The demand for retail real estate data is stronger than ever. Retailers, landlords and owners are all investing in numbers—from customer profiles to demographics, store traffic, conversion rates and social media metrics—to help make smarter, better strategic decisions. As the analytics and technology landscape constantly evolves, what can industry players do to ensure successful and sustainable data strategies? Below are a few key guiding tenets that we believe underpin successful approaches to big data in retail real estate:
Start With a Strong Platform
Just as important as the data sources themselves are where and how that data sits. Purchasing or subscribing to excellent data sets is near pointless without the right tools to store, access and analyze that data. Whether this platform is built in-house or outsourced, having a strong foundation that can assemble, visualize and share multiple data sources ensures access across an organization and its departments. It also, importantly, ensures scalability as new and more data sources are added to the mix.
Beware of Silos
When leveraging multiple data sources, the whole is always greater than the sum of its parts. Looking at different data sources in isolation often misses out on key insights that can be gleaned when information is cross-referenced. How, for example, does store or mall traffic data from vendor 1 correlate with social media activity delivered from vendor 2? And what can that tell you about the effectiveness of email or Twitter campaigns? Innovative data discoveries happen when information is cross-referenced and analyzed in new and interesting ways. That requires true synergy and integration between data sources, plus the dedicated brain power.
Don’t Forget Your Translator
Too often, we tap into great data sources, download the numbers and expect a brilliant insight to jump off the page. Though this can happen, it’s rare. The reason is this: Data itself doesn’t solve problems; it’s what we do with the data that does. When investing in data, it is crucial to ensure the proper resources are in place to translate that data into insights and, ultimately, smart business decisions. On the back end, this may mean data scientists and analysts to manage and process data (especially in the case of large volumes of information); on the front end, this means people or teams with the analytical expertise to turn the data into insights. These are the “translators” —the people with the skillset to interpret large and varied datasets, and transform them into what many call “strategic insights.” Many companies have invested heavily in data, but failed to budget or plan for translation. In these cases, all that data amounts to a pricey paperweight.
So, in conclusion, having data is great, and having great data is even greater. But remember that what you do with it and how you leverage it are critical to success.
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