The retail property landscape is constantly evolving. As disruption occurs, conditions shift, and audience mixes change, it’s important for retail property groups to have the right tools to make the best decisions. Data can help leasing managers solve almost any challenge these retail shifts bring and help aid in retail property business decisions, especially when it comes to leasing. Data can help provide insights on leasing decisions associated with tenant mix, rate adjustment, and tenant store positioning to name a few.

Because the amount of data available is so substantial, it’s important to harness it in a platform that can ingest multiple datasets at high volumes and provide both analysis and action. Then, the ways that data can help retail property managers improve, innovate, and be proactive are vast. Here are the top datasets we believe are the most helpful in aiding retail property managers with leasing decisions.

Market and trade area analysis

  • Government industry data – It’s important for retail property groups to consider historical government industry data and trends to aid in leasing decisions. This data helps determine the growth of various industries and predicts future performance of tenants. It helps to not only choose the best retail mix but ensure your tenants are able to pay rent in the future.
  • Traffic and transit data – Google directions API data can determine how far your target audience must travel within a specified area to your retail outlet. It can also distinguish which competitors or alternatives are most convenient within that same specified area. This data helps retail property groups understand which brands will effectively acquire customers based on travel times (convenience) and trade area. These brand options will modulate with travel time based on hour of day, day of week, and week of year. Changes in infrastructure can also impact the target demo’s retail options long-term.
  • Transaction data – Retail transaction data can help evaluate all types of spending patterns across a trade area. This helps retail property groups understand a retailer’s affinity which can help determine the right brands that fit the desired growth and spend. In addition, trade area analysis can evaluate how your trade area is changing, how that affects the audience that is likely to visit, and what anchors and categories are attracting new audiences or extending your trade areas.
  • Cellular data (4G/5G) – Cell data helps retail property groups understand where a customer originated and the route they took to get to a retail outlet. Cell data can provide a customer’s geo coordinates which helps retail property groups understand the travel path customers take to get to their retail venues. This helps determine a retail property’s trade area.

In-venue behaviors

  • People countersPeople counters can detect people with a high degree of accuracy, and can help you measure visitor traffic to, and throughout key areas, in your retail spaces. When you understand and measure footfall, you can use this data to inform tenant mix and rental premiums.
  • Wifi data – Location data from sources like WiFi can provide an understanding of when and where customers shop. WiFi data can determine the destination of a customer’s visit and plot points of engagement within the shopper journey. This helps retail property groups create destinations of stores that can then share traffic with associated tenants. This sensor data can also determine customer behavior outside and across stores which helps optimize retail mix. Having the right retail mix to match a trade area is a tenet of successful retail property management. Data enables this by distinguishing how to position retailers based on their relationships to the target market outside of a store. This is shown to increase conversion to retailers and ensure success of those brands.

WiFi data can determine the destination of a customer’s visit and plot points of engagement within the shopper journey.

  • Retail spend – Retail spend data can help a retail property group understand the category of retailer customers are shopping at and the type of products purchased. This data can determine the most popular and best performing retail categories which helps inform the optimal tenancy mix.

Customer feedback

  • Surveys – If you correlate when and where customers shop with sentiment data captured via survey, you can understand how satisfied customers are with their individual experiences. This provides a deep understanding of both category and retailer performance. Surveys delivered through a WiFi captive portal or a digital touch screen provide a low cost and scalable way to capture customer demographic, geographic, preference, and satisfaction data. This survey data can also help you understand perceptions and attitudes toward retail brands and identify the best options for future tenant inclusions.
  • Public forums, like Google reviews – Google review analysis can help evaluate how satisfied customers are with tenants within your asset and how they feel about competitors as well. This data can determine which tenants are engaging well with your audience.

The importance of expertise and a data platform

We believe these datasets are the most helpful to aid in leasing decisions. When you take a holistic view of these data sets and the analytics they provide, you will be empowered to make smarter leasing decisions. The key is to house data in a platform that turns it into actionable analysis enabling you to be proactive and better informed.

It is also important to understand that most data-driven decisions are coupled with human expertise. An expert on leasing negotiations is an integral piece of the equation that can help drive more informed decisions. Qualitative data like talking with retailers is also a key piece of information that complements any data source.

Interested in learning more?

At Skyfii, we’ve developed a leasing performance model that analyzes tenant performance and generates a consolidated dashboard to support leasing teams on a range of use cases. The model provides recommended rental rates, performance forecasts, and projected rental optimization opportunities based on actual leasing contract dates. It’s designed to help you get better insight from your existing data assets instead of going through third party sources. Interested in seeing how the model can help your retail property business? Request a free demo.