Add to Cart: Deal Makers and Deal Breakers of the Online Shopping Experience
The current generation of technologies have ushered in a new era in retail where nearly any item is available for purchase at the push of a button, and customers can shop from the comfort of their own home. Existing B-to-C communication strategies place emphasis on segmented content specific to a large group or the use of discounts, but consumers have become disillusioned to these tactics. Increased convenience in online retail has given customers almost unlimited choice in how, where and when to buy, and that abundance of choice has introduced an entirely new set of challenges for online retailers. Brands have trained customers to expect generic and discount-heavy communications, but customers no longer purchase because of these tactics alone. Successful retailers foster a sense of community and brand loyalty by remaining convenient and relevant.
In a recent consumer study, Add to Cart: Deal Makers and Deal Breakers of the Online Shopping Experience, comprised of both male and female consumers ages 18 to 56 in the United States, consumers shared their insights as to what makes or breaks their online shopping experiences. The report revealed that 62 percent of respondents choose to shop online instead of in-store more than half of the time because technology empowers them with choice and convenience. A large majority of respondents (64 percent) indicated they shop online vs. in-store because they're able to shop at any time from any device.
Respondents also indicated that a larger inventory of products is a draw (50 percent), and 49 percent of consumers believe shopping online is faster than shopping in-store. The biggest frustration for consumers, with 86.4 percent of shoppers listing this as their top deal breaker, are online experiences where the retailer has no personalization — wherein the retailer either cannot or does not remember the shopper was ever there before. Consumers also consider dropped shopping cart items (82 percent) and irrelevant or inaccurate product recommendations (80 percent) equally important factors in a subpar online shopping experience.
Results also show that these frustrations lead to churn and lost business. While 40 percent of customers react positively (will purchase/will most likely purchase) to what they consider good recommendations, the overwhelming majority of consumers (69 percent) would abandon a retailer if they received poor recommendations and another 16 percent said they would consider abandoning the retailer for poor recommendations. For retailers, poor implementation of personalization may potentially result in an 85 percent customer churn.
Additionally, the survey highlights that relevance and convenience go hand-in-hand. Respondents answered questions about product listing pages, which typically feature a static listing of content that each individual consumer must scroll/filter/search through to find their desired item. More than 40 percent of shoppers would only scroll up to two pages before losing interest in their product search and leaving the site, demonstrating the need for retailers to immediately surface the most relevant products for each individual shopper.
Today’s shoppers want to go to a site and see what's interesting to them, not their neighbor or the shopper that bought before them. Consumers don’t want to be included in a broad segment of buyers, nor do they want to be inundated with products they don’t want, never wanted or already bought. The traditional browsing experience with a one-size-fits-all approach is no longer acceptable; shoppers want sites to recognize them, infer their preferences, and use artificial intelligence to predictively rank and sort the merchandise. Shoppers don’t want to be one of millions — they want to be one of one.
Retailers need to go beyond personalization to predictions and provide the online buying experience that brings about increased engagement, conversions, revenue and, ultimately, happy customers.
Eldar Sadikov is the founder and CEO of Jetlore, an artificial intelligence-driven prediction platform.