{"id":25100,"date":"2023-11-07T10:33:39","date_gmt":"2023-11-07T10:33:39","guid":{"rendered":"https:\/\/www.revuze.it\/blog\/?p=25100"},"modified":"2024-01-22T11:17:41","modified_gmt":"2024-01-22T11:17:41","slug":"overcome-the-big-data-challenge-why-manual-review-tracking-doesnt-cut-it","status":"publish","type":"post","link":"https:\/\/www.revuze.it\/blog\/overcome-the-big-data-challenge-why-manual-review-tracking-doesnt-cut-it\/","title":{"rendered":"Overcome the Big Data Challenge: Why Manual Review Tracking Doesn’t Cut It"},"content":{"rendered":"
How are you accessing VoC?<\/span><\/p>\n If you\u2019re like most brands out there, you\u2019re probably using a combination of survey\/focus groups, social listening, and review syndication.<\/span><\/p>\n Ever thought about the other data set waiting to be mined? Online reviews from verified buyers.<\/span><\/p>\n For some companies it\u2019s the third rail. They are so overwhelmed, they don\u2019t even know where to begin and ultimately do nothing. Others are brave, venturing to take it on manually or attempt to build a system of their own without success. With consumer trends changing at light speed, <\/span>you can\u2019t afford not to look at online review analytics. <\/i><\/b>This data is vital since it\u2019s the only dataset with a direct correlation to sales and impacts buyer decisions. Keep in mind that organic reviews are completely unbiased and provide qualitative data about your brand and products.\u00a0<\/span><\/p>\n Here we will delve into why online review analytics can support strategic decision-making and buyer-based decisions for your brand and why a third-party system powered by generative AI, like Revuze is the way to go.<\/span><\/p>\n So let\u2019s tackle the first challenge. The data set is HUGE!<\/span><\/p>\n There can be thousands of reviews on one site for one product! Multiply that by all the online retailers, Amazon, Macy\u2019s, Target, Walmart, and all the products. The sites I mentioned are just in the US. Now think about all the global Amazon sites, Boot\u2019s, Marks & Spencer, and others.\u00a0<\/span><\/p>\n That\u2019s a whole lot of data! How do you begin aggregating the data?<\/span><\/p>\n Given the amount of consumer review data from online retailers, the first challenge is to collect it.\u00a0<\/span><\/p>\n Some companies have staff cutting and pasting the reviews into complicated Excel spreadsheets. It\u2019s not so practical given the number of reviews out there and of course there are new ones added every day. Manual work also increases the chances of human error which can impact crucial calculations like consumer sentiment. Trend detection is challenging when dealing with these quantities of data as well.<\/span><\/p>\n For example, let\u2019s focus on one product that may have 20,000 reviews. How can they be read and analyzed to identify trends? It\u2019s impossible.<\/span><\/p>\n Others have tried to create their own in-house solution. They realize it\u2019s a no-go very quickly.<\/span><\/p>\n Why, you ask?<\/span><\/p>\n There are a number of reasons. Many online retailers actually update the backend of their site to prevent crawlers from taking their data.\u00a0<\/span><\/p>\n Another issue are the <\/span>types of reviews<\/span><\/a>, organic and incentivized. Incentivized reviews are when consumers get some form of promotion to write a review as in the screenshot<\/a> below. Every retailer labels these reviews differently. Others don\u2019t label them at all. Plus you can easily have the data set filled with duplicate reviews from the syndication.<\/span><\/p>\n <\/a><\/p>\n This review from Target<\/a>, clearly highlights that it is syndicated with the phrase “originally posted on influenster.com”.<\/p>\n <\/a><\/p>\n Incentivized and syndicated reviews at least represent authentic data. However, can humans easily detect fake reviews? Can they identify the key markers and remove it from the data set to ensure that it doesn\u2019t impact numbers?\u00a0 <\/span><\/p>\n What you may not realize is that an important part of data collection is also <\/span>cleansing<\/span><\/a>. It\u2019s categorizing the reviews, deduping, and just ensuring the highest quality of data. This is an arduous process that can\u2019t be done manually either. Revuze\u2019s platform actually does this entire process with generative AI and sophisticated machine learning algorithms.<\/span><\/p>\n After all of the data collection and cleansing, come the consumer insights and analysis. This supports decision-making for many teams: marketing, innovation, ecommerce.\u00a0<\/span><\/p>\n One consumer review can have multiple opinions about different facets of the products and each opinion needs to be categorized by product topic. The<\/span> 2 star review below<\/span><\/a> for MAM pacifiers. This review has two opinions, one positive and the other less so. The mother discusses how she loved the product initially and gives a couple of clear reasons that can be classified into topics. She then goes on to detail a very negative experience with the product hurting her baby.<\/span><\/p>\nThe Data Set<\/span><\/h2>\n
Data Collection and Other Challenges<\/span><\/h2>\n
Make the Voice of Customer Shine Through<\/span><\/h2>\n