Developing Marketing Strategies Through Social Media Platforms: An Analysis of Social Media’s Impact on Consumer Behavior

Developing Marketing Strategies Through Social Media Platforms: An Analysis of Social Media’s Impact on Consumer Behavior

Dilara Büyükköz^* and Elif Güven.

Management Information Systems Department, Marmara University, Istanbul, Turkiye.

*Corresponding author

DOI: https://doi.org/10.51244/IJRSI.2024.1111007

Received: 22 October 2024; Accepted: 28 October 2024; Published: 26 November 2024

ABSTRACT 

Social media, which holds an important position not only in daily life but also in business, is now seen as one of the most suitable channels for marketing. Regardless of the sector, companies build their marketing strategies around social media platforms, which are more accessible and cost-effective, and promote their products or services through these channels. These platforms, which have significant positive effects on customer and brand communication, provide benefits for both companies and consumers. In this context, understanding which types of social media content resonate with customers is a crucial step toward achieving success. Considering the growing importance of social media, this study seeks to explore how businesses create and develop marketing strategies using social media platforms, as well as examine the impact of these platforms on consumer behaviors.

To this end, a survey was conducted with a total of 305 participants from different demographic groups, asking them about their frequency of internet and social media use, online shopping habits, preferred social media platforms, and how these platforms influence their purchasing decisions. The data obtained from these responses was analyzed using the SPSS software package. The analysis showed that while pre-purchase and post-purchase consumer behaviors vary by gender, only post-purchase behaviors vary by age. On the other hand, both pre-purchase and post-purchase behaviors are not affected by educational status, monthly income, or occupation. Additionally, a significant positive correlation was found between pre-purchase and post-purchase consumer behaviors on social media.

Keywords: Social Media; Social Media Marketing; Consumer Behaviors; Purchase Decision.

INTRODUCTION

The term “Social Media” combines “Social,” derived from the Latin word socius, meaning friend, and “Media,” which refers to channels of communication like radio, television, newspapers, and the internet. Essentially, social media is a web-based platform that facilitates interaction, allowing people to share information, opinions, and knowledge. As described by Safko and Brake, it serves as an umbrella term for activities and behaviors where individuals engage with communities online, exchanging content and ideas (Satpathy & Patnaik, 2019).

When examining the history of social media, it can be seen that it first emerged in 1979 with ‘Usenet’, created by Jim Ellis and Tom Truscott. Usenet was essentially a discussion platform that allowed internet users worldwide to send messages. However, the first appearance of the type of social media we use today began with the website ‘Open Diary’, founded by Bruce and Susan Abelson in 1998. The purpose of this site was to bring together people who wrote diaries within an online community. That same year, the concept of the ‘blog’ emerged, and a year later, bloggers started using the phrase ‘weblog’, leading to the formation of the term ‘blog’ in its fullest sense. Subsequently, as internet speeds increased, new social networking sites such as ‘MySpace’ in 2003 and ‘Facebook’ in 2004 were established (Kaplan & Haenlein, 2010, p.60).

Today, with the rapid development of technology, devices such as phones, tablets and computers have become indispensable for our daily lives, concept of social media has become more powerful than ever. New social media platforms and tools have emerged due to the increased speed and accessibility of the internet. Blogs, which began as personal sharing platforms, have now evolved into essential tools for businesses to connect with their market and implement marketing strategies. Companies use blogs to build communication links, monitor engagement, and adapt tactics based on audience interactions and feedback (Cross, Parker, & Sasson, 2003).

Microblogs have gained popularity as a means for users to share their daily thoughts and actions with a broader audience. These daily updates provide a unique source of information to analyze and interpret a user’s real-time interests, intentions, and activities (Banerjee, Chakraborty, Dasgupta, Mittal, Joshi, Nagar & Madan, 2009).

Social networking sites, like Facebook, Instagram, LinkedIn and Ravelry and media sharing sites connect individuals by allowing them to interact, form groups, and share content (Yavuz & Haseki, 2012, p.129). Users can create profiles that function like personal websites, sharing demographic details, personal and professional interests (Habib et al., 2018, p.169).

