Perceptions of the Video across the 4 Psychographic Groups - Assignment Example

Student’s Name: Instructor’s Name: Course Code & Name: Date of Submission: Table of Contents EXECUTIVE SUMMARY 1 DESCRIPTIVE ANALYSES: 2 INFERENTIAL ANALYSES: 9 RECOMMENDATION 22 REFERENCE: 25 LIST OF FIGURES Fig1. Device used for psychographic………………………………………………..……………………2 Fig2. Perceptions of the video……………………………………………………….…………………….3 Fig3. Means plot……………………………………………………………………………..………………5 Fig4. Perceptions of the video across the 4 psychographic groups................................................6 fig5. Central tendency......................................................................................................................7 Fig6. Gender of users and their interaction times of the video.......................................................8 Fig7. The social media site influences users’ interaction times....................................................10 fIg8. The time of day users access the social media site................................................................11 Fig9. How users’ interaction time with the video influences their perception………………….....13 Fig10. How users’ perceptions of the video influence their click through...................................16 Fig11. How users’ interaction time with the video influences their click-through………….…….18 Fig12. Social media sites the user has followed of the brand influences their click-through......20. EXECUTIVE SUMMARY The market research is an essential aspect for every company. The market research ensures that the product gain desired publicity at the lowest cost as possible. In addition, it eliminates ineffective advertisement campaigns carried out by the company. The introduction of the warm wear by the company has made it necessary to determine the best strategy in advertising the new product line. The research was carried out within four psychographic groups who are potential customers to the product. These groups are camping enthusiast, hiking specialist, indoor type and water sports warrior. The understanding of their needs will help the company in tailoring the advertisement to suite these groups. The advertisement is based on video demonstrating the new line of product. Therefore, the study on the current content is evaluated in this market research which will be helpful in ensuring that the company re-tailors the content of video in order to achieve the best results. The measure of the effect of the video in the market was measured using the statistical method on central tendency, Chi-square and regression models. The use of the statistical models ensures that the data obtained is reliable and can be relied by the researcher in coming up with sound conclusion and recommendation. The data is presented in tables and charts in order to ensure that users of the information can easily understand the research. The major objective of the research is to determine the effect of video clip in publicity of the warm wear brand. The results will ensure that company eliminates the faults, increase attention on positive aspects or maintain in order to ensure that the campaign achieve maximum publicity at a cost-effective level. The company should ensure that it follows through the research and adopt the recommendation given to ensure that they improve its current marketing campaign. DESCRIPTIVE ANALYSES: The extent to which the device used differs across psychographic groups. Fig1. Device used for psychographic According to the findings, hiking specialist recorded high number of users of all the devices since they are in need of warm clothing. This makes them always on look out on the best warm clothing in the market. The order for the need of warm clothing reduces with the visits by the users in various categories to the site. Water sports warriors recorded the lowest since they only require swimming costume which are readily available in market. In addition, it can be noted that high number of the users who accessed the site used mobile phones followed by computer and lastly, tablet. This is because people prefer to use phones because of their portability. The second preferred device is computers because of better display compared to tablets. The extent to which the perceptions of the video differ across age groups ANOVA Perceptions_of_Video Sum of Squares df Mean Square F Sig. Between Groups 1.450 3 .483 .927 .429 Within Groups 78.737 151 .521 Total 80.187 154 Fig2. Perceptions of the video According to the findings, noted that the age group of 18-29 years formed the highest number of users who accessed the site. They dominate since they are in active stage where they are involved in various activities like hiking, camping and sports. The individual of 48-57 years where second highest users, this is attributed to the fact that they like taking holidays spending their saving from active employment. The third group is individuals of 30-47 year. This trend is attributed to the increased commitment in employment thus reduced time to engage in various recreational activities such as hiking. Lastly, those individuals of 58 years and above showed to be the lowest users due to reduced aggressiveness to actively participate in recreational activities. The F-test of 0.927 obtained from the data collected checks on sensitivity of the variables. It shows that the variables are sensitivity and therefore, it rejects the null hypothesis to be false. The mean plot below shows the variability of