In this article we discuss how AI Automated Product Tagging software can be leveraged to boost the quality of your guided sales recommendations.
As detailed in our guided selling blog article here, guided selling software helps brands understand who their customers are, and helps make the right product recommendations for them so the customers don’t need to do the work.
Guided sales or guided selling, sometimes referred to as conversational search, addresses the choice overload and decision fatigue shoppers experience when presented with too many product choices or with technically complex product choices.
Gobot’s guided selling functionality is quite straightforward on the surface but there is lots going on “under the hood.”
On the surface, shoppers engage in a casual conversation and answer various questions and Gobot recommends various products based on this conversation.
In the back end, Gobot needs to arrive at the best recommendations for this particular shopper based on the conversation and other factors. This is where things get interesting.
Uniquely, Gobot arrives at these recommendations in a number of different ways depending on the scenario, which results in more relevant and higher converting recommendations.
One approach involves leveraging product tags. Gobot’s deep Shopify integration makes it super easy to leverage your product tagging to enhance your guides sales on your Shopify store.
By way of example, if a shopper on a Shopify store expressed during the course of his/her conversation with Gobot a strong dislike for a particular color, Gobot can exclude all products tagged that color.
For Gobot to leverage product tags, however, a store needs to actually have products tagged in a manner that Gobot can leverage.
It sounds simple to tag your products according to their attributes but this gets complex and tedious real quick, especially when you have a large product offering and/or complex products.
This is where AI Automated Product Tagging comes into play!
What are product tags and why are they used?
Products sold in most e-commerce stores are typically tagged with labels intended to describe their characteristics, features, and category.
Every product has different tags, so every product is unique.
These tags should include everything about the product—color, size, type, brand, use, sale, etc. For example, a dress could have these tags—red, midi, evening, spring, Zara, cotton, short-sleeve, sale, etc.
Visitors and shoppers are supposed to get information about the product from these tags.
Tags allow shoppers to filter products based on the categories they want to explore, and thus impact shoppers buying decisions.
Descriptive tags improve the online store’s filters, allowing shoppers to find the products they are looking for faster.
Descriptive tags also yield deep insights into customer intentions, even without shopping history. They are key to smart analytics, which helps make smart business decisions.
What has driven the advancement of AI Automated Product Tagging tagging?
As detailed above there are many benefits to accurate product tagging.
However, AI Automated Product Tagging has come a very long way in the past couple of years mainly because the importance of catalog management has become more recognized.
What is catalog management you may be asking?
A product catalog is a place where online retail store products are precisely ordered.
Catalog management is a process that organizes the products in a catalog in a specific way in order to make them consistent and relevant.
It’s this strategic cataloguing process that ensures the quality of your product data across all sales channels.
It includes how merchants organize, standardize, and publish their product data to each sales channel.
Whether your product data is created in-house or is from third parties like suppliers, you need to manage its accuracy.
The catalog should provide information such as names, categories, price, brand, color, suppliers, and other relevant information.
These categories need to be accurately ordered in order to make it easy for customers to find the right product.
Structured product catalogues help companies bring to market their products across channels and increase product exploration.
PIMs allow you to house your product information in a single place so its easier to roll out this information across all of your sales channels in a consistent way.
It is critical to catalog management that products be tagged correctly and in context.
With the recognition of the importance of the product catalogue more attention has been paid to the tagging process as tags form an important part of the product catalogue.
So it was a natural progression for tools to be developed that make the tagging process easier and more accurate.
What exactly is AI Automated Product Tagging?
AI Automated Product Tagging was designed to eliminate manual product tagging.
AI Automated Product Tagging organizes and tags products in the product catalog based on photos and other information a store may have for each product.
AI Automated Product Tagging enables machines to assign relevant keywords to digital pictures.
Some software tools actually leverage advanced AI algorithms to generate metadata for catalog assets.
Thanks to Deep Learning, these algorithms speed up the tagging process making it automated and largely eliminate the need for human intervention.
Many of these systems are capable of scanning product images and detecting features that are connected to particular keywords.
Let’s take clothing as an example. AI Automated Product Tagging in this context is trained to recognize clothing in images as we ordinary humans do.
For us, it takes only a glimpse of an image to recognize a clothing item to decide what it is, i.e., a dress, a blouse or jeans.
For computers this is not an easy task, and that is why the current process of catalogue tagging is done by humans.
With the advancements of artificial intelligence, scientists have developed neural networks that mimic the human brain, and can be trained to recognize what is in an image just as we humans do.
These neural networks can take an image, process it, and give us semantic information in the form of text.
In order for an AI system to recognize a fashion item depicted in an image, it needs to be trained on many images of clothing and apparel, and the attributes used to describe them.
And this is exactly what AI Automated Product Tagging for fashion is.
A trained AI system that has been trained on countless fashion images that are carefully tagged can effectively replicate what it has been trained on and tag images with fashion categories and attributes.
With AI Automated Product Tagging, every image gets several attribute labels attached. These attribute labels are much more than generic tags—they add deep, specific insights about the products.
Products often are tagged with many different tags. For example, an image of a red shirt with flowers can have several tags attached by the machine learning technology—”red shirt”, “flower shirt”, “slim-fit”, “formal”, “short-sleeve”, “buttoned-shirt”, etc.
AI Automated Product Tagging reduces the time it takes to tag products, improves the accuracy of the tags and the website’s search results, and plays a significant role in the reduction of operational costs.
Advanced image algorithms allow the entire tagging process to be automated. The process is typically performed in a single day, replacing days and weeks of manual human work.
Who are the AI Automated Product Tagging players?
This space has been growing quick but here’s a list of providers we are aware of in no particular order. Of note, a number of these providers have Shopify apps designed specifically for Shopify stores.
How can stores further benefit from their AI Automated Product Tagging software?
If you are already leveraging AI Automated Product Tagging, you understand the benefit of tagging and are likely using your product tags for various purposes, e.g., product filtering, shopper analytics, etc.
You can make further use of your product tags by implementing a guided sales tool that can tap into your product tags.
As indicated above, Gobot leverages a number of different approaches to making product recommendations – one important approach involves tapping into your store’s product tags.
A store that has invested in well curated product tags can seriously boost its conversion rate by implementing a guided sales tool that leverages this pre-existing investment in tagging.
As they say, pick the low hanging fruit first, right?