Through podcast platforms, like Spotify, Apple Podcasts, and YouTube, users can broadcast audio or video files serially, similar to modern radio programs. Additionally, collaborative tools such as wikis enable users to develop, organize and update content, which is vital for organizations to verify and correct information about their products and services when needed (Scott, 2007).

Forums provide another platform where users can exchange views by posting under existing or new topics, allowing them to chat and share interests. Companies consider forums valuable for understanding customer opinions and maintaining transparent communication, while customers can gain insights about the company and its products or services (Cross, et al., 2003). Online communities also serve as valuable marketplaces for companies, enabling them to adapt products and services based on user feedback and preferences (Akar, 2011).

As seen through the widespread use of various digital tools, unlike traditional media, social media enables real-time interaction between companies, institutions, and customers, fostering two-way communication. Customers can now follow brands on social media, access detailed information, and directly share feedback, highlighting the speed and efficiency of social media interaction. Today social media can be defined as a collection of virtual tools that facilitate digital communication and collaboration with brands, easily accessible to both consumers and companies. Recognizing the amount of time consumers spend on websites and social media platforms, businesses have strategically used these channels to deliver their messages effectively.

In today’s competitive environment, traditional marketing methods are no longer sufficient to reach consumers. Consequently, businesses aim to convey their messages across all available platforms. This shift has led companies to carry out marketing activities through virtual environments such as the Internet, online platforms and social media.

Active and efficient use of social media marketing can enable companies to promote their products and services, attract more customers and significantly increase their sales. By reaching a wider target audience, directly interacting with customers, and boosting brand awareness, companies can achieve these goals in a cost-effective manner.

These benefits can be shown as the reason why companies take such an active role in social media and the number of shares and participation they share from their official accounts day by day. Some sources consider the business use of social media as marketing and public relations, in-house networking, and external peer networking (Kotler, Kartajaya, Setiawan, 2010). In this context, when the contributions of social media to companies and the business world are considered, several topics come to the fore. These are Communication and Interaction with Consumers, Market Research, Integrated Marketing Communications, and Relationship Marketing with Social Media.

LITERATURE REVIEW

Social media and its effect on consumers’ decision-making process, in light of developing technologies, has become a popular area of study, with numerous research works available in the literature. Due to its growing importance and popularity in today’s world and business processes, there is a wide range of studies across various industries, geographic regions, and countries exploring the impact of social media on consumer behavior. Voramontri and Klieb (2019), using the Engel-Blackwell-Miniard (EBM) Model, showed in their study, that social media positively affects consumer satisfaction. Chopra, Gupta, and Manek (2020) explored how social media alters consumer experiences, particularly in the stages of information search and alternative evaluation. Fotis (2015) examined how consumers use social media throughout the entire holiday travel process and its impact on consumer behavior. Unni (2020) investigated the positive and negative impacts of digital and social media marketing on consumer behavior. Akayleh (2021) studied the impact of social media advertising on consumer buying decisions in Riyadh City, Saudi Arabia. Ramnarain and Govender (2013) aimed to explore how social media influences the purchasing behavior of youth in South Africa. Bigne, Andreu, Hernandez, and Ruiz (2018) analyzed how both social media and offline environments influence tourists’ online purchasing and recommendation behaviors for low-cost airline services, using the Theory of Reasoned Action (TRA). Salampasis, Paltoglou, and Giachanou (2014) investigated how social communication and microblogging services, like Twitter, can be used for continuous monitoring and analysis of consumer behavior by examining branding comments, sentiments, and opinions about food products. Vidani and Das (2021) focused on the evolution and effectiveness of influencer marketing. Various studies, including those by Diffley, Kearns, Bennett, and Kawalek (2011); Hu and Wei (2013); Moustakas (2015); Chitharanjan (2016); Ziyadin, Doszhan, Borodin, Omarova, and Ilyas (2019); and Singh (2021), have focused on the impact of social media on consumer behavior.

