growth, particularly with targeted digital strategies. Additionally, the bettering CVR in all locations highlights the success of continuing marketing activities and platform updates in converting visits into purchases.

User Trends

All five cities saw a decline in total users from Jan to Mar. Mumbai: 58,106 → 28,272, Bengaluru: 44,349 → 33,943, Delhi: 37,608 → 32,595, Patna: 32,821 → 36,676 (slight growth) and Pune: 31,712 → 22,319

Purchase Revenue

Despite fewer users, revenue remained strong or grew in some cities. Patna: ₹8.5L (Jan) → ₹12.2L (Mar), Bengaluru: Slight fluctuation but steady performance, Pune: ₹6.9L (Jan) → ₹6.7L (Mar) (minor dip)

Purchase Quantity (Order Volumes)

Purchase quantities increased in Patna and Pune. Patna: 519 → 783 and Pune: 365 → 428, indicates growing customer intent and repeat orders.

Conversion Rate (CVR%)

Consistent improvement in CVR across all cities. Mumbai: 1.3% → 1.7%, Patna: 1.6% → 2.1%, Pune: 1.2% → 1.9%, Suggests better user experience and targeting efforts.

Insights & Implications

Tier-2 cities like Patna and Pune show strong growth potential. Higher CVR despite fewer users signals better quality traffic or loyal customers. Performance suggests opportunity for Geo-targeted campaigns and customer retention programs and inventory optimization. The city that observed a decline in purchase revenue from January to March is

Mumbai-January: ₹15,37,407.34, February: ₹13,99,504.23, March: ₹10,21,525.90, and overall decline of approx. 33.6%.

This indicates that while other cities like Patna and Delhi showed growth or stability, Mumbai's revenue consistently declined over the three months, despite an improvement in CVR%. This could be due to a drop in high-value purchases or a shift in consumer behavior in the region.

CVR

These three bar charts show the monthly conversion rate (CVR%) for five cities Mumbai, Bengaluru, Delhi, Patna, and Pune from January to March. In January, Patna had the highest conversion rate at 1.6%, while Delhi had the lowest at 1.1%. In February, both Mumbai and Bengaluru improved to 1.7%, but Patna continued to lead with 2.0%. Pune also saw a noticeable rise to 1.6%. By March, conversion rates increased across most cities, with Patna reaching 2.1%, the highest overall, and Pune rising to 1.9%, showing strong improvement. Overall, Patna consistently had the best conversion performance, while Delhi lagged behind initially but improved over time.

Comparative City-Wise Performance

City-Wise Performance

The graph shows a city-wise breakdown of Raymond's e-commerce performance during January to March in five cities: Mumbai, Bengaluru, Delhi, Patna, and Pune. In terms of monthly purchase revenue, Mumbai consistently posted the highest revenue, followed by Patna, which performed surprisingly well for a smaller city. Pune posted the lowest revenue among the five cities. If we consider the monthly conversion rate (CVR%), which indicates the percentage of users who completed a purchase after visiting the site, Patna was the one with the highest conversion rates across all three months. Pune and Delhi, on the other hand, had the lowest CVR, meaning that fewer visitors from these cities completed a purchase.

The monthly purchases data (number of orders made) also consolidates the robust performance of Patna, which had the largest number of purchases on a monthly basis. Pune had the lowest activity in purchase quantity, meanwhile. Lastly, the graphical presentation of the monthly website users illustrates that Mumbai attracted the most website users, followed by Bengaluru and Delhi. Patna and Pune, meanwhile, had comparatively very few users. Overall, although Mumbai was top in traffic and revenue generation, Patna demonstrated outstanding performance in conversion as well as purchase activity with lower user numbers. Pune also trailed across all performance measures, indicating potential for user interaction and conversion improvement.

Monthly Performance

Total Users: Both January and February saw a healthy number of visitors to the site approximately 30,000 users per month on average. But in March, visitors fell to about 25,500. This might be due to the fact that January saw New Year offers and discounts, and February may have seen campaigns as well, while March was a slower month.

Revenue: February was the highest revenue month, with approximately ₹7.48 lakhs on average. January and March were lower ₹5.74 lakhs and ₹6.08 lakhs respectively. The rise in February may be because of effective marketing or seasonal promotions.

Purchases: February once more had the highest daily average purchases at 419. March was next at 369, and January was the lowest at 301. This indicates that February wasn't merely about visitors’ people actually spent more.

Conversion Rate (CVR): The CVR, which indicates the number of visitors who actually bought something, increased each month. January stood at 1.03%, February at 1.37%, and March at 1.43%. What this translates to is that while fewer visitors came in March, they were more likely to make a purchase likely because they were more serious or more targeted.

 Findings

Raymond's city-by-city e-commerce performance between January and March 2025 showed some interesting trends. There was a steady decline in overall user traffic across all five cities of Mumbai, Bengaluru, Delhi, Patna, and Pune. In spite of this decrease, cities such as Patna and Pune showed a strong increase in purchase numbers and conversion rates, indicating their increasing potential as Tier-2 markets. Mumbai and Bengaluru, though experiencing declining traffic, were able to preserve steady purchase revenue, indicating a solid base of high-intent users. One of the core findings is a high lift in Conversion Rate (CVR%) in all the cities, with fewer yet higher-quality or committed users finalizing purchases. Of particular note here is the healthy growth curve indicated by both revenue and unit number of purchase sales in Patna, signifying increased consumer trust and e-readiness among upcoming markets.

Suggestions

Strengthen digital campaigns in Tier-2 cities like Patna and Pune, where rising conversion rates and purchase volumes indicate growing customer engagement. Focus on retention strategies for metro cities such as Mumbai and Bengaluru to maintain revenue levels despite falling user counts. Enhance personalization and targeting to reach high-intent users through data-driven marketing. Invest in loyalty programs or exclusive offers to retain existing customers and improve repeat purchase rates. Explore hyperlocal marketing initiatives in emerging cities to build brand presence and trust at the grassroots level.

Conclusion

This research concludes that although overall user traffic on Raymond's online portal fell between January and March 2025 across all cities, purchase trends and conversion rates paint a better picture. Tier-2 cities such as Patna and Pune were among the standout performers, reporting higher volumes of purchases and better conversion rates, which indicate increasing digital activity and shopping maturity in these markets. Cities like Mumbai and Bengaluru, while facing lower user visits, still played a major role in overall revenue, indicating the existence of a stable and loyal customer base. The overall increase in conversion rates in all cities indicates the success of Raymond's marketing efforts and enhancements in user experience. These observations highlight the value of geo-targeted advertising, one-on-one marketing, and strategic targeting of high-potential markets. Based on these findings, Raymond can enhance its online campaigns, improve customer satisfaction, and sustain growth in mature and growth markets.

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