Five Ways to Build Your Business on Data
From finding the right location for your store to content creation, data has loads of applications within any business. The process of adding at least one informative and meaningful label to data in machine learning is called data labeling. The subsequent labels give Artificial Intelligence (AI) the context to learn from.
Use cases for data labeling include natural language processing, speech recognition, and computer vision. Humans provide supervised learning for AI, labeling data to train the machine. For instance, a photo of a bird is an example of properly labeled data. Once the data has been labeled, the bird image becomes the “ground truth” used in machine learning. And the AI trained on this data can subsequently make predictions regarding similar data.
- Finding a location for your brick-and-mortar business
Brick-and-mortar stores are a must for many businesses.
BestPlace is an application that businesses can use to find their best location options. Using machine learning, BestPlace creates a map of an area. The technology infuses data from the surrounding business locations so that the brands can predict future customer behavior and preferences.
BestPlace collects data from a variety of source types, including geospatial, social, and historical data. The software uses the data to identify the consumer behavior patterns used to predict customer behavior.
2. Converting paperwork into digital format
Another use for data is converting your paperwork into digital format. Once performed, it will be easier for businesses to make it easier for your organization to finish work and gauge progress.
Handl is a tool that converts paperwork into digital format and performs other relevant tasks. Their family of algorithms includes the classifier, the aligner, the field cropper, and Optical character recognition (OCR).
Each has its purpose. The classifier helps you classify the document type. The aligner aligns images. The field cropper enables you to find document fields. Lastly, the OCR helps you structure the data after the tool extracts it for you.
3. Training self-driving cars
Self-driving cars are becoming increasingly popular. To make this experience safe for both drivers and pedestrians, the machine is being fed data that is labeled by humans. This data includes all sorts of images such as cars, pedestrians, surroundings, etc.
Some of the self-driving cars are already on the road. For example, recently, Yandex Rover and GrubHub launched delivery robots on selected college campuses in the United States with the help of Toloka. It also helped the Yandex Self-Driving Group to release the largest AV dataset in the industry to date comprising 600,000 scenes (or more than 1,600 hours of driving).
4. Improving marketing
By collecting and annotating data in bulk, businesses can improve the way their customers search for products and where they find them. Data labeling helps build a predictable pipeline that impacts your NLP algorithms. It makes it easy for businesses to annotate named entity recognition, sentiment analysis, speech recognition, text and intent classification, text recognition, and more. For example, Toloka provides services that review images, assigning a label to them. The latter helped IVI, a popular video streaming service known for its recommendations and deep personalization, label 72,000 facial movie images fast to create a marketing campaign for the service.
As a result, the businesses improved the marketing of their products and reached their customers in a more efficient way.
5. E-commerce pricing
In e-commerce, a well-devised pricing strategy is paramount to stay ahead of the competition. To make sure that your business is doing everything right, you need to collect and process tons of information.
Toloka can help you do that. In 2022, it gathered, analyzed, and labeled prices from all major retailers for Yandex.Market, one of the largest marketplaces offering a wide assortment of products. Particularly, it assessed the quality of automatic matching, improved the quality of existing matches by removing incorrect matches, and increased match coverage by finding more URLs of matching items on competitor sites. As a result, Yandex.Market received a set of accurately labeled data that made sure that the platform has up-to-date information about the products on various e-commerce websites, thus informing its pricing strategy.
All in all, there are a myriad of ways on how data labeling can help to grow and advance a business, although not all companies understand how to use it most effectively yet.
