UX & UI
CITI-DPO
Deal Price Optimizer
WHAT
Price optimizer AI-powered price optimization software enables organizations to determine the best prices for Citi's services using highly detailed quantitative analysis.
Calculations consider various factors, from market conditions and competitor research to ongoing promotions and negotiations. The company’s targets and future expansion plans may also play a role in determining the optimal prices for its products.
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WHY
Quickly generate as many price scenarios and models as possible, baseline metrics, run analysis on guidance levels, competition rates, and current rates, and automate the price-setting process. It helps the sales team to predict a potent advantage during negotiations, and future revenue by current rates.
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SPECS
Team:
Bhakti Mai & Thomas Bill
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Duration:
One Month
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Tools:
Figma, Sketch, Invision, Tableau
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Skill :​
Task Flow, Wireframe, Interaction Design, Prototype.
Problem Description
Software used to manage pricing optimization relies on the power of data. The software analyses an enormous range of various metrics, and the most accurate calculations for its capabilities that drive profitability. Metrics are continuously monitored by pricing optimization software, with all analyses available for team members to explore in real-time. The software will then use these figures to determine the best possible pricing at any given time. To make a brighter look, we team redesigned the software by following Citi's style guidelines.
Task:
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Understanding Mainframe, Underlying Performance & Database systems.
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Sympathizing teams' future needs.
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Designing for the components of Dashboard.
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Learned about the database, performance, notification system, pull/push API's requests.
Ideation
We started with the basics workflow, where how users are using Optimizer systems. We focused on the problem and explored new possibilities to presents data and generate reports.
Task Flow
We started the project by reviewing all the known requirements and old DPO software with my Product manager & Developers. We began examining the common onboarding issues. We performed task analysis and defined the flow, where we made a solid line for the happy path and dotted lines for the optional path.
Wireframe
We draw our ideas on paper and convert them into wireframes. Later, we created components for the low-fidelity prototype. We iterated the DPO tool design based on insights from the product manager's feedback, as well as new requirements for exceptional cases.
Baseline Metrics
Interactive Charts
My Scenario Draft
High-Fidelity Prototype
We created a high-fidelity prototype and conducted usability tests with the Sales team. Once we had high confidence in the new design, we began implementation. We then made countless tweaks to the compliance onboarding flow after its launch to accommodate unique use cases.
Outcome
The compliance onboarding redesign decreased the time to become compliant by the sales team. Before the redesign, the majority of team members needed help from developers to complete the process, and unique situations were onboarded offline. The redesign helped us to serve the product manager and sales team that were previously unprofitable due to confusion in the tool.