Retention Based Marketing

Where Data Meets High Converting Messaging

Amplify your messages and scale your customer relationships

Retention marketing, sometimes referred to as lifecycle marketing or loyalty marketing, is a term that essentially boils down to keeping customers engaged, happy, and spending. However, successful messaging starts with a strong framework of the buyers journey while layering in data to validate the proper messaging.

Step 1
Segmentation
The first step in implementing a retention-based marketing strategy lies in identifying your multi-buyers, 1x buyers, and non-buyers. From there, you assess how each segment engages with different types of mailings (full price vs promo). Once you have figured out how each cohort engages and converts to various types of messages, you can then begin to implement a strategic strategy to drive customer engagement and CLV.
step 2
Automation 
While batch campaigns will require continuous creative thinking, for those looking to automate their workflows, you can then begin to create automated workflows that are dedicated for each segment group. For example, your typical e-commerce business should have 3 different journeys that speak to non-buyers (with the goal of the first conversion), one-time buyers (with the goal of engagement and conversion), and multi buyers (with the goal to keep these users engaged in the brand).
step 3
Testing 
The next step after implementing a few dedicated journeys is to optimize the customer journey through testing. The best way to continuously provide a lift in revenue is via testing. For example, a non-buyer may be more likely to convert on an offer. However, a muli-buyer who is more engaged with the brand may be more likely to convert with a higher degree of personalization.
Strategy implementation

Data-Driven Strategies That Stand The Test of Time

How I Do It:

Raw-Data Analyses

The first step in creating an omni-channel strategy starts with raw data extraction and assessment. After discovering which channels perform the most efficiently from a return stand point, an initial strategy can be then drafted to capatalize on past learnings.

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