users’ categories accessing the site; Means Plots Fig3. Means plot The variability of users’ perceptions of the video across the 4 psychographic groups Fig4. Perceptions of the video across the 4 psychographic groups According to the standard deviation obtained, it is noted that majority of the camping enthusiasts found the videos enjoyable since it has the highest standard deviation (1.329). The lowest number of users found the video entertaining with standard deviation of 1.178. On the other hand, majority of the hiking specialist found the videos enjoyable to watch with standard deviation of 1.504. The indoor type also had majority of the users enjoying the videos with standard deviation of 1.116 which is the lowest compared to the other two psychographic groups which found the videos enjoyable. Water sports warriors is the only group that majority of the users did found videos enjoyable but entertaining at a standard deviation of 1.668. The variability of the perception of the videos showed that water sports warriors are less attracted to the message conveyed by videos compared with the other three groups. The difference is brought by fact that water sport warriors activities requires light clothing thus making the video interesting rather than enjoyable. Use measures of central tendency to comment on “Camping Enthusiasts” and “Indoor Types” interaction times with the video. fig5. Central tendency According to the mean obtained from the data collected on users interaction time, it can be noted that majority of the users with higher interaction time were camping enthusiast. The camping enthusiast has the highest mean than indoor type which is evident that on overall time spent, indoor type had the lowest time of interaction. It is evident that camping enthusiast interacted more with the video compared to indoor type. Camping enthusiast had the highest median compared to the Indoor type. This shows that majority of the camping enthusiast had higher interaction time with the video than Indoor type. This is because median measures the middle number when the users’ interaction times are arranged either in ascending or descending order (Nuttall 1958). The two groups showed that they had the same mode of 30. Despite the difference in the median and mode, the camping enthusiast and indoor type users had a common time of interaction with the video. The relationship between the gender of users and their interaction times of the video. Fig6. Gender of users and their interaction times of the video There is sensitivity on clothing between male and female thus comparing the two is essential in marketing the line of clothing. The data explaining the users interaction with the video according to the gender shows that the variability explained is 73.8%. This shows the reliance the company should place on the data obtained. According to the findings, female formed the majority of the users who interacted with the video compared to male. This shows that the company should concentrate more on female gender since they have shown higher interest than male by interacting with the video. Though, Male shows higher standard deviation than female. Female gender is more concern on clothing and fashion more than male. This explains the difference in the mean. The female will take more time watching the video in order to see all the aspects of the clothing which forms basis of buying it. On the other hand, male users are less concern searching for clothes online or watch advertisements on clothes. INFERENTIAL ANALYSES: How the device used to access the social media site influences users’ interaction times with the video. Chi-Square Tests 1 = 18 - 29, 2 = 30 - 47, 3 = 48 - 57, 4 = 58+ Value df Asymp. Sig. (2-sided) 1 Pearson Chi-Square 9.791b 6 .134 Likelihood Ratio 10.114 6 .120 Linear-by-Linear Association 1.890 1 .169 N of Valid Cases 37 2 Pearson Chi-Square 5.540c 6 .477 Likelihood Ratio 6.537 6 .366 Linear-by-Linear Association .943 1 .331 N of Valid Cases 42 3 Pearson Chi-Square 12.661d 6 .049 Likelihood Ratio 13.009 6 .043 Linear-by-Linear Association 1.662 1 .197 N of Valid Cases 35 4 Pearson Chi-Square 3.497e 6 .744 Likelihood Ratio 3.902 6 .690 Linear-by-Linear Association .680 1 .409 N of Valid Cases 41 Total Pearson Chi-Square 9.934a 6 .127 Likelihood Ratio 10.566 6 .103 Linear-by-Linear Association 4.249 1 .039 N of Valid Cases 155 a. 5 cells (41.7%) have expected count less than 5. The minimum expected count is .70. b. 10 cells (83.3%) have expected count less than 5. The minimum expected count is .16. c. 9 cells (75.0%) have expected count less than 5. The minimum expected count is .24. d. 9 cells (75.0%) have expected count less than 5. The minimum expected count is .09. e. 9 cells (75.0%) have expected count less than 5. The minimum expected count is .20. Fig7. The social media site influences users’ interaction times There is need for the company to analyses the device used, age group, and the interaction time in adopting and measuring the success of the line of product. The CHI-SQUARE is essential where company measures expected performance and actual results. According to the data obtained, Pearson Chi-Square can be evaluated in order to evaluate data fit to the observed distribution (Kazmier et al. 2003). The data showed that age group of 48-57 showed that there are higher chances of the expected results being observed in the distribution since it has the highest value of 12.661 compared to other age groups. The group also shows higher likelihood