Acerca de

Please visit desktop site for full case study

Product Lists

Platform

Web, iOS, Android

Role:

Research, Design

The problem

Our customers tended to place multiple orders for the same kind of product week in week out as they completed their own specific types of jobs. This required them to search for each product over and over again when they wanted to purchase through maX.

Issues that this causes:

Having to manually search for every item they use
Items being forgotten from an order
Wasted time
Disruption to customers' workflow
Frustration with the maX platform

Research

Guerilla research

Guerilla research was done in our extensive branch network across 3 business units and 2 states to gain insights from customers in real time, in real life

Automated usability tests using Maze were conducted with customers on our research panel to ensure a smooth experience

Maze testing

Design

The aim of product lists was to create an experience that would enrich the lives of our customers and save them time on the repetitive task of ordering with Reece. Instead of having to search for highly technical named products multiple times a day, product lists facilitated the creation and use of repeatable "template" orders.

To help enhance this, we used a smart model to create and recommend lists for frequently used brands, categories and job types. These lists are strategically placed in high traffic areas and appear contextually to capture customer attention at the perfect time.

The web version was geared towards admin and staff that were less "on the go", incorporating more features such as printing as PDF, copying to other lists and generating quotes.

Substitutions

After careful analysis of usage patterns and sales data as well as customer and store research to back this up, we realised that customers usually create a list and almost never go back to update the included products, simply ordering again and again even if the product is no longer sold. This resulted in branches having to swap the item to another in stock item for the customer.

The model used for substitution data was driven by real time analysis of the swaps that were happening at a branch level, ensuring that our feature was echoing the expert advice given by our operations staff. As mistakes and false positive do happen, we allow customers to revert our substitutions if required.

Smart recommendations

Another solution to this problem came in the form of smart recommendations, using a smart predictive model to proactively suggest complementary products while customers shop in real time.