With the limitations caused by the Covid 19 pandemic, we have seen several companies expanding their offerings by implementing an e-commerce platform to sell their products and services. A plus to enhance your e-commerce portal is the implementation of machine learning algorithms, which can contribute to the success of your online business in a decisive way. The first contribution brought by machine learning is the increase in conversion rates, which is the metric that allows us to understand how effective an e-commerce site is in achieving the purposes for which it was designed, the most typical, for example, that of conducting the user to make a purchase.
Machine learning is, in fact, able to analyze the expressions that the user types in the search bar and propose results that are closer and closer to what he wants. With machine learning applications, this becomes possible even if the user does not type in the name of a product or a specific description: it is no longer the user who has to strive to understand how the programmers have designed the site, but it is the software that goes to meet the user, offering them a spontaneous and rewarding user experience.
The second plus with which machine learning enriches the user experience is the management of product recommendations. Recommendations are a crucial vehicle to upsell the site and offer a complete and attentive response to users’ needs. The product recommendations take effect during the first purchase, showing the user complimentary articles or services to improve the purchase experience or when the user returns to the portal again, enriching his navigation with satisfying suggestions.
Many companies invest a lot of time to understand, for their reality, which are the most suitable parameters to manage inventories and warehouse enhancement. Often, with the passing of the years, one realizes that a cost criterion is no longer suitable for the evolution of one’s organization, and the adjustment implies another colossal waste of resources. In such a scenario, a machine learning algorithm would analyze inventory turnover and consumption, developing the most effective strategy for valorization.
Machine learning also finds application in MRP, that is, the strategy used by some companies to have an automated purchase order flow. Also, in this case, the data analysis could –+–be helpful for significant optimization of reorders and, therefore, warehouse and production costs. For example, based on the production use flows of a specific raw material, machine learning could suggest to the department head a Just In Time purchasing strategy instead of maintaining a constant inventory.
An often significant cost center for a company is represented by the aggregation of expenses dedicated to customer service. A slice of these costs can be represented by the staff who manage the first level responses, both by e-mail and telephone. This first level of engagement, in fact, often consists of requests that are repeated frequently and that give rise to similar flows of problem management (for example: reordering a spare part or passing the file to a specialized technician). This first level assistance can be delegated, in its most basic meanings, to Artificial Intelligence algorithms which, using an intelligent chat, can manage the triage of the request, triggering the next appropriate level.
This solution would allow the company to free people from routine unproductive activities and specialize them in other tasks, commercial or technical, with more excellent value. These are just a few examples of how machine learning can be applied to the daily life of a company’s processes. Machine learning software has a soft learning curve, and the required computational capacity is increasingly optimized. As part of a business digitization process, it is, therefore, advisable to evaluate the integration of machine learning logic to make one’s working environment increasingly intelligent and resilient.
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