Building Production-Grade spaCy Text Classification Pipelines for Business Data
Unlock the full potential of spaCy with this guide to building production-grade text classification pipelines for business data.
Automating manual tasks out of your ecommerce business is one of the most valuable things you can do when looking to scale your revenue or optimize your current processes to improve margins. As the intersection of artificial intelligence and ecommerce continues to grow it's becoming more prevalent to see these organizations leverage these tools across backend operations to eliminate the need for manual effort on low ROI tasks. Not only does task automation software remove the costs and manual resources required, but scales to support increased work more efficiently than human resources do. The days of needing huge backend teams to manage fulfillment and inventory levels are seeing an end with the rise of software automation.
Most decision makers at these organizations decide if task automation is worth it with a few common metrics:
Asking these questions allows you to gauge the possible ROI of this new process relative to any risk that is involved with making the switch. Businesses want to understand all of the pros and cons of making this switch, and any short or long term effects it might have.
Most of these automation tools focus on removing rather low ROI tasks relative to the amount of time or money it takes to complete the process. Most of the time these are things like social media posting and updating, updating onsite products based on inventory levels, sending coupons to loyal customers, and other tasks that don’t require much in-domain knowledge, expensive labor, or a lot of labor. For that reason, these tools are normally targeted toward smaller ecommerce operations that don’t quite have the manpower yet to hire true experts for these tasks (experts that can use their knowledge to actually produce ROI past the cost). These automation tools often still require a decent level of manual intervention and quality expertise to see any large shift in ROI past just the reduction of total manual labor required.
Many of these long time popular automation tools have gotten a makeover in recent years through integrating Ai and machine learning to fill any gaps in the previous automation pipeline and enhance the quality of the process you’re automating. This includes doing things such as:
The ROI added through Ai is certainly not something to ignore, especially with the idea that it closes many of the gaps that had to be manually worked through in some of the previous pipelines. Ai has added a level of creativity to automation that in the past would require manual input and could bog down these pipelines.
You’ll notice that most of these automation tools used today in ecommerce are generic tools that could be used across a wide range of industries. The tools are focused on business processes (marketing, content, inventory) that exist in most businesses and could be moved to various industries. The generalized nature of these products can limit the potential ROI you can reach or require tuning towards the ecommerce industry to enhance the results.
What about specific software tools that help ecommerce businesses automate tasks that grow in required labor as your business grows? There are a ton of backend ecommerce processes that become more of a burden as your sales, inventory, and product catalog grow. On top of this, most of these tasks require industry specific knowledge that normally requires an experienced expert in ecommerce.
There is a clear lack of tools in the market specifically built to automate ecommerce related tasks for scaling businesses.
Product categorization and cataloging is a manual process that all ecommerce and online marketplaces must do any time they want to add a new product to the store or when looking to improve on-site search capabilities. This task requires knowledge of a few key topics to be completed efficiently and correctly:
- An in-domain understanding of how specific ecommerce products fit a product taxonomy. Taxonomists of a cat supplies brand need to understand the product well enough to say if it’s non-prescription or prescription to fit the taxonomy.
- A general understanding of the SEO and on-site search goals to either fit products correctly or update the taxonomy trees products.
As with most ecommerce tasks, the amount of work required to complete these tasks grows rapidly over time. More products, more SKUs, and more categories lead to more manual effort. The more frequently you add new products also leads to needing new categories.
In the above example let’s say you add a new niche in Cat Supplies called “Cat Car Items”. You then have to go through all surrounding categories in Cat Supplies to determine which products actually belong in your new category.
The manual work surrounding product taxonomy and categorization goes past just backend processes. A customer's ability to find the exact product they’re looking for is critical for the conversion rate of ecommerce brands and marketplaces. When a customer does an on-site search they have an ideal product in mind and are rarely interested in searching too much for it. 47% of potential customers give up their search after one attempt, and only 23% try 3 or more product searches. It’s critical that your products are laid out correctly in your taxonomy and that your taxonomy layout is user-driven.
That’s not the only sales and marketing related pipeline related to product taxonomy. Search engine optimization is a key traffic source for ecommerce brands and ranking your product pages and category pages high in Google leads to increased traffic. How your product data is structured is a key part of how Google’s crawlers identify relevancy between your pages and the keywords you care about to rank you better. It’s no surprise that this Forrester study shows that “poorly architected retail sites” sell about half as much as highly organized sits.
Let’s look at how ai leveraged automated product categorization can make all of these processes a breeze.
Ai based product categorization and taxonomy allows us to automate huge chunks of the above ecommerce & online store specific tasks. Not only do we remove the manual labor of the tasks with high accuracy, but many of the tasks can be completed in minutes rather than hours. Software scales much easier with your business than teams of manual employees meaning you won’t see your cost rise along the way.
We’ve built a complete SaaS product Pumice.ai that uses ai to automate product categorization, tagging, and cataloging without any manual effort. Let’s take a look at the benefits of automated product categorization tools like Pumice.ai.
- 95% reduction in manual labor required when generating new categories, fitting new products to a taxonomy tree, or refitting a product catalog to a new product taxonomy tree. Automated product categorization allows you to upload your products and automatically fit them to a taxonomy tree or categories list. The ai models learn the relationship between product data such as title, description, price, and tags and the taxonomy tree categories. This allows you to completely remove the manual processes from above that require industry and store specific knowledge.
- Automated product categorization is 17x faster than manual product categorization and becomes even more efficient as the scale grows. Think about how many hours it takes for a multi-seller ecommerce store to add 40,000 products to their catalog from a new seller. Pumice.ai can fit these products in minutes with insanely high accuracy.
