For the longest time, digital marketing strategies have focused on technology innovation and optimization to drive sales conversion up. While it’s been constant, it’s proven ineffective in some areas. With ecommerce recommendation engine developed on AI (Artificial Intelligence) personalization, retailers are transforming shopper experiences. Today, connected commerce has evolved in significant ways where AI-powered recommendation engine is taking consumer interaction to new horizons.
In earlier days, online marketers and retailers relied primarily on keyword searches to predict or motivate future shopping trips. Now, e-commerce personalization/recommendation engine has stepped up gamification with visual elements. Although widespread deployment to digital platforms is still ongoing, mobile channels continue to reveal impressive conversion rates.
This concept thrives on acknowledging the uniqueness of every consumer. It’s different from traditional e-commerce personalization concepts that gave precedence to recommending products and/or services based on shopping history. In comparison, ecommerce recommendation engine powered by artificial intelligence explores every aspect of a lifestyle to suggest meaningful recommendations.
While shopping online adds convenience to a hectic lifestyle. It’s not truly balanced when shopping isn’t organized. The integration of e-commerce recommendation solutions helps a shopper achieve this balance without compromises. Retailers aim to help the consumer escape the frustrations associated with last minute shopping. With immediate access to real-time product recommendations based on lifestyle and data analysis as consumer interests evolve. What the larger retail marketing community keeps forgetting is that a consumer purchase can be influenced by changing events. Furthermore, environmental factors is another key determinant influencing when and how a buyer shops.
Internet marketers also exploit the services of ecommerce recommendation engine to harvest real-time data from consumer social media activities and updates. This strategy is quite effective because of how social media has impacted today’s ever-connected digital shopping community. The problem entrenched in common marketing strategies proven ineffective is the use of outdated consumer data. Intelligent commerce recommender takes a direct approach to analyze real-time web data. As a potential consumer choices evolve, this technology maps the activities to suggest services or products that a shopper really values.
Modern-day intelligent commerce engine exploits deep machine learning help retailers better understand consumer behavior towards certain services/products. On this account, creating a truly unique shopping experience every time a consumer visits an ecommerce storefront is light work. To create intelligent recommendations, it’s imperative that retailers virtually invade the consumer’s mind. With machine learning solutions and ecommerce recommendation, analyzing consumer real-world habits is an adventure.