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](/content/images/2018/01/WP01-Table---Outreach-as-Intervention.png)
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](/content/images/2018/01/WP01-Table---Audience-Expansion-Examples.png)
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.