The following is a guest post from our partners at RetailOps
As a marketplace and omnichannel retailer, your product titles and descriptions – next to price and reviews – are two of the strongest assets you have to win over customers. Better marketplace visibility gets you in front of customers. Great product descriptions are your opportunity to own the customer experience by digitally putting your merchandise in the customer’s hands.
Handcrafting detailed product descriptions and optimized titles can take a LOT of time, especially if you sell across multiple marketplaces. This manual effort is neither scalable nor recommended. To scale and automate you need great product data. Having great data and knowing what to do with it allows the most successful marketplace retailers to manage large and varied catalogs across website storefronts, Amazon, eBay, and other available marketplaces to outsell the competition with ease.
Great product data is marketplace agnostic, factual, and granular. Once a system for your data is set up properly and a good process is in place in your organization, it’s easy to maintain.
To get started you need the right tools to clean your existing data, the right processes to collect great data going forward, and the knowledge of what great data looks like; the last of which we answer for you here with these 5 tips:
Keep the data factual and descriptive
This is the key to keeping data clean as well as making it easier on your warehouse team to accurately receive new inventory. Long-term, factual and descriptive data will limit obscure descriptions which lead to duplicate entries, bad data, and ultimately the inability to effectively scale across marketplaces. An example we see time and again is recording material, dimensions, texture, or other details about a product in a single description field. This is often done as there is no other place to put this important product information. The issue is that this data, being recorded in a single field, is not flexible to populate into distinct fields for filtering on a storefront or refining in a marketplace to get your product found.
Granular data is good data
Data is best broken down to its most atomic and factual level. That means not looking at data in terms of what fields marketplaces are asking for and what you’d like to populate in them, but rather creating data based upon the common components in those fields and recording facts. At RetailOps, we often use the analogy of ingredients and recipes to describe the difference between data and the fields they may eventually populate. If your ingredients are kept granular and pure, there are more recipes you can use them for. Conversely, if all your ingredients (data) are packed into a single frozen dinner (description field) – that’s the only real option you have. To give an example of a frozen dinner vs individual ingredients, let’s look at the Amazon listing below:
The product title listed in Amazon is “Nike Men’s Lunar 306 Running Shoe.” Rather than having all this data in a single data field, granular data practices would instead give you the following attribute fields:
- Brand: Nike
- Gender: Men’s
- Style: Lunarfly 306
- Product type: Running Shoe
Think of your listings as recipes
Going from granular data to great product listings and descriptions at this point is easy with a Feed Editor like that in RetailOps or a bit of Excel Wizardry. In the above listing, ‘Product Title’ is a recipe composed of ‘Brand’ + ‘Gender’ + ‘Style’ + ‘Product Type’. Two big advantages come into play at this point:
- Product listings for any marketplace can now be easily optimized and improved by simply changing your ‘recipe’ instead of updating countless lines of product data.
- Product titles, and other descriptive data can be easily tailored to the needs of each marketplace individually.
Let’s take a look at the same shoe above, but across additional sales channels:
Website: ‘Brand’ + ‘Style’
Amazon: ‘Brand’ + ‘Gender’ + ‘Style’ + ‘Product Type’
Ebay: ‘Brand’ + ‘Style’ + ‘Custom description’ + ‘Color’ + ‘Lace type’ + ‘MPN’ + ‘Gender’ + ‘Country size’ + ‘Size’
Design your data for where you are going/growing
If you know your company is headed for cross-border sales or expanding into new product segments, begin considering how your data can be structured now so that you’re not hamstrung expanding your business due to poor data structure when everything else is ready to rock. One tool we’ve found useful in RetailOps to provide flexibility and remove the need for extensive data planning is a concept of Meta-Fields. These are additional fields which can be can mapped to an attribute value and used within specific marketplace data feeds instead of the original value, effectively making your product data dynamic. As an example, this is an easy way to go from imperial measurements to the metric system or simply translating colors from English into Spanish or Japanese characters. RetailOps’ Feed Editor drives selected sets of data to each marketplace individually, allowing you to easily craft your product descriptions by marketplace to match customer audience – maximizing sales.
Get your data right
Data errors are some of the hardest to catch and can cause a product to go unseen to the intended customer, due to mixed up search and filtering results. Example: a size 7 men’s shoe and a size 7 women’s shoe are two separate facts according to most search results. How do you prevent this mix-up? Though human error can be a challenge to eliminate completely, a good PIM can greatly reduce this error by putting the right information in front of your receiving team to ensure they get your data right. This is done by showing only Men’s sizes when the gender attribute is set to “Men,” and only Women’s sizes when the gender attribute is set to “Women.” In this way, receivers can still do their job quickly while still maintaining data accuracy and search results.
An omnichannel strategy presents an amazing opportunity for sellers to put their products in front of more customers and maximize selling opportunities, but the unique nature of each marketplace, how they rank listings, and how they may change those rules makes the process more complex than simply copying and pasting into the new channel. Great data gives retailers a competitive edge, which they can control and use to outsell the competition across marketplaces and borders.
Want to learn more? RetailOps and Wiser are co-hosting a webinar to help retailers learn more about how they can get the most out of data.
About the Author
Sam Moses is the CEO of RetailOps, an enterprise-level ERP providing small to mid-size retailers the tools to help them scale from multi-million dollar operations to $100+ million a year businesses. From inventory-in to order-out, RetailOps puts all the tools a growing retailer needs to run their business on one platform.