ratio of 13.009 which increases the chances of occurrence. The age group of 18-29 years is also showing higher chances of the observed data occurring where the Pearson Chi-Square is at 9.791 and likelihood ratio is 10.114 which are the second highest. The age of 30-47 and 58+ showed the lowest Pearson Chi-Square of 3.497 and 9.791 with the likelihood ratio being 6.537 and 3.902 respectively. The trend showed by the Ch-test is valid because the age that is actively involved in the recreational activities and biggest spenders are 18-29 and 48-57 since they have time and resources to spend in such activities. The difference between the two age groups can be associated to fact that those individuals of 48-57 years have more resources from savings more than 18-29. In addition, the low score between the ages of 30-47 years is associated to active involvement in the employment thus little time spent for recreational activities or social media. The individuals above 58 years score is attributed to reduced level of aggressiveness or excitement on recreational activities, social media and use of electronic device. How the time of day users access the social media site influences their interaction time with the video. Chi-Square Tests 1 = FEMALE, 2 = MALE Value df Asymp. Sig. (2-sided) 1 Pearson Chi-Square 5.004b 9 .834 Likelihood Ratio 5.132 9 .823 Linear-by-Linear Association .418 1 .518 N of Valid Cases 70 2 Pearson Chi-Square 4.294c 9 .891 Likelihood Ratio 5.313 9 .806 Linear-by-Linear Association 1.350 1 .245 N of Valid Cases 85 Total Pearson Chi-Square 4.960a 9 .838 Likelihood Ratio 6.211 9 .719 Linear-by-Linear Association 1.751 1 .186 N of Valid Cases 155 a. 5 cells (31.3%) have expected count less than 5. The minimum expected count is .97. b. 11 cells (68.8%) have expected count less than 5. The minimum expected count is .14. c. 10 cells (62.5%) have expected count less than 5. The minimum expected count is .88. fIg8. The time of day users access the social media site The company should measure the chances of expected observed data on time the users access the site and gender of the user in order to determine the best strategy. According to the data obtained, female have higher Pearson Chi-Square of 5.004, while male have 4.294. Though, the likelihood ratio of the Male is higher than that of female because of the degree of association between the data collected. The data collected shows that there is a lower relationship (0.418) of data when it comes to female users than male (1.751). Therefore, it can be concluded that distribution for the female is more fit compared to data distribution of male users. The data concludes the assumption that most of the male users access the site but due less interest on the clothing, they spend little time in watching the videos (Corlett 1966). On the other hand, female Pearson is evident to the fact that female are interested in the clothing or fashion and they want to understand every bit of any clothe line being introduced in the market. The understanding is important since female gender uses the information obtained in making purchase decision. Unlike male users who only look for information when the need for the clothing arises i.e. when they want to engage in hiking or camping. How users’ interaction time with the video influences their perception of the video. Age Group: 18 -29 Age Group: 30 -47 Age Group: 48-57 Age Group: 58+ Fig9. How users’ interaction time with the video influences their perception The relationship between the interaction times with the perception of video is essential in determination of the intensity that the company will place in the campaign. The relationship is determined according to the Age group of the users. The linear relationship between the time of interaction and perception of the video for the age of 18-29 years shows that the two are positively related. That is when duration of interaction is high, perception of the video is good and vice versa. In addition, it is noted that for every 0.021 seconds spent by the user watching the video, user have perception of 1. The significance of the regression model is at 0.127 that is confidence interval of 74.6%. The relationship between variables for the age group of 30-47 shows that perception of the video and duration has negative relationship. The increase in duration of interaction by 0.006 leads to reduction of perception of the video by factor of 1. The above significance of the linear relationship which shows the extent at which variables are explained by the regression model is at 0.718 or 71.8%. This is important in deciding on the extent to rely on the model in making major decision. The age group of 48-57 years shows that there is a positive relationship between the interaction time and perception. The regression model shows that when interaction time increases by 0.007 seconds, the perception increases by a factor of 1. The regression model has significance of 71.6% which variables are explained. The age group of 58+ years showed there is negative relationship between interaction time and perception of the video. According to the model, increase in interaction time by 6.395E5 results to decrease in perception of video by factor of 1. In conclusion, the video interest the age group of 18-29 and 48-57 years since they are more involved in recreational activities and high spenders. How users’ perceptions of the video influence their click through. Group: Camping Enthusiast Group: Hiking Specialist Group: Water Sport