Digital Footprint Analysis of Raymond on Consumer Search and Web Traffic

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

Dr Vidhu Sekhar P, Assistant Professor, Department of Fashion Management Studies, National Institute of Fashion Technology, Ministry of Textiles, Govt of India, Daman Campus.

Abstract

In the evolving landscape of digital marketing, understanding consumer behaviour has become an essential metric for strategic decision-making. This study aims to investigate the correlation between Google search volume and website clicks for Raymond and its set of brands over a two-year time. The research aims to uncover patterns in consumer interest and assess how effectively it translates into online engagement. The analysis also incorporates campaign performance metrics— measured through growth of google search volume of keywords—to explore their influence on web traffic. Using statistical methods including correlation analysis, the study evaluates whether fluctuations in digital search interest are significantly associated with variations in website engagement. The findings provide insights into the effectiveness of Raymond’s digital marketing efforts and identify potential gaps in the consumer journey from online interest to website interaction. This research also offers a comparative study by benchmarking Raymond's performance against its competitors, thereby scrutinizing brand visibility and engagement in India. Overall, this study contributes to a deeper understanding of the digital consumer journey in the Indian fashion and lifestyle sector.

Keywords

Digital Marketing, Consumer Behaviour, Google Search Volume, Campaign Effectiveness, Competitive Benchmarking.

1. Introduction

In the era of digital transformation, the fashion and lifestyle industry are increasingly leveraging data-driven strategies to understand consumer behaviour and optimize marketing effectiveness. With the widespread use of search engines and digital media platforms, consumer journeys often begin online, making it essential for brands to monitor and analyse metrics like search volume, impressions, reach, clicks, and website visits. These indicators offer critical insights into customer interest, brand visibility, and engagement. As online visibility becomes a determinant of competitiveness, businesses must assess whether their digital marketing efforts are effectively converting consumer interest into actionable engagement.

Raymond Group, a legacy brand in the Indian fashion and textile sector, has diversified its portfolio through sub-brands such as Raymond, Park Avenue, ColorPlus, Ethnix and Raymond Ready to Wear. As consumer attention continues to shift to digital channels, understanding how these brands perform online is vital to maintaining market leadership. While Raymond has historically been known for its offline retail dominance, its ability to engage consumers online is now equally important. Given the competitive pressure from brands like Van Heusen, Allen Solly, and Arrow, it becomes imperative to analyse Raymond’s digital performance.

The primary objectives of this research are to analyse monthly trends in digital search and engagement, explore the statistical relationship between Google search interest and website clicks, and benchmark Raymond’s performance against key competitors. By employing correlation analysis on two years of time-series data, this study aims to derive actionable insights that can enhance the effectiveness of future marketing strategies. The results will not only aid Raymond’s marketing teams in optimizing campaign targeting and timing but also contribute to broader academic literature on digital consumer behaviour and performance analytics in the Indian fashion industry.

1.1 Digital Marketing

In the rapidly evolving landscape of modern commerce, digital marketing has become a critical component of strategic business decision-making. As consumer interactions increasingly shift to online platforms, advertising too has moved from traditional channels to the digital space, offering brands measurable, data-driven approaches to target consumers with precision (Taneja & Vij, 2018). The rise of digital ecosystems enables businesses not only to reach a wider audience but also to create interactive pathways where consumers can discover, engage, and convert—often within a single online journey (Rzemieniak, 2015). Rzemieniak (2015) emphasizes the significance of performance-based models—such as cost-per-click (CPC) and cost-per-action (CPA)—as critical methods in tracking campaign return on investment.

One of the defining advantages of digital marketing is its ability to generate real-time performance metrics. Campaign effectiveness can now be assessed through detailed indicators such as impressions, click-through rates (CTR), leads, and conversions—allowing marketers to optimize campaigns dynamically and efficiently (Kolesnyk & Kostynets, 2023). This evolution has led to a greater reliance on analytical frameworks to understand not just reach, but engagement, sentiment, and the causal impact of advertisements.

Moreover, digital marketing integrates various tools such as search engine optimization (SEO), content marketing, and social media advertising to create a comprehensive brand experience. Platforms like Google and Facebook empower marketers to set specific objectives and target consumers based on their behaviours, demographics, and interests. For instance, Facebook metrics such as frequency, reach, and link clicks offer detailed insight into user engagement, which is crucial for measuring campaign success (Kolesnyk & Kostynets, 2023).

1.2 Consumer Behaviour

Consumer behaviour broadly refers to the cognitive and emotional processes individuals undergo while discovering, evaluating, and interacting with products or services online. With the proliferation of data analytics tools, marketers today are increasingly able to observe and interpret these behaviours in real-time, making strategic adjustments accordingly (Theodorakopoulos & Theodoropoulou, 2024).

