For the majority of history, good retails would have kept a tab on whatever goes within their store, not only to care for their wares but also to read into the subtle behavior ques his clients subconsciously made.
Nowadays a major part of our commerce is done online, eCommerce sales are booming and the footprints we used to leave in the store are now easier to track and collect in the digitalized, AI, era we live in.
The efficiency of collecting data introduced a new challenge as the amount of data that can be collected is immense and can become overwhelming. Therefore, it demands the keen eye of a great salesman in order to know which data is relevant and which is not: Enters AI and machine learning.
Machine learning works by feeding algorithms with data in order to train them to recognize and produce results of the designer’s choice. A well-trained algorithm can help not only to process massive amounts of data but also to adjust its performance in real-time to a fluctuating market and consumer behavior.
With eCommerce becoming an integral part of our society more and more eCommerce sellers rely on artificial intelligence to improve their ability to collect, analyze, and utilize the newly accessible data in order to stay competitive and grow.
Understand user behavior and improve conversion rates
Guaranteeing the return of a customer or ensuring a potential client will complete a transaction on the seller’s page and not on another’s is heavily reliant on the customer experience. Frequent visitors are accustomed to receiving appropriate recommendations, A customer might have questions or issues he encounters and a proper response might be the difference between a successful deal to a failure. Chatbots and personalized recommendations artificial intelligence operated tools will comply with the user’s requests while having the benefit of evolving over each new interaction, leading to a higher satisfaction level and a positive experience resulting in transformation into more purchases and repeated dealings.
Understand and analyze user-generated data (UGC)
Prior to the customer’s arrival at the product’s page, a search inquiry will occur, how and where it will lead is a factor of understanding the associations and cultural influences on the entered search query. Therefore choosing the correct keywords in the product description will help to channel the traffic away from competition into the seller’s destination. AI solutions might do so by scouring social networks, online reviews, and even competitor’s pages to ensure the seller will have the best choices at hand.
Even the small business owners will find the task of processing and reassessing prices in order to reach optimality on a daily basis to be overbearing, to say the least, even absurd. Taking it up a notch and a larger business can have double to triple the number of products and with it the increased workload of keeping them up to pace with market prices. This problem might cost a business its growth and its competitiveness in a marketplace that becomes globalized.
That is why companies had made striding steps in developing AI tools that can learn how to optimize pricing based on product portfolio, point of sale, and the customer itself. Doing so not despite but thanks to the massive amounts of collected data, which gives the algorithm an opportunity to explore all possible scenarios and make predictions based on fiscal developments and provide dynamic pricing reflective of supply and demand.
For example, limited quantity brands can maximize profit by constantly updating their price tag when left attended by the sensitive eye of an AI, forming a solid data line to strategies future eCommerce business decisions of the product.
The change from the physical plain to the digital world is one that brought many advantages for both the clients and sellers but alongside it came numerous challenges and risks. A transaction that is now processed entirely without the need for a face to face handshake has raised the cases of fraud and deception.
To combat this Machine learning tools took several approaches in an attempt to hunt misconduct, when possible a supervised AI is being produced but in other cases, it’s by understanding the norm and learning what is the outlier that helps to detect the fraud. In addition, some eCommerce AI relies on tracking the consistency of geolocation registered from the user or other suspicious user activities such as blocking attempts of necessary data transference.
Smart business owners and sellers understand that going on the eCommerce global train is the smart choice for the future and while staying afloat on a local market was hard, in a global market it’s immensely harder. That is why using any advantage, like eCommerce AI, is a necessity that can’t be spared and with no human means of processing the amount of data acquired – automation and artificial intelligence becomes the best and only way a seller can push himself forward.
In some cases such as the distribution of promotional email and sending receipts of successful purchase automation is all that is needed, but in many cases, it is the artificial intelligence that will be needed to produce the native language in a chatbot or the defensive capabilities that will secure the business and its connections from fraud and theft.