The report predicts that by 2028, the global market for data annotation will be worth USD 8.22 billion. Additionally, a CAGR of 26.6% is predicted for the global market for data annotation services through 2030; by that time, it is likely to be worth US$ 5.3 billion. The traditional supply of intensive manual labeling has not been able to keep up with the rising demand for labeled data.

Three variables account for a substantial portion of the market’s need for Data Annotation Tools..

1. Tools for automatically classifying data and an increase in the utilization of cloud computing services.
2. Companies are adopting data annotation tools more frequently to precisely classify vast amounts of AI training data.
3. To enhance driverless ML models, there is a growing requirement for well-annotated data as investments in autonomous driving technologies rise. Data annotation is anticipated to advance significantly and become increasingly more integrated as the digital environment changes in the twenty-first century. The development of mobile computing and digital image processing is a significant driver of such changes.

Here’s how Data Labeling is paving the way for E-commerce Sector

Data labeling in new retail has been hailed as a revolutionary idea and is now being sold commercially in some areas. It can save labor expenses, enhance customer service, streamline business operations, and increase consumer insights. Due to its seamless blending of the physical and digital worlds, new retail is quickly taking over as the dominant model in our culture.

1. Object recognition
   Models for object recognition and classification in unmanned stores aid in automating the
   entire shopping process. In order to assist a virtual checkout, machine learning models for
   automatic product recognition, for instance, can determine which items a customer has in
   their cart. To grasp what goods are on each image, which article numbers correspond to
   each product, which brand, which packaging size, supplier information, etc., these models
   first need to be fed with thousands of tagged images.
   Additionally, inventory management and visual merchandising can be automated, making it
   simpler to identify when items need to be restocked on the shelf or alerting the visual
   merchandiser to adjust how their products are displayed in-store.
2. Customer data

Without clean and pertinent data, the e-commerce industry cannot grow. Consider the data
on the consumer and their preferences for the product or brand, as well as the underlying
information about the costs, special offers, payment options, etc. This data contains the
customer’s interactions with and impressions of the website where the good or service is
offered.
E-commerce firms and merchants must explore the various client categories in order to
better serve customers. Which segments of consumers behave the best? What are their
tastes, and which extra item are they likely to add to their basket along with the current
one? These data points can be categorized or labeled to assist define customer categories
and better service customers.

3. Facial Recognition
   For a more individualized customer experience, facial recognition technologies can be
   utilized to identify consumer profiles, behaviours and produce predictive styling. Consumer
   analysis can be completed and saved for use in persona profiling and subsequent visits.
   Sadly, not all client segments have been adequately represented in the datasets that already
   exist, leading to outliers, access denials, or biased data insights. Therefore, it’s crucial that
   databases for facial recognition are impartial, diversified in all respects, and indicative of the
   people who actually go to that particular place or store.
4. Visual Search
   Using recognition software, visual search is a developing technique that enables users to
   take photos of apparel or advertisements and link them straight to product pages. Because
   it’s now much simpler to find the item you’re looking for, this greatly enhances the
   consumer experience.
5. Receipt Transcription
   A significant amount of data, including information on purchases, shipment, and handling, is
   produced through receipt transcription. The back-end system will be simplified and labor
   expenses will be reduced thanks to the automatic transcription and labeling of this data
   from the POS system. Data labeling will thus greatly lessen the workload of store workers
   and reps.
   

Want to engage your AI projects by taking the initial step and gaining access to precise, high-
quality data sets?
Data Labeler delivers high-quality, annotated training data with the help of qualified experts
in order to deliver the finest services possible.
To learn how Data Labeler can assist you on this path, get in touch with us.