Contextualizing Customer Data

With over 80% of companies adopting hybrid/WFH policies, office demand has become highly variable. This variability is driven by factors like company policies, commute realities, and work types. While opinions abound on the future of work, objective data is our most reliable guide. To navigate this evolving landscape, we must analyze each customer's data and understand their specific needs.

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To ensure we're all on the same page, let's define some foundational metrics.

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• Occupancy: The number of people using the workspace at a specific time.
• Capacity: The total number of individual workspoints available, including desks, focus rooms, and phone booths. This is a key metric for calculating utilization.
• Occupancy Cost: The total annual cost of leasing a space.

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Introducing REKalibrate’s Demand Curve

To truly understand customer data, we need to illuminate "Dark Data" – siloed and inaccessible information – and connect disparate data sources for a complete picture. Our proprietary Demand Curve fuses Occupancy, Capacity, and Occupancy Cost data into a single, dynamic view.  By transforming the traditional calendar-based view of utilization into a demand-ordered series, the Demand Curve simplifies complexity and reveals underlying patterns of demand across any given period.  This approach consolidates three key data sources that are often siloed, providing a more holistic and insightful understanding of workspace utilization.

Transforming demand data is just the first step. We then enrich this foundation with additional layers of data. This multi-dimensional view provides a richer understanding of occupancy data and its implications for optimizing space requirements.

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Understanding the Demand Curve

The Demand Curve visualizes key metrics to reveal occupancy patterns and cost implications. Here's how to read it:

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Primary Y-Axis (People & Capacity): Shows the workspace's total capacity as a horizontal line, along with daily peak occupancy levels. In this example, the line at "120" represents a capacity of 120 individual workpoints.

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X-Axis (Time): Represents the observed period in working days, typically one year (245 days), excluding weekends and holidays.

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Secondary Y-Axis: (Occupancy Costs): Displays the annualized cost of the workspace. In this example, the Occupancy Cost is $3M/year.

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Demand Curve Interpretations

The colors on the Demand Curve represent different aspects of space utilization and cost:

• Dark Green: Represents the Cost of Unused Capacity. This is the difference between the total capacity and the peak occupancy, indicating cost of space that was never used, relative to total cost.
• Blue: Represents the Cost of Utilization. This is the cost of space consistently used every day, relative to total cost.
• Light Green: Represents the Cost of Under-Utilization. This indicates the cost of space that was used at least, but not every day relative to total cost, suggesting potential for optimization.

Real-World Examples: Analyzing Occupier Behavior

Let's examine the occupancy patterns of three real-world (anonymized) occupiers that REKalibrate has analyzed and uncover the valuable insights provided by their data, starting with how each customer utilizes their space daily and then broadening our perspective to understand long-term trends and space utilization.

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Each customer exhibits unique occupancy patterns throughout the week, reflecting different workplace strategies and preferences:

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• Customer A: Shows consistent office usage throughout the week, with a slight dip on Mondays and Fridays, suggesting a general "work from the office" policy.
• Customer B: Exhibits a strong preference for Tuesdays and Thursdays, with some Wednesday activity and minimal usage on Mondays and Fridays, indicating a "come in 2-3 days of your choosing" policy.
• Customer C: Uses the office exclusively on Tuesdays and Wednesdays, reflecting a company-wide mandate for in-office presence on those days.

While analyzing daily occupancy trends can reveal valuable insights for week-to-week operations, it doesn't provide the comprehensive understanding needed for long-term real estate procurement decisions. To gain a broader perspective, we need to expand our time horizon and examine utilization patterns over a longer period. This is where the Demand Curve comes in. By visualizing occupancy trends over time, the Demand Curve reveals a richer story about each occupier's space utilization and potential needs.

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Analyzing Occupancy Efficiency with Demand Curves

The Demand Curves above provide valuable insights into how efficiently each occupier utilizes their leased space:

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• Customer A: Exceeds their designed capacity for approximately 20 days per year, with peak utilization surpassing planned capacity. They demonstrate minimal underutilized capacity.
• Customer B: Peak occupancy aligns with their space needs, but they exhibit a considerable amount of underutilized capacity.
• Customer C: Shows significant room for optimization, with peak occupancy falling far short of capacity. This results in a substantial portion of unused capacity and a considerable amount of underutilized capacity.

This broader view of trends over time, including seasonal variations and sustained shifts in usage, is critical for understanding customer decision-making around leasing, expansion, or optimization.

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Looking Ahead

Mastering Demand Curve interpretation takes practice, but getting there unlocks a wealth of insights into customer data. Join our next whiteboard session to see how these insights apply to real-world scenarios. We'll explore how to leverage Demand Curve insights to derive comparable efficiency scores for each occupier and how these measurements can inform future transaction and product forecasting. We'll also delve into how leasing, customer success, and asset management teams can use these insights to deliver compelling value to customers.

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