{"id":23,"date":"2014-01-23T07:15:57","date_gmt":"2014-01-23T07:15:57","guid":{"rendered":"http:\/\/www.recolize.com\/en\/blog\/?p=23"},"modified":"2018-09-11T18:32:31","modified_gmt":"2018-09-11T16:32:31","slug":"reduce-large-product-lists-mobile-shops-presenting-relevant-products-customers","status":"publish","type":"post","link":"https:\/\/www.recolize.com\/en\/blog\/reduce-large-product-lists-mobile-shops-presenting-relevant-products-customers\/","title":{"rendered":"Reduce large product lists in mobile shops by presenting only relevant products to your customers"},"content":{"rendered":"
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As everybody knows Mobile Commerce is a trending topic these days (e.g. here<\/a>). Designing an online shop for mobile devices is all about reducing elements and the rethinking of existing page structures to make them more adaptive to devices with constraints like bandwidth, screen size, storage amount, button sizes etc.<\/p>\n

Analogous to reducing visual website elements and contents, images and page sizes shrinking the amount of presented products is also an essential ingredient in the mobile cocktail.
\nNobody wants to scroll down long lists of products on mobile devices with a small screen while sitting in the bus with only Edge\u00a0bandwidth speed available – that’s for sure.
\nSo as a consequence to improve customer satisfaction and your conversion rate you should present those products to your online shop customers most prominently that are probably the most relevant ones for them.<\/p>\n

But how to find out which products are most relevant for your customers?<\/b>
\nWell, that’s the point where recommendation engines come into play. They enable you to reduce your product catalog size by pre-filtering your products to your customers’ needs. The visitor has searched for green socks recently. So why don’t recommend him items that other customers bought together with them?
\nTherefore the view and click behaviour of your webshop visitors as well as additional information like given product ratings and purchases are saved. Comparing this data to other visitors enables the recommendation engine to deduce information for product recommendations. Extensive filter mechanisms allow merchants to influence the automatically calculated products, e.g. to display only upselling products in the cart or to filter for a certain category. More enhanced recommendation engines may also take the current location into consideration as well as other circumstances like weather, hour of the day or likes of your Facebook friends. With increasing the amount of data the recommendations can be improved and deliver even better fitting results.<\/p>\n

According to the following graphic the usage of product recommendations can lead up to a 150% conversion rate increase. We assume in mobile shops this rate might even be higher.<\/p>\n

\"Maximize<\/a>Monetate Marketing Infographics<\/a>