A landmark study by Jerath, Ma, and Park (2014) emphasizes that consumer engagement with search engine results—particularly the variance in click behaviour on organic versus sponsored links—is highly dependent on keyword popularity. Their findings reveal that users who search for less popular keywords are generally more involved in their decision-making process and exhibit a greater likelihood of clicking on sponsored results, indicating a higher intent to purchase. This behaviour aligns with the "purchase funnel" theory, which suggests that deeper engagement is typically observed in the later stages of the consumer journey.

In contemporary digital ecosystems, user activity—including page visits, click-through rates, navigation patterns, and time spent on site—can be tracked and analysed to map detailed consumer journeys. Big data analytics plays a transformative role in this context by allowing firms to decode user intent and personalize interactions across digital platforms (Theodorakopoulos & Theodoropoulou, 2024). Moreover, search behaviour offer more than just descriptive metrics—they are indicative of consumer involvement, readiness to purchase, and overall engagement levels. These behavioural signals not only inform ad placement and keyword bidding strategies but also contribute to real-time campaign optimization (Jerath et al., 2014).

1.3 Google Search

Search engines, especially Google, play a crucial role in digital marketing by offering insights through search volumes and intent. Understanding user intent—whether to buy, explore, or research—enables marketers to segment audiences and tailor strategies. For example, transactional searches signal conversion readiness, while informational ones reflect early-stage interest. This alignment between intent and marketing goals forms the basis for effective SEO and SEM strategies.

While not tied to a single study, both Ziakis et al. (2019) and Durica & Svabova (2015) emphasize the convergence of SEO and SEM in driving online visibility. SEO provides a cost-effective foundation by enhancing organic discoverability, while SEM offers speed, control, and targeting precision through paid strategies. Best practices involve leveraging Google Analytics and search console tools to identify high-intent keywords, optimizing landing pages for those terms, and tracking the user journey from click to conversion.

SEO refers to the practice of improving a website’s visibility in unpaid (organic) search results. It involves technical enhancements, keyword targeting, and content optimization to align with search engine algorithms (Ziakis et al., 2019). Factors such as keyword inclusion in title tags, meta descriptions, URL structure, page loading speed, backlink quality, and mobile responsiveness all contribute to higher rankings on Search Engine Results Pages (SERPs).

On the other hand, SEM involves paid strategies, such as Pay-Per-Click (PPC) advertising, that ensure visibility in search results through bidding on keywords. Both SEO and SEM are complementary: while SEO builds long-term visibility, SEM allows for immediate reach and targeting precision. As Durica and Svabova (2015) highlight, combining both approaches lead to a more robust marketing strategy, especially when backed by analytics-driven insights on local search behaviour and website engagement.

2. Literature Review

Taneja & Vij (2018) present a detailed framework for digital advertising, covering planning, execution, and evaluation. They highlight the shift from traditional to digital media, focusing on elements like objective setting, budgeting, agency collaboration, and content creation. The study emphasizes SEO, keyword selection, and tools like Google Ads. It outlines steps from scheduling to audience targeting and analysis, stressing digital advertising's flexibility, control, and measurability, and the value of digital metrics in assessing campaign impact.

Kolesnyk & Kostynets (2023) examine how Facebook-based digital activations enhance national advertising campaigns. They highlight audience targeting, measurable outcomes, and brand perception shifts. Key metrics discussed include reach, impressions, frequency, and clicks, along with evaluation tools like ad recall and sentiment analysis. The study also critiques Facebook’s algorithmic biases and limitations, emphasizing the challenges of campaign assessment. Their comparison of experimental and observational methods offers valuable insights for analysing behavioural data and time-based campaign performance.

Rzemieniak (2015) analyses online advertising models, including impression-based (CPM), interaction-based (CPC), and action-based (CPA, CPL, CPS) strategies, assessing their impact on brand visibility and customer acquisition. The study highlights performance metrics like ROI, CTR, and conversion rates for campaign optimization. Emphasizing digital ads’ role in boosting entrepreneurship, it underlines their market reach and cost-effectiveness. The research offers valuable insights into user engagement and conversion behaviour.

Jerath & Park (2014) analysed over 1.6 million keyword searches from a top Korean search engine to explore how keyword popularity affects consumer click behaviour. They found that users of less popular keywords clicked more and showed higher engagement with sponsored links, indicating stronger purchase intent. The study supports the purchase funnel model and highlights the value of behavioural segmentation for targeted digital marketing, emphasizing the strategic use of keyword data and ad placement.

