Introduction

Data-driven marketing has become a revolutionary method in the digital age, enabling organisations to utilise data analytics to customise marketing tactics, enhance consumer interaction and stimulate corporate growth. With technological advancements, the quantity and diversity of available data have markedly increased, resulting in a transition from intuition-driven marketing to tactics based on real-time data insights. Data-driven marketing utilises customer behaviour, preferences and demographics to guide decisions, with the objective of improving personalisation, targeting and response in a highly competitive digital environment. Recent literature underscores the significance of real-time data analytics in facilitating rapid adaptation by corporations to market trends, shifts in customer behaviour and competitive challenges.

A study by Kumar et al. (2022) indicates that real-time analytics enable marketers to consistently observe and adapt to changing client requirements, thus promoting a more dynamic and flexible marketing approach. These data-driven insights have been crucial in enhancing customer lifetime value, decreasing customer acquisition expenses and optimising return on investment (ROI) on marketing efforts. Moreover, progress in artificial intelligence (AI) and machine learning (ML) has enhanced the capabilities of data-driven marketing. Wang et al. (2023) examine how AI-driven technologies empower marketers to forecast future trends and tailor content with exceptional precision, enhancing both engagement and conversion rates. These solutions also provide predictive analytics, essential for forecasting client needs and personalising interactions across several digital touchpoints. A crucial element of data-driven marketing is the capacity to integrate extensive unstructured data from various sources, such as social media, search engines and customer feedback. SEMrush, a premier tool in this field, illustrates the utilisation of data-driven marketing by offering insights into search behaviour, competitive positioning and content efficacy. Chaffey (2023) asserts that these technologies enable organisations to obtain an extensive understanding of the client journey and to strategically modify marketing initiatives in real time. SEMrush is recognised for its comprehensive search engine optimisation (SEO), pay-per-click (PPC) and content marketing functionalities, utilised by organisations such as eBay to enhance their marketing campaigns through real-time data and actionable insights.

Real-time analytics has emerged as an essential element in the decision-making processes of contemporary enterprises, especially for substantial e-commerce platforms such as eBay, where consumer preferences and competitive dynamics fluctuate swiftly. Through the integration of real-time analytics technologies, eBay may acquire instantaneous data on customer behaviour, search trends and market dynamics, facilitating more agile and accurate marketing campaigns. SEMrush, a premier analytics tool, has been important in assisting eBay and other prominent corporations in monitoring and adapting to these developments in real time, utilising insights from SEO, PPC campaigns, content marketing and competition analysis. A paper by Chen and Chen (2022) highlights that real-time data access allows marketers to optimise campaigns in real time, enhancing engagement rates and ROI by adjusting plans based on rapid feedback. This functionality is especially advantageous for e-commerce leaders such as eBay, as SEMrush’s tools provide real-time modifications to keywords, content and ad placements based on live data, hence optimising the relevance and efficacy of marketing initiatives.

Research conducted by Johnson et al. (2023) revealed that e-commerce firms employing real-time analytics experienced markedly enhanced customer satisfaction and retention rates, enabling them to promptly resolve concerns, modify product displays and optimise their value propositions. SEMrush offers eBay critical insights into organic search performance and competitive strategies, allowing the marketing team to identify popular goods or keywords and promptly modify their approach. This adaptability is particularly vital in the dynamic online retail sector, where demand can vary significantly due to external influences such as seasons, events or economic changes. Gupta and Singh (2024) demonstrate that real-time data analysis improves strategic alignment among departments, promoting a more unified decision-making process. At eBay, SEMrush’s real-time insights enhance collaboration among marketing, product and customer service teams, enabling coordinated actions based on common data. This collaborative strategy enhances decision-making efficiency and connects activities with shared organisational objectives, such as boosting sales or strengthening brand visibility.

Review of Literature

The essence of data-driven marketing is its capacity to leverage real-time insights to build plans that are pertinent and adaptive. Chen and Zhang (2023) assert that organisations leveraging customer data to shape marketing strategies experience enhanced ROI and customer engagement, facilitated by the capacity to create more targeted communications that appeal to particular populations. By utilising data from sources such as web analytics, social media engagement and purchase behaviours, businesses may develop campaigns that effectively attract and retain customers.