OBJECTIVES OF THE STUDY

The objectives of this study are:

1. To reveal the effect of social media on consumer behavior.
2. To understand how different social media platforms shape consumer actions at various stages, both before and after a purchase.
3. To discern whether there are variations in consumers’ behavior on social media before and after buying, concerning their demographic characteristics.
4. To determine if there is a significant correlation between behaviors observed during the pre-purchase scale and post-purchase scale.
5. To understand how extensively consumers within Turkey engage with social media platforms and to what extent these platforms influence their purchasing habits.

METHODOLOGY

To investigate this dynamic, a survey was created and distributed online to 305 participants using social media, with different demographic characteristics, living in Turkey. The survey consists of 14 questions, including demographic queries, with multiple expressions and options in each. In preparing the survey questions, research on social media and marketing was conducted and existing studies from the literature on these subjects were taken into consideration.

The survey study consists of three sections. The first part includes statements and questions designed to determine how much, how often, and for what purposes consumers use the Internet and social media platforms. The second part aims to examine pre-purchase and post-purchase consumer behaviors separately, with statements measuring the degree of social media’s influence during these processes. The third and final part contains demographic questions.

Five-point Likert scale was used to measure how often social media platforms are used. Another set of questions focused on why consumers follow the accounts of companies/brands on social media, using the same scale. The scale was also applied to statements about the use of social media tools, as well as to measure the impact of social media platforms on consumer behavior before and after purchasing.

The survey was conducted between 08.05.2021 and 22.05.2021. SPSS 27.0 software was used for data analysis. Reliability analyses were performed to determine the consistency and stability of the survey. Additionally, t-tests, variance analyses and correlation analyses were carried out to test the various hypotheses explored in this study, as listed in Table 1 below.

Table 1. The Hypotheses Tested in the Study

Alternative Hypotheses

:  Pre-purchase consumer behavior varies by gender.

:  Post-purchase consumer behavior varies by gender.

:  Pre-purchase consumer behavior varies by age.

:  Post-purchase consumer behavior varies by age.

:  Pre-purchase consumer behavior varies by educational status.

:  Post-purchase consumer behavior varies by educational status.

:  Pre-purchase consumer behavior varies by monthly income.

:  Post-purchase consumer behavior varies by monthly income.

:  Pre-purchase consumer behavior varies by occupation.

: Post-purchase consumer behavior varies by occupation.

: There is a significant correlation between pre-purchase and post-purchase consumer behavior.

DATA ANALYSIS

Initially, descriptive statistical analyses were performed for each question in the survey. Table 2 presents the frequency distribution of the participants for their daily average time spent on the internet and social media, while the corresponding percentages are shown in Figure 1.

Table 2. Average Time Spent on the Internet and Social Media

	Hours	Frequency		Hours	Frequency
Average Daily Internet Usage	[0 – 2)	15	Average Daily Social Media Usage	[0 – 2)	101
	[2 – 4)	95		[2 – 4)	138
	[4 – 6)	98		[4 – 6)	56
	[6 – 8)	55		[6 – 8)	6
	[8 +)	42		[8 +)	4
	Total	305		Total	305

Figure 1. Percentages of Average Daily Internet and Social Media Usage

As is seen in Table 2 and Figure 1, there are five time categories for daily internet and social media usage. For daily internet usage: 4.92% (15) for 0-2 hours, 31.15% (95) for 2-4 hours, 32.13% (98) for 4-6 hours, 18.03% (55) for 6-8 hours, and 13.77% (42) for 8 hours and above. For daily social media usage: 33.11% (101) for 0-2 hours, 45.25% (138) for 2-4 hours, 18.36% (56) for 4-6 hours, 1.97% (6) for 6-8 hours, and 1.31% (4) for 8 hours and above, with the number of participants indicated in parentheses. It is also observed that the average daily internet usage of the 305 survey participants is 5.09 hours, while the average daily social media usage is 2.86 hours.

