Plain Talk

In today’s big data crazy world, broad, macro level datasets such as demographics, psychographics, and business locations often get a lot of attention. Given the fact that this data was not readily available just a few decades ago, the enthusiasm for leveraging it isn’t surprising.

But in our rush to embrace the possibilities that macro level data presents, it’s possible to overlook the contributions that micro level data makes to real estate business intelligence.

Defining and Applying Micro Level Data
The terms macro and micro are used in many different contexts. For purposes of this discussion, we use the term macro level data to refer to broad datasets, such as those used to examine a trade area in real estate analysis. Macro level data is typically purchased from a third party. Micro level data, by contrast, refers to data not associated with the trade area – such as site characteristics. This data can be either qualitative (e.g. visibility) or quantitative (e.g. square footage). Micro level data is often proprietary and specific to your business.

Why does looking beyond the trade area matter? In short, the trade area is only one piece of the performance puzzle. Understanding and analyzing performance drivers requires identifying and quantifying, to the extent possible, all relevant factors. Micro level data variable relationships can be tricky to quantify and may not be continuous, linear, or monotonic, but often are relevant because they define real estate requirements. For example, a business may need a certain minimum square footage, visibility ratings, and parking space in addition to a strong trade area in order to be successful.

Unlike macro level data, which is most often aggregated and purchased from third party sources, most consumer-facing businesses have access to their own site characteristic and other micro level data. It’s an untapped source of intelligence that can help to explain the nuances seen in macro level data. And because micro level data includes variables unique to your business, it can provide a competitive advantage through insights that only you can access.

The Quality vs. Quantity Debate
For smaller businesses in particular, site characteristics and other similar datasets present opportunities to produce deeper levels of intelligence based on relatively small amounts of data. This is due to the fact that the quality of a dataset is often more important than its quantity. Yes, there needs to be a sufficient sample set for the analysis to be deemed robust and statistically reliable, but using a huge dataset yields no additional benefits if the data quality is poor.

Fortunately, the quality of micro level data is something that businesses can control. By following best practices for data collection, such as establishing a scoring framework and/or working with a neutral third party to collect data on each site, you can gather valuable information to enhance your business intelligence solutions.

Finding the Balance: Combining Macro and Micro Level Datasets
Ultimately, the contributions that both macro and micro level data make to business intelligence are strengthened when the two types of data are combined. There is no one-size-fits all solution when it comes to site selection analytics, so it’s important to carefully evaluate each dataset to determine which features are the most valuable predictors of performance and help the organization to answer its specific business questions.

Don’t allow your organization to be caught in the trap of big data vs. “small data.” Use both resources to aid in achieving your goals.

Learn more about using data in real estate intelligence

Buxton is the leading customer analytics firm that helps organizations identify who their customers are, where those customers are located, and the value those customers have to the organization.

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Fort Worth, TX 76137
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