Personalisation is a paramount advantage of data-driven marketing. Research conducted by McCarthy and Willis (2023) indicates that 80% of consumers are more inclined to make a purchase when firms provide personalised experiences. Personalisation may manifest as product recommendations, tailored communications or dynamic website content that adapts according to user behaviour. The transition from uniform marketing to personalised experiences has been facilitated by developments in ML and data analytics, allowing organisations to predict client demands and customise offerings accordingly. Real-time data analytics is essential in data-driven marketing, allowing organisations to make rapid adjustments based on live data.

Wang et al. (2024) highlight the influence of real-time information on decision-making, indicating that organisations employing live analytics can enhance campaigns throughout execution, hence preventing missed chances. Real-time data can detect changes in client mood or behaviour, enabling organisations to adjust their strategy for immediate pertinence. For example, enterprises such as eBay utilise SEMrush to track SEO, PPC and content efficacy in real time, facilitating immediate modifications based on prevailing data trends. The capacity to respond swiftly provides a competitive edge, especially in e-commerce, where consumer tastes and market dynamics can fluctuate rapidly.

Gupta and Rao (2024) assert that real-time analytics allow organisations to enhance targeting precision, minimise advertising expenditure waste and attain elevated conversion rates, highlighting the significance of timeliness in efficient data-driven marketing. Data-driven marketing is progressively guided by sophisticated analytical models, including predictive and prescriptive analytics, enabling marketers to foresee future trends and formulate plans proactively. Predictive analytics, enhanced by AI and ML, utilises previous data to anticipate future behaviours, such as purchase probability or churn risk, therefore, aiding marketers in more efficient resource allocation (Kumar & Smith, 2022). Prescriptive analytics advances by suggesting specific activities to get desired objectives, providing firms with a data-validated and results-oriented strategy playbook.

A recent study by Rosen and Hall (2023) emphasises the incorporation of these models into customer relationship management (CRM) systems, hence optimising the workflow for marketers by directly associating data insights with actionable strategies. The incorporation of these models into CRM platforms enhances efficiency and elevates the uniformity of data-driven marketing initiatives across various customer touchpoints.

SEMrush provides a comprehensive array of analytical tools that jointly facilitate strategic decision-making in marketing. A research by Roberts and Hill (2023) indicates that tools such as SEMrush enable marketers to assess keyword efficacy, optimise advertising expenditures and evaluate rival actions—all in real time. The capacity to operate in real-time is especially vital in digital marketing, as minor delays in responding to emerging trends can result in lost opportunities. SEMrush’s comprehensive keyword research assists businesses in aligning their content with customer search behaviours, hence improving exposure and engagement in competitive markets.

Evans et al. (2022) assert that SEMrush’s competitor analysis tool is essential for marketers aiming to distinguish their methods. SEMrush enables users to track competitors’ keywords, PPC campaigns and content efficacy, offering insights into effective and ineffective strategies within a specific industry. This competitive data can guide decisions regarding content creation, budget distribution and campaign scheduling, enabling organisations to secure an advantage by responding to market shifts ahead of their competitors. The research indicated that firms utilising SEMrush for competition analysis achieved a 25% enhancement in campaign performance relative to those employing restricted or conventional solutions.

Content strategy is another domain in which SEMrush excels as a decision-making instrument. SEMrush’s SEO toolset allows marketers to examine organic search trends, keyword ranks and backlink profiles, all of which are essential elements of an effective content marketing plan. SEMrush’s content analysis tools furnish organisations with an in-depth comprehension of their audience’s search inclinations, enabling the production of more focused and captivating content. This data-driven methodology for content production enhances the probability of attaining superior search engine ranks while aligning with consumer preferences, hence augmenting engagement and facilitating transactions.

SEMrush functions as an effective instrument for administering and enhancing PPC campaigns. SEMrush enables marketers to make swift modifications that improve the ROI of their advertising expenditure by offering real-time information into ad performance, keyword bidding tactics and competitor PPC analysis. According to research by White and Chen (2023), organisations utilising SEMrush for PPC campaign optimisation achieve an average cost efficiency gain of 30%, attributable to its capacity to identify high-performing keywords and discard poor ones. SEMrush’s ad builder and analytics tools enable marketers to perpetually test and optimise advertisements, leading to a more efficient allocation of money towards the most effective PPC campaigns.

