Top 3 tips to boost revenue via mobile apps

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First, simplify ‘add to cart’ features

“Even though Ecommerce shopping patterns have been standard for more than 25 years, some websites are poorly designed, confusing users and preventing them from adding products to their cart or, even worse, abandoning their carts entirely before reaching the final milestone: checking out and completing their transaction,” said Sree Singaraju, SVP of AI and Cloud Solutions, Mobiquity. “Creating an experience that helps retailers mimic the in-store experience virtually must provide shoppers with key pieces of information. This includes easy-to-find product descriptions and reviews, comparison capability, images, prices, discounts, and in-stock availability to improve the chances of a consumer adding directly to their cart without having to navigate to find the information they need. In-store experiences rely on checkout lines and clever marketing displays to entice shoppers to add last minute items. While traditional shopping habits rely on some ‘latency’ to give shoppers time to peruse, online shopping relies on quick decision making and a website or mobile app that keeps up with those decisions.”

Second, activate associated product features

“Setting up machine learning models will be primarily driven by aggregate shopper behavior and trends, rather than individual shoppers’ tendencies. By collecting and analyzing overall behaviors, retailers can better predict what an average shopper would select and prioritize those offerings through targeted suggestions and prompts. The objective of using a machine learning model is to ensure a shopper looking for a certain category of products is provided the opportunity to add additional products associated with that behavior. For example, if the aggregate data shows that customers who buy sponges also buy dish soap, the retailer should prioritize navigation to dish soap for any customers who add sponges to their cart,” said Singaraju.

Third, remember those recommendations

“While providing recommendations and site navigation based on aggregate data from typical shoppers can aid in upselling, taking shoppers’ individual habits into account is another useful tactic to be used in tandem,” said Singaraju. “Providing recommendations based on individual shopping habits includes recommending items a shopper has purchased in the past that need to be re-ordered based on duration, favorite items, and additional items that similar shoppers have recently purchased. This model can also be used to provide situational discounts that entice users to add items to their carts due to their ability to increase basket size and foster checkout. For example, if a shopper usually purchases shampoo every three months, but has not purchased in the last four months, sending a discount code for their preferred shampoo may help push the shopper to be loyal to a specific retailer’s brand, rather than building loyalty with a new retailer.”

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ABOUT THE AUTHOR

Kristina Knight is a freelance writer based in Ohio, United States. She began her career in radio and television broadcasting, focusing her energies on health and business reporting. After six years in the industry, Kristina branched out on her own. Since 2001, her articles have appeared in Family Delegate, Credit Union Business, FaithandValues.com and with Threshold Media.