As a consumer there’s nothing more frustrating than spending hours deliberating over your new bathroom, only to find that your sink and tap choices aren’t compatible later down the line. Our product discovery service helps retailers manage complex product configurations, dependencies and relationships, meaning consumers can now choose and visualise a product and the accessories that match from the outset. Find out how our technology solves this complex retail problem below.
To demonstrate the complexity and benefit of a full product configuration solution, we’ll look at an example typical in the bathroom space. The diagram below shows two configurations of the same vanity unit, one configured with a countertop basin, and the other configured with an integrated basin:
This presents some challenges, namely, how can we describe this in such a way that we can determine which products are compatible with others, and can enable an interactive planning experience?
One approach to this situation would be to produce models of the countertop and integrated basin configurations. This could be represented as two rows in a database referring to two models, and their associated materials, in a content distribution network. However, with customers demanding increasing personalisation, and the proliferation of product lines, this is unlikely to be a viable solution long term — the amount of work required to model each configuration would grow exponentially as additional products, such as taps or worktop materials, come online. Conversely, a completely flexible solution, such as a rule-based system based on the physical constraints of products would become costly to maintain from a data perspective. Developers would need to maintain the rules, and precise physical measurements would need to be managed for each product.
Our goal for the DigitalBridge full product configuration solution was to find a balance between these approaches, enabling:
The power of this approach can be demonstrated by breaking apart the previous example into its constituent parts, shown below.
Full product configuration allows us to reuse the same set of models and data across entire product ranges. In this example, models for the vanity unit, integrated basin, worktop, countertop basin, and lever tap are built separately. Data is only needed to describe how the integrated basin and worktop attach to the vanity unit, how the countertop basin attaches to the worktop, and how the lever tap attaches to both types of basin. From this, our platform allows for the data-driven generation of any valid permutation of these models as a product. If we wanted to add another vanity unit, we can model it independently and describe where an integrated basin and worktop attach to it, and we can reuse the rest of our models and product data for free.
The DigitalBridge Product Discovery API is our solution to the problem of full product configuration. It helps to manage this complexity by breaking apart the product configurability problem into a few manageable entities.
These entities allow our solution to represent complex product configuration data an a way that can be consumed by our own offerings as well as directly integrated into customer platforms without the complexity of managing a rule-based PIM solution. As well as being the source of full product configuration data for our own offerings, it can also be used as a data source for recommending related products, or for powering interactive visualisations of product configurations inside a customer’s product catalogue.
Looking to the future, we’ve already extended the Product Discovery API to support the concepts of ranges and product roles. In the medium term, this means that our platform will allow customers to globally or selectively swap the products in their designs from one product range to equivalent products in another range.
Finally, we manage product data for our clients in spreadsheets that our automated data maintenance tools can interpret. These tools check the validity of the product configuration relationships and ensure that invalid data can never be released to the public. This means that our turnaround times for data deployments are in the order of hours, not days, but there is still room for improvement. Over the coming months, we will be working closely with our product teams and customers to discover workflows that can simplify the data maintenance process, and packaging this into a simple dashboard for customers to maintain their own product configuration data.