SEMrush’s utility transcends the marketing department, since its insights facilitate cross-functional decision-making across organisations. SEMrush’s comprehensive dashboards and reporting tools facilitate the seamless sharing of data across departments, including sales, product development and customer service, thereby aligning decisions with unified business objectives. Roberts (2023) discovered that organisations utilising SEMrush data across several departments showed significant enhancement in strategic alignment, as teams could converge on common metrics and KPIs, hence improving the overall efficacy of marketing and sales initiatives.

Research Objective

• To analyse the Website Traffic of the eBay using SEMRUSH tool.
• To analyse the organic Search vs. paid search eBay.
• To identify the Traffic channels of eBay.
• To identify the devices used by the users of eBay.

Research Gap

Although SEMrush is commonly utilised by corporations such as eBay, limited research examines the direct influence of real-time data from SEMrush on eBay’s decision-making and competitive strategies. The research is deficient in detailing how eBay employs SEMrush to promptly modify marketing strategies in reaction to real-time market changes.

Research Methodology

The study methodology employs a methodical approach to data collection, analysis and interpretation to examine the website traffic of eBay’s e-commerce platform using SEMrush. This study seeks to examine traffic sources, user interaction, keyword performance and other critical metrics to deliver a comprehensive evaluation of eBay’s online operations. The primary instrument utilised for data collection is SEMrush, which offers extensive insights into website traffic and SEO performance. The research employed a non-probability purposive sampling technique, designating eBay’s website as the focus for traffic analysis. Data segmentation is predicated on traffic sources (organic, paid, referral and social), devices (desktop versus mobile) and geographical areas. This digital marketing tool provides a comprehensive dashboard for tracking website traffic, keywords, backlinks and competitive analysis. This study primarily uses the ‘traffic analytics’ and ‘organic research’ modules.

Analysis and Interpretation

Demographic analysis is the methodical investigation of population characteristics, emphasising specific demographic aspects such as age, gender, income, education, occupation and geographic location. It enables organisations to comprehend the makeup and dispersion of their target audience, facilitating informed decisions about marketing, product development and strategic planning. By analysing trends and patterns in these demographic characteristics, organisations may more effectively customise their offers to satisfy the requirements and preferences of various segments, optimise resource distribution and improve overall consumer engagement. The demographic overview indicates that eBay’s user base is predominantly male, consists largely of younger individuals and is primarily located in the United States. These data can inform eBay’s marketing and user experience strategies to coincide with the interests and behaviours of these critical categories.

 

Figure 1. Demographic Analysis Using SEMRUSH.

 

Figure 1 reveals that eBay possessed over 214.7 million users globally across all devices during this timeframe. The data indicate a 2.16% decrease in users relative to the prior period. This decline indicates a minor reduction in traffic, either due to seasonal factors, competitive market fluctuations or shifts in user preferences. The research indicates that 51.38% of eBay’s users are male. This predominantly male user base indicates a nearly even gender distribution, albeit with a small preference for males. Comprehending this demographic analysis enables eBay to customise its marketing and user engagement techniques to appeal primarily to a male audience, while also promoting inclusivity for female users. The 18–24 age group comprises 22.8% of eBay’s user base, rendering it a substantial demographic component. This demographic comprises younger folks, frequently regarded as technologically proficient and digitally engaged. Focussing on this demographic may be crucial for eBay, as it could guide plans for platform functionalities, product classifications and marketing tactics that resonate with younger consumers. The statistics indicates that 57.42% of eBay’s traffic originates from the United States, signifying that a predominant portion of the platform’s users resides in the United States. The substantial US user base is pertinent to eBay’s regional marketing and commercial strategy, affecting aspects such as product offerings, language preferences and localised promotions aimed at American consumers.

Socioeconomic Analysis

Socioeconomic analysis examines the interplay between economic activities and social aspects within a specific population or community. This examination investigates variables including income levels, education, employment, housing, health and social class to comprehend socioeconomic position, quality of life and general well-being of individuals and communities. Through the evaluation of these characteristics, socioeconomic analysis yields insights into inequities, economic possibilities, resource accessibility and social mobility, thereby informing policy formulation, company strategy and community development.

 

Figure 2. Socioeconomic Analysis of eBay Using SEMRUSH.

