Append
Segment customer records and foot traffic data
New Segmentation Report
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File Formatting Guidelines
Please follow the formatting guidelines below to ensure a successful upload.
File Format
CSV Only
Required Fields
Customer ID and at least one (1) matching variable
Column Names
Match column names to the parameter names below (e.g. Address). Custom metrics (e.g. Spend) can have any name.
Matching Variables
Include as many clean matching variables as you can for a better match rate.
Parameter
Description
Variable Type
Examples
Customer ID
A customer number (unique identifier) for matching back records after PII is removed. This field is required.
ID
871Y781983
1
1
Address
Full address including line 1 (street info), line 2 (apt #), city, state, and postal (zip) code. Skip this if the components are split across columns.
Matching Variable
13924 S SPRINGS DR, CLIFTON, VA, 20124
1008 Race Street, Cincinnati, OH, 45202
1008 Race Street, Cincinnati, OH, 45202
Address Line 1
Primary address line. Include the street number and name. Skip if you are using the 'Address' field (full address in one column).
Matching Variable
1008 Race St
13924 S SPRINGS DR
13924 S SPRINGS DR
Address Line 2
Secondary address line (if any). Skip if you are using the 'Address' field (full address in one column).
Matching Variable
Apt A
2E
319
2E
319
City
Include full city name (no abbreviations). Skip if you are using the 'Address' field (full address in one column).
Matching Variable
Cincinnati
Miami
Miami
State
Abbreviated state letters or full state names allowed. Skip if you are using the 'Address' field (full address in one column).
Matching Variable
OH
Florida
Florida
Zip
5-digit code. Including the 4-digit extension may improve your match rate. Skip if you are using the 'Address' field (full address in one column).
Matching Variable
45202
94303-5300
94303-5300
Full Name
Combined first and last name. Don't include prefixes (e.g. Mrs.) or suffixes (e.g. Jr.). Accents are allowed.
Matching Variable
John Smith
ZOE DOE
Lucia Gonzales
ZOE DOE
Lucia Gonzales
First Name
Don’t include prefixes (e.g. Mrs.). Accents are allowed.
Matching Variable
John
ZOE
Lucia
ZOE
Lucia
Last Name
Don’t include suffixes (e.g. Jr.). Accents are allowed.
Matching Variable
Smith
smith-jones
Pèrez
smith-jones
Pèrez
Email
Include a domain name (e.g. gmail.com). Remove spaces between the email address.
Matching Variable
example@email.com
john@gmail.com
john@gmail.com
Hashed Email
Emails should be converted to all lower case and then hashed with SHA-256.
Matching Variable
d731872b173d1a503cf
Spend
Total amount spent by the customer. This is an example of a custom (amount) metric. It could be anything relevant to your business (total spent, order count, lifetime value, etc). This is not required.
Custom Metric
100.67
23
23
Subscriber
Indicates the customer is a subscriber. This is an example of a custom (category) metric. It could be anything relevant to your business (subscriber or not, customer type, etc). This is not required.
Custom Metric
Y
N
Freemium
N
Freemium
Blockgroup
Census Block Group ID. This should be a 12 digit number.
Matching Variable
060371011101
170318426003
170318426003
Latitude
Approximate latitude of an individuals residence. Must also include longitude.
Matching Variable
39.10955
Longitude
Approximate longitude of an individuals residence. Must also include latitude.
Matching Variable
-84.51767
clat
'Central Latitude'. Use 'clat' and 'clng' columns to search for individuals by first and last name within a specific radius around a known location. This is helpful when you have a customer's name but only an approximate location, like the vicinity of a coffee shop.
Matching Variable
39.10955
clng
'Central Longitude'. Use 'clat' and 'clng' columns to search for individuals by first and last name within a specific radius around a known location. This is helpful when you have a customer's name but only an approximate location, like the vicinity of a coffee shop.
Matching Variable
-84.51767
This is one example of a well formatted file; yours doesn't have to exactly match it. This example data file includes a Customer ID as well as First Name ,Last Name, Address, and Email for matching. It also includes two custom metrics: Spend and Subscriber.
Customer ID
First Name
Last Name
Email
Address Line 1
Address Line 2
City
State
Zip
Spend
Subscriber
1
John
Smith
john@gmail.com
1008 Race St
Cincinnati
OH
45202
100.67
Y
2
Zoe
Smith
zoe@gmail.com
1420 Main St
2E
Cincinnati
OH
45202
59.36
N
3
Allen
Jones
allen@gmail.com
990 W Bluegrass Ave
Louisville
KY
40215
12.99
N
4
Lisa
Gonzalez
lisa@gmail.cin
127 Rose St SW
Grand Rapids
MI
49507
77.93
Y
This is one example of a well formatted file; yours doesn't have to exactly match it. This example data file includes a Customer ID as well as First Name, Last Name, Email, Zip, Latitude, and Longitude. It also includes one custom metric: Customer Type. Providing the zip code allows us to look for individuals with the given name in the zip code. The lat, lng coordinates allow us to get more specific (at a household or census block group level).
