Solicitation and Activity History
Overview
Cheetah Digital recognizes that you may need to utilize solicitation and activity history for Campaigns executed from your previous provider(s), in order to properly target customers for Campaigns that will be sent from Engage+. For example, you might need this solicitation and activity history to apply the appropriate Filter segmentation logic to identify cohorts (such as Active Buyer, Lapsed Buyer, Non-Buyer, or High-Value Buyer for example), or to target "actively" engaged customers (for example, opened or clicked a certain number of emails in a certain period of time).
The Onboarding team recommends that clients wishing to load a prior vendor’s solicitation and activity history to the Messaging platform do so at an aggregated customer level. Data as this level is "summarized," which speeds up load times and query performance. As an example, let's say that you sent five email messages to a particular consumer in the past three months. Instead of providing all the granular details regarding each of those five messages (details which aren't necessary to correctly target this consumer), you would simply provide the summarized count of how many messages were sent in that time period.
If you're unable to provide aggregated solicitation and activity history, but still require its use within Messaging, the Onboarding team can potentially handle the Extract-Transform-Load (ETL) of granular data into an aggregated format. This ETL would occur outside Messaging and the resulting aggregated feed would be loaded to the platform. A separate statement of work would likely be required for this effort, unless overtly included within the Onboarding scope.
Solicitation History Feed
Cheetah Digital suggests that you provide a solicitation history feed whose unique identifier is the same as the intended sending table (to be determined during the Discovery stage).
The data points to be included in this feed should be reflective of your necessary targeting selection criteria, such as: count of emails sent in the past 0-3 months, count of emails opened in the past 0-3 months, count of emails clicked in past 0-3 months, etc.
Further, it's often useful to also include individual columns that reflect the date / time value for each customer’s most recent send, most recent open, and most recent click. These "most recent" date / time fields are particularly valuable for IP warming purposes. "IP warming" refers to the gradual process of establishing a good reputation with Internet Service Providers (ISPs). ISPs will refuse to deliver email messages from IP addresses that they view as spammers. By effectively targeting your most engaged customers first, you have the best opportunity to establish a good reputation and high inboxing rates with the ISPs.
Below is an example of a suggested file layout that could be utilized to load aggregated solicitation and activity history into Messaging. This sample is provided only for illustration purposes and should not be interpreted as the only option available to you.
|
Field Name |
Field Description |
Format |
|
|
Email Address |
|
|
Emails_Sent_0_to_3_mos |
Count of emails sent in last 3 months based on DateasOf |
integer |
|
Opens_0_to_3_mos |
Count of opens in last 3 months based on DateasOf |
Integer |
|
Clicks_0_to_3_mos |
Count of clicks in last 3 months based on DateasOf |
Integer |
|
Emails_Sent_4_to_6_mos |
Count of emails sent in last 4-6 months based on DateasOf |
Integer |
|
Opens_4_to_6_mos |
Count of opens in last 4-6 months based on DateasOf |
Integer |
|
Clicks_4_to_6_mos |
Count of clicks in last 4-6 months based on DateasOf |
Integer |
|
Emails_Sent_7_to_9_mos |
Count of emails sent in last 7-9 months based on DateasOf |
Integer |
|
Opens_7_to_9_mos |
Count of opens in last 7-9 months based on DateasOf |
Integer |
|
Clicks_7_to_9_mos |
Count of clicks in last 7-9 months based on DateasOf |
Integer |
|
Emails_Sent_10_to_12_mos |
Count of emails sent in last 10-12 months based on DateasOf |
Integer |
|
Opens_10_to_12_mos |
Count of opens in last 10-12 months based on DateasOf |
Integer |
|
Clicks_10_to_12_mos |
Count of clicks in last 10-12 months based on DateasOf |
Integer |
|
Emails_Sent_13_to_15_mos |
Count of emails sent in last 13-15 months based on DateasOf |
Integer |
|
Opens_13_to_15_mos |
Count of opens in last 13-15 months based on DateasOf |
Integer |
|
Clicks_13_to_15_mos |
Count of clicks in last 13-15 months based on DateasOf |
Integer |
|
Emails_Sent_16plus_mos |
Count of emails sent in last 16-18 months based on DateasOf |
Integer |
|
Opens_16plus_mos |
Count of opens in last 16-18 months based on DateasOf |
Integer |
|
Clicks_16plus_mos |
Count of clicks in last 16-18 months based on DateasOf |
Integer |
|
MostRecent_OpenDT |
Most recent date opened based on L18 mos of data |
mm/dd/yyyy |
|
MostRecent_ClickDT |
Most recent date clicked based on L18 mos of data |
mm/dd/yyyy |
|
MostRecent_SentDT |
Most recent date sent based on L18 mos of data |
mm/dd/yyyy |
|
DateAsOf |
Date aggregated solicitation & activity data is thru |
mm/dd/yyyy |
Database Tables
Historic solicitation and activity data from your previous vendor can NOT be incorporated into the Messaging’s internal Campaign history tables. These tables have their own internal primary keys that are assigned by the system upon execution of a Campaign within Messaging. Therefore, any solicitation and activity history brought into Messaging will be done via your entity tables.
Further, Cheetah Digital recommends that this solicitation and activity history data be loaded into a separate entity table dedicated specifically to this purpose, rather than integrating the data into the sending table used to deploy Messaging Campaigns. By loading this data into its own entity table, when the data becomes stale and outdated over time, the table itself can be purged without impacting other actively-used entity tables.
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