 

Figure 2 suggest that the predominant demographic of eBay users comprises households consists 3–4 individuals (41.27%). This suggests that eBay’s user demographic may be family-oriented, with a considerable segment residing in medium-sized households. Smaller and larger households are under-represented. The low-income sector is the most significant, including 65.55% of users. This indicates that a significant proportion of eBay users may be price-sensitive, perhaps in pursuit of bargains and economical alternatives on the platform. 42.55% of eBay users declare full-time employment status, suggesting that nearly half possess steady jobs. This may indicate they possess a consistent income; nevertheless, as evidenced by the income level measure, they can still belong to lower-income demographics. Compulsory or high school education is the highest attained level by 49.84% of the user base. This indicates that almost 50% of eBay users have not attained higher education, which may be associated with the reported lower income level. The research indicates that eBay’s principal users are predominantly from low-income origins, employed full-time, residing in households of 3–4 persons and possessing an educational attainment of high school or lower. This profile may affect eBay’s marketing and product strategy to focus on affordable items that resonate with this audience.

Website Visit Device Analysis of eBay Users

Device analysis of website visits involves the assessment of data concerning the various devices (including desktop computers, tablets and mobile phones) utilised by visitors to access a website. This analysis elucidates user preferences, behaviours and trends by examining the impact of various devices on user interaction with a website. It frequently encompasses information such as the quantity of visits from each device category, bounce rate, session duration and conversion rate. Device analysis enables website proprietors and marketers to enhance user experience by customising site design, content and functionality according to the devices predominantly utilised by their audience. A significant proportion of mobile visitors may signify a requirement for mobile-optimised design or application development. Ultimately, device analysis facilitates more efficient digital marketing strategies and improves user engagement across platforms.

 

Figure 3. Website Visit Device Analysis of eBay Users.

 

Figure 3 illustrates the distribution of visits to eBay.com by country as of September 2024, encompassing all devices. The United States commands a substantial 69.28% of the user base, equating to 462.6 million visits, with a relatively even distribution of 54.66% on desktop and 45.34% on mobile devices. This signifies robust interaction from the United States, with approximately equivalent preference for both desktop and mobile platforms. Other nations contribute lesser fractions to eBay’s overall traffic. Mexico represents 1.86% of total visitors (12.4 million), with 66.03% of users using mobile devices. The United Kingdom ranks next at 1.47% (9.8 million visits), demonstrating a strong inclination towards mobile usage, with 66.98% of visits conducted via mobile devices. India follows with 1.35% of visits (9 million), exhibiting the highest mobile usage rate among the top five nations at 79.39%. This indicates India’s robust mobile-centric online behaviour, perhaps attributable to extensive smartphone penetration and the affordability of mobile data. Finally, Canada accounts for 1.28% of visitors (8.6 million), with the majority (56.11%) utilising desktop access, demonstrating a pronounced desktop preference relative to other nations on this list. This data indicates that eBay’s greatest user base is in the United States, with mobile usage prevalent in developing markets such as India and Mexico, while desktop usage is more prominent in developed nations such as Canada and the United States.

Device Analysis of Unique Visitors of eBay

The device analysis of unique visitors of eBay refers to the examination of data related to the specific devices (such as mobile phones, desktop computers and tablets) used by individual visitors who access eBay’s website. This analysis focuses on identifying how many unique visitors, or distinct users, access the site from each type of device within a given time frame.

In Figure 4, the September 2024 data illustrates the distribution of unique visitors to eBay.com categorised by nation and device type. The United States accounts for 57.42% of unique visitors, totalling 123.3 million people. Of these, 63.12% utilise mobile devices to access eBay, while 36.88% employ desktops, demonstrating a pronounced preference for mobile in the US market. Mexico, accounting for 2.45% (5.3 million unique visits), demonstrates a significant mobile inclination, with 71.87% accessing via mobile compared to 28.13% on desktop. India ranks second at 2.43% (5.2 million unique visits), exhibiting an even greater dependence on mobile devices—84.08% of users reach eBay via mobile, establishing India as the nation with the highest mobile usage rate among the top five countries listed. The United Kingdom accounts for 2.28% (4.9 million unique visits), predominantly on mobile devices at 73.4%, while 26.6% access via desktop. Finally, Canada accounts for 1.6% of total unique visits (3.4 million), exhibiting a little higher desktop usage rate of 42.78%, while mobile usage remains predominant at 57.22%.

 

Figure 4. Device Analysis of Unique Visitors of eBay.

 

Traffic Channels of eBay.com Using SEMRUSH

In digital marketing and analytics, a traffic channel denotes a particular source or route by which users access a website or digital platform. Channels classify and monitor the sources of online traffic, enabling marketers to comprehend user origins and evaluate the efficacy of various marketing techniques.

