Retailer brands generate a lot of data at their stores and warehouses:
- Customer data
- Inventory data
- Sales data, etc.
Big data and secure cloud computing with built-in AI and Machine Learning capabilities allow retailers to leverage these data points to optimize production and operations and deliver top-class service to customers.
One such example is American retail giant Nordstrom.
When Nordstrom decided to launch its discount department store chain, they knew they would compete directly with T.J.Maxx, Marshalls, and Ross Dress for Less.
Instead of competing with these brands on price alone, Nordstrom decided to look at their data and see where they could improve to stand out.
They stopped concentrating on what the competition was doing and put their focus on improving customer service.
Nordstrom took their customer feedback data and ran it through an NLP model to understand the most pressing issues that led to bad customer experiences at their stores.
A common complaint that kept cropping up was that customers had trouble locating in-store salespeople quickly.
Nordstrom’s in-store employees wore street clothing, making it difficult for customers to differentiate between store employees and fellow shoppers.
As the NLP model analyzed tons of customer feedback text and brought this issue to light (among other issues), Nordstrom quickly put their store workers in bright-colored t-shirts, making it easy for customers to spot them.
Within two days of that pilot, the company saw a 30-point jump in the key metric to evaluate sales staff effectiveness.
This is but a small example of how retailers can analyze their data and optimize business that immediately affects sales and revenue.
Actually, how does NLP work?
While machine learning models can run through tons of data very quickly, what makes them effective is labeled data.
Labeled data tells ML models where to look and what they mean.
So the following question is “who labels the data?’
It’s not like retail brands have data labelers on their payroll read to jump in.
Finding skilled text data annotators is an enormous challenge.
While many reputed data annotation companies specialize in image and video data labeling, very few are good at text data annotation.
This is because text data labeling is a particular skill, and a piece of text can be interpreted in many ways.
For example, anybody with minimal training can label image data by drawing boxes over objects on photographs. Drawing bounding boxes around video footage isn’t that difficult either—purely because there are no subjective elements in labeling visual data. However, text annotation is a different ball game altogether.
You can address the subjectivity in text data labeling by training data labelers in the relevant domain.
Do retail enterprises have the time and budget to hire and train data labelers?
That’s why we built Traindata, Inc.—a data annotation company with bespoke data labeling tools and highly skilled, certified, and qualified data labelers.
to learn more and hire us to label your data.