When your assets include 300 buildings in 75 countries, with over 250,000 occupants, even an incremental decrease in overhead costs translates into an enormous gain in productivity. WeWork knows this better than anyone.

The company recently invested in numerous data science initiatives to evaluate the use of space, heat, light, and sound in its hundreds of co-working spaces. What may seem like a meticulous chore (3-D scanning every inch of space before turning a single screw, for example) has already delivered a 15-20% cost savings — and given WeWork a strategic advantage over its competition.

Here are a few interesting ways WeWork is leveraging big data to create better processes:

  • Deal Flow: By using location data on a target neighborhood’s proximity to key amenities, including coffee shops, shopping, restaurants, hotels, and gyms, the company has drastically increased the speed of sourcing & securing properties.
  • Resource Efficiency: Unused space is another liability that WeWork strategically avoids. By evaluating space-usage data in current offices, its lowered the cost of adding a new desk by 33% in one year, according to Bloomberg.  
  • Customer Delight: WeWork is developing smart furniture that captures individual preferences and adjusts to each client (think standing desk height, room temperature, background music, etc).
  • New Enterprise Products to Fuel Growth: In the long-term, WeWork can position itself as an end-to-end service for office management for enterprise customers, its bread & butter clientele.

For WeWork, operationalizing its data is leading to faster deals, better business outcomes, and incredible growth. While some see their value in terms of square-footage, its real asset is in the business intelligence they possess from an increasingly sophisticated data set on how we, well… work.

So, how can you take a page from WeWork’s playbook to turn your data into an asset?

The answer is predictive analytics. Predictive analytics apply statistics and machine learning using historical data to predict behaviors and outcomes. If harnessed properly, companies can see into the future to guide product development, inform customer offers and promotions, and cultivate growth in new markets.

Below are some examples of how predictive analytics can be applied to your business:

  • Sales forecasting – Predict monthly and quarterly bookings based on historical conversion rates.
  • Fraud detection – Find inaccurate credit applications, fraudulent transactions both offline and online. Identity thefts and false insurance claims.
  • Campaign optimization – Model future campaigns targets based on deep analysis of customer behaviors, preferences, and profile data.
  • Marketing & customer analytics – Collect data from all customer touch points, and use the historical customer information to anticipate future needs and purchases.
  • HR analytics – Analyze key factors impacting productivity and employee engagement, including voluntary termination and absences, and identify gaps in skills and resources.
  • Risk management – Maximize return and minimize risks by yielding more accurate forecasts on investment and strategic initiatives.

To learn more, including process recommendations and steps to take to improve your success, get the Insider’s Guide to Predictive Analytics here.