Theodorakopoulos and Theodoropoulou (2024) conducted a systematic review on how big data analytics enhances understanding of consumer behaviour in digital marketing. Tools like Google Analytics and SimilarWeb help track metrics such as clicks, visits, and session duration, mapping the customer journey. The study highlights the growing use of predictive analytics and personalization to refine strategies but also raises ethical concerns around privacy. The authors call for further research on how data-driven personalization influences long-term consumer trust and brand loyalty.

Ziakis, C., et al., (2019) conducted a two-phase study combining a literature review and empirical research to identify key SEO factors influencing Google rankings. Analysing 24 factors using Spearman correlation, they found strong links between rankings and elements like keyword in URL, SSL presence, backlinks, domain age, and content length. Structural aspects like internal linking were also significant, while factors like keyword density showed weaker impact. The study underscores SEO’s evolving nature and the need for ongoing strategy updates.

Durica & Svabova (2015) examined how company characteristics affect Google local search rankings. They identified qualitative factors like business photos and accurate descriptions, and quantitative ones such as citations and Google+ followers. Using correlation and regression analysis, they found that complete, keyword-rich profiles with visuals ranked higher. Even small optimizations improved visibility and engagement. The study emphasizes the importance of structured SEM practices in boosting local digital presence and driving potential customer conversions.

3. Research Methodology

This research is based on a quantitative approach, employing statistical methods to investigate the correlation between digital interest (as measured by Google search volume) and online engagement (measured through website clicks) for Raymond and its associated brands over a two-year period.

3.1 Objectives

• To analyse the relationship between Google search volume and website click data for Raymond and its associated brands over a two-year period.
• To evaluate the effectiveness of digital campaigns by observing trends in keyword-based search interest and web engagement.
• To identify behavioural patterns in digital consumer journey.
• To benchmark Raymond’s digital performance against its competitors within the Indian fashion and lifestyle market.

3.2 Research Design

The research follows a descriptive and analytical design, focused on identifying patterns, relationships, and trends over time. The study is non-experimental and relies on observational data collected through digital platforms. The design is cross-sectional for competitor benchmarking and longitudinal for analysing changes in search volume and website interaction over time.

• Type of Study: Quantitative, descriptive, and correlational
• Data Source: Secondary (Google Search Volumes, campaign reports, Power BI analytics dashboards)

3.3 Tools and Techniques Used for Analysis

To process and analyse the data, the following tools were used:

• Microsoft Excel: Used for organizing, cleaning, and visualizing raw data. Excel functions and pivot tables were applied to summarize key variables.
• SPSS (Statistical Package for the Social Sciences): Used for performing statistical test, Pearson correlation to explore relationship between search volume and website traffic data.

4. Data Analysis

4.1 Hypothesis Testing

4.1.1 H₀: There is no statistically significant correlation between Google search volume and website clicks.

H₁: There is a statistically significant positive correlation between Google search volume and website clicks.

Interpretation

A Pearson correlation analysis results revealed a strong, positive, and statistically significant correlation (r = .695, p < .001), indicating that increases in search volume are significantly associated with increases in web traffic. This supports the assumption that higher online search interest reflects greater consumer intent and engagement.

The primary purpose of conducting the Pearson correlation test was to statistically evaluate the relationship between Google search volume and store website clicks for Raymond and its associated brands. In the context of digital marketing, understanding this relationship helps determine whether increased consumer interest, as indicated by higher search volumes, translates into actionable engagement in the form of store website clicks.

Chart 1. Chart representing Growth Trend of Sum of Google Search Volume and Store Website Clicks for Raymond Brands

In the subsequent quarter, October to December 2024, Raymond broadened its targeting to include both NCCS A and B segments across all age groups in key campaign regions. This shift significantly expanded the brand’s reach, resulting in a substantial increase in search volume—from approximately 2,50,000 to nearly 7,00,000 searches. Store website clicks also rose sharply during this period, mirroring the increase in search interest.

H0: There is no significant variation in consumer search behaviour for Raymond's products across keyword intent types, regions, or months. H1: Consumer search behaviour for Raymond's products varies significantly by keyword intent, with higher search volumes in specific regions and during specific months, indicating seasonal and region-specific purchase intent.

Chart 2. Chart representing Growth Trend of Type of Keywords over the span of 3 years for Raymond Lifestyle Limited

Interpretation

• Transactional keyword search volume grew steadily, with a sharp rise post July 2024, peaking in Nov–Dec 2024. This may align with festive or wedding season campaigns.
• Neutral keywords also show a rise in 2024, especially from August onward, implying increased interest or demand.
• Informational searches remained relatively flat, hinting at the audience being more purchase-driven than research-driven

Chart 3. Chart representing Search Volume for Type of Keywords across States and Regions of India for Raymond Lifestyle Limited.

