How to Boost Product Classification With AI

How to Boost Product Classification With AI

Here’s the golden rule for every online enterprise or marketplace: Your online catalog must always be up-to-date and search friendly, despite the rapid influx of inventory and fluctuation in product availability. That is easier said than done, especially if you handle a wide variety and large volume of products from different countries. 

Well-organized product categorization and classification are key to your website’s traffic and customer search engine, but also contribute significantly to your logistics and operations behind the scenes. In order to prevent costly errors due to faulty product classification, you need an AI-based platform that automatically and accurately categorizes merchandise. 

What is Product Classification

Product classification is a crucial process for any eCommerce business. It involves categorizing products based on their attributes, such as size, color, and material. However, manually classifying products can be time-consuming and prone to errors, which is where AI comes in. In this blog post, we’ll explore how AI can boost product classification and help eCommerce businesses save time and improve accuracy.

Use machine learning algorithms

One of the most effective ways to boost product classification with AI is to use machine learning algorithms. These algorithms can learn from existing product data and use that knowledge to classify new products automatically. For example, an algorithm could learn that a certain set of attributes is associated with a particular product category, such as shoes, and then use that knowledge to automatically classify new shoes based on their attributes.

Train your AI models

To get the most out of AI for product classification, it’s important to train your AI models on a large and diverse set of data. This can include product images, descriptions, and other attributes. The more data your AI models have to learn from, the more accurate they will be at classifying products.

Incorporate natural language processing

Natural language processing (NLP) is a branch of AI that can help improve product classification by analyzing and understanding text-based product descriptions. By using NLP, AI can automatically extract relevant product attributes from product descriptions and use them to classify products more accurately.

Use image recognition

Image recognition is another powerful AI technology that can be used to boost product classification. By analyzing product images, AI can automatically identify product attributes such as color, shape, and pattern. This can help improve the accuracy of product classification and reduce the need for manual intervention.

Integrate with your eCommerce platform

To get the most out of AI for product classification, it’s important to integrate it with your eCommerce platform. This can help automate the classification process and reduce the need for manual intervention. For example, an AI-powered product classification system could automatically categorize new products as they are added to your eCommerce platform, saving time and improving accuracy.

What are the benefits of AI-powered product classification? 

Empower accurate product mapping

In order to ensure accurate product mapping, you must first create organized product groups. This means forming macro-categories (dresses, for example) and micro categories that logically fall underneath the main category (casual dresses, summer dresses, etc). AI-powered product taxonomy enables accurate macro and micro categorization predictions based on category ID and taxonomy tags. Product classification using machine learning prevents costly manual mistakes.   

Optimize tariff classification and customs compliance

When dealing with a large volume of products from numerous countries and various retailers there is much room for error. An AI-based system has the ability to use data such as manufacturer, product description and other input to automatically ensure customized classification and accurate tariff codes. The machine-learning classification module can be modified based on each country’s code in the Harmonized Commodity and Coding System, facilitating faultless shipments to customers worldwide.  

Recommend products based on past customer experiences

AI-based classification can be used to recommend new products based on a customer’s past browsing and purchase history. Today’s customers desire personalized content and special offers based on their individual preferences. AI-categorization, which provides actionable insights, enables the delivery of individualized messages. This machine learning tool is used by large businesses like Netflix and Spotify. 

Correlate crucial data of different classes

Aside from the ability to match data within a given class, AI-based techniques also enable the correlation of data from different classes that share association. Machine learning integrates meta-data analysis to achieve a deeper understanding of data. This input enables more discerning automatic cataloging for easy discovery through search. By combining in-depth classification and correlation capabilities, AI provides enterprises with the tools to locate, inventory and map crucial data.

The days of manual categorization for online catalogs are long gone. With inventories counting tens of thousands or even millions of products, most large enterprises are transitioning to automated AI-based platforms to ensure consistency, prevent duplications, and increase customer traffic. 

Uncovering Insights: How to Get the Most Out of Your Amazon Data

Uncovering Insights: How to Get the Most Out of Your Amazon Data

Amazon is a powerhouse in the online retail world. With its vast array of products and services, it is no surprise that Amazon is the go-to platform for many businesses to get their products or services in front of the right customers. However, it can be challenging to understand how to effectively use Amazon to its fullest potential when it comes to leveraging data and gaining insights about customer behavior.

At Cluster, we understand that as a business owner, you want to make the most of your online presence. In order to do so, it’s important to have a deep understanding of how your competitors are performing on platforms like Amazon. That’s where our Amazon Insights tool comes in.

