Buying Stats

Average Annual Transactions - Group

The number of transactions your customers made per year, on average, throughout their lifetime. For easier use, the transactions are grouped into buckets.

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Buying Stats

Average Order Value - Group

The average value of the customers’ transactions in their lifetime.

“25-50”, “200-300”, “300-400”, “Non Buyer”

Buying Stats

Channel Mix - Last 12 Months

The sales channels that your customers purchased in the last 12 months.

“Unidentified”, “Other Channel Buyer”, “Non Buyer”

Buying Stats

Discount - Lifetime

The total discount amount used by your customers in their entire lifetime.

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Buying Stats

First Transaction Date - Digital

The date of your customers’ first transaction in the Digital sales channel.

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Buying Stats

First Transaction Revenue

The revenue of your customers’ first transaction.

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Buying Stats

First Transaction Sales Channel

The transaction channel in which your customers made their first purchase. The default value is “Unknown”.

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Buying Stats

Last Transaction Date - Digital

The date of your customers’ last transaction in the Digital sales channel.

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Buying Stats

Last Transaction Interval

The interval, in days, between your customers’ last transaction and the previous one.

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Buying Stats

Last Transaction Sales Channel

The transaction channel in which your customers made their last purchase. The default value is “Unknown”.

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Buying Stats

Months Since Last Transaction

The number of months since your customers’ last transaction.

“7 to 12 Months”, “13-24 Months”, “More than 24 Months”, “Non Buyer”

Buying Stats

Product Category Count

The number of categories your customers purchased from you in their lifetime.

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Buying Stats

Product Count

The number of distinct products your customers purchased in their lifetime.

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Buying Stats

Revenue - 13-24 Months

The total amount of revenue generated by your customers between 13 and 24 months ago (ie “2 years ago”).

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Buying Stats

Revenue - Last 12 Months

The total amount of revenue generated by your customers in the last 12 months (ie “last year”).

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Buying Stats

Revenue - Lifetime

The total revenue generated by your customers’ transactions in their lifetime.

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Buying Stats

Revenue Decile - 13-24 Months

The total amount of revenue generated by your customers between 13 and 24 months ago (i.e. “2 years ago”), grouped by deciles, with decile 1 being the biggest spenders and decile 10 being the lowest spenders.

“1”, “2”, “3”, “4”

Buying Stats

Revenue Segment - 13-24 Months

An easier way to use “Revenue decile”:

• “1 - High Value” represents your top 10% customers in terms of revenue on this period

• “2 - Medium Value” represents your 80% average customers (ie not in the top/bottom 10%) in terms of revenue on this period

• “3 - Low Value” represents your bottom 10% customers in terms of revenue on this period

“1 - High Value”, “2 - Medium Value”, “3 - Low Value”, “Non Buyer”

Buying Stats

Revenue Trend Segment

A segmentation representing the progression of revenue for your customers between 13-24 months ago and the last 12 months. It characterizes whether they spent less (“Downward”), the same (“Stable”), or more (“Upward”) in the last 12 months than in the 13-24 months ago period. If a customer was acquired in the last 12 months, then she gets marked as “New Buyer” instead. “Reactivated” customers are buyers who made a purchase in the last 12 months, did not make a purchase in the previous 13-24 months, but did make a purchase before 24 months ago. “2 Period Inactive” customers are buyers who did not make a purchase in the last 24 months but made a purchase before 24 months ago. “Lapsed”customers are buyers who made a purchase in the last 13-24 months but not make a transaction in the last 12 months.

“Stable”, “Upward”, “Downward”, “Non Buyer”, “New Buyer”, “Reactivated”, “2 Period Inactive”, “Lapsed”

Buying Stats

Second to Last Transaction Date

The date of your customers’ penultimate (second to last) transaction.

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Buying Stats

Transaction Count - 13-24 Months - Group

Same as Transaction Count - 13-24 Months, but grouped in buckets for ease of use.

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Buying Stats

Transaction Count - Last 12 Months - Group

Same as Transaction Count - Last 12 Months, but grouped in buckets for ease of use.

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Buying Stats

Transaction Count - Lifetime - Group

Same as Transaction Count - Lifetime, but grouped in buckets for ease of use.

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Campaign Activity

Were included in an CDP legacy Campaign

Filter out individuals who were previously included in an CDP legacy campaign. This filter references data stored in CampaignHistory.

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Coupon History

Received a coupon

Filter out individuals who received a coupon.

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Customer Attributes

Address Certified

A flag to mark whether the address has been certified by the CDP Data Quality Engine (this is the USPS CASS certification) :

• “True” means that the address was CASS certified

• “False” means the address is not CASS certified (and is likely invalid)

• “Unknown” means that there is either no address, or that the address is not in the US/Canada (those are the only countries we can certify right now)

“True”, “False”, “Unknown”

Customer Attributes

Age

The age of the customers. This is coming from your standard customer feed.

