Data is in the center of business. From tracking buying habits in the retail world to predicting fraud in the banking world, data scientists are figuring out the best ways to extract the most value for their customers and companies.  

Gone are the days where it’s okay if the only data your company collects is descriptive, i.e. name, number of site visits, number of purchases, etc. Instead, data must now take things at least a step further and go from the “what” to the “why”, i.e. customer A visited our site because they were on site B where they clicked on our ad C.

However, in most cases, this diagnostic data is still primitive and begs for more. To build the most competitive moat and differentiate from competition, companies must go 2 steps further, to predictive and prescriptive analytics. Predictive analytics is when you know X, you know that Y is likely to happen next. For instance, because a customer purchased flooring, she is most likely remodeling her home and therefore needs to look at kitchen cabinets as well. Prescriptive analytics is when you figure out what you need to do to get to the outcome you want (or, in many cases, what you need to do to avoid the outcome you don’t want). For example, because of many factors, such as age of pipes and weather conditions, a smart device has predicted a leak in pipe A. Analytics software has a prescription to this problem: an automatic push notification is sent to a plumber to fix pipe A on Monday before the leak happens.

Let’s go through a few more examples of some ways real estate technology companies have been leveraging big data to cut costs, maximize profitability, and provide the best service to their customers:

  1. Construction companies collect field data to predict and prevent design and safety issues.
  2. Project Management companies collect data to predict problems before they happen, and therefore cutting replacement costs for building owners.
  3. Hospitality companies collect data to predict occupancy, enabling them to price their rooms to maximize profitability.
  4. Furniture companies collect data to predict customer preferences and buying patterns, therefore spending less money on sitting inventory. 
  5. Brokerage companies collect data to predict the market dynamics and negotiate the best terms for their clients.

If a company doesn’t have any intellectual property, what is preventing fast followers from copying its business model and eventually providing a better service to its customers? For this reason, it is important for entrepreneurs to think about the best ways to extrapolate data and use it to their advantage to build the best possible company they can.

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