Why Analyzing Amazon Data is Important

Analyzing Amazon data provides valuable insights that can be used to optimize your Amazon business. It can help identify areas of opportunity, uncover customer preferences, and inform strategic decisions. By understanding your data, you can make informed decisions on how to best grow and optimize your business.

For example, analyzing your Amazon data can help you identify products that are underperforming, as well as identify which products are most popular with customers. This knowledge can help you make decisions on which products to prioritize, as well as which products to discontinue or improve.

Analyzing Amazon data can also help you understand customer behavior, such as which products customers are searching for, how often they are returning to your store, and how long they are staying on your website. This information can help you tailor your marketing and promotional campaigns to best suit the needs of your target audience.

In addition, analyzing Amazon data can help you identify trends in customer behavior, such as which products are most popular in different demographic groups or in different locations. This information can help you target the right segments of customers and better cater to their needs.

Best Practices for Analyzing Amazon Data

Now that we’ve discussed why analyzing Amazon data is important, let’s go over some best practices for data analysis.

1. Start with the Basics

Before diving deep into your data, it’s important to start with the basics. Begin by gathering data on key metrics and trends, such as sales, revenue, customer engagement, and customer reviews. This will give you a good starting point and provide you with an overview of your performance and customer behavior.

2. Identify Your Goals

Once you have an understanding of your data, it’s important to identify your goals. What do you want to achieve with your data analysis? What insight do you hope to gain? Knowing your goals will help ensure you are focusing your analysis on the right areas.

3. Clean and Organize Your Data

Once you have identified your goals, the next step is to clean and organize your data. Data can be messy and difficult to interpret, so it’s important to make sure it is organized and easy to understand.

4. Analyze Your Data

Once your data is organized, it’s time to start analyzing it. Begin by looking for patterns and trends that can help you understand customer behavior and identify opportunities for improvement.

5. Visualize Your Data

Data visualization is an important step in data analysis. Visualizing your data can help you identify patterns and trends more quickly and easily. There are many tools available to help you visualize your data, such as Tableau, Microsoft Power BI, and Google Data Studio.

6. Take Action

Once you have identified insights from your data, it’s time to take action. Use the insights you’ve gained to inform your decisions and optimize your Amazon business.

How to Use Insights Gained From Your Amazon Data

Now that we’ve discussed best practices for analyzing Amazon data, let’s talk about how to use the insights gained from your data.

1. Optimize Your Product Listing

Analyzing your Amazon data can help you optimize your product listings. You can use the insights gained from your data to determine which products are most popular with customers and which products are underperforming. This information can help you make decisions on which products to prioritize and which products to discontinue or improve.

2. Tailor Your Marketing and Promotional Campaigns

Analyzing your Amazon data can also help you tailor your marketing and promotional campaigns. You can use the insights gained from your data to understand customer behavior and target the right segments of customers. For example, you can use the data to identify which products are most popular with different demographic groups or in different locations.

3. Improve Your Customer Service

Analyzing your Amazon data can also help you improve your customer service. You can use the insights gained from your data to understand customer preferences and identify areas of improvement. For example, you can use the data to determine which products customers are searching for and how often they are returning to your store.

4. Sales Performance Analysis

One of the most important factors in your Amazon strategy is sales performance. With an Amazon Insights tool, you can gain a deep understanding of how your competitors are performing in terms of sales volume and revenue. This information can help you identify areas where you can improve your own sales strategy, and stay ahead of the competition.

5. Product Pricing Analysis

A variety of Amazon Insights tools can provide you with detailed data on product pricing for your competitors’ products. This information can help you identify areas where you can improve your own pricing strategy, and stay ahead of the competition.

6. Competitor Analysis

Analyzing your competitors’ data can also provide valuable insights. By analyzing their sales data, pricing, and customer reviews, you can identify gaps in the market and opportunities to differentiate your products from theirs.

For example, you might discover that your competitors are offering a similar product at a lower price, which could prompt you to adjust your pricing strategy. Alternatively, you might notice that your competitors are receiving consistently negative reviews for a certain feature or aspect of their product. This can help you identify areas where you can differentiate your product and offer a better customer experience.

In addition to analyzing your direct competitors, you can also look at broader market trends and patterns. For example, you might notice that certain product categories or trends are gaining popularity among customers. This can help you identify new product opportunities or adjust your existing product line to meet changing customer needs.

7. Advertising Campaign Analysis

Amazon offers various advertising options to sellers, including Sponsored Products, Sponsored Brands, and Sponsored Display. By analyzing the performance of your advertising campaigns, you can determine which campaigns are generating the most sales and adjust your strategy accordingly.