From Figure 5, the traffic channel analysis for eBay.com in September 2024, depicted in the graphic, elucidates the allocation of website visitors by source. eBay receives the majority of its traffic directly, accounting for 69.32% and around 462.9 million visits. This direct traffic is significant, however it has diminished, suggesting that while eBay maintains a robust brand presence, user engagement from direct sources may be slightly declining. Organic search is the second largest source, at 18.6% of total traffic (124.2 million visits), which has experienced growth. This indicates that eBay’s prominence in search engines is robust, perhaps attributable to efficient SEO tactics and user search patterns that favour eBay’s content. Referral traffic accounts for 8.27% (55.2 million visits) but has experienced a reduction, suggesting that eBay’s inbound links from external sites may be less successful or diminishing in influence. Paid search constitutes 1.73% of the traffic (11.5 million visits) and indicates an upward trend, implying that eBay is allocating resources to paid search initiatives that are producing favourable outcomes, while it remains a little segment of the overall traffic. Organic social traffic is negligible at 0.79% (5.3 million visits), reflecting a tiny decline, suggesting that eBay’s organic social media presence may not substantially contribute to traffic. Paid social traffic constitutes the smallest source, accounting for about 0.1% (637k visits); however, it has experienced a minor growth, indicating a minimal effect from social media advertisements. E-mail traffic accounts for 1.03% (6.9 million visits) and has experienced a slight growth, indicating eBay’s continued involvement in e-mail marketing. Ultimately, display advertisements constitute merely 0.17% of the traffic (1.1 million visits) and have experienced a modest decline, indicating that display advertising is not a primary emphasis for eBay or is less efficacious in generating traffic relative to alternative channels.

 

Figure 5. Traffic Channels of eBay.com.

 

Traffic Journey of eBay

A traffic trip denotes the route a person follows to access and engage with a website. It encompasses the various channels, interactions and touchpoints that guide users from initial awareness to site visitation and potentially to subsequent actions, such as registration or purchase. Comprehending the traffic journey enables businesses to delineate how users find, interact with and engage with their brand across various platforms, channels and stages.

From Figure 6, the traffic trip chart for eBay.com in September 2024 offers a summary of the main traffic sources and destinations. On the left, we observe the primary sources generating traffic to eBay. The predominant share of traffic (69.32%) is sent to eBay’s website; nonetheless, this direct traffic has undergone a minor reduction of 3%. Organic search from Google constitutes 15.36% of visitors, reflecting a positive growth of 2.76%. eBay’s referrals account for 2.83%, reflecting a decrease of 13.64%. Google-sponsored advertisements contribute 1.63%, reflecting a growth of 2.02%, while referrals from PayPal account for 1.16%, exhibiting a substantial gain of 36.24%. The Top Destinations box on the right side depicts the subsequent sites visitors visit following eBay. A significant 36.76% of users continue to utilise eBay, although a decline of 13.73%. PayPal obtains 8.03% of its traffic from eBay, reflecting a minor decrease of 0.32%. Google accounts for 7.05% of eBay’s outgoing traffic, whereas USPS and Facebook garner 6.37% and 2.33%, respectively. USPS and Facebook experienced significant reductions of 6.22% and 6.86%, respectively. This visualisation underscores eBay’s reliance on direct traffic, Google’s substantial influence as a search and advertising source and PayPal’s notable status as an outbound link, presumably associated with eBay’s payment operations. Variations in traffic percentages may indicate alterations in user behaviour or the efficacy of eBay’s marketing and engagement activities.

 

Figure 6. Traffic Journey of eBay.

 

Top Organic Keyword Research of eBay

Organic keyword research entails the identification and analysis of keywords (search terms) utilised by individuals in search engines such as Google, without the influence of paid advertisements. The objective is to identify pertinent, high-traffic and low-competition keywords that can generate organic traffic to a website when adequately optimised within the site’s content.

