Product data to ease the navigation on e-Commerce platforms

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When developing an e-Commerce platform, there are three basic goals driving the strategy: attract customers (SEO), retain them up to the buy action and of course, convert visits into sales.

This article will focus mainly on the navigation/product selection step, from landing page to buy button, which is often measured by the “bounce rate”.

It seems this topic has already been tackled and analyzed deeply, but we usually miss one important aspect: the impact of unstructured product categories and product data model.

Unstructured product categories are also generating bounce

Weak product categories

When I navigate on an e-Commerce platform, I often struggle to find in which product category to find the product I am looking for. Several pain points in the mega-menu can generate customer dissatisfaction:

  • Product categories with unclear scope:
    For example, I want a surge protection device so should I look under “electrical protection device” or under “electrical safety device”? Both could work.
  • Too generic product categories:
    For example, if I want tires, I expect to find a dedicated “tires” category and not find them under “Accessories”.
  • Some product pages are not linked to any product category:
    Often, I was only able to find a product by searching with the product reference as the product page was not linked to any category.

All these issues point to an unstructured product hierarchy that a thorough work on the product catalog’s structure along with a solid product mapping could solve.

Filtering capacity or how to narrow down a product list

Once I have chosen a product category, I come across other navigation difficulties related to the same lack of structure and product mapping. Indeed, many e-Commerce platforms include too many products under a single product category.

So how can I easily find one specific product? If by chance, there is an efficient product filter or a product selector, I might be able (if I am patient enough!) to reach the correct product page to then eventually buy the product.

But in reality, performing product filters on e-Commerce platforms are quite rare. They often include inadequate characteristics or incomplete ones (I would still get 5,000 references despite having ticked all filters!). This is causing huge customer dissatisfaction with many visitors leaving the web site abruptly.

One solution (among others): product data standard   

Over the past 20 years, several initiatives were led by companies (energy, automotive, chemicals…) to standardize product classification and product description. In electrical distribution, ETIM standard was set up along with eCl@ss to address Industry 4.0 and BIM needs. eCl@ss aims to cover all kinds of products (not only electrical ones).

Beyond the value brought by a unique and shared way of classifying products and describing their characteristics, it appeared to us, at Schneider Electric, that both standards could be valuable for e-Commerce platforms. This applies not only for product categories but also for product filters.

  • For customers

The first value of having these standards applied is to help the navigation by providing product categories which each have a clear definition (no overlaps), a limited scope and a solid product mapping.

By describing similarly all similar products (using the same date model), these standards are allowing the creation of a very performing product filter just by putting each characteristics of the data model as filter criteria.

As a result, the product search is simplified, and customer experience consistency is improved. Both are major benefits for the customer.

Not convinced yet? Then please continue reading.

  • For e-Commerce platform

Obviously, the customers’ interest goes in hand with the interest of the e-Commerce platform. The more satisfying the customer experience will be, the more the platform will sell. Nevertheless, applying a product data standard also simplifies complex data management issues by:

  • Measuring product data completeness and quality
  • Facilitating product data maintenance when adding a new brand or a new product category for example

Of course, if all e-Commerce platforms are using the same data standard, in the same way and for the same brands, it will undermine their competitive advantage. But this won’t happen overnight as we know companies are not addressing digitization through the same strategy.

  • For manufacturers

As a manufacturer, we see, at Schneider Electric, a real value to simplify data models as we have been creating a lot of data in the past in many different data models to fit to the needs of each distributor and software company.

Rationalizing data models in the frame of those standards has generated big wins among which a clarified data strategy, more efficiency as less data has to be created, so overall costs savings in the long term.

It also helps to review the product portfolio and adapt the marketing strategy towards offer gaps.

Of course, creating product data under specific standards is a huge effort but it is without doubt key for us to compete in the race of e-Commerce!

To conclude, product data standards on e-Commerce platforms can positively impact the online customer experience by improving the navigation and the product selection. Of course, it requires efforts from both manufacturers for data creation (and maintenance) as well as e-Commerce platforms. However, it is also necessary to build solid foundations to address the next generation’s digital needs: AI, personalized content, omni-channel, SaaS.