How to Collect Hi-Quality Data for Machine Learning Models in Retail?

“Data is the new science. Big Data holds the answers.” –Pat Gelsinger.

Credits: Exterro

Are you a retailer enthusiastic about introducing Artificial Intelligence to make your in-store and off-line shopping experience better but dont have clarity on how to collect data to kick start the creation of machine learning models? 

Then read on, because this blog will give you insights on how easily you can capture data using existing systems to build next-gen computer vision and natural language processing models. 


In the consumer-centric world of retail achieving high levels of customer satisfaction is the topmost priority for every retailer. The most important part of this process is collecting high-quality data and basing decisions on them. 

Computer vision, Natural Language Process models can come in handy in doing predictive analysis and helping brands increase sales but the basis for this is data. If the data fed into the systems is not reliable then the output will be of no practical use to the retailers – “Garbage In, Garbage Out”

Often retailers who want to introduce Machine Learning models into the system face difficulties in identifying effective ways to capture the right input data. In this blog, we will explore ways in which retailers can collect high-quality datasets to build efficient machine learning models. 

Data for Machine Learning in Retail

Traditionally predictive analytics was the method of mining insights using the existing data to uncover futuristic predictions like crafting customer lifetime value (CLV). It was a slow and semi-automated process and relied heavily on classic statistical techniques as Regression. 

Another disadvantage of continuing with predictive analytics is it only works on “cause” data and has to be re-done with “change” data. And since this is done on spreadsheets, it cannot reveal the association between the reasons and results. 

Today with the advent of artificial intelligence, machine learning models are built to automate predictive analytics and expedite the strategic decision-making processes. 

Machine Learning models use advanced computational algorithms such as decision trees or Random Forest. The machine can learn by itself and respond to the changes in the training data without human intervention. The output of a machine learning predictive model is vastly impressive and retailers today place their bets on its success in driving higher ROI. 

Progressive retailers are adopting machine learning like never before. According to a report, the global artificial intelligence in retail market size is expected to grow from USD 736.1 Million in 2016 to USD 5,034.0 Million by 2022, at a Compound Annual Growth Rate (CAGR) of 38.3%.

Sources for Capturing Data in Retail to Build Machine Learning Models

How can a retailer begin its machine learning journey? What type of data is required and how can a retailer gather that data? Will it be a costly affair? These are some typical questions every retailer has when they want to switch from legacy systems to modern ones. 

AI-based models feed humongous raw data and by smart techniques that data can be captured in cost-effective ways:


Retail websites can gather a lot of value when it comes to user segments and preferences. The browsing history of the consumer highlights key matrics required by the machine learning model to make futuristic predictions. 

Location, gender, purchase behavior, etc can help the retailer build a perfect route for marketing activities that are focused on enhancing customer loyalty. 

According to research, brands that put customer data to good use outperform competitors by 85 percent in sales growth and by more than 25 percent in gross margin.


Point of Sales (POS) data collection is often referred to as passive data collection where the customer is not required to take any action. The data can reflect repeated sales, time of sale, items purchased, payment method, geographic location, demographic data, etc. 

This data is very instrumental in building a machine learning model as it helps in predicting customer’s user preferences based on a series of historic purchases. POS systems have been a huge hit with the retailers. 

Every business, whether a restaurant, retail, or grocery store should adopt an appropriate POS system to gather as much information about the customer as possible. 

CCTV Cameras

Having a CCTV camera in any retail store is now a norm, primarily installed to identify theft or to monitor the happenings inside a store. These recordings can be extremely helpful in building computer vision models. 

Video footages of customers strolling through the store are the best insight a retailer can get to enhance the effectiveness of their planograms. Offline or in-store retail can leverage this data to identify loyal customers and notify the in-store professionals to offer customized deals. 

In the long run, these CCTV footages can help retailers streamline their business success by learning about the micro-influences that affect consumer behavior.

There are many applications that give meaning to the captured video footage and help in making “visual sense” out of it. Installing simple applications like this can make your data machine ready. 

Bangalore-based is utilising artificial intelligence and machine learning to fulfil the dream. After running pilots across various use cases using CCTVs in hospitals, offices, schools, restaurants, etc., the founders found an excellent product-market fit for the retail sector where extracting visual insights from a brick and mortar store had a significant impact for the businesses. Wesense raised an angel round of 1.3 crores fund last year from serial entrepreneurs. 

Loyalty Cards

Discounts, exclusive rewards, and early sale intimation – Loyalty cards are very lucrative. When a retail store gives out loyalty cards to customers it’s a method to enhance repeated visits and subsequent purchases both online and offline.

These cards are loaded with rich data, they contain diverse personal information about customers (name, place of stay, birth date, anniversary date, etc) this information helps in formulating the buyer’s persona and sharing customized promotion offers. It can also influence dynamic segmentation and help in building predictive content. 

In-Store Sensors

“Just walk out” themed store AmazonGo created quite a buzz with the retail community. The entire store is sensor active and every item you pick or every move you make is being captured.

In-store sensor data is used to build computer vision applications. It helps in identifying who the customer is and progresses on to understand their customized preferences. They are like eyes – they watch and remember, everything!

How to Utilise data to develop Artificial Intelligence capability in Retail

AI in Fashion: The captured data can be annotated with the help of a smart data annotation platform which can be used to identify and tag detailed product attributes. The catalog data can then be used to analyze the user behavior and enable better product discovery and personalization.

AI in Grocery Retail: Data captured can be fed in the computer vision and natural language processing models which can help in analyzing shoppers’ lifestyles. These profiles can be used to market offers, cart abandonment emails and push notifications. 

AI for Electronics: A.I. can help retailers identify categories, products, and brand affinities built from data collected through every click of the customer. 

AI for Beauty: The customer data can help in building a powerful AI application to suggest personalized beauty products to customers, increasing the chances of purchase

For any machine learning model to perform at its best the data needs to be labeled accordingly. To best annotate your data check out Labellerr

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