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Customer Attributes

Likelihood to buy

The likelihood to buy of the customer, in deciles (decile 1 is the highest likelihood to buy, decile 10 is the lowest). This is part of the machine-learning models provided by CDP.

“1 - High”, “2 - Medium”

Customer Attributes

Birth year

The birth year of the customers. This is coming from your standard customer feed.

“1988”, “1989”, “1974”

Customer Attributes

Closest store

The closest store for your customers. The default value is “Unknown”.

“Inkanta Hacienda Santa Barbara”, “Hakata Hankyu”, “Maison Mode Chengdu” , “DFS”

Customer Attributes

Distance to closest store

The distance, in miles, between your customers and the store closest to where they live. This filter can work only for US, Canada and UK addresses (for both stores and customers).

“23.5”, “40.3”, “5”

Customer Attributes

Distance to a store

Select any customer living within [N] miles of store [ABC], e.g. : select all customers with distance to store [San Francisco store] is less than [20] miles.

This can be used to send them an email about an event going on in a specific store.

This has a few limitations for obvious reasons : we support distances of up to 40 miles (greater distances are considered useless by most retailers, since not a lot of people would make such a long trip), and we compute the distance to the 20 closest stores for each customers, so if there are more than 20 stores within a 40 miles radius around a given customer, we will only compute the distance to the closest 20.

This filter can work only for US, Canada and UK addresses (for both stores and customers)

“San Francisco store” and “20”

Customer Attributes

Email Status

The validity of the email address of your customers :

• “V” means “Verified” and means the syntax is valid, the domain is good/ known and this email address is not a known spam trap

• “U” means “Unverified” and means the syntax is valid, but the domain is unknown/bad

• “X” means “Invalid” and means the syntax is invalid

“V”, “X”, “U”

Customer Attributes

Customer Status

A flag to distinguish customers that are buyers from the non-buyers. Customers who have ever made a purchase are considered buyers.

“Buyer”, “NonBuyer”

Customer Attributes

Propensity to convert

Your customers’ propensity to convert in the next 30 days, as computed by CDP’s propensity to convert predictive model. This classifies Non-Buyers into deciles, with Decile 1 being the most likely to convert and Decile 10 being the less likely to convert.

(Not available yet) “1 - High”, “2 - Medium”, “9 - Low”, “Non-Buyers”

Customer Attributes

State

The state in which your customers live. The default value is “Unknown”.

“MI”, “AL”, “GA”

Customer Attributes

NCOADateUpdate

This is the date at which the record last went through NCOA processing. This is currently in a UNIX epoch format (e.g. “1507060487001”), but we will soon release a “human-readable” translation (e.g. YYYYMMDD format) of that filter.

“1507060487001”, “in the last 3 days”

Customer Attributes

MoveDate

This is the date at which the move was registered with USPS in the NCOA database. This is in the format “YYYYMM”, e.g. “201401” is a move that was registered on January 2014 (the actual move could have happened earlier or later, the NCOA database cannot provide that information).

“201401”

Customer Preferences

Call campaigns opt-out

This will replace the “Preferences” filter in the future.

This filter allows you to select customers that are opted-in/opted-out of your Call campaigns.

• Y : contacts explicitly opted-out

• N : contacts explicitly opted-in

• Ucontacts that have no explicit values (and are usually contacts

  that you can/want to contact).

“Y”, “N”, “U”

Customer Preferences

Email campaigns opt-out

This will replace the “Preferences” filter in the future.

This filter allows you to select customers that are opted-in/opted-out of your email campaigns.

• Y : contacts explicitly opted-out

• N : contacts explicitly opted-in

• Ucontacts that have no explicit values (and are usually contacts

  that you can/want to contact).

“Y”, “N”, “U”

Customer Preferences

Primary device type

The device that your customers used the most for their purchases in their lifetime. The default value is “Unknown”.

“COMPUTER”, “MOBILE”, “Unknown”

Customer Preferences

Primary store

The store through which your customers made most of their purchases in their lifetime. The default value is “Unknown”.

“Inkanta Hacienda Santa Barbara”, “Hakata Hankyu”, “Maison Mode Chengdu” , “DFS”

Email Activity

Date of last email opened

The date at which the customers last opened one of your email campaigns.

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Email Activity

Email click count between 31 and 60 days ago

The number of clicks on email campaigns’ links between 31 and 60 days ago (ie “2 months ago”)

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Email Activity

Email click count in the last 30 days

The number of clicks on email campaigns’ links in the last 30 days (ie “last month”).

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Email Activity

Email open count between 31 and 60 days ago

The number of emails opened by your customers between 31 and 60 days ago (ie “2 months ago”).

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Email Activity

Email open count in the last 30 days

The number of emails opened by your customers in the last 30 days (ie “last month”).

