While a picture (or visual) paints a thousand words, written-text product attributes can be the difference between your client clicking that “Add To Cart” button or moving on in their shopping pursuit.
While hundreds of thousands of products are sold online every day, you want to be sure that the work you put into your product listings pays off and is effective.
You may agree, though, that the amount of work connected to extracting and entering data to fill in product attributes for each item can get quite overwhelming pretty fast.
The burning question? What exactly can be done to streamline product attribute-related data entry? Yes, it can… read on to see how.
One thing is certain – to manage data, you must first obtain it. While a data management system may be a no-brainer among all the available options out there, including MDM (Master Data Management), PIM (Product Information Management) or PLM (Product Lifecycle Management), Excel, or ERP (Enterprise Resource Planning), the question of what information can be extracted from a product label and how to get them in a fast and frictionless way requires a closer look.
So, let’s dive in.
What Kind of Product Data You Can Find on Product Labels?
From big to small, product labels must check a few boxes when it comes to the information included on them. This means that due to rigorous regulations, the type of data they include also depends on the product type it features (clothing vs. food, cleaning detergent vs. electronic good or medical product, and more).
A general range of information, as you may suspect, will include:
- Marketing information and elements, e.g., brand tagline, brand icons
- Product properties such as ingredients, volume, quantity or weight of the product, best by date
- Sales-related information, including barcode, batch number, and supplier identification
- Guidelines on safety information ranging from allergens to usage instructions, age warning markings guidelines, and hazard statement
- Regulatory compliance information required for specific product categories, e.g., manufacturer traceability, name and address, or information based on local regulations (EU, US acts, or other)
The list of products that need to feature a label is simply endless.
And as much as we all love a nice and eye-catching product label the endless combinations of shapes, sizes, fonts, colors, patterns, graphics and more can be eye-boggling (holographic elements, small print, etc.)
Based on the above, we can all agree that product-label information is simply invaluable to both the seller and the customer.
On the one hand, helping you to internally manage data (e.g., product data stored, edited, and distributed using a PIM system) and helping the client find and identify the exact listing they are after as part of product attributes.
So, what’s the issue?
Problems with Data Extraction from Product Labels
Well, it’s not uncommon when sourcing, importing, and purchasing products for your e-commerce store that you may simply receive your products from the distributor or manufacturer and will be left to assemble all relevant information about it – mostly from the product label.
Let’s imagine one such situation, for example, an online retail site selling personal care items (such as cosmetics, beauty, and household cleaning products) and other drugstore products. The company sells numerous products from multiple manufacturers.
While the e-commerce logistics models vary, our model company, like many other firms out there, receives the items as they arrive at the warehouse, accompanied by a shipment note.
This document only states the information needed for the later settlement of the payment needed by, e.g., the accounting or AP department.
Therefore, the shipment note confirms the order but provides no more specifications that the e-commerce shop needs.
So, besides the information contained within the barcode (which has a limited amount of characters and usually mainly includes the date of manufacturing, inventory details expiry date, price, name of the manufacturer), the seller is left to find a lot of data that he may want to feature as part of the listing and product attributes but must do the information sourcing and extracting by hand.
Where may he find this information? That’s right, on the product label.
With a constant inflow of products reading and retyping long lists of information, ingredients, and directions for use into their online system is no easy or fun job.
This is where an OCR for data extraction automation comes in.
OCR and Data Extraction Automation – Solution to the Problem
Let’s take a closer look at how such an OCR tool manages to make the job of data extraction from labels so much easier.
An OCR, which stands for Optical Character Recognition, is designed to identify and capture written data fields within images or photos.
Result? Data in unprocessable formats, such as documents in their physical sheet of paper format or a photo or a scan of a document, can be converted into text that is processable digitally and can be used further, whether in product management systems, placed straight in an online product listing or even used for analysis.
This technology has been long used in the business sector by, e.g., accounting teams, financial institutions, or debt collection sector to streamline how they process large quantities of documents, from invoices to timesheets to receipts where data also needs to be extracted from physical copies of documents and fed into further steps of the procurement process.
There is one element to consider, though. A big similarity between documents such as invoices, timesheets, receipts, and purchase orders is that they are all…. printed on a flat piece of paper where the text is easy to read, not only by a person but also by a machine.
Getting back to our example of an e-commerce store, it’s not rocket science to realize that the majority of products sold there will be either on cylindrical, conical, convex, or other irregular shapes.
This means that extracting text will be difficult as the curved surface edges may leave the text deformed and/or blurred.
The Mechanics of an AI-Powered Tool – Converting Labels to Text
Extracting data from such product labels simply requires a different approach. By taking multiple photos of e.g., a cylindrical object, the OCR can merge these images in a way that removes the curvature of it, making the text readable. From then on, similarly to documents, the OCR can read these with a high threshold of certainty.
