It is impossible to overestimate how much ecommerce transactions depend on product data. For customers shopping online, product data is the only way to interact with advertised goods in virtual stores. It is the only way for buyers to discover products on search engines. Because they cannot physically see or touch products, the available information is all customers need to inform their purchasing decisions. As a result, product data enrichment has become a key area of focus for forward-thinking, customer-centric companies. However, it can be difficult given the tons of data involved in optimizing product information to the highest standard. But just like in other industries, AI once again emerged as a solution to this problem.
WHAT IS PRODUCT DATA ENRICHMENT?
Product data enrichment is the process of updating published product information to increase the relevance and usefulness of the available data to customers and prospects. In other words, it is the process of creating a comprehensive story around each product to support the decision making of potential buyers. This is also called Product Experience Management (PXM). When customers come across a piece of clothing or electronic device, they immediately look for more details. Color, size and material are examples of details that someone needs to buy clothes. On the other hand, the electronics shopper will look for battery life, weight, and warranty. More often than not, the quantity and quality of information they find determine whether a purchase is made. Also, the accuracy of the data correlates with the satisfaction resulting from the use of the purchased item.
The information required for product data enrichment varies by product and industry. In general, the following information is required to complete the product attribution:
- Specifications: Including weight, height, color, size, production materials, etc.
- Defining Features: Covers attributes such as capabilities, battery life, warranty information
- Reviews and Comparisons: Comments from verified users of the products along with the graphical representation of how the product compares to alternatives in its price range
- Images: Preferably high resolution interactive images
- Videos: Usually includes the display of product data in short or long videos
- Prices: current price and discounts and offers available
- SEO data: meta descriptions, alt text, focus and related keywords
Before a company can claim to have enriched its database, all of the above information must be available for each product in its catalog. Attempts to manually update this information leave room for inconsistent, duplicate, or incomplete data. The introduction of PIM software to the ecommerce market has certainly improved the prospects for product data enrichment. But there is always a next level; in this case, that level is AI-enriched data enrichment.
HOW DOES AI CONTRIBUTE TO PRODUCT DATA ENRICHMENT?
Good things happen for customers and businesses when AI is involved in product data enrichment. Customers enjoy better shopping experiences and have fewer reasons to return products. For companies, AI is a time and cost saving tool. It offers opportunities to increase staff productivity, search engine visibility and sales through increased cross-selling and upselling.
So, how exactly does AI drive the product data enrichment process? Below are five ways this can happen:
CLASSIFICATION OF PRODUCTS
Product categorization is essential for findability. It simplifies the process of finding relevant products when browsing search engines and e-commerce stores. AI contributes to product data enrichment by introducing sophistication into the categorization process in many ways. For example, Natural Language Processing (NLP) can scan large amounts of text, photos, and graphics and use keywords to place products in the right categories.
Similarly, machine learning and clustering algorithms can categorize products using labeled and unlabeled datasets. Ultimately, AI can break down large product catalogs into apparel, food, smartphones, and several other categories in a short amount of time and with high accuracy using one or more of the above techniques.
MATCHING PRODUCTS
AI can use similar natural language processing, machine learning, and clustering techniques to match similar products. Therefore, it becomes easier to cross- and upsell through relevant product recommendations. The AI solution uses attributes to suggest products that complement each other or have striking similarities. So, with this kind of product data enrichment, companies can offer personalization while improving the customer experience.
KEYWORD EXTRACTION
Shoppers need keywords to find relevant information on search engine results pages. Since keywords are crucial for prospects discovering products online, keyword extraction is important for product data enrichment. The extracted keywords and phrases can be used to build metadata, product descriptions or product categories. But how do you extract keywords from hundreds of megabytes of text?
Well, AI has several techniques that can solve this problem. Ecommerce brands can train their AI to use NLP or ML based techniques to find important keywords. Advanced extraction methods such as Term-Frequency Inverse Document Frequency (TD-IDF) and neural network-based models can also be useful. TD-IDF determines keywords by considering the frequency of a word throughout the document. On the other hand, neural network-based models use deep learning methods to identify keywords. Regardless of the technique used, AI can be trusted to facilitate the keyword extraction process for product data enrichment.
EXTRACTION OF PROPERTIES FROM IMAGES AND VIDEOS
Images and videos are rich sources of data and AI can efficiently extract the necessary information. Using computer vision techniques, AI can search through thousands of photos and videos, identifying product features along the way. This may include features such as color, size, texture, and warranty information. The information obtained can then be used to update product descriptions, perform product matching, and make product recommendations.
GENERATION OF PRODUCT DESCRIPTIONS
Instead of using AI to extract information for product descriptions, your company can entrust the entire task to the AI. ChatGPT, the recent AI tool that is creating a buzz in the market, will be perfect for this method of product data enrichment. ChatGPT and similar AI solutions can generate accurate product descriptions with the necessary keywords. The AI model naturally requires large datasets and extensive training to perform this task well. Once these requirements are met, the machine begins to produce excellent descriptions with minimal human supervision.
In short, the product data enrichment is a task that every e-commerce company has to perform these days. There is high competitiveness in the market and enriching the product database is an important step to stay ahead of business rivals. Artificial intelligence can accelerate the path to better customer experiences and greater market visibility through its remarkable data processing techniques. Therefore, companies looking to expand and scale should consider introducing AI into their long-term product strategies.
