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Optimize your marketing outreach with AI-powered audience expansion

Perry McDermott on

Customer Lifecycle Marketing Audience


This post is part 4 of our 4-part Customer Lifecycle Optimization series.

First, identify your most predictive customer lifecycle data

B2C revenues are often tied directly to customer lifecycle outcomes. Targeting likely-to-convert leads, identifying good up-sell candidates amongst your existing customers, and proactively engaging churn-prone customers are all good ways to boost revenues while reducing overall customer acquisition costs.

The key to identifying those likely-to-convert leads, up-sell candidates, and churn-prone customers is mapping out your customer lifecycle and identifying predictive lifecycle data to better predict future behavior. With data-driven predictions, you can optimize your outreach initiatives by targeting better audiences at each stage of the customer lifecycle.

Motivating transitions between customer lifecycle stages

Whether you want to increase conversion rates from lead generation campaigns or reduce churn from existing customers, properly targeted outreach is essential to engaging and motivating the right leads and customers to take a desired action.

Outbound communication is the strongest and most versatile intervention at an organization’s disposal to compel progress and therefore expand revenue. This includes individually targeted digital advertising, a form of direct outreach.

Consider the following examples of outreach initiatives:

Example Marketing Outreach Intervention

Having identified a stage transition you’d like to motivate with an outreach intervention, the question becomes, “Who do I reach?” Regardless of the desired transition, the general technique is called audience expansion, also known as “lookalike” audiences.

3 examples of audience expansion

The following table contains three examples of audience expansion. In each case, an existing audience is “expanded” by identifying individuals with similar attributes in a larger set of candidates.

3 Examples of Audience Expansion

Note that in the third example, retention, we’re looking for candidates (current customers) likely to join the indicated audience (lost customers) thereby entering the destination stage (reactivation) so that we can act to prevent that transition.

Audience vs candidates

To apply the audience expansion technique, we must always identify the audience and a set of candidates with similar characteristics and attributes.

An audience is a sample of the customers you’re trying to find more of. This can be as simple as matching a lifecycle stage (i.e. retention and expansion), or as specific as trying to find more high-value customers.

Candidates consist of the universe of possible recipients of your outreach within which to expand your audience. Using lead generation as an example, your candidates would be your current leads, while the corresponding audience would be your current customers. Your audience (customers) is expanding into these candidates (leads) with motivation from your outreach initiatives.

Applying your predictive groundwork

With these groups defined, the next step is to apply your predictive groundwork. This could involve using patterns you identified in your data to look for similar opportunities among your candidates, or in more advanced cases, using artificial intelligence to build a predictive model trained to discriminate between likely and unlikely transitioners.

Finally, you will be left with a well-defined group of candidates likely to transition into a desired lifecycle stage when reached with relevant content.

Putting it all together

As mentioned earlier, this article is the fourth and final part of our customer lifecycle optimization series. To summarize the CLO practice, it’s useful to recall our original motivation.

We employ AI-powered predictions to build and defend revenue by improving outcomes within our customer lifecycle. We know this means motivating transitions from one stage to the next.

    4) Leverage these patterns to guide outreach.


For more information on anything discussed in our customer lifecycle optimization blog series, download our free whitepaper: AI for the customer lifecycle: Making the most of your data.

Faraday Whitepaper|Customer Lifecycle Optimization

Predicting customer lifecycle outcomes with data analysis and machine learning

Perry McDermott on

Predictive customer lifecycle marketing


This post is part 3 of our 4-part Customer Lifecycle Optimization series

Expanding on your customer lifecycle map

In our last article, Lifecycle mapping: uncovering rich, predictive data sources, we discussed the importance of mapping out your customer lifecycle to better understand where your most predictive customer data is hiding.

Lifecycle mapping is the first step to using artificial intelligence (AI) to optimize your customer lifecycle marketing initiatives.

Now, we’ll pose some questions to help identify your predictive customer attributes and lifecycle events, pinpoint where that data is located, and recognize patterns to predict outcomes for future prospects, leads, and customers.

Step 2: Data discovery

Data discovery is the second stage in the customer lifecycle optimization (CLO) process. The primary task of this stage is to expand on your lifecycle map to identify authoritative data sources that establish progress.

As a reminder, your customer lifecycle map should look something like this:


Customer lifecycle mapping tool

At each stage, prospects, leads, and customers will complete certain events that will individually or collectively trigger a transition in or out of that stage. Individuals will also have different attributes, which help determine whether or not they belong in any given stage.

Events typically emerge from data that represents behaviors and change. For example, your e-commerce system records purchases, your CRM records wins, and your ESP records email engagement. When individuals click a link in an email, sign up for a demo, or make a purchase, they’re completing transition-triggering events.

Attributes exist in the data that describes the customer themselves. For example, your CRM will contain contact information, lead status, and possibly physical addresses. If you service a specific geographical market, a determinant attribute will likely be the individual’s household address. If they live outside of your market, they will fail the litmus test.

Go through each cell of your customer lifecycle map and think about which events or attributes in your data could be used to trigger a transition or pass the litmus test. Think about where you can find that data.

Step 3: Predictive groundwork

Now that you’ve formalized your lifecycle and its representation in data, you can start recognizing patterns and eventually predict outcomes.

Imagine loading up your converted leads (customers in “retention and expansion”) alongside your stale leads that never converted. What differences can you find?

This comparison analysis is especially effective when you’ve added depth and breadth to your existing data (see our previous article for more information on data depth and breadth).

As you experiment with this type of pattern recognition, you’ll quickly realize that it’s the kind of thing that computers do very well. That’s where machine learning comes in handy.

Machine learning refers to a class of artificial intelligence techniques in which previous outcomes are algorithmically analyzed to uncover patterns, a process known as “training” a predictive model. The model can then be used to predict likely outcomes given new data by looking for indicative patterns.

What's next?

Once you’ve mapped out your customer lifecycle, identified where to find your most predictive data, and developed models to facilitate future predictions, you’re ready to use those predictions to guide your outreach.

For a deeper dive into the four step customer lifecycle optimization process, download our free whitepaper, AI for the customer lifecycle: Making the most of your data.

Whitepaper — AI for the customer lifecycle

Lifecycle mapping: uncovering rich, predictive data sources

Perry McDermott on


Customer audience with limited data


This post is part 2 of our 4-part Customer Lifecycle Optimization series.

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


This is post part 1 of our 4-part Customer Lifecycle Optimization series.

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