Customer lifecycle optimization: a 4-step process...
We recently talked about the role of data and artificial intelligence (AI) in the customer lifecycle optimization process.
To sum it up, because revenue is linked directly to the customer lifecycle, applying predictive data and AI yields powerful, fact-based predictions that help optimize our targeting, outreach, and overall spend at every stage.
This process is customer lifecycle optimization (CLO), and it involves four prescribed steps: lifecycle mapping, data discovery, predictive groundwork, and guided outreach.
Let’s dive into the first step: lifecycle mapping!
What is customer lifecycle mapping?
The first step in any CLO initiative is always identifying and defining key lifecycle stages and transitions between stages.
Mapping these stages and transitions to a uniform customer lifecycle is crucial to uncovering rich, predictive data, which we’ll discuss more in a later article. For now, we'll focus on lifecycle mapping.
The uniform B2C customer lifecycle:
While terminology will change from business to business, we found that this formulation is rich enough to capture important boundaries, yet simple enough to avoid stages with ambiguous transitions.
Think about your organization’s customer journey. How are stages defined? Which attributes qualify individuals to be placed in those stages? What events trigger transitions between stages?
How to map your customer lifecycle...
Now that you’ve given some thought to your customer journey, it’s time to formalize your observations. Let’s map out your customer lifecycle to the uniform lifecycle we presented above.
Use the following table to help organize your findings:
Transition in — What events trigger someone to land in that stage? Did they submit a webform, sign up for a newsletter, or call in from a direct mailer?
Litmus test — Do individuals in this stage meet all the necessary criteria to belong in this stage? Are there any grey areas?
Transition out — What conditions trigger someone to transition out of this stage? You’ll notice that your “transition out” should overlap with the following stage’s “transition in.” This prevents leads and customer from slipping through the cracks.
Your customer lifecycle mapping is something that everyone in your organization should agree with. If you run into confusion or disagreement, the process is working! Collaborate with your team until you can all agree on various stage attributes and transition triggers.
Now you’re ready to discover those rich, predictive data sources. We’ll explore the data discovery process in the next article, but if you’re not the waiting type, download our whitepaper, AI for the customer lifecycle: Making the most of your data!