Self-service analytics powers intelligent real estate investments

Self-service analytics powers intelligent real estate investments

Self-service analytics powers intelligent real estate investments

A leading Canadian commercial real estate developer wanted more accessible insights, and a deeper understanding of its market, competitive landscape, and consumer profiles, to enable quicker and more data-driven decision-making companywide.
Our team built a cloud-based and open-source one-stop shop dashboard application, intended to reduce the time spent on ad-hoc querying, improve access to data across departments, deliver deeper insights by way of advanced analytics and new metrics, and empower business users with true self-service analytics.

The value proposition:

More than 2 full-time employees in annual time savings. Better decision-making in investment planning, leasing negotiations and marketing initiatives yield the potential for tens of millions of dollars in revenue and investment gains.

Milestones and learnings:

A key milestone came in navigating through a variety of the company’s existing data sources, and tying the raw data to business outcomes in the form of user stories. As in many analytics projects, data discovery and cleanup were the backbone of our solution.
The creative process of determining which internal data sources to focus on, and which external data sources would help enrich the insights, was crucial. We initiated a clear line of communication with the client early on, iterating and building on an established feedback loop.

Our solution roadmap consisted of:

• Interviewing stakeholders to identify the current state, pain points, and target state.
• Assessing available data and exploring new data to enrich analytics.
• Cleaning and aggregating the data for analytics.
• Evaluating a variety of BI tools and analytical techniques.
• Building an open-source, cloud-based dashboard app.
• Embedding advanced analytics and creating new metrics and visualizations.
• Leading client workshops, receiving feedback and agilely actioning those learnings.
• Testing and documenting to ensure quality and encourage user adoption.

Technical and analytical environment:

• R, JavaScript, CSS and HTML programming
• RShiny cloud applications
• Fuzzy clustering
• Text analytics
• Social media engagement analytics