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Sumaiya Khatun
Aug 01, 2022
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For example, you can immediately see what your lips look like with a certain lipstick. To apply this within your own web shop, you can search for industry-specific solutions that already exist. If no tools can be purchased under license yet, you can hire a developer who develops a tool specifically for your type of product, for example by using Tensor flow to train an ML model yourself. Also read: A convincing product description for your web shop in 10 steps 2. Contextual Search Results One of the most obvious uses for machine learning is the personalization of search results in the web shop based on the visitor's behavior. Currently, the search of most web shops works on the basis of an algorithm that simply matches the entered keywords with the product title or description. A better way of matching the results can be achieved by also looking at the context. Contextual search machine learning An example of contextual search is if a visitor has purchased job function email list Nike sneakers and shirts several times. If this visitor then searches the web shop for 'shorts', it is probably better for the conversion to show Nike shorts that are closer to the customer's taste. This instead of the general results you would normally have gotten. A huge number of these kinds of patterns and variables can influence the conversion from the search. With machine learning, the computer itself looks for these connections and constantly adjusts the results to achieve the best result. If you have your own Woo Commerce or Magento web shop, you can take a look at the implementation of services such as Fancify . These platforms help you make your web shop search more relevant with contextual search. 3. Targeted user experience With artificial intelligence you can also provide a targeted user experience, in which the visitor is more central. You can use this to adapt the content of your web shop to the visitor behaviour.
Your Customers Based on Their Previous content media
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Sumaiya Khatun

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