Figure 2 below presents the distribution of evaluation of how frequently social media users utilize platforms such as Facebook, Instagram, Twitter, YouTube, Snapchat, LinkedIn, blogs, podcasts, and other online communities, based on a 5-point Likert scale. According to the data given in Figure 2:

Facebook: 31.80% (97) never, 26.56% (81) rarely, 17.38% (53) sometimes, 17.05% (52) often, and 7.21% (22) always;

Instagram: 7.54% (23) never, 12.79% (39) rarely, 16.07% (49) sometimes, 40.33% (123) often, and 23.28% (71) always;

Twitter: 40.98% (125) never, 19.67% (60) rarely, 15.08% (46) sometimes, 17.05% (52) often, and 7.21% (22) always;

YouTube: 8.52% (26) never, 20.33% (62) rarely, 28.52% (87) sometimes, 25.90% (79) often, and 16.72% (51) always;

Snapchat: 91.15% (278) never, 5.57% (17) rarely, 1.64% (5) sometimes, 0.98% (3) often, and 0.65% (2) always;

LinkedIn: 56.07% (171) never, 17.05% (52) rarely, 13.44% (41) sometimes, 9.18% (28) often, and 4.26% (13) always;

Blogs: 69.51% (212) never, 20.00% (61) rarely, 8.85% (27) sometimes, 1.31% (4) often, and 0.33% (1) always;

Podcast: 78.69% (240) never, 11.15% (34) rarely, 8.85% (27) sometimes, 0.66% (2) often, and 0.66% (2) always;

Online communities (e.g. eksisözlük): 43.28% (132) never, 31.48% (96) rarely, 19.67% (60) sometimes, 3.93% (12) often, and 1.64% (5) always.

Focusing on the categories of ‘Often’ and ‘Always,’ it is observed that 63% (194) use Instagram more frequently than other platforms, followed by 43% (130) for YouTube and 24% (74) for Facebook.

Figure 2. Frequency of Social Media Platforms Usage by Users

Figure 3 below presents the distribution of responses regarding the positive effects of blogs, microblogs, social networking sites, media-sharing sites, podcasts, wikis and online communities on participants’ purchasing decisions. According to the data given in Figure 3:

Blogs: 31.48% (96) strongly disagree, 23.61% (72) disagree, 30.49% (93) neither agree nor disagree, 13.11% (40) agree, and 1.31% (4) strongly agree;

Microblogs (Twitter, etc.): 41.97% (128) strongly disagree, 22.95% (70) disagree, 26.89% (82) neither agree nor disagree, 7.54% (23) agree, and 0.66% (2) strongly agree;

Social Networking Sites (Facebook, Instagram, LinkedIn etc.): 20.66% (63) strongly disagree, 20.00% (61) disagree, 32.79% (100) neither agree nor disagree, 22.30% (68) agree, and 4.26% (13) strongly agree;

Media Sharing Sites (YouTube, Flickr, Slideshare, etc.): 30.49% (93) strongly disagree, 15.74% (48) disagree, 28.20% (86) neither agree nor disagree, 21.97% (67) agree, and 3.61% (11) strongly agree;

Podcasts: 59.34% (181) strongly disagree, 15.08% (46) disagree, 20.00% (61) neither agree nor disagree, 4.59% (14) agree, and 0.98% (3) strongly agree;

Wikis (Wikipedia etc.): 57.05% (174) strongly disagree, 17.05% (52) disagree, 19.02% (58) neither agree nor disagree, 5.90% (18) agree, and 0.98% (3) strongly agree;

Online Communities (Eşki Sözlük, etc.): 36.07% (110) strongly disagree, 21.31% (65) disagree, 27.54% (84) neither agree nor disagree, 11.48% (35) agree, and 3.61% (11) strongly agree.

Focusing on the categories of ‘Agree’ and ‘Strongly Agree’, it is observed that social networking sites have a more positive impact on purchasing decisions than other platforms, with 27% (81), followed by media sharing sites with 26% (78).

Figure 3. User Responses on Positive Effects of Social Media Platforms

Reliability analyses were conducted to assess the consistency of the statements measuring the degree of social media impact. The Cronbach’s Alpha coefficients for the pre-purchase and post-purchase consumer behavior scales are presented in Tables 3 and 4.

