This post is 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 machine learning and predictive modeling can optimize marketing and customer experience outreach at every stage. We call this customer lifecycle optimization.
What is customer lifecycle optimization?
Customer lifecycle optimization (CLO) is the practice of using big 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.
For a deeper look into each step, check out our free whitepaper, AI for the customer lifecycle, Making the most of your data.
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 big 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.