GEO Target the US? The Myth - The Reality

I started working on a database to do with income, costs of living, property values, building permits (as well as data to be added) in the 20,000+ GEO segments in the USA for the purpose of geofencing ads for a few projects I am working on. Micro segmentation ...

CONCLUSIONS:
My indexes are showing a variation of .068 to 7.5 of the values of different US geographic local areas

By advertising ANY item (that is not massive adopted [like food, clothing, smartphone cases]) you are wasting at least 2/3 of your ad spend when you target the 300+ millions of US Americans that you may think are such a lucrative market.

Even with Facebook ads or SEM PPC -- unless you are segmenting the geographic elements within the US Market <<< you are wasting a lot of money on people that will never convert.

You could segment low income areas for items that might appeal to them -- this is not limited to a certain segment.

Do you live in Europe? Would you advertise to the EU as a block and unsegmented by nation or language -- of course not! That is what many are doing in the USA GEO currently.

 

I'm designing a database with an algorithm -- one factor is an approximation of spendable income by zip code.

This could be used inversely also -- poorer area tent to buy certain products and services too -- the spending patterns are different.

I am starting to work on database unions and joins

mysql> SELECT
-> zip_names.zipcode_nb, zip_names.state_iso, zip_names.metro_area,
-> zip_lat_long_usa.latitude, zip_lat_long_usa.longitude, zip_lat_long_usa.county
-> FROM zip_names
-> INNER JOIN
-> zip_lat_long_usa on zip_names.city = zip_lat_long_usa.city
-> WHERE zip_names.city REGEXP 'College' and zip_names.state_iso ='TX';
+------------+-----------+-----------------------+-----------+------------+---------+
| zipcode_nb | state_iso | metro_area | latitude | longitude | county |
+------------+-----------+-----------------------+-----------+------------+---------+
| 77845 | TX | College Station-Bryan | 34.709342 | -92.228271 | PULASKI |
| 77840 | TX | College Station-Bryan | 34.709342 | -92.228271 | PULASKI |
| 77845 | TX | College Station-Bryan | 30.582241 | -96.289328 | BRAZOS |
| 77840 | TX | College Station-Bryan | 30.582241 | -96.289328 | BRAZOS |
| 77845 | TX | College Station-Bryan | 30.57258 | -96.327044 | BRAZOS |
| 77840 | TX | College Station-Bryan | 30.57258 | -96.327044 | BRAZOS |
| 77845 | TX | College Station-Bryan | 30.65212 | -96.341012 | BRAZOS |
| 77840 | TX | College Station-Bryan | 30.65212 | -96.341012 | BRAZOS |
| 77845 | TX | College Station-Bryan | 30.65212 | -96.341012 | BRAZOS |
| 77840 | TX | College Station-Bryan | 30.65212 | -96.341012 | BRAZOS |
| 77845 | TX | College Station-Bryan | 30.65212 | -96.341012 | BRAZOS |
| 77840 | TX | College Station-Bryan | 30.65212 | -96.341012 | BRAZOS |
| 77845 | TX | College Station-Bryan | 30.579234 | -96.293826 | BRAZOS |
| 77840 | TX | College Station-Bryan | 30.579234 | -96.293826 | BRAZOS |
+------------+-----------+-----------------------+-----------+------------+---------+
14 rows in set (0.06 sec)

mysql> show tables;
+-----------------------------+
| Tables_in_entries |
+-----------------------------+
| 3zip_FHA_index |
| MSA_county_populations2010 |
| median_sale_res2018 |
| ranked_building_permits2018 |
| ranked_housing_price_change |
| zip_income2016 |
| zip_lat_long_usa |
| zip_names |
+-----------------------------+
12 rows in set (0.00 sec)

mysql> describe ranked_building_permits2018;
+--------------+----------------+------+-----+---------+-------+
| Field | Type | Null | Key | Default | Extra |
+--------------+----------------+------+-----+---------+-------+
| rank_permits | int(5) | NO | | NULL | |
| local_area | char(50) | NO | | NULL | |
| nb_permits | int(7) | NO | | NULL | |
| pct_total | decimal(10,10) | NO | | NULL | |
+--------------+----------------+------+-----+---------+-------+
4 rows in set (0.00 sec)

mysql> select * from ranked_building_permits2018 limit 0,6;
+--------------+-----------------------------------+------------+--------------+
| rank_permits | local_area | nb_permits | pct_total |
+--------------+-----------------------------------+------------+--------------+
| 1 | College Station-Bryan, TX | 130982 | 0.0576569994 |
| 2 | Ithaca, NY | 104625 | 0.0460549049 |
| 3 | Hot Springs, AR | 103466 | 0.0455447244 |
| 4 | Asheville, NC | 77323 | 0.0340368307 |
| 5 | Lake Havasu City-Kingman, AZ | 65090 | 0.0286519834 |
| 6 | North Port-Bradenton-Sarasota, FL | 62247 | 0.0274005225 |
+--------------+-----------------------------------+------------+--------------+
6 rows in set (0.00 sec)

 

So I check Truila and Zillow and Many houses are new construction hmmm

 

based on IRS returns for 2016 by the zipcode filed from

mysql> SELECT
-> state,
-> zip,
-> CEIL((SUM(nb_returns)/aggregate_income)*1000) as "Income Rank"
-> FROM
-> entries.zip_income2016
-> WHERE
-> zip = 77845;
+-------+-------+-------------+
| state | zip | Income Rank |
+-------+-------+-------------+
| TX | 77845 | 281 |
+-------+-------+-------------+

77845 | TX | College Station-Bryan

200 might be an average have yet to work that out

 

I have used government Census Bureau 2010 stats, HUD, other US government public sources and private housing data.

I found an interesting list compiled by medicare on cost of medical indexed by state they have lists of allowed reimbursement by institution that may be more geofenced by region but they need to be analyzed and indexed.

We used to be able to buy lists by zip code and approximate credit ratings 1-2-3 many years ago when I worked for a CFL lender (like household or beneficial finance) we got the addresses for direct mail ...

My goal is just to better identify micro locations for marketing.

The other thing is this type of advertising is expensive so the customer acquisition CR and payout must be high 1 to 4 dollars per CTR so you might have $10-$40 per conversion /or customer(member) acquisition.

 

I have found some DSP's. Google ads and Bing has geofencing.

 

just a teaser of what is out there
I have
mysql> SELECT COUNT(*) FROM PersonalIncomePerCapitaUSA;
+----------+
| COUNT(*) |
+----------+
| 3248 |
+----------+
1 row in set (0.01 sec)

Sorted by USA State and County of 2017 now