Maps are so important to us that I’m writing about them again (see my earlier post).

It seems like everyone loves a good map. Maps help us get from one place to another. They help us visualise data and are a powerful resource for storytelling.

Today, with a nearly unimaginable amount of data available, and data decision making becoming the norm, we are building value by integrating actionable data sets into more sophisticated mapping.

Mark Benson, cartographer for Bee Media, is in charge of creating a flexible, fluid mapping environment that helps us develop more opportunities for our clients.

By flexible and fluid, I mean up-to-date (and updatable), interchangeable data sets that cross various levels of geography and data points. Data points that include competitor locations, opt-in customer locations, drive time to store buffers, and available localised digital or print media buys.

Some of the questions and important points we want to illustrate to our clients are:

• Where are my best customers (via segmentation) in relation to my location or locations?
  
  
• Where are they in relation to my competitors?
  
  
• Are there opportunities that I am not seeing geographically?
  
  
• How do I help our clients visualise our powerful mapping solutions?

Layers of data:

Integrated for revealing actions we can take on behalf of our clients:

And now, integrating publicly available data sets like parcel (home owner lot size) data adds an amazing amount of rich, detailed information.

Lot boundary files are available from your local county government offices.

Integrating parcel boundary and data files can add great depth to the knowledge you now have at an address level.

Each parcel is identified by a street address. Each parcel likely has other variables tied to it like:

• USPS address.
  
  
• Lot size in square feet.
  
  
• Year of last improvement.
  
  
• Land use.
  
  
• Square footage of building on parcel.

With the USPS address as the matching variable, we can also append a PRIZM NE lifestyle segment and data from our client customer files (proprietary to the client). And, how about adding a layer showing the address of homes that just sold and the amount they sold for?

Now, zoom in to a zip code of interest integrated with Google Earth for a natural view:

Turn on the land use layer and segment by Year Home Built:

Point out older and larger parcels:

Add another variable, such as Millennials by number in household (Nielsen household level data purchased):

And now, what if we added flags to indicate the addresses of homes that have just sold and for how much?

Many types of clients can benefit from such data views.

We can now segment by:

• Lot size by address (county government file).
  
  
• Square footage of structure (county government file).
  
  
• PRIZM lifestyle segment (Nielsen PRIZM NE, address level).
  
  
• Household income ranges (Nielsen address level).
  
  
• Age of head of household (Nielsen address level).
  
  
• Homes that have just been sold (MLS or local real estate information source).
  
  
• Integrate a client’s own customer file (PRIZM segments, amount spent, frequency of transaction, etc.).

Clients are very interested in seeing such data views and learning how you can be the source of information, strategy, and tactics that will help them use this data to build their businesses.

Just think about the types of businesses that would want to sit down with you and review information that can help them connect with target customers and sell more merchandise.

Nurseries and landscapers will want to know where the larger lots are, and those that have just been sold. What happens when a home is sold? The new owners will change things. They might look for new plants and redo the landscaping.

New owners are also more likely to buy new furniture, appliances, floor coverings, paint, windows, window coverings, etc. The larger the lot, the more likely the larger the budget. Knowing the neighborhood lifestyle (PRIZM) also provides background information useful for what message or method might work best.

Combine data layers of varying geographies, address-level point location data (client-owned customer file or target profile), and home sales transactions, and you have a lot to work with. A lot of valuable conversations that will result in business.

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