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Lifecycle mapping: uncovering rich, predictive data sources

Perry McDermott on


Customer audience with limited data

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:

B2C customer lifecycle stages

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:


Customer lifecycle mapping tool

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.

What's next?

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!


Whitepaper — AI for the customer lifecycle

Optimize your customer lifecycle with artificial intelligence

Perry McDermott on

Customer audience with predictive data

AI-powered customer lifecycle marketing is the future. Why are so many companies missing the wave?

We all know we should be doing more with our data. If you’re a CEO, you’re hearing it from your board. If you’re a manager, you’re hearing it from your CEO. So why are so many companies having trouble operationalizing data-driven business processes?

Spoiler alert: it’s not due to a lack of data… corporate IT systems are better than ever at capturing data. Rather, as data volumes grow, it becomes increasingly difficult to access, analyze, and make meaningful predictions from all that data.

In an ideal world, we could analyze and make predictions using every single data point in our systems, but in reality, the “do-everything” approach to analytics is overkill. Focusing our analyses and predictions on specific business issues lets us extract immediate value from the right data and the right sources..

When it comes to B2C, our lowest-hanging fruit for a truly data-driven process is the customer lifecycle; where proven AI-powered predictive models can optimize marketing and customer outreach efforts at every stage. We call this customer lifecycle optimization.

What is customer lifecycle optimization?

Customer lifecycle optimization (CLO) is the practice of using data and AI to make predictions that measurably improve outcomes at each stage of the customer lifecycle.

As with other practices, CLO involves a series of prescribed steps to be done effectively.

The canonical CLO process comprises four steps: lifecycle mapping, data discovery, predictive groundwork, and purposeful outreach. We’ll dive deeper into each individual step in future posts, but keep in mind that this process is near-universal for B2C customer lifecycle optimization.

So what’s the point? Why does CLO matter?

Let's take a step back to answer that...

For nearly every B2C company, revenue is linked to the customer lifecycle. Lead generation, opportunity conversion, and customer retention outcomes will have a direct impact on revenues. So using data and AI to optimize these outcomes is the most logical way to systematically build AND defend revenue.

This probably isn’t news to you. The concept of customer lifecycle marketing has been around for a while, and many of you have tried to “use data” throughout the process. While that’s a smart decision, it’s probably safe to say that nearly all of you have been somewhat disappointed with the results.

Why is that?

Why is data-driven marketing so difficult to implement?

We’ve narrowed the answer down to two overarching factors: The Data Gap and Data Myopia...

The Data Gap: nearly all CEOs believe they have operationalized data-driven business, while nearly all employees feel the opposite. CEOs aren’t necessarily out-of-touch with the concept of data-driven business, and employees aren’t necessarily incapable of using analytical tools. The problem is that most enterprise analytics tools and BI systems take several days to deliver reports, use data with limited depth and breadth, and are therefore largely unused by employees.

Data Myopia: traditional enterprise analytics tools and BI systems suffer from “blind spots” in the underlying source data used to create reports. In other words, these systems report on data with limited depth and breadth, severely limiting the quality of analysis and prediction. For a deeper look into the importance of data depth and breadth, check out the the whitepaper linked at the end of this post.

Am I ready to use AI throughout my customer lifecycle?

Thanks to the uniformity of the B2C customer lifecycle, nearly every company can adopt and implement a truly data-driven approach to lead generation, opportunity conversion, and customer retention.

Check out AI for customer lifecycle: Making the most of your data to learn how to map out your customer lifecycle, discover relevant data sources, and setup the predictive groundwork necessary to guide your outreach.

Whitepaper — AI for the customer lifecycle