Where is the real value of your online retail store? It’s not necessarily in your SKUs or most recent collection that the customer sees, but in the product data that sits behind the scenes. The most innovative retailers understand that. They know too that collecting data is just half the challenge. Equally important is the ability to act on the intelligence to extract insight. Here’s how Lily AI can optimize your product data management and extract the full range of customer behavioral insights.
Why Product Data Management Matters
Good product data management achieves two important objectives. First, leading up to a purchase, granular product attributes allow the retailer to show the most relevant results for an individual customer based on their search. In particular, they provide the content that search engines such as Google and Bing will use to compose their ads and free listings automatically. And secondly, post-purchase, product data feeds valuable data back into the system to help with demand generation and inventory management. With a detailed view of what your customers are searching for and buying now, and how they feel about their purchases, you can better plan for what they’ll be searching for in the future. Broad, generic product data sets won’t cut it. Analytics applications that drill down into the granular detail, on the other hand, can improve the bottom line by up to 10% according to McKinsey.
What Does Product Data Management Cover?
A single item in an online retail store can generate multiple data points, all of which need to be managed consistently and efficiently. In that respect, Lily AI goes deeper into the data than any other platform, with a taxonomy of over 15,000 product attributes. Data points for a single dress, for example, could include:
- Measurements, sortable by filters and facets
- Product name and description
- Category; for example, summer, formal, luxury
- Key features such as style, color (again sortable)
- Unique catalog ID for inventory management.
Where data is visible to the customer, as in product descriptions, it has to be user-friendly and consistent. Where it is back-end data for inventory management or order processing, it has to be clean and structured.
Best Practices for Product Data Management
Any big data set delivers the most value when it provides volume, velocity, variety and value. These are unlikely to be realized if you’re just loading the supplier’s generic product descriptions into your PDM system. Organizing your product data into broad categories will ultimately frustrate your customers by serving them irrelevant suggestions. Here’s how to set up your product data strategy for automation:
- Prioritize your top-selling products. Any fashion store has a handful of products (the golden 20%) that generate most of the revenue (around 80%). Focus on these first to drive business growth.
- Take a customer-centric approach to product data. Brick-and-mortar stores can pile their products high and let footfall do the rest. Retail ecommerce, by contrast, has to leave a trail of breadcrumbs for customers to follow. That means using descriptions that trigger curiosity and resonate with a specific need, context or psychographic profile.
- Use enhanced product data to inform quick decision making. Every extra click a customer takes on your site is also an opportunity for them to abandon their search. By using granular product attributes and targeted product descriptions, you can help your customers find, sort and purchase products before fatigue kicks in.
- Test and optimize. With an intelligent automated platform like Lily AI, you’re not going to have to spend time managing product data. That leaves you more time to test and optimize based on the data insights received. Try different images, keep finessing product descriptions, alter the color palette or change the position of a button. Whatever it takes, be restless until the data shows an upturn in results.
- Stand out and differentiate with regular promotions, local listings, customer reviews and ratings. It’s not just about the products in your inventory. Customers want to see how other customers are reacting too.
What Optimization Looks Like
An online retail store is correctly optimized when it returns different suggestions and recommendations for two different visitors. That’s a sign of granular product attributes working to their full potential. By incorporating a range of personalization options, you can maximize customer experience. Ultimately, the idea is that when a customer searches, they don’t see what you’ve got. They see what you’ve got for them.
Because product descriptions and names are pulled through automatically by search engines, it’s important to lead with the key details too — such as brand, gender, promotion. Given that many listings will be truncated, especially on mobile, the most important features need to be visible first.
A consistent and well-structured information hierarchy also supports a better user experience. Arranging product attributes in tiers helps customers filter the options, compare them according to key criteria, and explore alternatives based on their initial choice. All these point toward an eventual conversion.
For instance: Shoes > Men’s > Leather > Formal > Brown > Oxford
Another sign of a well-optimized store is that data is synched and updated continuously. When customers reach the end of a search sequence to discover that a product is out of stock or comes with a high shipping fee, they are unlikely to return, let alone purchase. Since Lily AI assigns unique IDs and product attributes for each item, and automates corresponding data for price, taxes, shipping and so on, there are no frustrating surprises for the customer.
Finally, consistency matters. Product descriptions should be organized in such a way that exactly the same information (e.g., color, style, cut) is displayed from SERP to the landing page to checkout. When customers are invited to select from variants, they should be able to cycle between options smoothly without having to reenter their choices. That’s especially important on mobile, where customers are likely to abandon if their preferences are not saved.
Lily AI offers the retail industry’s leading customer intent platform for cleaning up your product catalog, personalizing your customer experience and making search relevant. That starts where powerful automation using artificial intelligence and machine learning meets deep, structured product data.
McKinsey - Success in the Apparel Industry Relies on Retail Data Analytics
University of Rhode Island - Data Analytics and Applications in the Fashion Industry: Six Innovative Cases
CGS - How Big Data Is Impacting the Fashion Industry