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Email Activity

Emails sent between 31 and 60 days ago

The number of emails sent to your customers between 31 and 60 days ago (ie “2 months ago”).

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Email Activity

Emails sent in the last 30 days

The number of emails sent to your customers in the last 30 days (ie “last month”).

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Email Activity

Performed an event with message

Select customers who received/opened/clicked/other event on a campaign with a message (all email campaigns events are supported provided that they tie to a message, and it also applies to any custom event type configured for you if that event ties to a marketing message).

E.g. : you can select customers who “performed an event [emailOpen] where message name is [Abandoned Cart]”, but you cannot select customers who “performed an event [emailUnsubscribe]” because the emailUnsubscribe event generally doesn’t tie to a specific message (especially in the case of people unsubscribing from a preference center for instance).

The event type can be specified by using event type values (listed on the right). Note that no web events are available there because they are not tied to marketing campaigns.

You can then select the timeframe in which this event should have happened, and you can refine further by customer attributes/summaries, or by the campaign’s message name and/or subject.

• emailSend

• emailClick

• emailSubscribe (only if tied to a message)

• emailOpen

• emailBounce

• emailUnsubscribe (only if tied to a message)

Email Activity

Performed a number of events in a timeframe

Same as Performed an event in a time period, but you can select the number of times the selected event type should have happened within the selected timeframe. This filter can be refined as well.

Performed the event “emailOpen” greater than 3 times in the last 30 days

Purchase Activity

Made a transaction

Selects customers who made a transaction in a specific timeframe. You can refine this rule further by adding refinements.

Made a transaction after 08/01/15 where Average Order Value (AOV) lower than $50

Purchase Activity

Placed first order

Selects customers who made their first transaction in a specific timeframe. You can refine this rule further by adding refinements.

Placed first order in the range of 7 to 14 days ago where Likelihood To Buy equal to “2 - Medium”

Purchase Activity

Purchased a product

Selects customers who purchased a product in a specific timeframe. You can refine this rule further by adding refinements.

Purchased a product before 08/01/16 where Organization Name equal to “San Francisco Store” and Transaction Item Revenue greater than $100

Purchase Activity

Customers who spent certain amount in a timeframe

Select customers based on the amount of revenue they spent within a specific timeframe e.g. “spent more than $300 in the last 30 days…”. This filter can be refined further.

Spent more than $300 in the last 30 days where subtype in Shipped, Returned

Web Activity

Browsed a product

Selects customers who browsed a product in a specific timeframe. You can refine this rule further by adding refinements.

In particular, you can refine by :

• UserClient : this is “A” for events that happened on your mobile application (if you are sending us such events), and “B” for events that happened on a regular browser.

• Domain : this lists out the domain on which the specific event have happened, which can be useful for filtering the events if you use a lot of subdomains for marketing purposes or special sales and don’t want to target people that browsed on those websites.

• OperatingSystem : as its name indicates, this is a high-level summary of the operating system of the visitor. It can help you seleect specific segments of users based on their device/OS.

• DeviceType : as per its name, this is a very high-level summary of the device used by your visitors. It can help you segment further the people you want to target/avoid targeting for your campaigns, based on the device on which those events happened.

Browsed a product in the last 10 days where Product Name equal to “ProductABC” and Category Name equal to “CategoryXYZ”

Web Activity

Date of first web visit

The date of the customers’ first visit to your website.

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Web Activity

Days since first web visit group

Same as Date of first web visit, but grouped into buckets for ease of use.

“0”, “8-15”, “16-30”, “31-60”

Web Activity

Web visit count between 31 and 60 days ago

The number of website visits between 31 and 60 days ago (ie “2 months ago”).

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Web Activity

Web visit count in the last 30 days

The number of website visits in the last 30 days (ie “last month”).

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Purchase Activity

Placed first order

Customer, Transaction, Organization

Made first order in the range of 7 to 14 days ago where Likelihood To Buy equal to “2 - Medium”

Purchase Activity

Purchased a product

Customer, Organization, Product, Product Category, Transaction, Transaction Item

Purchased a product before 08/01/16 where Organization Name equal to “San Francisco Store” and Transaction Item Revenue greater than $100

Purchase Activity

Purchase a number of products in a category

Customer, Organization, Product, Product Category, TransactionSummary

Purchased 5 products in the last 30 days where Category Name equal “Accessories”

Purchase Activity

Customers who spent certain amount in a transaction

Customer, TransactionSummary, Organization

Spent more than $500 in a single transaction in the last 30 days where Organization Type is Physical

Web Activity

Customers who abandoned their cart

Customer, Event

Abandoned their cart 2 days ago and Likelihood to Buy equal to “1 - High”

Email Activity

Performed an event

Customer, Event

Performed the event “cartUpdated” in the last 30 days where User Client equal to A

Household

Belongs to a household

Household summary

Belongs to a household where Average Order Value is greater than “$500”