Personalizing customer marketing on the basis of customers’ previous behavior can be very valuable. Sending specialized offers/incentives to customers based on what they have done in the recent (or not-so-recent) past is a powerful way to increase customer spend, engagement, and loyalty.

You can use this approach in Optimove by defining Target Groups using the Activity History selection category.

The selection criteria in this category consist of the following elements:

Selection Element	Examples
Type of history	Customers with activity Customers with no activity
Activity type	Metric Days with activity
Function	Average Maximum Minimum Total
Activity	Number of order days Number of login days Total discounts
Condition	Greater than (>) 5 Between 5 and 10 Less than (<) 3
Time frame	On 10/12/2019 During the past 60 days This week/month/quarter/year Last calendar week/month/quarter/year In this date range

Additional Timeframes

When using "any campaign", the only available timeframe is during the previous X days.

When using the additional activity options ("while a member of a target group" or "One or more actions"), the available timeframes are:

During the previous / exactly (number of days) ago - customers who were targeted over the past X number of days or exactly X number of days ago.
This week (to date) – customers who were targeted during the week (Mon-Sunday). For example, if today is Wednesday, this timeframe will include Monday and Tuesday’s customers.
This month (to date) - customers who were targeted during this month.
This quarter (to date) - customers who were targeted during the current quarter.
This year (to date) - customers who were targeted during this year.
Last calendar week – customers who were targeted last week. For example, if today is Tuesday, August 29th, "last week" would be Monday the 16th – Sunday the 22nd.
Last calendar month - customers who were targeted last month.
Last calendar quarter - customers who were targeted during the previous quarter.
Last calendar year - customers who were targeted during last year.
On this date - customers who were targeted on a specific date. * In this date range - customers who were targeted on a specific date range.

How to Segment Customers Using Activity History

There are three ways to segment customers:

1. Selecting customers with a numeric metric

Selecting customers with activity, based on a numeric metric such as the number of orders, purchase amount, or the number of logins. To use a numeric metric as your activity type for the selection criteria follows the steps below:

Go to Activity History in the Selection Criteria navigation bar
Select customers “with activity”
Under Activity Type select “metric”
Under Function select whether you’d like the segmentation to be done based on customers’ Total, Average, Minimum or Maximum value.
Under Activity select the attribute on which the segmentation should be based. For example, select Number of Order Days if you wish to select customers based on the total number of logins to your platform or app.
Under Condition select the numerical condition you would like to use to segment your customers. For example, “Greater Than”.
Set the value you wish to use
Define the timeline for which the conditions should be met. For example, between March 1st and April 30th.
You should now have criteria set up similar to the one below. All that is left is to click “Add”.

2. Selecting customers with a number of activity days

Selecting customers with activity, based on the number of separate days on which the customer’s activity caused a change in a given metric such as the number of days with a login, the number of days with a purchase amount, etc. To use a number of activity days as your activity type for the selection criteria follow the steps below:

Go to Activity History in the Selection Criteria navigation bar
Select customers “with activity”
Under Activity Type select “Days with activity”
Under Activity select the attribute on which the segmentation should be based. For example, select Number of Order Days if you wish to select customers based on the total number of logins to your platform or app.
Under Frequency select the number of days this activity should have taken place in order for customers to be selected for this segment. For example, “at least 5 login days”.
Define the timeline under which the activity days should have taken place. For example, between March 1st and April 30th.
You should now have criteria set up similar to the one below. All that is left is to click “Add”.

3. Customers with no activity

Selecting customers with no activity, based on customers not having days with the desired activity in a given metric time period. To select this group follow the steps below:

Go to Activity History in the Selection Criteria navigation bar
Select customers “with no activity”
Under Activity select the attribute on which the segmentation should be based. For example, select Number of Order Days if you wish to select customers based on the total number of logins to your platform or app.
Define the timeline under which the activity days should have taken place. For example, between March 1st and April 30th.
You should now have criteria set up similar to the one below. All that is left is to click “Add”.

Example Use Cases

Reimburse Player Losses to Encourage Continued Activity

An online gaming operator is looking to identify those customers who lost a large amount of money on the previous day and to compensate them in a way that will mitigate their negative experience and encourage continued engagement.

For example, the marketer can select all players whose “net revenue” was less than “minus $300”, (i.e., with a daily loss in excess of $300). This kind of campaign will generally be set to run every day, in order to compensate all* such players who lost more than $300 the previous day.

Here is how this selection appears in Optimove’s Target Group selection criteria panel:

Note that you should typically combine this selection criterion with others, to make it more intelligent. For example, it could be combined with a campaign history criterion (to limit the number of times that a single player will receive this reimbursement), and also a “win ratio” threshold (because $300 might be a small loss for some high-rolling players).

Missed the Black Friday Sale

An online apparel retailer wants to generate some extra sales volume on the heels of Black Friday by enticing particular customers who did not buy anything on Black Friday to make a purchase.

The targeted customers are those who made a purchase on the prior year’s Black Friday but did not buy anything this year.

This campaign could specifically mention that “We missed you at our Black Friday sale this year.” To set this up in Optimove, the marketer will combine two Activity History criteria. The first will include all customers who made a purchase on the date of Black Friday the previous year: The second will be all customers who did NOT make a purchase on Black Friday this year, which was three days before.

Activity History Update Frequency

Activity History updates daily. Read more about update frequency here.