With an increasing number of search clicks being lost to featured snippets, it's getting more important to learn to optimize your posts for featured snippets. And now with Google and most other search engines using featured snippets for providing results for voice searches, it's high time you learnt how to.
Imagine the user experience you could provide to your customers if they could trial the products, say, a sofa, without actually visiting a physical store or outlet? Ikea has already started offering this feature and it has gained a lot of attention and buzz. The possibilities in this industry are endless, with potential applications in the healthcare, fashion, and many other areas. Videos are evolving to form an integral part of the marketing strategies of most brands. We expect that will see a huge growth in the usage of video.
This trend has been evident over the past few years- noticed how the biggies like Entrepreneur, Inc, Forbes etc have videos on almost all their posts? And we aren't just talking about video product descriptions. Video will become one of the major channels for both content marketing and advertising.
For starters, you can use tools like Lumen5 to create simple videos like this one, which you can use for both branding and driving traffic:. You can see the entire step by process for creating a video using Lumen 5 here. Lastly, Shopify has its own coding language called 'Liquid', so any customization in the system comes at an additional expense.
For beginners with conventional requirements, Shopify remains the ideal ecommerce solution. But if you have unique features and functionalities in mind for your store, it might be better to look elsewhere. Launched in , BigCommerce currently hosts over 55, online stores and is among the leading ecommerce software providers.
And considering the comprehensive list of built in features it offers, BigCommerce is often considered the best option for those who have little to no technical knowledge, or simply don't have time to mess with the code.
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Powerful support with extremely helpful setup wizard, educational videos, and extensive guides with visual representations. One concern with BigCommerce is the lack of free themes. Currently it offers only 7 free themes, while most of their immediate competitors offer more than 20 free themes. However, the platform has a comprehensive list of premium themes, and it also allows customization of these themes if you want to give your store an entirely unique look and feel.
Consumer Purchase Decision- Making Process Based on the Traditional Clothing Shopping Form
With almost one-fourth of the market share, Magento is often considered the market leader among ecommerce platforms. This platform is considered the best ecommerce platform for enterprise level online stores. Extensive resource library for store management, growth, demos, and online community of store owners as well as plugin developers you can tap for help.
One of the critical factors associated with Magento is the pricing of its versions. Additionally, the flexible, open source nature of the platform also makes it quite complex, so you need very competent developers for customization. So even with the Community version -considering hosting, SSL, extensions, customization, etc.
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Magento is considered the right ecommerce platform for enterprise-level merchants with a large volume of products. Given the complexity of store setup and management with Magento, the platform is not recommended for beginners. With its feature richness and smart pricing models, YoKart is built with a specific focus on helping startups build ecommerce ecosystem easily. Think of it as the one-stop solution for launching multi-vendor stores like eBay and Etsy.
Although many other ecommerce platforms also provide their multi-vendor version, YoKart does this seamlessly. Other than the framework, you own the source code and can customize the portal to your requirements. YoKart is built on an encrypted framework. Which means that customization would require knowledge of that framework.
In general, an experienced PHP developer would quickly understand the framework and how to modify it as needed. Comprehensive manuals for store owners and merchants, video tutorials, FAQs, and month free support for store management. Being a relatively new entrant in the industry, there are no third-party extensions currently available for YoKart. But the system offers built in analytics tool, over a dozen supported payment gateways, rewards and discount coupon management features. A big drawback, however, is its lack of social commerce capabilities, meaning that that it currently lacks the features to allow merchants to sell on Facebook and other social media platforms.
For multi-vendor stores such as eBay and Etsy, YoKart is a great choice. The data for the study was gathered in two stages. In the first stage, data was collected to select products from each of the four classes—search, experience-1, experience-2, and credence—for use in the subsequent study. Thirty graduate students from a large urban university in the Southeastern United States participated in the first phase of the study. The average age of the students was in the mid-thirties. The sample consisted of an equal number of males and females, from clerical, supervisory, and technical professions.
Each had used the Internet before on a regular basis. After each description they were asked to list four products that they believed represented each category. The four descriptions are given below:. A total of 64 products were listed by the respondents under the search product category, 70 products were listed under the experience-1 category, 87 products were listed under the experience-2 category, and 69 products were listed under the credence product category. Based on the examination of the product list provided by the respondents, the authors selected the two most representative products for each product class see Table 2 to be used in the second stage of the study.
We also focused on those products that are more readily available on the Internet so as to make the second stage of the study more relevant to the respondents. In the second phase, the data for the study was collected in two major metropolitan areas with a population of 3. The respondents were contacted during different days of the week and different times of the day. The survey was administered only to those who were at least 18 years of age and had used the Internet regularly in the past. In comparing our sample to the Census of the local area we found that our sample was skewed towards the more highly educated and higher income respondent, and to skilled as well as managerial types of occupations.
This was expected as we surveyed only those who were regular users of the Internet. However, the sample profile is similar to the national profile of the Internet users as reflected in the last GVU survey. Percentages do not add to per cent due to missing values. Sample demographic characteristics continued. The demographic profile of sample respondents of four product categories actual count.
The survey instrument had four sections. In section one, we asked each respondent to indicate his or her preference for purchasing from the Internet each of the eight products selected from stage one of the study. The subjects were given a list of 50 statements designed to capture 11 different dimensions of internet retailers. The items were chosen from an exhaustive search of the literature in the area of Internet retailing as well as direct marketing e. A separate set of respondents was asked for the same information but assuming they were purchasing products such as clothing and perfume experience A third group of respondents was asked for the same information but assuming they were purchasing products such as cellular phones and televisions experience The fourth group of respondents was asked for the same information assuming they were purchasing products such as vitamins and water purifiers credence.
