The Price prediction platform is an AI SaaS solution that boosts revenue by optimizing the service or product price based on demand and supply. The app is backed by data science and AI which is perfect for Sports, Cinema, Live-entertainment, Parking, Attractions, and Ski Resorts.
Ticket price strategy
Creating the effective dynamic ticket pricing strategies
The AI Price prediction platform improved business pricing strategy. Results include:
Consisted of: Two product/UX designers, remote development team, product manager, business analysts, data analysts, and many more.
Ux management and hands-on UX design
LEAN UX/Agile product development. Back-to-back design-development sprints
Initial discovery and ideation, followed by ongoing with bi-weekly design deliverables based on Agile development sprints.
Project team based the discovery, input from customers, and initial hypotheses on existing e-commerce and marketing site data analytics. We mapped out customer behavior patterns and produced basic use cases. These helped us validate initial assumptions and ideate further.
Validation sessions with stakeholders, SMEs, and then lottery customers were performed both: in-person and remotely. The business had clear expectations and goals that had to be reflected within a few months of the redesign.
After use cases got validated and sign off, initial design ideas were presented in low to high fidelity wireframes, covering the initial responsive redesign concept. After signing these feature sets off were split into BTC which would allow us to explore and produce new designs in a more agile way.
Based on the findings and agile development cycles, the design team had to figure out the way to work in streamlined production. With a caveat that the final solution would achieve the success criteria set by both: the business and the customer.
I planned UX work to be split into two-week sprints consisting of LEAN UX-based discovery (with a remotely held SME, stakeholder and product team forum), ideation and production days, followed by validation and front-end implementation efforts to follow. For example, after the UX team was done with a design sprint, the development team would pick up the deliverables and start their development sprint.
After we reached agreed MVP fidelity to roll changes out to the general public the prototype was released to capture user feedback and tangible outcomes using data analytics.
Having performed focus groups across the business and involving the users we captured feedback that had only a few shortcomings.
With positive initial customer reception, we knew that analytics are going to show even greater engagement improvements.
The Price prediction platform proved to be successful in optimizing ticket pricing strategy for businesses. Having listened to the end-users and simply giving them what they truly needed we managed to not only satisfy their wants but also deliver enough value to the business. From the UX standpoint, this was also a learning opportunity to deliver great solutions within limiting constraints.
Analytics-based results captured 2 months after the go-live date reflected that:
+10% Increase in attendance during less-visited days
Increase in overall revenue
+40% Increase in share of online sales