Customer ID
First Name
Last Name
Email
Zip
Latitude
Longitude
Customer Type
1
John
Smith
john@gmail.com
45202
40.7128
74.006
Trial
2
Zoe
Smith
zoe@gmail.com
45202
34.0522
118.2437
Premium
3
Allen
Jones
allen@gmail.com
40215
41.8781
87.6298
Premium+
4
Lisa
Gonzalez
lisa@gmail.cin
49507
25.7617
80.1918
Trial
This is one example of a well formatted file; yours doesn't have to exactly match it. This example data file includes a Customer ID and an Email for matching. It also includes one custom metric: Average Order Total. We can only match this file using 1-to-1 email matches. Always include as many matching variables as you can, but this is an example where the only customer data you have available is email.
Customer ID
Email
Average Order Total
1
john@gmail.com
100.67
2
zoe@gmail.com
59.36
3
allen@gmail.com
12.99
4
lisa@gmail.cin
77.93
File Upload
Important: You are uploading data to Spatial.ai’s secure matching environment. PII is encrypted and deleted immediately upon matching. You may download your customer file consisting of Customer IDs and segment match levels with PII removed in the table on the Append page.
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Match Column Names
We've matched 4 columns from your customer list. Please check if they're correct.
Address Line 1
City
State
Zip
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Specify Market Base
Choosing a proper market base helps identify which segments your customer base over-indexes on relative to the nearby general population.
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Confirm Details
Customer List
Name
my-customer-list.csv
Size
2.5MB
Mapped Columns
Total Spend
Churn
None
Market Base
National
OH, KY, IN
File Formatting Guidelines
Please follow the file format guidelines specific to your mobile data provider to ensure a successful upload.
Important: Mobile data files must be uploaded in CSV or TSV format (they can be zipped). 'Census Block Group ID' or 'Latitude' and 'Longitude' are required; it is recommended to include both if possible. 'Visitor ID' is required to generate visit frequency statistics. Ensure header column names exactly match one of the Example Names below. Incorrect formatting can lead to an upload error or inaccurate results.
It is recommended that you upload the unaltered zip file which you received. Please upload either shape file or CSV format.
Watch Tutorial
You can upload a ‘Common Evening and Daytime Expanded Report’ or a similar report from Azira without any additional formatting. You may upload the CEL visit data or the entire downloaded zip file.
Upload the file with "Catchment Area Origins", "Trade Area" or similar in the title. This contains data for visitation origin. Please ensure you only include data for visitation from home not work (e.g. "habit_area_type" should always be "HOME" in the file)
File Guidelines
Parameter
Description
Example Names
Census Block Group
A location component is necessary to segment mobile data records.
CBG, Blockgroup, Blockgroup ID, BG, Common Evening Census
Latitude
A location component is necessary to segment mobile data records. Must also include 'Longitude'.
Latitude, Lat
Longitude
A location component is necessary to segment mobile data records. Must also include latitude.
Longitude, Lng
Location/POI ID
The ID for the place being visited. We need this field to accurately determine what market(s) this data should be compared to.
Location ID, POI ID, Polygon Name, Polygon ID
Visitor ID
We can use this field to remove/reduce outliers. Additionally, it allows us to calculate frequency of visit statistics.
Visitor ID, Hashed Ubermedia ID
Date
Allows us to report the time range of the data.
Date, Visit Date
Visit Count
Allows you to include data aggregated at a visitor level or not. In the dataset, each row could represent a visitor (when you include this parameter) or a visit (without this parameter).
Visit Count, Visits
File Upload
Upload your foot traffic report below. Enter the site's address to view the segmented report on the Analyze page.
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Review File Details
Foot Traffic Report
Name
my-foot-traffic-report.csv
Size
2.5MB
Site Address
123 Main St, Cincinnati, OH, 45202
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Specify Date Range
Select a date range for visits to the location. For most reports we recommend using the past 12 months to account for seasonal fluctuation and ensure significant data volume.
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Provide Site Address
Enter the postal address of the site you drew your polygon around.
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Review Details
Custom Polygon
Site Address
123 Main St, Cincinnati, OH, 45202
Date Range
Past 12 Months
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