Table 3. Reliability Analysis Results: Pre-Purchase Consumer Behavior Scale

Clauses		SD	Inter-item correlations	Cronbach’s Alpha coefficients
1	3.593	1.219	0.689	0.869
2	3.902	1.114	0.657	0.874
3	3.269	1.184	0.754	0.858
4	3.308	1.261	0.701	0.867
5	3.289	1.116	0.762	0.857
6	2.984	1.179	0.647	0.875
Reliability alpha coefficient () = 0.886

X= Mean; SD = Standard Deviation.

Table 4. Reliability Analysis Results: Post-Purchase Consumer Behavior Scale

Clauses		SD	Inter-item correlations	Cronbach’s Alpha coefficients
1	2.525	1.277	0.694	0.828
2	2.682	1.298	0.754	0.817
3	2.793	1.308	0.630	0.840
4	2.757	1.200	0.582	0.848
5	2.692	1.329	0.653	0.836
6	2.754	1.311	0.593	0.847
Reliability alpha coefficient () = 0.860

The alpha reliability coefficient of the pre-purchase consumer behavior scale  is calculated as 0.886, while for post-purchase consumer behavior scale , it is 0.86. The fact that Cronbach’s Alpha coefficients are above 0.70 for both pre-purchase and post-purchase consumer behavior scales indicates that the scales are reliable and internally consistent.

After the descriptive statistical and reliability analyses, the hypotheses listed in Table 2 were tested. As shown in Table 5, the mean pre-purchase consumer behavior scale score was 3,534 for women and 3,082 for men. The mean post-purchase consumer behavior scale score was 2.823 for women and 2,438 for men. According to the results, gender has a significant effect on both pre-purchase and post-purchase scales at the 5% significance level, with and  .

Table 5. Independent Sample t-Test Results for Scales by Gender

	Gender	N		SD	t-value	p-value
Pre-purchase consumer scale	Female	208	3.534	0.859	3.996	0.000
	Male	97	3.082	1.039
Post-purchase consumer scale	Female	208	2.823	0.958
	Male	97	2.438	1.002	3.218	0.001

Number of samples; = Mean; SD = Standard Deviation.

As shown in Table 6, the mean scores for the pre-purchase scale across the age groups,  [18 – 26), [26 – 36), [36 – 46), and [56+) were calculated as 3.833, 3.434, 3.350, 3.443, and 3.117 respectively. Variance analysis for these groups showed . At the 5% significance level, it can be said that there is a difference between the means of age groups, indicating that age has a significant effect on pre-purchase consumer behaviors.

On the other hand, the mean scores for the post-purchase scale across the age groups,  [18 – 26), [26 – 36), [36 – 46), and [56+) were calculated as 2.912, 2.862, 2.727, and 2.552 respectively. Variance analysis for these groups showed . At the 5% significance level, it cannot be said that there is a difference between the means of age groups, indicating that age does not affect post-purchase consumer behaviors.

Table 6. Variance Analysis of Consumer Behavior Scales by Age

	Age	N		SD	F-value	p-value
Pre-purchase consumer scale	[18 – 26)	38	3.833	0.687	3.7756	0.005
	[26 – 36)	53	3.434	0.912
	[36 – 46)	91	3.350	0.960
	[46 – 56)	56	3.443	0.924
	[56+)	67	3.117	1.003
Post-purchase consumer scale	[18 – 26)	38	2.912	1.072	1.571	0.182
	[26 – 36)	53	2.862	0.920
	[36 – 46)	91	2.727	0.976
	[46 – 56)	56	2.552	0.908
	[56+)	67	2.542	1.049

Number of samples; = Mean; SD = Standard Deviation.

As shown in Table 7, the mean scores for the pre-purchase scale across the educational status groups — primary education, associate degree, bachelor’s degree, master’s degree and doctorate —were calculated as 3.250, 3.509, 3.400, 3.372, 3.415 and 3.167 respectively. Variance analysis for these groups showed . At the 5% significance level, it can be said that there is no difference between the means of educational status groups, indicating that educational status does not affect pre-purchase consumer behaviors.

The mean scores for the post-purchase scale across the educational status groups —primary education, associate degree, bachelor’s degree, master’s degree and doctorate —were calculated as 2.667, 2.882, 2.640, 2.657, 2.771, and 2.574 respectively. Variance analysis for these groups showed . Similar to pre-purchase consumer behaviors, at the 5% significance level, it can be said that there is no difference between the means of educational status groups, indicating that educational status does not affect post-purchase consumer behaviors.