Interpretation

• Transactional keywords dominate across all regions, especially in Maharashtra, Uttar Pradesh, Delhi, and Karnataka followed by Gujarat, Tamil Nadu, Bihar, and West Bengal– indicating high commercial intent.
• Neutral keywords show consistent but lower engagement. Informational keywords are minimal in comparison – suggesting a lower focus on awareness-driven or educational search

These patterns confirm that both regional and seasonal factors influence consumer search behaviour for Raymond's products, thereby supporting the alternate hypothesis and rejecting the null hypothesis.

H0: There is no significant difference in consumer online search behaviour between Raymond's Brands and their competitors across all the categories.

H1: There is a significant difference in consumer online search behaviour between Raymond's Brands and their competitors across all the categories.

Chart 4. Chart representing Search Volume of Raymond’s Brands.

Interpretation

• Park Avenue, ColorPlus, and Ethnix show a significant increase - 101.43%, 349.57%, 198.89% respectively - in 2024 as compared to brand search volumes in 2023 — indicating growing interest and consideration towards the brands.
• Raymond-Ready to Wear and Made to Measure also saw a sharp rise – 3871.70% and 326.77% - in brand search volumes in 2024 compared to minimal volumes in prior year.

Chart 5. Chart representing Search Volume of Competitor Brands.

Interpretation

• Manyavar, Allen Solly, and Van Heusen dominate consistently across all three years, with

Manyavar peaking among the competitors.

• Emerging brands like Rare Rabbit and Tasva are gradually gaining
• Arvind, Siyaram’s, and The Linen Club have lower search volumes compared to market leaders but remain steady.

In 2024 the top competitor brand, Manyavar, recorded over 2.5 million searches, while Raymond’s brand, Ethnix, peaked at around 5,67,680 searches — showing approximately 4x gap in consumer search interest.

Raymond’s brands like Ethnix and ColorPlus experienced over 100% growth in search volumes from 2022 to 2024. In contrast, competitor brands like Allen Solly and Van Heusen showed a more modest growth of approximately 10–15%, indicating Raymond's improving momentum despite lower base volumes.

Chart 6. Chart representing Search Volume of Raymond’s Competitor Brands Categorically.

Chart 7. Chart representing Search Volume of Raymond’s Brands Categorically.

The search volumes for categories like T-shirts, jeans, and shirts are notably higher for competitor brands such as U.S. Polo, Tommy Hilfiger, and Van Heusen, indicating a clear consumer preference for casual and semi-formal wear outside of Raymond’s portfolio. On the other hand, suits and blazers see greater search interest for brands like Rare Rabbit, Blackberry, and Louis Philippe, suggesting that these competitors offer more appealing style choices or better value propositions in the occasion wear and business-formal segments.

While Raymond’s suiting category does generate considerable search volume, it is primarily concentrated within a few sub-brands, reflecting a lack of diversity and broader appeal within the full Raymond portfolio. Hence, Raymond’s focus remains more on formalwear like suiting and shirting, which, despite moderate search volume, lack the broad appeal and scale of competitor brands.

Additionally, competitors such as Manyavar and Tasva dominate the traditional and ethnic wear markets, including sherwanis and kurtas, further highlighting Raymond’s limited reach in festive and occasion wear categories. Brands like Tommy Hilfiger with jeans, Manyavar with sherwanis, and Louis Philippe with blazers exhibit clearer consumer associations with specific categories, showcasing a more focused brand positioning. Raymond’s stagnant or declining search trends signal the need for digital revitalization and a more targeted consumer strategy to improve visibility and align with changing fashion preferences.

2. Findings

A strong positive correlation (r = 0.695, p < 0.001) was found between Google search volume and store website clicks for Raymond’s brands, highlighting that higher online search interest is linked to increased consumer engagement, especially during campaign periods.

Transactional keywords were the most prevalent across all three years, indicating a consumer base with high purchase intent. Maharashtra, Uttar Pradesh, Delhi, and Karnataka recorded the highest volumes of transactional keywords, marking them as key priority markets.

The period between July and December 2024 saw the most significant rise in search volumes, coinciding with India’s festive and wedding seasons. Campaigns during this time, especially Performance Max activations, led to a sharp rise in store website clicks, from around 250,000 to nearly 700,000.