From Figure 7, keyword enumerates the terms often input by users in search engines that subsequently direct them to eBay. The primary keywords consist of variations of ‘eBay’ (e.g. ‘eBay’, ‘eBay Motors’, ‘eBay login’, ‘eBay.com’, ‘eBay USA’, and ‘My eBay’), signifying robust brand-centric enquiries. Each keyword’s aim is categorised into numerous sorts, including N (navigational), T (transactional) or a mix thereof. The majority of terms have an N intent, indicating that users are specifically searching for the eBay site, whereas ‘eBay login’ encompasses both N and T intents, implying that users either seek to access their accounts or intend to engage in activities such as purchasing or bidding. The position indicates eBay’s ranking on the search engine results page (SERP) for each keyword. eBay occupies the top rank for all these keywords, signifying robust SEO performance for branded search terms and elevated visibility on search engines. SF (SERP features) denotes the existence of distinctive elements on the search results page, such as sitelinks or FAQs. The presence of ‘N’ adjacent to certain keywords indicates that eBay is featured in SERP elements that augment its visibility and promote click-through rates. The traffic column indicates the projected monthly traffic generated by each term for eBay. The keyword ‘eBay’ generates the highest traffic, with 24.3 million visits, signifying that numerous users access eBay directly via search engines. Alternative keywords generate somewhat lower traffic yet nevertheless yield substantial volumes, exemplified by ‘eBay motors’ with 658.4k visits. The traffic % denotes the proportionate contribution of each keyword to eBay’s overall search traffic. The term ‘eBay’ constitutes 26.23% of eBay’s organic search traffic, significantly surpassing contributions from other keywords such as ‘eBay motors’ and ‘eBay login’. Volume denotes the monthly search frequency for each keyword—the aggregate number of searches conducted across the internet. For instance, ‘eBay’ exhibits a substantial search volume of 30.4 million, signifying robust brand recognition and regular enquiries for eBay. Additional terms, such as ‘eBay Motors’ and ‘eBay login’, exhibit significant search traffic, indicating particular domains of user interest. The term difficulty (KD %) measure signifies the level of challenge in achieving a ranking for each term. Elevated numbers indicate increased competitiveness. The keyword difficulty for these terms is elevated, with scores between 63 and 100, indicating that eBay contends with other websites in a competitive search environment for these popular terms. The URL column displays the particular URL on eBay’s website that ranks for each keyword. The homepage ranks for ‘eBay’, whereas individual pages, such as the ‘auto parts and vehicles’ category page, rank for ‘eBay motors’.

 

Figure 7. Top Organic Keyword Research of eBay.

 

Managerial Implications

The demographic analysis of eBay’s user base depicted in the image reveals various managerial implications. eBay boasts 214.7 million users across various devices; nonetheless, the 2.16% decrease in users signifies a necessity to prioritise user retention measures, including the enhancement of user experience and the amplification of interaction through targeted advertising. The marginal male majority (51.38%) indicates that eBay might advantageously pursue marketing strategies aimed at attracting female users, including expanding its product offerings or messaging to appeal to a more equitable gender demographic. The 18–24 age demographic constitutes 22.8% of the audience, suggesting that eBay should prioritise trends and functionalities appealing to younger consumers, such as mobile accessibility, social commerce and gamified purchasing experiences. Additionally, with 57.42% of eBay’s users located in the United States, the platform might prioritise US-specific product offerings, promotions and customer service improvements while also investigating tactics to expand its international market presence. Customising marketing strategies, product suggestions and communication informed by these analytics can enable eBay to boost engagement, elevate user satisfaction and perhaps augment user retention and growth, especially within under-represented demographic groupings.

The data regarding eBay’s visitor distribution by country and device holds substantial management implications for enhancing marketing strategies, user experience and technological investments. The significant mobile usage in major economies, particularly in India (84.08%), Mexico (71.87%) and the United States (63.12%), indicates that eBay should adopt a mobile-first strategy. This entails improving the mobile application’s performance, speed and user interface to guarantee a seamless buying experience, especially for these mobile-centric markets. Customising mobile-targeted promotions, enhancing mobile payment alternatives and incorporating location-based services can enhance engagement and conversions on mobile devices, aligning with user preferences in these areas. Conversely, the comparatively elevated desktop usage in Canada (42.78%) and the United States (36.88%) indicates that these markets may still prioritise a more conventional, comprehensive browsing experience typically linked to desktop usage. eBay should concentrate on enhancing the desktop website experience for these customers by assuring compatibility with high-resolution photos, comprehensive product information and an easy interface that improves shopping on larger displays. The demographic differences suggest that marketing strategies may be customised according to device preference. Mobile-first initiatives in India and Mexico may produce superior outcomes, while a balanced strategy for the United States and Canada might accommodate both desktop and mobile customers. eBay’s management should prioritise resource allocation for mobile infrastructure in mobile-centric nations and focus on improving app performance. Moreover, these observations underscore the necessity for localised tactics. Countries with significant mobile usage, such as India, could gain from app-exclusive promotions, localised content and language-specific interfaces to enhance accessibility. The significant desktop usage in the US market suggests that eBay may explore desktop-oriented technologies, including virtual fitting rooms or product customisation tools, to improve the desktop purchasing experience.

