Experienced consultants James Gurd and Paul Rogers discuss why source product data often isn’t fit for purpose for an ecommerce site, giving advice on how to identify and plug data gaps, and use the data effectively to deliver CX and efficiency wins.
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We recently talked about fixing ISNO issues for ecommerce sites using smarter automation with the team at Sweft. In this episode we’re diving deeper into product data and looking at how ecommerce teams can enrich base data to improve ecommerce trading and operations. It’s an area that doesn’t always get enough attention during site migrations, and has platform specific considerations as well.
We explore why source product data is not always fit for ecommerce needs, the impact partial data has, tools you can use to plug data gaps and ways to add richer data to product records, and the operational benefits this has.
Key discussion points
- Let’s start at the beginning: why is source product data not fully fit for ecommerce, what gaps do you usually find?
- Impact on CX
- Impact on direct channel
- Effectiveness of marketing
- Who should be responsible and how should you approach?
- Should you just bring in PIM and / or headcount?
- Or should you try and drive change and work with stakeholders etc.?
- What impact does partial product data have?
- Inaccurate search, SKUs not being indexed for relevant queries
- Insufficient use of filters: people can’t refine the product grid based on what’s important to them, or some SKUs don’t show up when they should
- How should this be approached when replatforming?
- What tools can ecommerce teams use to find product data gaps?
- External search engine keyword research as a proxy: what language do people use when searching for your categories and products
- Q&A search: are there product or category related questions you don’t answer right now? – can identify FAQ snippets to enhance PDPs
- Onsite search: what queries don’t you handle well or have zero results for?
- Customer enquiries / reviews: what issues does the Customer Service team have to deal with, what do customers want to know?
- VoC feedback loops onsite: what are customers saying
- How can we add richer data to product records?
- Attribute data: structured data in name_value pairs with primary aim to drive filtering and smarter search + enable list based content on the PDP to reduce long paragraphs. Example where attribute data has had a big impact: wine company adding ‘vegan’ as attribute rather than in the title due to character limit, indexing attributes for search = incremental sales
- What can custom metafields be used for? e.g. adding downloadable documents to a product, displaying environmental credentials with origin data, tech specs (essential for electrical), add a promotion countdown timer
- The value of tags & what a good tag taxonomy looks like e.g. use of tagging to enable automated processes e.g. dynamic categories/collections based on tags so new products are auto-allocated
- Adding shipping instructions; display custom info based on product attributes/tags e.g. items that can’t ship internationally, or which require 2-person delivery
- Now let’s move on to the operational side, which is where we think a lot of opportunity lies:
- Order management: for example, if a SKU has to be routed to a specific carrier due to delivery restrictions, prioritise specific orders based on custom fields (a customer of mine does this for Express shipping as WMS recognises the field)
- Customer segmentation
- Personalisation: great example is a food company displaying a custom metafield for customers it knows have a food allergy, asking them to check their order before purchasing for the ingredients they’re allergic to
- Customise order records: automatically add comments based on fields/tags that will help the warehouse e.g. contains fragile items, which have specific packaging needs
Want to suggest a topic or guest for a future episode? Contact us via the website or on LinkedIn.
