Cart Abandonment by Mr. Sethuraman J, Practice Leader – Big Data Analytics, Machine Learning & Data Sciences at Happiest Minds Technologies
The rising e-commerce marketplace
Exponential rise in internet penetration, changing work habits and demographic shifts have led to an increase in e-commerce transactions. Estimates show that the total value of global e-commerce transactions was USD 29 billion in 2013 and it is expected to reach USD 34 billion in 2014. One of the key areas affecting revenue loss through ecommerce transactions is cart abandonment. In cart abandonment, the visitors to the online shopping website put items in their shopping cart, but leave the site without completing the purchase.
Cart Abandonment – How money is left on the table
Cart abandonment is a common phenomenon in e-commerce, resulting in lost sales to the tune of over 67 %, according to a latest survey conducted by the Baymard institute, a web research company in the U.K (Source). If we try to calculate a dollar value to this loss based on the two statistics mentioned earlier ( global e-commerce sales value for 2013 and the abandonment rate of over 67%), the money lost due to shopping cart abandonment would have touched approximately USD 19 billion in 2013 alone.
According to a statistics from Statista , key reasons for shopping cart abandonment by consumers are found to be unexpected costs (56%), casual browsing (37%), discovery of better price (36%), very complicated website navigation (25%), crashing of websites (24%), very lengthy process (21%), excessive payment security checks (18%) and website timeout (15%) among others.
Cart abandonment: How predictive analytics can help
Predictive analytics has been used quite frequently across different business platforms to find patterns and figure out trends. These patterns and trends when applied to the consumer’s online behavior on the e-commerce site can help retailers convert at least a reasonable amount of these abandoned carts into sales.
Understanding the consumer behavior
Predictive analytics can help recognize the pattern for cart abandonment based on the data generated from the visiting customer base. The customers can be segmented based on behavior. Few of the popular categories include first timers, regulars and existing customers who have abandoned the cart. Based on the behavioral patterns of the three segments, the e-tailer can focus on, say, two categories of apparent interest – first timers and existing customers. These segments can then be addressed in a focused manner, through targeted ad campaigns, e-mails providing special offers and customized welcome messages when they visit the site the second time.
Enhancing the possibility of cross sell and upsell
The taste of the customer or visitor as gleaned from his digital foot print and behavior can indicate what else can be sold to him. He might be willing to buy other associated artefacts in the same category or related category which can be showcased to him during his online shopping experience. Stocking the site with such artefacts is an added feature that can be pursued by e-tailers to ensure customer interest remains on the site.
Discovering the most effective promotion methods
Some of the customer segments might be more susceptible to e-mail promotions while others may be more inclined to social media marketing, search engine marketing or direct marketing. This can be done by predictive analytics based on the purchase history, education, age, nationality, gender and marital status of the customer. It can also predict which potential customers can be encouraged to come back to the site by using re-marketing e-mails. We can also figure out product categories where remarketing is going to be most successful and accordingly channelize marketing spend towards such product categories.
Arriving at the potential reasons for shopping cart abandonment
The customer segment behavior can also indicate the potential areas where there was a possibility of the customer abandoning the cart and take corrective measures. For example, if a particular customer segment abandons the cart in the catalogue, it might indicate the need for addition of more product categories to provide the requisite choice to the customer. If the customer abandons the cart close to payments, it might indicate the need for a more streamlined payment process. If cart abandonment is abrupt due to the session expiring, it can be linked to the activity and search of the customer around different goods. The session window could be made to track the interest level or engagement of the visitor and likewise keep expanding till the user is active on the site and proceeds to the checkout.
Enhancing post sales service
Predictive analytics can be used to keep tab on the post sales behavior of the customer and we can get important insights on suppliers also – how good they have been in providing service to the customers and for what period, have they kept their promises about after sales service etc. The e-tailer can thus zero down on the best supplier and retain them to ensure enhanced customer service and overall service quality standards which will definitely increase repeat purchase.
Conclusion
Predictive Analytics can definitely help with reducing shopping cart abandonment in e-commerce by obviating lack of understanding consumer behavior, enhancing the cross sell and upsell rates, zeroing on the most effective promotion methods and dynamically arriving at the key reason for the abandonment. Accurate analytics can ensure post sale quality of service thereby reducing lost sales through avoidable reasons which would bolster both the e-tailers topline and bottom line as well as create loyal customers amidst heavy competition.
(The views expressed in this article by the author are his own. Technuter.com doesn’t own any responsibility for it.)
