Sentient AI – Ecommerce Recommendation Engine

Challenged by the entry of artificial intelligence (AI) into digital marketing and eCommerce, business conversion goals are becoming more reliant on customer purchase history in real time.

For one thing, part of the evolving AI-e-commerce recommendation engine is to identify tasks that have notably been time-consuming for companies looking to improve their effectiveness.

Advances in technology are changing the way people utilize information; therefore, the recommendation engine is invaluable when developing new software—and especially new Apps.

Melting before our very eyes are the days when direct marketing involved snail-mail. AI marketing strategies and product presentation improvement are incentivizing e-commerce through audience segmentation and personalized approaches.

Social Media Marketing reports that based on a Forrester study, “60 percent of large retail and e-commerce firms are expected to implement AI technologies within the next 12 months.”

Fundamentally, it makes sense because e-commerce, recommendation engine algorithms have already proven that shoppers are willing to buy more and stay longer when they are engaged in personalized shopping experiences that move them through a myriad of related possibilities.

While it is true that the ever-growing role, that AI technologies are taking in digital marketing and e-commerce, is changing the landscape for giant businesses like Amazon and Alibaba, the small digital and e-commerce businesses are still somewhat dependent on legacy practices.

However, through the application of personalized email marketing and market automation, these companies can begin to take advantage of recommended systems that are still within their grasp. Once holiday shopping is passed, businesses should have captured more real-time data–which is a major opportunity to harness customer information.

For example, by increasing personalization of this information, smaller companies can focus on their target audience in more relational ways than a larger business might venture to use as an approach. The margin of error with this type of marketing could be wider when dealing with the masses as opposed to a smaller audience. Learn more about Sentient at Crunchbase.

Perspective is relative. Correspondingly, artificial intelligence requires machine learning, which requires big data, which requires colossal amounts of memory in order to process the information into usable intelligence.

Therefore, from the perspective of e-commerce, recommendation engines suggest marketing and management techniques that actually influence a conversion from the future backward.

By predicting what a customer will buy and when they will buy it–and by making sure that predicted products or services are ready and waiting at the moment the customer comes online, selects a product and hits the “check-out” button to pay for the sale—the engine has done its job. stated in a recent article that there are already in-the-moment personalization platforms based on customer relations management (CRM) data.

One such example is “Moveable Ink which delivers dynamic email content that changes based on a variety of factors—as well as contextual information, such as the weather in the user’s location at the very time they open the message– which can dramatically impact the way a customer may react.”

Successful e-commerce businesses waste no time staying abreast of AI technology and understanding the ways in which applications presented by e-commerce recommendation engines can affect their bottom line. Smart marketers know that they do so at their peril.

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