Problem
The ML/AI platform is an AI driven price recommendation and insights platform to maximize profit and customer loyalty. Pricers and merchandisers need easy and intuitive ways to view price action recommendations, pricing strategies, and take actions on the pricing strategies. This would lead to maximizing profits and building customer loyalty.
Solution
How might we facilitate taking action on the price action recommendations for pricers and merchandisers, so that they can adjust price, change price, add a discount, and choose a pricing strategy at the FHC, Platform, Series, and Order Code levels, simply and intuitively.
Impact
80% Time decrease for price actions
- Decreased the time to act on price recommendations at the order code level, series level, platform level, and FHC level
- Increased the competitive advantage for various global pricing teams to sell and close deals by reducing the time to search for, analyze, and accept recommended price actions.
- Decreased the time to view and select pricing strategies at all levels.
Constraints
Design System not completely designed or coded, so we had to leverage the design system from AG Grid to speed up production and time to market.
The product team was in the initial stages of digital transformation, and as a design team, we had to help relationships to move them along that journey while proving .
Use a stakeholder map to map out the entire team that is responsible, accountable, consulted, and informed for the product at every level.
Hypothesis
We believe that global pricers and merchandisers need an easy way to act on price actions, which currenctly the process leads to time and revenue lost to implementing pricing strategy, and that if we created a fly in drawer so they can see, choose, and implement a pricing strategy, it would lead to global pricers viewing and implementing pricing strategies more quickly. This would decrease the cost of the time to implement a pricing strategy and increase revenue by allowing for a seamless way to take action on pricing strategies.
How might we. . . .
How might we facilitate price action changes for pricers and merchandisers, so that they can adjust price, change price, or add a discount, to take action on the price recommendations and apply them to the
individual order codes.
User Interviews
Initial user interviews were conducted with the black and white wireframe prototye created from the initial business hypothesis.
Interview's Transcription
Recorded user interviews where uploaded to Otter.AI for transcription.
Interview's Data Synthesis
The transcribed interviews were synthesized in a Khanban board to find the recurring themes. This was done to discover the most importnat things to prioritize and go build
A task flow was created to figure out the critcal path that would solve the information and benefits flow based on data from the user research and the main persona.
Design Artifacts
Initial business solution - invalidated.
- Fly in drawer to satisfy the price action need.
Sketches
Sketches on a whiteboard where created, in collaboration with business analyst, subject matter experts, developers, and project managers, implementing the initial hypothesis.
B&W Wireframes
Low resolution wireframes where created by the design team, in collaboration with business analyst, subject matter experts, developers, and project managers, to test, get feedback on, and solve for the wanted outcomes.
