Product data is like the lifeblood of ecommerce. When customers can’t physically interact with products, they need a lot of information to compensate that lack of knowledge and security in what they’re purchasing. Data are the building blocks of product listings on sales channels and adverts on marketing channels – if the data isn’t good, everything else is affected.
That’s what makes product data enrichment so important to get right. In this article we’ll explore exactly how to enrich product data for ecommerce, whether you’re creating ads on Google Shopping or listing your products to a marketplace like Amazon.
What does the customer need?
While there are hundreds of different channel options for brands to sell or advertise through, the needs of the customer on all of these channels are actually relatively similar, which is good news for your ecommerce or digital team.
Pretty much everywhere, customers are going to want to know what your product looks like, see how much it costs, read a description of it, and various other data points which depend on the product in question. These are always important and should always be a priority for enrichment and optimisation.
What does the channel need?
Unfortunately, while customers all want this data, none of the channels refer to it in the same way as any of the others – they all have their own formats and structures by which they accept this information.
So, a big first step in product data enrichment is all about understanding the channels you’re sending your data to, and knowing what attributes and field names they expect to see when you send them data.
Work on your data in one place
Unsurprisingly, data enrichment is much easier when you’re not switching between 3 different spreadsheets and several systems. It’s not just on an individual level, though – the whole team should be working from the same single data source. That way work never gets duplicated and it’s much easier to oversee what’s being done.
Target the most important work first
It’s an obvious-sounding statement, but it’s a lot easier said than done. Too often enrichment is seen as a pure bulk task, which has to be rushed through as fast as possible in order to list large quantities of products online.
While speed to market is vital, brands and retailers leave money on the table if they rush out poor-quality listings just to get online. Targeting the most important enrichments first (think about those titles, images and descriptions which most if not all channels use) and continuing to work through the long tail of optimisations over time makes teams significantly more effective and valuable.
Adopt new-generation ecommerce technology
Previous tools have been able to help ecommerce and digital teams to move their work out of the spreadsheet and into a graphical user interface. However, they struggled to actually automate the manual process of enriching product information.
Now new technology harnessing artificial intelligence can scan incoming product data and automatically enrich key elements, adding information by understanding relationships between products and constantly learning from every product.
Additionally, machine learning can make the first step of this guide massively simpler by allowing listing teams to focus on what their customer needs, rather than what channels need. It does this by mapping different channel structures into one master structure, so that the team can truly work on one set of data, rather than having to figure out how to label the information uniquely for each channel.