{"id":25014,"date":"2023-10-24T12:31:13","date_gmt":"2023-10-24T12:31:13","guid":{"rendered":"https:\/\/www.revuze.it\/blog\/?p=25014"},"modified":"2024-01-22T13:35:24","modified_gmt":"2024-01-22T13:35:24","slug":"online-review-analytics-from-the-category-level-beyond","status":"publish","type":"post","link":"https:\/\/www.revuze.it\/blog\/online-review-analytics-from-the-category-level-beyond\/","title":{"rendered":"Gaining a Competitive Edge: Delving into Category Insights"},"content":{"rendered":"

Brands are always looking for data to up their game and stay ahead of the competition. In today\u2019s digital world it\u2019s more important than ever for brands to be agile to adapt to the ever-changing market landscape. Underlying emerging trends can quickly become a threat to brand or product loyalty. That\u2019s why companies need to stay on top of their category, brands, and product offerings.\u00a0<\/span><\/p>\n

So what data source packs a punch for brands? Consumer insights from online reviews.\u00a0<\/span><\/p>\n

The breadth of online retailers out there give companies unparalleled access to big data from verified buyers from around the world. Amazon recently shared that <\/span>45 consumer reviews<\/span><\/a> are written every second across all their retail sites! That\u2019s just from one retailer. Consider the amount of data when you aggregate from sites like Target, Walmart, Macy\u2019s as well. That’s where online review analytics come in. Survey and focus group insights don\u2019t even come close to the size of the data set from online retailers. It provides unique access to unbiased consumer insights data on the category, brand, and product level.<\/span><\/p>\n

This blog will explore the different levels of consumer insights, data challenges, and why it\u2019s important for your marketing and product development.<\/span><\/p>\n

Category Insights<\/span><\/h2>\n

Many consumer brands have category level insights however, they are only for their own brands and products. They don\u2019t have category level insights that include all their competitors. The fact is, online reviews analytics is the way to do it. It provides an overall snapshot of the market from verified buyers, meaning this is post-purchase data.\u00a0<\/span><\/p>\n

This entails accessing a range of online retail data sources from around the world. Sounds easy enough, right? Not exactly.<\/span><\/p>\n

To illustrate, let\u2019s take the cosmetics category. It\u2019s quite broad, and can be further divided into subcategories like face, eye, lip, nails, and more.\u00a0<\/span><\/p>\n

Creating a cohesive category is the first hurdle that needs to be overcome. Revuze\u2019s generative AI does all the work behind the scenes by creating a unified product taxonomy. Why is this so critical? Every online retailer has a different way of mapping out the products on their website, but Revuze generative AI is adept in creating a unified category no matter the number of data sources.<\/span><\/p>\n

The screenshots below highlight the same Essie nail polish being sold on <\/span>Amazon<\/span><\/a>, <\/span>Target<\/span><\/a>, and <\/span>Walmart<\/span><\/a>. However, each page has variations including the product taxonomy, product name, as well as image variations. For instance, on the Amazon page, the product name is \u201cBright Purple Play Date\u201d while on the other pages it\u2019s just called \u201cBright Purple\u201d. Now let\u2019s focus on the breadcrumb navigation at the top of the Target\u2019s PDP. Here we see that Target gets very granular by placing the product in the nail polish category and not just in the overall nail category. Both Target and Walmart have multiple images and Walmart highlights that the product is vegan.<\/span><\/p>\n

\"Creating<\/a><\/p>\n

To make sense of the data on the category level, Revuze\u2019s generative AI engine is doing a lot of work behind the scenes. The AI creates a unified category taxonomy, merging the names, and pulling the images and ensuring that it can provide the most accurate insights. The data set is so big, it’s challenging to do this manually.<\/span><\/p>\n

The category data empowers companies to keep their finger on the pulse of their brand and their known and emerging competitors. The screenshot below highlights cosmetics brands that are above and below the average consumer sentiment. This already is an indication of how your company measures up against the competition. Beyond sentiment, the chart highlights the share of discussion for various brands.\u00a0<\/span><\/p>\n

\"Online<\/a><\/p>\n

You can do the same for a subcategory. The chart highlights the brand positioning for nails. We see that Sally Hansen nail care products have above average sentiment and the highest share of discussion. How does your brand measure up in comparison?<\/span><\/p>\n

\"Category<\/a><\/p>\n

Your category positioning already can provide direction with your marketing strategy. For instance, is your brand sentiment above or below the average among your competitors? Is that indicative of your marketing strategy or should it set off a red light about product issues? The data can be leveraged to do pre-campaign research. Digging through the reviews will allow you to pinpoint opportunities to strengthen your brand positioning and refine messaging using your own consumers\u2019 words.<\/span><\/p>\n

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