- Improve site navigation for potential customers to increase conversion rates. By better placing your products in the correct categories it becomes easier for potential customers to find the exact product they’re looking for. If they want to search for specific “Hiking Sneakers” they should be able to find these products without sifting through “Outdoors Sneakers” which is much broader. Pumice.ai also allows you to use ai to generate new categories and tags that might better fit a product based on the input data.
- Enhance the velocity of all your PIM related tasks. These same ai models can be used for many tasks that enhance your product information management and how you go about understanding the relationships between products. We’ve built product similarity models that help you understand the relationship between two products based on any combination of fields. The information learned from these relationships can help you better understand when it’s time to create new categories or further refine the depth of your taxonomy tree.
Truly generalized ecommerce ai products need to support a level of input variability that covers the many different ways that ecommerce and retail stores catalog their data. Some stores save only fields such as product title, product description, price, and SKU - while others add tags, GTIN, attributes, and more. In traditional machine learning, this would cause issues when trying to fit any ecommerce store to the required inputs of an automation tool. Specific use cases that ha too many or too few inputs would lead to lower accuracy or even not being able to run the software.
The natural language processing (NLP) models used in these pipelines are perfect for their ecommerce automation specific use case. They support any amount of different input fields past the standard product title and description. The pumice.ai pipeline allows you to use a huge variation in the length of the data as well. Product descriptions with just a few words or 1000 words can be categorized correctly. Each field can vary in its “importance” level to the corresponding category/product/taxonomy based on your specific store and quality of field without being retrained. Category names don’t have to be related to the product itself (pants category for jeans product) as we’ve seen a ton of weird naming conventions fit products perfectly fine.
This is due to the deep underlying relationship the models have learned between various levels of product data to categories, tags, and other products, and the overall pipeline's ability to adjust dynamically to inputs and understand relationships on the fly.
The ecommerce focused nature of these NLP models and pipelines comes with even more benefits related to automating PIM tasks. These different pipelines can build on top of each other to conquer new PIM tasks and learn new relationships that better fit the level of data you have. Combine our product similarity endpoint and dynamic categorization endpoint to create a new pipeline that categorizes new products based on the current categorization of products vs a historical data focused relationship between a single product and single category. This allows not only for lower risk automated categorization but a much more real time match.
New tasks with different business objectives can also be accomplished with ecommerce focused NLP models. We can leverage the same pipelines seen above to gauge the similarities between our product catalog and our competitors. The benefits of this aren’t always easy to see but can lead to powerful data such as:
- How to improve on site SEO and site navigation.
- How to better categorize our products.
- What information/keywords we should include to improve our product listings.
- When is it a good time to create new product categories or refine existing ones.
The ecommerce task automation space has been waiting for the powers of ecommerce specific ai tools.
Pumice.ai allows you to start automating out PIM tasks in just a few steps with different levels of data. Go from raw product data to structured data relationships in minutes.
You’ll want to collect the required data for the PIM task you’re looking to automate out. Most of the ecommerce tasks and benefits we outlined above require this data:
- Product data with any available fields. Fields such as attributes, features, price, model etc can either be added as optional fields or appended to the product description.
- Your store taxonomy tree or categories we want to fit product data to. This can be as simple as just the names of the categories and the tree layout. You should include all levels to the tree as the pipelines use all information to best fit products or generate categories.
- Existing product to category matches. This data is very easy to acquire as it normally is already attached to the product data.
These different data points can often be exported from your product information management system or internal product database. Many ecommerce platforms such as Shopify and Woocommerce support exporting this data as CSV or JSON.
Based on the PIM related task you’re looking to automate you need to choose which ai pipelines need to be used and how to order them. Pumice.ai supports three main pipelines in the baseline version:
This pipeline focuses on categorizing product data into a product taxonomy tree or categories list. The dynamic nature allows you to use any set of categories or tree at runtime without training specific to your set. This feature lets you quickly make changes to the structure of your categories or use multiple trees on the same products.
This pipeline generates categories & attributes for product data without your specific categories or taxonomy tree. This is a great way to understand what ai thinks are the best categories and tags for your product data based on similar stores and industry competitors.
This pipeline not only generates standard categories such as “Swimwear” but can combine product data + knowledge of categories to generate higher level ones such as “Summer”.
Being able to understand the similarity between two different product data records is a key task that leads to so many of the benefits outlined above. This NLP tool compares any amount of fields for two products for similarity based on what their best fit category would be.
The platform allows us to deploy any of our custom models on top of the baseline ones such as product data matching, image based matching, visual search, and many more tools we’ve built.
Make sure your inputs and output formats match what is required for the pipelines and your own internal systems. We support CSV, JSON, and direct API calls to the various pipelines as input. Output data will come back in the same format that was provided. Custom integrations allow for data to be returned in a different structure or automatically integrated into PIMs or ecommerce platforms.
Once the output systems are connected, you’ve created an entirely automated PIM task pipeline! The level of manual effort required before or after the Pumice.ai block is completely up to you. The output integration module can be automated with our most common custom integrations we’ve built:
- Sales Layer PIM
- Plytix PIM
- Google Sheets
- Internal DBs
- Classification models as decision makers
Pumice.ai is a PIM enhancement platform that leverages our proven artificial intelligence tools to automate repetitive tasks and easily setup workflow automation in your backend on one easy dashboard. With the help of our Width.ai development services you can leverage custom models and integrations built directly into your dashboard. Contact us today to learn more about how you can automate your product management and get thousands of hours back in a few minutes.
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