Table 7. Variance Analysis of Consumer Behavior Scales by Educational Status

	Educational Status	N		SD	F-value	p-value
Pre-purchase consumer scale	Primary education	4	3.250	1.134	0.257	0.936
	High school	38	3.509	1.057
	Associate degree	25	3.400	0.767
	Bachelor’s degree	178	3.372	0.962
	Master’s degree	51	3.415	0.890
	Doctorate	9	3.167	0.874
Post-purchase consumer scale	Primary education	4	2.667	1.009	0.421	0.834
	High school	38	2.882	1.060
	Associate degree	25	2.640	1.103
	Bachelor degree	178	2.657	0.998
	Master’s degree	51	2.771	0.903
	Doctorate	9	2.574	0.641

Number of samples; = Mean; SD = Standard Deviation.

As shown in Table 8, the mean scores for the pre-purchase scale across the monthly income groups, [0-10,000], [10,001-25,000], [25,001-50,000], and [50,000+) were calculated as 3.490, 3.583, 3.535, and 3.285 respectively. Variance analysis for these groups showed . At the 5% significance level, it can be said that there is no difference between the means of monthly income groups, indicating that monthly income does not affect pre-purchase consumer behaviors.

The mean scores for the post-purchase scale across the monthly income [0-10,000], [10001-25,000], [25,001-50,000], and [50,000+) were calculated as 2.775, 2.740, 2.852, and 2.607 respectively. Variance analysis for these groups showed . Similar to pre-purchase consumer behaviors, at the 5% significance level, it can be said that there is no difference between the means of monthly income groups, indicating that monthly income does not affect post-purchase consumer behaviors.

Table 8. Variance Analysis of Consumer Behavior Scales by Monthly Income

	Monthly Income	N		SD	F-value	p-value
Pre-purchase consumer scale	[0-10,000]	17	3.490	0.849	1.785	0.150
	[10,001-25,000]	16	3.583	0.691
	[25,001-50,000]	96	3.535	0.954
	[50,000+)	176	3.285	0.957
Post-purchase consumer scale	[0-10,000]	17	2.775	0.937	1.328	0.265
	[10,001-25,000]	16	2.740	0.913
	[25,001-50,000]	96	2.852	1.098
	[50,000+)	176	2.607	0.930

Number of samples; = Mean; SD = Standard Deviation.

As shown in Table 9, the mean scores for the pre-purchase scale across the occupation groups—student, officer, private sector employee, worker, freelancer, academician, retired, and others—were calculated as 3.646, 3.598, 3.344, 2.611, 3.672, 3.361, 3.249, and 3.346 respectively. Variance analysis for these groups showed . At the 5% significance level, it can be said that there is no difference between the means of occupation groups, indicating that occupation does not affect pre-purchase consumer behaviors.

The mean scores for the post-purchase scale across the occupation groups—student, officer, private sector employee, worker, freelancer, academician, retired, and others—were calculated as 2.715, 2.727, 2.752, 2.222, 2.870, 2.861, 2.573, and 2.654 respectively. Variance analysis for these groups showed . Similar to pre-purchase consumer behaviors, at the 5% significance level, it can be said that there is no difference between the means of occupation groups, indicating that occupation does not affect post-purchase consumer behaviors.

Table 9. Variance Analysis of Consumer Behavior Scales by Occupation

	Occupation	N		SD	F-value	p-value
Pre-purchase consumer scale	Student	24	3.646	0.588	1.409	0.201
	Officer	22	3.598	1.187
	Private sector employee	105	3.344	0.967
	Worker	3	2.611	1.295
	Freelancer	32	3.672	0.699
	Academician	6	3.361	0.859
	Retired	75	3.249	0.980
	Other	38	3.346	0.948
Post-purchase consumer scale	Student	24	2.715	0.775	0.486	0.844
	Officer	22	2.727	1.112
	Private Sector Employee	105	2.752	1.046
	Worker	3	2.222	1.347
	Freelancer	32	2.870	0.824
	Academician	6	2.861	0.645
	Retired	75	2.573	0.995
	Other	38	2.654	1.031

Number of samples; = Mean; SD = Standard Deviation.