The administration of the survey instruments was randomized to prevent a response bias. There were no statistically significant differences in the demographic profiles of the four groups of respondents. A total of valid surveys was obtained. The breakdown of the sample size in each product category is as follows: A first step in the analysis was aimed at ensuring that the survey instrument captured all the attributes of Internet retailer. Hence, Principal Component Analysis with Varimax rotation was performed on the fifty importance of Internet retailer attribute items to examine their discriminant and convergent validity.
The analysis produced a clean factor structure with items loading on the appropriate components Table 5. Ten dimensions were obtained with Eigenvalues greater than 1, and 66 percent of the cumulative variance was explained. Only seven items did not load on the underlying dimensions. These two items were retained where they loaded because they seemed to be relevant to the respondents' information search; therefore, they were suitable items to measure the Information Service component.
Since the two items did not measure either component rigorously, they were eliminated. Therefore, it was eliminated. A total of three items out of fifty were deleted. The rest of the items that loaded on the appropriate components produced ten dimensions with high Chronbach Alphas in the range of 0. Thus, our analysis confirmed the presence of 10 attributes labeled as follows: A scale for each attribute was created by summing up the responses to the items loading on the corresponding factor. The preference responses to the two products in each of the four categories were combined to measure the overall preference for purchasing products from each category on the Internet.
The reliability alphas of the four product preferences were in the range of 0. The first, second and third hypotheses were tested through six paired t -tests evaluating whether each of the four product classes was significantly different from the others, and an F -statistic was calculated to test overall significance. The results of the t -tests and F tests provided partial support for the three hypotheses.
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Descriptive statistics including the means and standard deviations are exhibited in Table 6. Hypotheses 1 and 2 were supported; i.
Hypothesis 3 was partially supported; i. This shows that products that are costly and difficult to evaluate are the least likely to succeed on the Internet.
Introduction
Examples of the experience-2 products may be automobiles or home appliances such as washers, dryers, air conditioners, television, and refrigerators. In addition, they cannot be tried before the purchase. To test hypotheses 4 through 7, ten separate one-way Analyses of Variance were performed in which product category was the independent variable and the summated score of each importance of Internet retailer attribute component was the dependent variable.
Additionally, mean values of each of the 10 Internet retailer attributes for each product class were compared to determine the rank order order of importance for the Internet retailer attributes for each of the product categories. The results of the ten separate Analyses of Variance to test hypotheses 4 through 7 indicated that product category produced significant differences in two Internet retailer attributes on a summated scale.
The comparison of the mean values of each Internet retailer attribute importance scores across all product types indicated that Perceived Value was the most important attribute followed by Information Services, followed by Convenience and then IR Reputation Table 7. However, for the Experience-1 and Experience-2 product classes, Perceived Value had the highest importance followed by Information Services.
Convenience, IR Reputation, and Order Services ranked third, forth, and fifth respectively for all product types. The remaining rankings of relative importance are shown in Table 7. Therefore, hypotheses 4, 5, and 6 were partially supported. Hypothesis 8 was tested through a two-tailed correlation analysis to determine whether significant associations or correlations exist between the importance of Internet retailer attribute components and online purchase preference for each product class. The results of the two-tailed correlation analysis indicated that product purchase preference from an Internet retailer had significant associations with Internet retailer attributes table 1.
IR Reputation and Customer Service did not have a significant association with the online purchase preference for experience-1 products. For experience-2 products, IR Reputation, Home Shopping, and Order Services had a significant influence on online purchase preference at the 0. Perceived Value and Customer Service had no significant effects on online purchase preference for credence products. In order to ensure that the results were not an artifact of the demographic variables of gender, occupation, education, and income the results were confirmed by running correlations while partialling out the effects of the four demographic variables.
The results remain almost identical except for change in the significance of six correlations out of a total of Those changes were primarily the artifact of lower sample sizes as the missing values for the four demographic variables lowered the sample size without changing the correlation coefficients in substance. Thus, the authors found support for hypothesis 8 without the moderating effects of the demographic variables. The findings of the present study support the hypothesis that product categories of search, experience and credence significantly influence consumers' purchase preference from an Internet retailer, as well as the importance consumers place on certain Internet retailer attributes and that the relative importance attached to this attributes vary significantly across the four product classes search, experience-1, experience-2, and credence.
These results have important implications for Internet retailers as well as non-Internet retailers. It is evident from the support of the first through third hypotheses that consumers prefer to purchase certain products online. The results of the present study indicate that the search category products are more likely to succeed than the experience and credence category products since online shoppers feel more confident about purchasing them online.
The findings are consistent with those of Ford, Smith, and Swasy that consumers give more credibility to search than experience and to experience than credence advertising claims. The findings also support Nelson's theory that consumers value advertising claims differently across product categories.
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Therefore, prompt updating of the information on e-tailers' Web sites is important. The results of the present study support the hypothesis that online shoppers prefer to purchase experience-1 products online less than they do the search products. The reason may be that information provided online for experience-1 products such as clothing and perfume may not be sufficient for online shoppers to purchase them online with confidence.
Shoppers would like to try the product in the store before making a purchase decision. If shoppers cannot find the right size, kind or color, they may choose to order the product on the Internet after seeing and trying it in the store.