Warrior Group: Indoors Type Fig10. How users’ perceptions of the video influence their click through The company needs to understand how the click is influenced by the perception of they have on the video. This is achieved by understanding direct probability between the variables p(x/y) in the model (Chadwick 2014). This is measured by categorizing users according to their psychographic groups. According to camping enthusiast, the probability of having one click depends on the user’s perception of 35.801 while the adjusted beta is 3.578 for one click. The relationship between the two variables is positive since increase in perception results to increase in number of clicks. The relationship is possible since user will click a link if his perception about it is good and the overall percentage is high. According to hiking specialist users’ data, the probability of having one click depends on the expected perception 22.346 or adjusted perception of 3.107 of the user. The relationship is positive since increase perception on the video result to increase in the number of clicks. There is high possibility of the above occurring due to the high overall percentage in block 1 of 88%. The probability of relationship between click and perception of the video for Water sports warriors is positive, where 2.390 of expected perception are expected to increase number of clicks by one. On the other hand, the adjusted value of perception (0.871) is expected to yield one click. The logic regression has high probability of being true because of small variation between the expected and adjusted beta. The group indoor type showed positive relationship between the perception of the video and click. The probability of having one click depends on user’s perception of 1.162 while the expected perception is at 3.197 for one click. This has high chances of occurrence due to confidence interval (83.2%) of the logic regression and little difference between expected and adjusted beta. How users’ interaction time with the video influences their click-through. Group: Camping Enthusiast Group: Hiking Specialist Group: Water Sports Warrior Group: Indoors Type Fig11. How users’ interaction time with the video influences their click-through The company need to compare probability of the relationship between click and interaction time in order to draw conclusion from the data gathered. This relationship according to psychographic groups is as shown below; The camping enthusiast showed a negative relationship between the time of interaction and clicks. The probability of having one click depends on the time of 0.788 while the expected interaction time shows positive relationship where increase by 0.455 of the interaction time results to increase by one click. This is less likely to happen because of inconsistency between the expected and adjusted beta. The hiking specialist users’ data showed positive relationship between the interaction time and clicks. The probability of expected and adjusted interaction time for one click is 1.487 and 0.397 respectively. The occurrence of the above has high probability of happening since the expected and adjusted beta have small deviation and consistent. The water warrior sports variables on the interaction time and clicks showed a positive relationship where increase in interaction time by 0.318 is expected to increase possibility of having one click. The expected interaction time to increase one click is at 1.374. The beta explains 51.9% of the overall logic regression. The interaction time and click showed negative relationship where increase in interaction time results to decrease in the number of click for the indoor type (Baliga and Vohra 2003). The result shows that increase in interaction time by 1.626 results to decrease in clicks by one. The expected beta is 0.197 thus showing inconsistency of the data which reduces the likelihood of the relationship of the data being true. How the number of social media sites the user has followed of the brand influences their click-through. Group: Camping Enthusiast Group: Hiking Specialist Group: Water Sports Warrior Group: Indoors Type Fig12. Social media sites the user has followed of the brand influences their click-through. The company needs to measure the relationship between the clicks and number of social sites followed by the user. In order to come up with the best strategy to that yield desired results. The camping enthusiast users show a positive relationship between the social sites followed by the users and the licks (Baliga and Vohra 2003). Increase in the number of social sites by 0.57 results to probability of getting one click. In addition, 1.768 expected number of social site followed was approximated to increase click by one. The expected results have high probability of happening due to high confidence interval of the logic regression model. The hiking specialist users showed positive relationship between the social site followed and the clicks by the users. Increase in the number of social site followed by 0.104 is expected to yield one click. The expectation has low probability of happening due to low significance level of the explained variables in the logic regression model. The water sport warrior data showed positive relationship between social sites followed and clicks of the video. The increase in the number of social site followed by 1.45 results to the increase in expected click by one. The variability between the expected and adjusted showed the same direction