Data labeling is the process of identifying raw data(images, text, files, videos, etc) and adding single or various informative labels to provide context so that a machine learning model can learn from it. This whole process takes time and can be costly and inefficient if it is done manually. Data labeling is scalable when a person works with the right data labeling software.

There are various forms of data labeling, including image, video, audio, text.

Of course, the process of using this labeled data is also important. So in this article, CloudVandana will describe five main uses of data labeling for marketing and some platforms that a marketer should use to get most of the data. Data labeling is used in countless industries including healthcare, finance, research, automotive, and technology. Data labeling has become an integral part of marketing with the growth of artificial intelligence.

The Process Of Data Labeling

Data labeling is a continuous process between humans and machine algorithms. The task is complicated with the help of software, processes, and people. The raw data is analyzed first, and then labeled based on context. The context is determined as per industry standards and needs. The context is then used to train and develop machine learning algorithms. The more context a marketer provides, the better the AI can learn. This helps the marketers to deliver better algorithms results in the future.

For the upper mentioned benefits, Google is encouraging businesses to adopt responsive display ads.

5 Key Uses Of Data Labeling In Marketing

Data labeling is common in various industries, precisely, healthcare and finance. As machine learning is a very popular tool for marketers, the need for data labeling is increasing. In this article, CloudVandan is sharing five key uses of data labeling in marketing.

1. Increase Personalization

Personalization increases the conversion rate. So, to boost the marketing efforts, marketers should personalize the experience throughout the customer journey. Marketing campaigns, social media posts, ad copy, and website flow are included in this process. Personalization is a process to fit a market demographic. It can be achieved through data labeling. Marketers can use personalization to increase the search query results, provide personalized product recommendations, and enhance product attributes for categorization.

2. Optimize Prices

There are various data sources to consider for analysis. To get ahead of the competition, consider analyzing competitor creatives in campaigns and advertisements. With the help of data labeling, marketers can train specialized software to recognize, analyze, and categorize competitor raw data. For price optimization, this will largely be text and images.

3. Arranging User-Generated Content

User-Generated Content is a great way to attract new customers. User-generated content can be used on the website, in email campaigns, and on social media. Data labeling services can effectively categorize this content for use.

4. Analyze Customer Reviews

In this digital world, online purchase plays a very essential role. So visitors or prospects generally trust the reviews given by other customers. Reviews help in the enhancement of any business. With the help of scaled data labeling, marketers can train the algorithm to analyze customer reviews. Data labeling can do so many things instead of just categorizing data based on keywords.

5. Boost Product Offering

Product assortment and availability are very important for effective marketing campaigns. With the help of properly trained machine learning algorithms, marketers can fill the assortment gaps and ensure the availability of specific campaigns. By using data labeling and annotation services, marketers can determine gaps in the offerings and availability based on an analysis of competitor assortment.

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