The effect of testers on return rate in the cosmetic field
- 1. Research: The effect of testers on return rate and customer satisfaction in the cosmetic fieldBY ANGELA SINGH SOPHIA ATTAF ANDREA VERESS
- 2. Introduction ▶ Cosmetic industry: $56.63 billion in US ▶ By 2016: total revenue will exceed about $ 62 billion and employ about 63,816 people ▶ Large rate of returns in this industry: 8.60% (according to the consumer returns survey, which included 60 companies) ▶ Products that have been returned cannot be sold to another customer
- 3. Secondary research ▶ Product return rate might attain 25% in some cases ($100 billion per year in lost and it decreases the company’s profits by 3.8%) ▶ Dissatisfaction with a product is the consequence of an emotional dissonance that has a positive relationship with product returns ▶ The reasons for high product return according to this research are: ▶ The client found a better product at a lower price ▶ The expectations of the customer were not met ▶ Even if males had a high dissatisfaction level, their rate of return was lower than for females. ▶ The rate of product returns varies according to the nature of the product and the period of the year
- 4. Problem definition ▶ In drugstores and other retail stores (Walgreens, CVS, Rite Aid, Target, Walmart) customers cannot try out cosmetic products before the purchase ▶ If customers could try cosmetic products would that decrease return rates and increase customer satisfaction?
- 5. Research questions ▶ Is there any relationship between product testing and customer satisfaction? ▶ Is there any relationship between prior product testing and return rate? ▶ Is there any relationship between gender and reaction to testers regarding satisfaction level?
- 6. Hypothesis ▶ H1: There is a positive correlation between product testing and customer satisfaction regarding the tested product. ▶ H2: There is a relationship between prior product testing and return rate ▶ H3: The rate of return is different according to the gender of the customer. ▶ H4: There is a relationship between rate of return and level of education. ▶ H5: There is a relationship between satisfaction level when testers are available and gender. ▶ H6: There is a relationship between rate of return in the last 6 months and the return frequency when testers are not available.
- 9. Quantitative Research Plan ▶ Total- 12 questions ▶ Specific research questions- 9 ▶ Demographic questions- 3
- 10. Questionnaire Design ▶ Non-comparative scaling technique 'Likert scale’ ▶ Other questions consist of nominal, interval and scales which provides data about the respondent's demographic information
- 11. Sampling population ▶ Target Audience ▶ Number of respondents ▶ Sampling techniques
- 13. Results
- 14. First Hypothesis ▶ Our first hypothesis: There exists a positive correlation between product testing and customer satisfaction regarding the tested product. ▶ Used test: Wilcoxon Signed Rank Test ▶ We used a seven-level Likert scale to collect these data where 1 represents “extremely dissatisfied” and 7 represents “extremely satisfied”.
- 15. First Hypothesis H0: The difference between the pairs follows a symmetric distribution around zero. P value is below 0.05, so we can reject the null hypothesis. Effect size: r= Z/ √n. The effect size in this case is 0.49 which is considered a large effect size. Testers have a statistically significant effect on customer satisfaction.
- 16. Second Hypothesis ▶ Our second hypothesis: There is a relationship between prior product testing and return rate ▶ Test method: Wilcoxon Signed Rank test ▶ We used a five-level Likert scale to collect these data where 1 represents “never” and 5 represents “very often”.
- 17. Second Hypothesis H0: The difference between the pairs follows a symmetric distribution around zero. P value is below 0.05, so we can reject the null hypothesis. r= Z/ √n. The effect size in this case is 0.24 which is considered a small/weak effect. According to this test, we can say that testers have a small effect on the returns.
- 18. Third Hypothesis ▶ Our third hypothesis: Different gender return cosmetic products with different frequency. ▶ Test method: Cross tabulation with Chi-square
- 19. Third Hypothesis H0: There is no difference between woman and man on whether they returned a cosmetic item in the last 6 months. Our P value (0.079) was higher than our significance, so we couldn’t reject the null hypothesis. According to the test, there is no statistically significant difference between women and men regarding their returns.
- 20. Fourth Hypothesis ▶ Fourth hypothesis: There is a relationship between return of products and level of education. ▶ We wanted to test whether education level and return frequency has any relationship. ▶ Test method: Independent Samples T-test ▶ Grouping variable: returned a product in the last 6 months ▶ Independent variable: education level ▶ We measured the education level on a 7-level scale where 1 was equal with “less than high school” and 7 was equal “doctorate”.
- 21. Fourth Hypothesis H0: There is no relationship between return of products and level of education. The Sig (2-tailed) value is 0.21, because this value is not less than 0.05, we cannot reject the Null Hypothesis. We couldn’t prove any significant relationship between education level and whether returns happened in the last 6 months.
- 22. Fifth Hypothesis ▶ Fifth hypothesis: There is a relationship between satisfaction level when testers are present and gender. ▶ Test method: Independent Samples T-test ▶ Grouping variable: gender ▶ Independent variable: satisfaction level if testers are available in stores ▶ We measured the satisfaction level on a 7-level Likert scale where 1 was equal with “extremely dissatisfied” and 7 was equal “extremely satisfied”.
- 23. Fifth Hypothesis H0: There is no relationship between satisfaction level when testers are present and gender. The Sig (2-tailed) value is 0.115, because this value is not less than 0.05, we cannot reject the Null Hypothesis. We couldn’t prove any statistically significant relationship between satisfaction level when testers are placed and gender.
- 24. Sixth Hypothesis ▶ Sixth hypothesis: There is a relationship between return of products in the last 6 months and the return frequency when testers are not available. ▶ Test method: Independent Samples T-test ▶ Grouping variable: returned a product in the last 6 months ▶ Independent variable: return frequency when testers are not available
- 25. Sixth Hypothesis H0: There is no relationship between return of products in the last 6 months and the return frequency when testers are not available. The Sig (2-tailed) value is 0.00, because this value is less than 0.05, we can reject the Null Hypothesis. This proves that there is a significant positive correlation between high return rate when testers are not available and returning a product in the last 6 months.
- 26. Other findings ▶ According to the results of this question, samples and testers could decrease the return intention of a customer. ▶ When we asked respondents whether they would feel more comfortable buying a new product if testers were available at the store. We got a response rate of 89.2%
- 27. Limitations ▶ Convenience sampling was used to gather the data. ▶ The survey has been conducted in the city of San Francisco only.
- 28. Managerial Implications ▶ The results from our study have some implications: ▶ Testing the product before purchase has a large effect on customer satisfaction. ▶ From our study, about 70% of respondents showed increased satisfaction when they were able to test the product before purchase. ▶ Managers should therefore, explore the idea to incorporate samples for cosmetic product categories as these products also have a very high margin.
- 29. ▶ In addition, after a cosmetic item is returned, the product cannot be resold. This will lead to wastage and thereby reduce revenue of the company. ▶ In the short-run it may cost companies to make testers/samples available in stores but in the long-run it will help them to get more customers and thereby increase their profitability. ManagerialImplications
- 30. ▶ Although small in effect, there appears to be a negative relationship between the presence of testers and the rate of return of products. ▶ Based on our sample, it was found that there is no statistical significance in difference between gender and return rates. ▶ However, we saw in our sample that women tend to return more often than men. However, more research can also be done with a bigger sample size to see if there will be any statistical significance between gender and return rates. ManagerialImplications
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