In the research, the relationship between pre-purchase and post-purchase consumer behavior was examined using correlation analysis, specifically, Spearman’s correlation analysis. The Spearman correlation coefficient was found to be 0.692, with a p-value of 0.000. Based on these results, it can be concluded that there is a significant positive relationship between pre-purchase and post-purchase consumer behavior at the 5% significance level.

Table 10 below shows a summary of the hypotheses tested in this study and the corresponding decisions based on the related statistical analyses.

Table 10. Summary of Hypotheses Testing and Decisions

Hypotheses in the Study	Decision
:  Pre-purchase consumer behavior varies by gender.	Accepted
:  Post-purchase consumer behavior varies by gender.	Accepted
:  Pre-purchase consumer behavior varies by age.	Accepted
:  Post-purchase consumer behavior varies by age.	Rejected
:  Pre-purchase consumer behavior varies by educational status.	Rejected
:  Post-purchase consumer behavior varies by educational status.	Rejected
:  Pre-purchase consumer behavior varies by monthly income.	Rejected
:  Post-purchase consumer behavior varies by monthly income.	Rejected
:  Pre-purchase consumer behavior varies by occupation.	Rejected
: Post-purchase consumer behavior varies by occupation.	Rejected
: There is a significant correlation between pre-purchase and post-purchase consumer behavior.	Accepted

CONCLUSION

The rapid growth of the Internet and lifestyle changes have led to increased interest in internet platforms, especially social media. The widespread use of mobile devices has made social media a significant part of daily life. Businesses across all sectors have recognized the power of these platforms, utilizing them for marketing activities to connect with consumers, as digital media is more cost-effective and accessible than traditional media. Additionally, consumers rely on social media for brand information through other users’ shared experiences, influencing brand images positively or negatively. Successful companies turn these platforms to their advantage by developing effective strategies.

This study thoroughly examined the process of developing marketing strategies within social media platforms and aimed to determine their impact on consumer behavior. A survey was conducted to assess participants’ frequency of internet and social media usage, online shopping behavior, and the influence of social media tools on purchasing decisions. The survey also investigated changes in consumer behavior before and after purchases, with demographic data collected to provide further context. A comprehensive analysis of the responses was performed to identify key patterns and effects.

The findings indicate that social networking sites, particularly Instagram, exert the most significant influence on consumer purchasing decisions, while the influence of blogs, Snapchat, podcasts, and LinkedIn is comparatively minimal. The analysis of pre-purchase and post-purchase behaviors revealed that demographic factors, such as education, income, and occupation do not affect pre-purchase behavior, whereas gender and age do post-purchase behaviors. However, showed no significant variation across these demographic groups. Additionally, the analysis confirmed a positive correlation between pre-purchase and post-purchase consumer behaviors within the context of social media interactions.

RECOMMENDATIONS

1. Since Instagram shows the most significant influence on consumer purchasing decisions, businesses can focus on promoting their brands on platforms like Instagram to gain a competitive advantage.
2. Because educational status, income, and occupation did not significantly influence consumer behavior on social media, businesses might consider universal approaches that appeal across these demographic lines while still adapting content based on other influential factors like age and gender.
3. The positive correlation between pre- and post-purchase consumer behaviors shows that companies should not only attract customers but also continue to maintain customer relationships after the sale. Loyalty programs, personalized messages, and effective customer support can be used for this purpose.

FUTURE RESEARCH

Although this study provides important analyses on the impact of social media on consumer behavior, some suggestions can be made for future studies:

1. Since the scope of the study covers a single country, the impact of the cultural structure of societies could not be investigated in the study. A global study could obtain different analyses and results by considering different cultural structure variables.
2. In future studies, different social media platforms, such as TikTok, could be included. Each platform has its own user demographics, which can have various effects on consumption behavior.
3. The effects of emerging techniques, such as augmented reality and artificial intelligence, could also be included in future studies.

REFERENCES

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