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Your first welcome email is a good way to introduce new leads to your key brand message, build value and drive the first conversion.
This tactic is available with all product packages.
Contents
Tactic details
Overview
This lifecycle Tactic ensures that your brand identity remains strong with your leads, pulling them back and driving them to make their first purchase.
It supports the Revenue objective by contributing to the following strategy:
- Convert leads to first-time buyers.
Basic workflow
The Tactic starts with a New contact node that adds all new contact registrations to the program. It waits 1 day before sending them an email with an incentive to buy, product news, a social media block and product recommendations. A reminder is sent 5 days later if no purchase has been made.
Advanced workflow
In addition to email, you can also add the contact to a Web Channel audience and a CRM Ads audience at the same time. The Tactic will check every day for 4 days to see if a purchase has been made, and remove the contact from both audiences if this is the case.
Automation Center program
The program itself looks like this, with the basic email workflow on the top path, and CRM Ads (1) and Web Channel (2) on the path below.
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Entry node
The program starts with a New contact node that returns all new contact registrations in your account, from all sources. -
Email path
The program then waits for 4 days after the initial email before filtering to see if a purchase has been made. If TRUE, the contact exits the program. If FALSE, a reminder email is sent. -
Web Channel / CRM Ads path
After adding the contact to the respective audience, the program filters each day for "predict last purchase date was in the last 2 days". If TRUE, the contact is removed from the audiences. If FALSE, they remain in the audience.
After 5 days the contact is removed from the audiences automatically.
Functionality overview
From the Advanced package, this tactic uses:
- Web Extend
- Predict Email Recommender
From the Max AI package, this tactic can use:
- Incentive Recommendation
- Send Time Optimization
The required data sources are:
- Contact data
- First name (subject line and content personalization
- Web behavior data
- Predict last purchase date (to exit contacts from the program)
- Product data
- Product name, price, image and URL (for the Predict widgets)
Available add-ons:
- Web Channel
- CRM Ads
Preparing the Tactic for use
First, you should check that your account assets are ready for use.
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Data sources
Make sure that the data sources listed above are correctly configured and contain values for all the fields you will need. -
Incentives
You will need to upload a pool of voucher codes to use as incentives, or configure Incentive Recommendation if you are using that instead.
Then modify the message content for each of the channels.
If you have already downloaded and updated the Tactics template, you will already have made most of the necessary changes to the email content, and you only have to make the changes specific to this actual campaign (see Configuring a Tactic > Email campaigns for details).
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Email
Make any necessary changes to the creatives.
Open the Predict widget in the 4-column product block and change its settings to the Personal logic. -
Web Channel
If you are using Web Channel, create a campaign to match the email and select this in the Web Channel - add node. -
CRM Ads
If you are using CRM Ads, create an ads campaign in your network of choice and create the audience that will link to it. Then select the audience in the CRM Ads - add to audience node.
Since this is targeting new contacts, the participation setting should be only once ever.
Activating the Tactic
When you are done, activate the program. You will be able to check on the performance in the reporting pages of the Strategic Dashboard.
Tips & hints
Differentiate content by size of first purchase
You can use block targeting to show different content based on whether or not the contact made a purchase by using web behavior fields such as:
- predict last purchase total price
- predict last purchase products
- predict last purchase categories