Suggestions and Recommendations

• Credibility in Payment System

Online frauds and breach are the biggest barriers to online sales. As a result, prospective buyers prefer staying away from revealing their credit card and bank details.

• Discount and lucrative offers

Use of credit card and E-banking can be encouraged by giving discount and lucrative offers while shopping online using it.  A large number of users search for online discounts and then go for shopping.

• Untimely Delivery of Products

It might take a few minutes to search, book and pay for products and services online, but the delivery of the product may take unreasonable time.

• Consumer Bias

Consumers often display a bias for brands that they know well and have had a good experience in the past. Thus products of brands with a favorable bias will score over the products of less popular brands. A few would risk buying expensive jewelry from an unknown jeweler online.

• Lack of ‘Touch –Feel-Try’ Experience

The customer is not sure of the quality of the product unless it is delivered to him and post-delivery of the product, it is sometimes a lengthy process to get a faulty or the unsuitable product changed. Thus, unless the deliverables are as per the customers’ expectations, it is hard to infuse more credibility in online shopping.

• Mounting Competitive Pressures

To attract customers, the competing online players are adopting all means to provide products and services at the lowest prices. This has resulted in making the consumers choice-spoilt, who in turn surf various websites to spot the lowest price for the product. Thus, although the number of transactions is increasing, the value of the products sold is continuously falling owning to high competition and leaner margins.

• Seasonality

Online market is facing seasonal fluctuations. Usually August to February is the peak seasons for sale, while March to July is the dry seasons for sale. During the peak season, occasions that drive the sales are Diwali, Rakhi, Valentine’s Day, New Year, Christmas, Mother’s Day, and Friendship Day etc. On these occasions younger generations prefers buying and sending gifts online.

Moreover, companies need to reduce the risks related to consumer incompetence by tactics such as making purchase websites easier to navigate, and introducing Internet kiosks, computers and other aids in stores. The feedback of an online buyer should be captured to identify flaws in service delivery. This can be done through online communities and blogs that serve as advertising and marketing tools and a source of feedback for enterprises.

Results And Interpetations

• There is a strong inter-dependence between a few variables affecting online buying behavior. For example, owning a credit card, gender and E-banking has a significant impact on the frequency of online purchases whereas age and income of the respondent does not. Also, gender does not have any impact on the average amount spent per purchase made online.
• Depending on the reasons for a person to be online, consumers have been divided into homogeneous groups. Based on cluster analysis we could divide the respondents in three clearly distinct groups. These are ‘Leisure Hunter’, ‘Regular Web Person’ and ‘Dedicated Surfer’.
• Consumers have been further divided into four clusters on the basis of factors which influence them while making an online purchase as ‘The Surgical Shopper’, ‘The Enthusiast Shopper’, ‘The Casual Shopper’ and ‘The Reluctant Shopper’.
• We could also arrive at five factors which can explain the data with 66.88% significance. These factors could be categorized into ‘Trust’, ‘Convenience’, ‘Risk propensity’, ‘The Power Shopping’ and ‘Neglect’.
• The most popular product category sold online is Air/Rail Tickets followed by books. 
• It must be noted that both the above products have relatively low touch-and-feel need. Gifts, Electronic Products and Car & Hotel rental are also very popular with the Online.
• Discriminant analysis shows that Gender, Credit Card, E-banking, Use of SNS and Age significantly differentiate between those who shop online and those who do not.

Results & Interpretation

Analysis of the data will conclude in the next spring semester 2013 after the sample size of 100 is collected for an efficient result.