Raymond’s brands demonstrated growth in search volumes but lagged behind competitors. Park Avenue had the highest search volume at 7,34,570 in 2024, far below competitors like

U.S. Polo, Louis Philippe, Van Heusen, and Allen Solly. Manyavar surpassed 2.5 million searches, indicating a significant visibility gap.

Ethnix and ColorPlus saw over 100% growth from 2022 to 2024, while competitors like Van Heusen and Allen Solly continued to lead in consumer interest, albeit with slower growth (10– 15%).

Raymond performed well in traditional categories like suiting and shirting but struggled in casualwear and occasion wear. Competitors such as U.S. Polo, Tommy Hilfiger, and Van Heusen dominated casual categories, while Rare Rabbit, Blackberrys, and Manyavar led in occasion wear, highlighting Raymond’s limited category visibility.

3. Suggestions

To enhance digital engagement and brand visibility, Raymond should prioritize region-specific and seasonally aligned campaign strategies, with a particular focus on high-performing markets such as Delhi, Gujarat, Bihar, West Bengal, Karnataka, Maharashtra and Uttar Pradesh. Emphasizing high-intent transactional keywords through both SEO and SEM efforts will allow for deeper penetration in purchase-driven segments.

Product category diversification must be addressed by increasing promotional efforts in underrepresented yet high-demand categories such as T-shirts, jeans, kurtas, and casual shirts. This should be supported by content-driven strategies including blog articles, influencer collaborations, and localized video content that builds awareness among new-age digital consumers.

Raymond must strengthen its digital infrastructure with real-time keyword performance tracking and optimising competitive benchmarking dashboard on Power BI. Optimize Raymond’s brand websites for high-intent transactional keywords and local SEO. Invest in paid campaigns (SEM) to compete with top-ranking competitor pages in key categories. This will enable the marketing team to identify category gaps, consumer shifts, and digital opportunities swiftly, allowing for agile marketing decisions and improved ROI from digital campaigns.

4. Conclusion

The research establishes a clear and statistically valid relationship between digital interest and consumer engagement in the case of Raymond’s online presence. A correlation coefficient of 0.695 confirms that increased Google search activity meaningfully translates into website interaction, affirming the critical role of search visibility in driving digital-to-store conversions.

While Raymond’s campaigns—especially in 2024—have delivered encouraging growth in engagement and keyword traction, the brand continues to trail behind its competitors in key digital metrics. The 3x disparity in search volume between Raymond’s top-performing brand and that of its leading competitors highlights a competitive gap in digital visibility and consumer consideration. Additionally, the brand’s over-reliance on formalwear categories limits its relevance in a market that increasingly values versatility, casual fashion, and festive occasion wear.

To remain competitive in India’s digitally driven fashion ecosystem, Raymond must adopt a more data-led, agile, and consumer-centric marketing strategy. This includes expanding category-level visibility, optimizing keyword and campaign performance, and building a more holistic digital presence. Only through strategic digital repositioning and continuous optimization can Raymond strengthen its brand equity and secure long-term relevance in the evolving fashion and lifestyle market landscape.

References

Taneja, G., & Vij, S. (2018). Dynamics of a digital advertising campaign. SSRN. https://ssrn.com/abstract=3308035

Kolesnyk, B., & Kostynets, I. (2023). Measuring digital advertising effectiveness on Facebook as part of a national brand advertising campaign. Economics, Finance and Management Review, (3), 68–82. https://doi.org/10.36690/2674-5208-2023-3-68-82

Rzemieniak, M. (2015). Measuring the effectiveness of online advertising campaigns in the aspect of e-entrepreneurship. Procedia Computer Science, 65, 980–987. https://doi.org/10.1016/j.procs.2015.09.063

Jerath, K., Ma, L., & Park, Y.-H. (2014). Consumer click behavior at a search engine: The role of keyword popularity. Journal of Marketing Research, 51(4), 480–486. https://doi.org/10.1509/jmr.13.0099

Theodorakopoulos, L., & Theodoropoulou, A. (2024). Leveraging big data analytics for understanding consumer behavior in digital marketing: A systematic review. Human Behavior and Emerging Technologies, Article ID 3641502. https://doi.org/10.1155/2024/3641502

Ziakis, C., Vlachopoulou, M., Kyrkoudis, T., & Karagkiozidou, M. (2019). Important factors for improving Google search rank. Future Internet, 11(2), 32. https://doi.org/10.3390/fi11020032

Durica, M., & Svabova, L. (2015). Improvement of company marketing strategy based on Google search results analysis. Procedia Economics and Finance, 26, 454–460. https://doi.org/10.1016/S2212-5671(15)00873-4