of deviation. This increases the probability of getting the expected results explained by the logic regression model. The indoor type showed positive relationship between number of the social site followed and the clicks (Baliga and Vohra 2003). Therefore, increase in number of social site followed by 0.064 results to increase in number of clicks by one. The data obtained has high probability of happening due to high confidence interval. RECOMMENDATION Strategies that need to be implemented to increase click-through rates of the video amongst a particular demographic group The company needs to adopt strategies that work well for the marketing campaign in order to achieve desired results. The analysis carried out from the data collected during the market research enables the research to recommend the following in order to increase rates of clicks within demographic groups; i. Camping enthusiast According to the research, the company needs to ensure that the videos receive positive perception in the public on how the demonstration of the product is carried. The gaining on the perception of the videos will increase the number of clicks which ensures that the marketing campaign objective is met by passing the intended information to the market. In addition, the video should be interesting in order to increase interaction time since interaction time and clicks are positively related. The company implementation of the above strategies will increase the number of clicks by the hiking specialist. ii. Hiking specialist The hiking specialist requires warm wear which makes it necessary for the company to ensure that the number of clicks on videos is increased. The clicks can be increased by increasing the number of the social site followed by the users through creation and advertising the social web pages. The increase results to increase in the number of clicks due to the positive relationship between the two variables. The increase in clicks results to improved publicity and information on the brand of warm wear in the market. iii. Water sports warrior The number of clicks by the water sports warrior can be increased by improving the interaction time, perception and social sites available for the users to follow. The positive relationship between this variables and clicks will automatically improve the number of clicks the video receives. iv. Indoor type According to the relationship between interaction time and perception of the video, only age group of 18-29 and 48-57 showed high interest. The other groups showed little interest due to small impact change in the two variables. The company should balance the two variables for the indoor type in order achieves high number of clicks since it has positive relationship. How to tailor the content of the video to best suit one particular psychographic group The content of the video plays a major role in determining the effectiveness of the publicity campaign of any brand. Therefore, video need to be tailored to meet the needs of various users using preferred devices. The research recommends the following in order to suit needs of diverse groups; The research showed that high number of users who interacted with video is female. The company should therefore understand what male gender requires in improving the number of male viewers. This can be achieved by tailoring the video to capture their attention for example involving well known athletes for activities such as hiking and water sports warrior. This will increase number of male who interact with the video since male are more interested in sports than fashion or clothing advertisement. In addition, the age group of 30-47 and 58+ years showed little interest in watching the videos. This can improve by tailoring the video to meet their needs. For example, video can contain people going to work during winter period wearing the product or old people showing their contentment with the product’s warmth. The above strategy will ensure that the groups increase their viewing to the videos posted by the company. The majority of the viewers used mobile phone followed by computers and tablets. The company should ensure that the videos are compatible to phone player format, tablets and computers in order to ensure all device potential customers watch the video effectively. The effectiveness will ensure that the company brand publicity gains desired objective. Lastly, videos need to be tailored specific psychographic group in order to persuade the potential customer to purchase the wear. For example, video for that targets water sports warrior should be tailored by showing a swimmer who swims and after the exercise he/she shows the contentment of warmth that he receives from the warm wear clothing. The other groups should be gathered for the showing the relationship between the clothing and activity. REFERENCE: Baliga, Sandeep, and Rakesh Vohra. 2003. 'Market Research And Market Design'. Advances In Theoretical Economics 3 (1). doi:10.2202/1534-5963.1059. Chadwick, Simon. 2014. 'Market Research Is Dead; Long Live Market Research!'. Research World 2014 (49): 6-7. doi:10.1002/rwm3.20164. Corlett, T. 1966. 'Statistical Applications In Market Research: An Introduction'. Applied Statistics 15 (3): 155. doi:10.2307/2985297. Kazmier, Leonard J, Michael K Staton, Daniel L Fulks, and Leonard J Kazmier. 2003. Business Statistics. New York: McGraw-Hill. Nuttall, C. 1958. 'Statistical Sources For Market Research.'. Applied Statistics 7 (2): 130. doi:10.2307/2985316. Read More