Responsive Product Vision
Concept, Flow, UI Design, and Interaction Design
The Concept Car
To help Infor Retail's partners and prospective clients envision how the product suite would look when released, an example of its ideal future state, or a "concept car", was created. The prototype and animated video not only showcase how the product could look, feel, and function, but also how its intuitive experience will streamline the workflow of its users.
This concept car demonstrates the full lifecycle of an assortment plan across all user roles, for both the Apparel and Grocery verticals.
When creating a new assortment plan, users start by gathering their ideas in a shared mood board. Since inspiration can strike at any moment, this mobile version of the application enables users to quickly document their findings on-the-go. The communal platform fosters collaboration between users, streamlining the formation of the new assortment plan.
For each vertical, users are unrestricted in the type of content they can post. Each submission is then marked with the contributor's initials.
When inspiration hits, this interface empowers users to capture their thoughts instantly and add them to the mood board. Since users can be contributing to more than one assortment plan at a time, they can easily select the relevant mood board to add their submission to.
With the features of adding geolocation and tags, users can provide additional context to the post as well as start to uncover themes for the assortment plan.
Determining the Assortment
Next in the process, users refine their plan by dragging the various styles into one of the three available categories. There is an ideal number of styles to include and a set budget to spend, both of which are being tracked at the top of the screen in real-time.
When users tap into one of the pictures, they will find additional information as well as historical performance data on the tagged keywords to help better inform their decision.
Reviewing the Plan
With a draft assortment plan determined, users now need to check that its projected sales meet the category goal. This also gives them a chance to see their assortment as a whole and how it fits in with the other assortments in the category.
Leveraging Machine Learning
For guidance on how to optimize their assortment and meet their category goals, users can turn on a Recommendation Engine, which is supplied insights by an underlying platform of machine learning and predictive and prescriptive analytics.
Once activated, the Engine will automatically filter potential supplemental products by the keywords it deems will provide the necessary additional value to the assortment. Users can then select a solution from these proposed options.
Analyzing the Numbers
In this pivot table, users examine how the detailed financial and inventory numbers break out over the lifecycle of the assortment.
Users are given full flexibility to adjust the table to meet their needs. They can filter what data is shown, input additional plan metrics into the y-axis, or add a z-axis of potential products.
Evaluating & Optimizing Performance
Once the assortment plan has launched, users are able access a comprehensive overview of how the products are selling. Users can evaluate how an individual product or an attribute is performing relative to the rest of the assortment or take a deeper dive into the data of a specific product to see if it is meeting its sales goal.
If a product is found to be underperforming, users can remedy the issue by either implementing adjustments or removing it entirely for the remainder of the cycle.