After human validation, the information captured and extracted by the OCR can be exported into a desired format, i.e., Excel, CSV. This data is now available to enter into the next desired step.
8 benefits of Alphamoon’s Software in Extracting Data from Product Labels
There are numerous benefits that come with using an OCR for your day-to-day data extraction. Here are the 8 main ones.
1. Consumers Want to be Informed
Google reports that users are increasingly putting search engines to good use in terms of product information research. It’s not enough to have only basic information on your website. Instead, it’s best to provide as much detail and inspiration to customers as possible.
This comes as no surprise as e-commerce sales are increasing so quickly and significantly that they are forecasted to make up approx 24% of retail sales worldwide by 2026, as reported by Oberlo. Nowadays, consumers are not only becoming more accustomed to the idea of online shopping, but are also more expectant.
As a sideline, it’s worth noting that these customers’ expectations are related to more than what is posted but also the language it’s in.
As noted by Shopify.com in a study done by Flow.io the majority of their shoppers agreed that product descriptions (67%), product reviews (63%), and checkout process (63%) needed to be available in their own language.
Suppose your listing doesn’t contain all the relevant attributes. In that case, the reality is you may lose your customer to a competitor who took their time to include the important information on their site. More information simply encourages customer trust and loyalty.
2. Manual Data Extraction is Mundane: Optimize the Work Experience
Manual data extraction, i.e., a Data Entry Administrator or Clerk needing to type out information by hand, is a slow and tiring task that doesn’t scale easily, meaning it can easily cause backlogs. Imagine being able to double or even triple your productivity by equipping your employees with an efficiency-boosting tool. Workers no longer need to spend hours on the tedious task of reading the fine print of labels to retype them. Instead, they can simply validate and approve the work that the OCR carries out for them, meaning more can be done at the same time.
3. Avoid Manual Extraction as it Leads to Costly Mistakes
According to The Data Warehouse Institute, errors in data entry within areas such as procurement, supply chain, and others can cost businesses over $600 billion each year.
Not only can such errors make you and your clients question the validity of your other data, but the time and effort involved in retracing faulty information and fixing is time that could have definitely been used for other tasks and jobs.
Relying on an OCR means that the tool works consistently and doesn’t get tired despite the large volumes of text it’s given to capture.
What’s more, if the OCR can’t ensure a high enough certainty threshold, i.e., isn’t entirely confident, if the captured data was identified correctly, it will mark this information asking for feedback that it will learn from.
4. Tool for Extraction Data from Product Labels Should Have Easy Onboarding and API Integration
Alphamoon’s Intelligent Document Processing software is a simple, ready-to-go, and no-code solution. Users can get started with it without the need for long onboarding processes or investments into any further equipment.
Want to take Alphamoon Workspace for a swing? Sign up and process first 50 documents for free.
The file types (Excel, CSV) that the extracted data can be exported as they were also created with further integrations and systems in mind.
5. Platform for Product Data Labels Should Suit Growing Companies
You might have heard that automation is only for large enterprises…
In reality, automation is invaluable to small businesses too. As businesses grow and scale with time, so do their processes and volume of products.
Automation can prevent unnecessarily complex workflows from forming by helping to form optimized processes from the very start, like the process of extracting data from product labels. The value of this solution can’t be underestimated, especially in the case of e-commerce.
If a given site starts out with a certain number of products, it’s best to introduce streamlining tools and optimized processes so they can be a robust foundation for when the business grows, and the number of products sold doubles or triples.
6. A Tool Should Handle Even the Tough Cases
When it comes to day-to-day data extraction, the tool in use needs to be able to handle more than the “perfect scenario” cases.
Product labels come in various shapes, sizes, formats… and states, so the OCR must be trained well enough to handle anything from texts printed on “busy” backgrounds or an irregular surface. There are many challenges when doing image-to-text extraction but they all can be handled by Alphamoon.
7. Automation Helps Cut Costs and Help the Environment
It’s now become common knowledge companies can save time, prevent errors and increase productivity thanks to AI-based automation tools.
At the same time, it’s important to know that such tools can play a role in cutting costs.
Opting for Alphamoon’s cloud-based data extraction tool can help to lower a company’s energy use when extracting data by up to 80%! This is a significant reduction that makes it possible for environmentally conscious businesses to make the right choice while also benefitting from decreased energy costs.
8. Choose a Multifunctional Data Extraction Tool
Alphamoon’s Intelligent Document Processing tool is not limited to only extracting data from product labels. The platform can be used to simplify the work of AP and accounting teams in their finance-document-related work.
And? What do you think about the above-stated documents? Are you ready to start your own journey with automating data extraction from product labels?