Insights from the traffic path on eBay.com in September 2024 indicate numerous critical management implications that might inform decisions in marketing, customer retention and partnership initiatives. The significant dependence on direct traffic (69.32%) underscores eBay’s robust brand recognition, although it also indicates a potential over-reliance on frequent users. This suggests that eBay should consider diversifying its acquisition approach by increasing investments in alternative channels, including paid and organic search, social media and affiliate marketing, to attract new audiences that do not already regard eBay as a primary destination. The data indicate Google’s substantial contribution as a source of both organic (15.36%) and sponsored (1.63%) traffic. In light of the organic search traffic increase (+2.76%), eBay has the chance to enhance its SEO strategies, particularly by concentrating on long-tail keywords or trending products, to optimise organic exposure. The increase in paid Google traffic (+2.02%) suggests that eBay’s paid advertising is producing favourable outcomes, warranting additional budget allocation for Google ads to potentially enhance returns. The 13.64% decrease in internal referrals from eBay indicates a necessity to boost cross-linking across product listings or other site parts, either via personalised suggestions or improved search algorithms to maintain user engagement. Furthermore, outgoing traffic to PayPal (8.03%) and USPS (6.37%) indicates eBay’s dependence on these partners for transactions and logistics. eBay’s management should consider more integrations or exclusive agreements with key partners to enhance the customer experience and fortify alliances, particularly with PayPal, to mitigate drop-off rates during payment. The decrease in outgoing traffic to Facebook (-6.86%) may indicate diminished engagement on the platform or a reduction in cross-promotional activities. This provides eBay with the opportunity to reassess its social media approach, potentially by investing in platforms that more closely fit with its target demographics or by exploring new social features on platforms with strong engagement levels.

eBay’s organic traffic is significantly reliant on branded keywords, highlighting the necessity of preserving and enhancing brand familiarity and loyalty. Managers must persist in optimising pages for branded and navigational searches, as these are essential for engaging users already acquainted with eBay. Nevertheless, dependence on branded searches poses a danger; a fall in brand awareness could substantially affect organic traffic. Consequently, managers ought to devise tactics to diversify traffic sources by focussing on non-branded, high-intent keywords that correspond with prevalent product categories (e.g. ‘online auto parts marketplace’ rather than merely ‘eBay motors’) to attract new audiences unfamiliar with eBay. The competitive keyword difficulty (KD%) indicates that achieving rankings for these terms is arduous, necessitating expenditures in high-quality content and focused SEO strategies in these tough domains. Moreover, developing focused campaigns centred on ‘eBay motors’ and ‘eBay login’ indicates opportunity for specialised, user-centric landing sites and support resources that elevate the user experience and augment conversion rates. By equilibrating branded and broader, category-oriented SEO, managers can diminish dependence on brand-specific traffic, engage new users and cultivate a more robust search strategy.

Conclusion

The traffic study for eBay.com in September 2024 identifies essential opportunities for development and enhancement. eBay’s robust brand devotion is shown in its substantial direct traffic; yet, this dependence also highlights an opportunity to broaden acquisition methods and engage new audiences. The consistent efficacy of Google as a traffic source highlights the necessity for ongoing investment in SEO and targeted paid advertising to facilitate more growth. Decreases in internal referrals indicate a necessity for improved user engagement techniques within the site; however, elevated outward traffic to partners such as PayPal and USPS underscores the importance of fortifying these partnerships for more seamless transaction and shipping experiences. Organic keyword research is an essential method for improving a website’s exposure and drawing targeted, high-quality visitors. By discovering and optimising relevant, high-volume and low-competition keywords, businesses may enhance their organic search ranks, effectively reach their audience and foster sustainable, long-term growth without dependence on paid advertising. Comprehending user intent associated with each keyword allows websites to produce content that corresponds with searchers’ requirements, enhancing engagement and conversion rates. A well-implemented organic keyword strategy enhances SEO performance and bolsters brand authority, rendering it a crucial element of any digital marketing strategy.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Surjadeep Dutta   https://orcid.org/0009-0004-4637-6844