Ultimate Guide to Know Everything About Data Labeling in Retail
Like it is said “if there is a data science hall of fame, it would have a section dedicated to labeling. Data labeling is an indispensable stage of data preprocessing. Large volumes of historic data are processed and trained using predefined target attributes.
With the advent of big data, machine learning models feed on a humongous amount of data. Organizations need people or tools to enrich this data so that it can be used as the perfect training dataset.
Just feeding the data into a machine learning model will not deliver the desired results. It is important for the data to be annotated accurately for machines to recognize.
How is Data Labeling Empowering Retail?
Unlabeled data is all around us. Thousands of CCTV camera footage of people walking inside a retail store, picking up their favorite products are captured each day.
Besides this, a ton of customer feedback emails discussing their experiences with the brand or the store is shared every day. Voice calls of customer complaints or requests or queries get stored.
There are so many business decisions that can be enriched if all of this data is induced into a machine learning model. All of these datasets are unstructured, so processing this raw data into a structured training dataset is necessary.
These training datasets can be read by the machine and businesses can derive maximum benefits by developing various relevant predictive models.
What is the Benefit of Data Labeling?
Labeled data is of great value because it provides an accurate estimation of the conditions of our world. The patterns of the labeled data are understood by the machines and it helps in training the machine to see things just like how humans do.
Machine Learning models are basically used to forecast various possibilities on the business front. Labeling the data helps with advanced classification and in building complex forecasting models.
Once the Machine Learning algorithm is trained it will automatically search for similar patterns in every new data set that’s fed into it. Without data annotation/ labeling no accurate and reliable machine learning models can be built.
Why is Manual Data Labeling not Preferable?
Let’s assume a retail giant wants to conduct a customer sentiment analysis based on the reviews their brand/ product has received on social media and website discussion sections. To accomplish this task the minimum number of data points they need to collect is 1 lakh reviews.
Using these 1 Lakh reviews as it is is not an option so the retail giant may choose to get the data labeled manually. Assuming that labeling a single comment takes 30 seconds, an average data annotator will take approximately 800 hours, 100 work shifts averaging 9 hours each to complete the task.
There are two important factors to understand here:
- By the time the machine learning model is fed with the labeled data, the feedback might be irrelevant.
- Considering that the median work hour rate for a data annotater in the U.S is $36 the entire task of data labeling can cost the firm approximately $29,000 dollars!
The best way to go about it is to adopt automation, a part of the dataset can be labeled to train a classification model, and then the rest of the data is simply picked up with the machine and labeled according to the data set that’s trained this data can be used to find target values – positive, negative and neutral sentiments.
What Would you Prefer – Labeling fast or Labeling Smart?
There is an exploding need for accurate data labeling methods. Manually annotating data can be a tedious task.
Manual Data Labeling Challenges:
- Managing and maintaining the quality of data labeling
- Workforce management
- Keeping a track of the cost incurred
- Compliance with data privacy requirements
- The task to ensure data security
Labellerr – The Fastest and Smartest Data Annotation Platform
Labellerr provides you simple, feature-rich, affordable data annotation solution.
Why Choose Labellerr?
- Data Labelling at scale is an important concern for an organization since creating labels on large data sets by hand is often too slow and expensive. Labellerr solves this problem with their agile ML-Powered data annotation platform.
- Work Quality and Worker productivity is difficult to track in the case of crowdsourcing and freelance data labeling services. Hence now with Labellerr’s marketplace, you can choose from our hand-picked and most trusted vendors to get data labeling tasks done.
- Domain and context capabilities specific to tasks are limited with workers on crowdsourcing platforms, contractors, and freelancers.
So, if you wish your data annotation task to be automated and error-free then choose Labellerr.
Benefits of Labellerr’s Data Annotation Platform:
- Label data at 10x speed using Labellerr’s ‘Auto Labeling’ feature
- Track work quality and worker productivity with a personalized dashboard experience.
- Get relieved from the hassle of reviewing each dataset, instead review only the ones having low confidence scores.
The above is still just an indicative list, the superior benefit of adopting an automated way of labeling dataset is peace-of-mind and trust. The idea here is simple – let the machine do all the work for you so that your focus can be on your customers!