When it comes to segmenting your customers, you have plenty of options. The right method depends on your business objectives and goals. Do you want to simply understand your current customer base? Do you want to predict what your customers will do next?
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When clustering data, it can be challenging to handle time series data, especially when the series have differing lengths. Check out this lesson for the code and theory on how to cluster your own messy time series data.
Data scientists often evaluate their own work based upon model accuracy, completeness, or some other criterion. However, it’s even more important to demonstrate the business impact of your analysis to stakeholders within your organization.
If you’re a growth marketer that has started measuring customer lifetime value instead of just return on ad spend, this lesson is for you. The next step is to reframe your decision making to optimize your marketing based on CLV.
Defining and rallying support for a vision of success for yourself and your team can help to not only invigorate your team and partners today, but it can also help to sustain momentum, celebrate wins, and structure learnings as you move forward.
Incrementality helps marketers optimize media investments by determining how much each channel or tactic contributes to results.
Apply incrementality testing to customer lifetime value to guide high-quality budget allocation and optimization decisions.
Customer lifetime value comes in many shapes and sizes. From the popular RFM approach at the cohort-level to predictive lifetime value, this lesson explores the various flavors of CLV.
There are several methods you can use to calculate customer lifetime value, from simple to complex. This lesson will explore how to predict CLV at the aggregate and individual level.