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Today, we are going to talk about \u201cpersonalized product recommendations\u201d technology, give you an overview of the technology, and discuss key elements, supported by examples.<\/span><\/i><\/p>\n If you are dealing with the personalized product recommendations software for the first time, it may look complicated but in fact, software is purely based on consumer behaviors. The tool works by gathering data about your consumer and analyzing their browsing and buying behavior patterns.<\/span><\/p>\n For instance, imagine that somebody visits your online store for the first time, and suddenly spots products on your main page, which they were looking for earlier the same day. It means that visitor was already looking for that product configuration across Internet, and now he visits your Ecommerce website, where exact product category appears to him in your sales catalog. Same thing happens if somebody browsed through a specific category, like \u201cRunning shoes\u201d in your store, the software will automatically generate a list of most popular running shoes models available in your store.<\/span><\/p>\n <\/p>\n Displaying most relevant products to visitors of your store makes their shopping journey less time consuming, more personal and inspiring.<\/span><\/p>\n However, your main page is not the only place where personalization happens. The product story would be also shown to your visitors at category page, product detail page, shopping cart page, and even your 404-error page.<\/span><\/p>\n Product personalization is not only about your website. Everybody has seen email newsletters featuring a block of recommended products at least once. The same goes for advertising banners, showing us same items we left in the shopping cart while browsing in a different online store or lightning-fast web push notifications with a link to a personally tailored selection of recommended products.<\/span><\/p>\n Throughout the time, product personalization still takes a crucial part of marketing strategy of an online retailer. There are some cases where an online retailer does not use personalization, like a mono-product store or a small local store. Though, it is unlikely that bigger eCommerce will remain without any kind of personalization.<\/span><\/p>\n However, we have recognized a few major flaws in product personalization. For instance, when dealing with Big Data \u2013 personalization can become cure or trap for your business. Further, we are going to discuss in detail main issues.<\/span><\/p>\n To summarize: <\/strong><\/p>\n Big Data, personalization is driven by statistical data. In other words, if a significant number of consumers recently purchased laser printer and crib, along with coffee maker, personalization would recognize that as a buying behavior pattern and generate a suggestion of this two items along with coffee maker.<\/span><\/p>\n Example 1: Shows that personalization is driven by statistical data not by logic.<\/span><\/i><\/p>\n In this case, there is no viable logic, as recommendations do not fit with primary product. However, even if we see it as an issue, Big Data completely relays on statistics, and you won\u2019t argue against it.<\/span><\/p>\n We have also recognized that Big Data is very precise to details. Earlier, we mentioned \u201crunning shoes\u201d example, here comes the question: What else we can add to the product? As we know already a list of most popular running shoes models would appeal to retailer visitor. But, is it really enough of a recommendation to attract and involve visitor to make a purchase or to buy above his personal needs? <\/span><\/p>\n According to our research, the final purchase decision is affected by a very specific set of factors. Those factors vary depending on a segment. It is also recognized that<\/span> Apparel & Accessories, Construction & Repair, Auto Products, Pet Products, Consumer Electronics or Home Appliances, Cosmetics & Beauty<\/span><\/i> segments, are the most sensitive when it comes to the final purchase decision.<\/span><\/p>\n Therefore, for improving the effectiveness of personalization tool; first, you have to alter your marketing strategy, by taking into consideration key elements of your retail segment.<\/span><\/p>\n For example personalization strategy for Apparel and Accessories online retail, has to generate products suggestions divide by gender, also it is crucial for products to correspond in size. Imagine, if your store visitor was suggested a pair of desired running shoes, and finds out at the same time, they are completely sold out in his size and only available in two sizes smaller or bigger. Then you fail to satisfy your customer needs. This issue is also illustrated by the following example:<\/span><\/p>\n Example 2: While visitor of Forever 21 chose and added to the cart an XS hoodie, he can still see recommended items in the wrong size<\/span><\/i>.<\/span><\/p>\n (Example 2.1)<\/span><\/i><\/p>\n Some items are in the wrong size, for instance, this white muscle tee is available only in XL:<\/span><\/p>\n (Example 2.2)<\/span><\/i><\/p>\n As we already said, Big Data takes actions relying on analytics, not logic. <\/span><\/p>\n Some businesses may overlook these issues, but on the other hand, there are retail segments, which don\u2019t really have a choice, and they are forced to overcome personalization issues, otherwise, it can harm their customers.<\/span><\/p>\n We have recognized that in FMCG<\/span> (<\/span>Fast Moving Goods consumed every day) sector, Big Data can be really harmful to consumers. Especially, when dealing with goods that contain specific allergen; the majority of prospective buyers would not react to an allergen, but there is a percent of people, who are actually allergic to it. There is also a percent of people with the medical condition, limited to a strict diet. As a result, software generating not accurate product recommendations, where lactose intolerant customer will be suggested to buy milk, or diabetic deliberately buys a milk chocolate bar full of sugar.<\/span><\/p>\n A situation like that is also harmful to your business, as you have to pay money to take space in a block, and it would be taking by unrelated products, while relevant products are skipped.<\/span><\/p>\n\n
Illogical Recommendation Logic<\/span><\/h2>\n
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<\/p>\nUniversal does not mean precise<\/span><\/h2>\n
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Pros and Cons of Big Data<\/span><\/h2>\n
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