For example, you might discover that your Sponsored Products campaigns are generating more sales than your Sponsored Brands campaigns, which could prompt you to allocate more of your advertising budget to Sponsored Products. Alternatively, you might notice that certain keywords or ad placements are generating more sales than others. This can help you optimize your ad targeting and improve the ROI of your advertising campaigns. In addition to analyzing the performance of individual advertising campaigns, you can also look at broader trends and patterns.

8. Inventory Management

Analyzing your inventory data can help you make informed decisions about inventory management. By analyzing your inventory levels and sales data, you can identify which products are selling quickly and which ones are not. This information can help you adjust your inventory levels and prevent stockouts.

For example, you might notice that a particular product is selling faster than expected. This can prompt you to order more inventory to avoid running out of stock. Alternatively, you might notice that a certain product is not selling as well as expected. This can prompt you to adjust your pricing or marketing strategy to boost sales.

You can also use data to forecast future demand. By analyzing historical sales data and trends, you can predict how much inventory you’ll need to order in the coming months. This can help you avoid overstocking or understocking, which can lead to lost sales or excess inventory costs.

How Cluster Can Help

To get the most out of your Amazon data, it’s important to have the right tools and resources in place. This might include data analytics software, customer feedback platforms, or inventory management systems. By investing in tools like Cluster, you can ensure that you’re able to collect and analyze data effectively and make informed decisions that drive business growth. Book a demo now.

Demand and Product Analysis: Present Relevant Items at Competitive Prices

Demand and Product Analysis Present Relevant Items at Competitive Prices

There is no question catalog integrity is truly the foundation of a successful marketplace, but you know what else is? Having high demand products of prices that will sell. Many marketplaces tell us time and time again that they struggle with the ability to stay apprised of pricing changes, which we know fluctuates by the minute. 

 

At the same time, shoppers want the reassurance that they’re getting the hottest items at the most competitive prices. To that end how does a marketplace stay ahead of new products or know what products are in highest demand outside of their own respective marketplace? 

 

On December 8, 2021, during a live webinar, we dove into this and more, starting with the importance of catalog integrity as the foundation of any marketplace business, followed by how to present the most relevant items, at the most competitive prices. Read the transcript below and watch the accompanying video

Getting Product Demand Analysis and Ranking Right

We can all determine why customers gravitate towards the Amazon marketplace. If I ask anyone if they ever had to go to the second page search results on Amazon, their answer would likely be no. Well, why is that? We know that Amazon has perfected product demand analysis and product ranking and always shows that best selling products at the most competitive prices. So despite this simple logic, this continues to be a pain point for many marketplaces in which low demand products are monopolizing the most important digital real estate, which is the first page search results.

 

According to BigCommerce, over 70% of sales are coming from page one. Having low-demand items displayed first will undoubtedly lead to a very poor user experience and leave your customers wanting more. Again, the obvious lack of competitive pricing will deter your buyers. 

 

In fact, according to Active8 more than 50% of online shoppers will now regularly compare sellers to find the best prices. So the bottom line is pricing is not static and it’s a marketplace’s responsibility to stay apprised of competitors’ pricing, sales campaigns and promotions.

 

This can change by the minute. So to that end I’m just going to pose a few questions:

 
  • How does a marketplace ensure they’re offering competitive pricing? 
  • What guidance, if any, are you providing to your sellers to help them price their items competitively and to win that basket? 
  • And if a marketplace knows that they have the best offer, how do they inform their shopper that they are in fact receiving the best offer? 
  • What can you do as a marketplace to increase conversions?
 

First, fill gaps into the competitors’ pricing. Having sufficient market research will allow you to understand competitor pricing across marketplaces to help you win the basket. Now providing pricing guidance to your sellers is an amazing opportunity for marketplaces to drive value to their seller community, as well as presenting high demand and relevant items first. Most marketplaces are relying on transaction history to dictate product rankings on their marketplace.

 

This works well for big organizations like Amazon. They have massive amounts of transactional data. However, this can be incredibly challenging for less mature marketplaces. Moreover, this doesn’t account for the demand of products on competitor sites. Another point to consider is that a merchant could be trying to list hundreds, if not thousands of items, and not just on your marketplace. So like I articulated earlier, they need your guidance.

 

Driving Value to Sellers with Market Intelligence

If you have the right data to back pricing recommendations to your seller community, it can be a tremendous way to drive value to your existing sellers and result in recruiting power sellers to your marketplace. So help your customers realize they’re getting a good deal. According to BigCommerce nine out of 10 U.S. customers price check a product on Amazon.

 

In closing, if you have the sales intelligence and the confidence that your marketplace has the best price, you can communicate this with your user and show it through a best offer badge. Next, be sure to dive into the importance of catalog integrity, promoting the most relevant products with the most informed, competitive pricing – and learn the characteristics of A+ sellers that are right for your marketplace.