Real-time Analytics
Lincoln Park Zoo needed a better way to show animals that are sometimes off exhibit to visitors while also replacing manual, siloed marketing reporting with a clearer view of performance and ROI.
Reported impact
The zoo created a new visitor experience for hard-to-see animals, gave marketers a unified decision dashboard, and saved roughly 50 hours each month by using machine learning instead of manual video review.
Lincoln Park Zoo used AI and analytics to solve two visibility problems at once: guests could not always see animals in their habitats, and marketers could not see a unified picture of performance across disconnected data tools. PwC helped the zoo pilot a machine-learning workflow that analyzes video and automatically creates polar-bear highlight clips for a visitor web app, giving guests a behind-the-scenes experience without disrupting the animals’ environment. In parallel, the team identified and integrated 10 marketing data sources into a Power BI dashboard so the zoo could replace manual reporting with clearer insight. The combined architecture pairs content automation with analytics, making it easier for staff to monitor engagement, explain marketing impact to leadership, and justify future funding. By connecting animal footage, digital content, and decision dashboards, the zoo turned fragmented information into visitor-facing experiences and faster internal decision-making.
Video from polar bear habitats is processed by a machine-learning model that automatically creates highlight clips for a visitor web app, while marketing and performance data from 10 sources is consolidated in Power BI. Staff use the dashboard to monitor engagement and leadership uses the same data to assess ROI and guide funding decisions.
50 hours saved each month using machine learning instead of manually reviewing video footage
Now, our marketing team has access to 10 different data sources pulled into a single dashboard.
PwC brought next-level innovations to the zoo, helping our team better use technology to help deliver on our mission.
PwC helped us use technology in smart ways, making it easier for visitors to see our animals and for our team to get the insights we need.
the PwC team built a machine learning model that automatically creates clips for a highlight reel that visitors can view from our web app.
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
The official Form 990 posted on lpzoo.org states 'Total number of individuals employed in calendar year 2023 (Part V, line 2a) ... 541.'
Related
PwC Sample Oct 9, 2026 PepsiCo PepsiCo’s Global Sustainability Office had to coordinate hundreds of processes and data sources, but forecasting and planning still took weeks and stakeholders needed faster, more consistent sustainability insight. 17 agentic loops designed across core process areas AI-enabled data management layer · agentic loops · governance framework 
Anthropic Sample Archived · Oct 4, 2026 International Rescue Committee IRC teams needed to analyze frontline health data quickly enough to influence staffing, outreach, and supplies, but the information came from ministry-run facilities in inconsistent paper and Excel formats and often had… 4+ hours per month saved by half of surveyed pilot users Claude Enterprise Anthropic Sample Archived · Sep 28, 2026 International Rescue Committee IRC wanted proof that AI could turn inconsistent frontline health data into useful local decision support despite electricity constraints, poor connectivity, and ministry-defined indicators that varied across facilities. 2,700+ health facilities across 40 countries Claude Enterprise 