Predicting customer behavior can be easy if you are analyzing your customers and leads. Using advanced segmentation, you can predict how your customer will respond in a number of scenarios including churn, offers, upsells and more. The challenge is that it requires data. Once you have the data, you can plug in the data to create mathematical models for AI-driven predictions. The key is to assign multiple segments to a customer representing different ‘variables' about the relationship – then these segments can be plugged into AI algorithms to predict behavior.
Beyond Demographics - Segmentation in the 21st Century
As marketers, we need to think about segmentation differently. Real life doesn't follow Marketing 101's 4-Ps – Pricing, Product, Placement, Promotion. People are unique, but you can identify patterns with their behavior.
Here are the types of information you can collect to create advanced segments:
Types of Predictions
Predicting the future starts with understanding the past. By collecting information about your customers and their behaviors and by creating micro-segments, you can begin to use this information in order to predict behaviors of current and new customers.
Here are a few types of customer behavior predictions you can make:
- Training Needs
Retention/Churn is probably one of the easiest metrics to calculate. Retention predicts the probability to remain a customer, while churn predicts the probability of cancellation. They are opposite metrics – just some people like the positive spin of ‘retention' over the negative sentiment of ‘churn'. Demographics can be used to predict this, but by leveraging all four types of segment information to create AI algorithms, you can drill down to create accurate predictions of cancellations and can proactively address problems.
Some of this is just using the data and information at your disposal – for example if someone gets an error and you proactively contact them to provide steps to resolve it – there's no AI involved in that – however, this is the next level of retention.
If someone can't afford your product/service – you were marketing to the wrong person in the beginning of the process. If someone experiences a problem that can be solved, then focused effort needs to go into saving the sale.
By going beyond basic demographics, you can see clear patterns that can help you identify marketing problems and can help improve long-term retention over time.
Creating a Data-Driven Culture
For many companies, this is a tremendous transformation in the way that they operate. If your team isn't used to being measured on their performance or isn't used to responding to issues prioritized by data, then they will have huge problems in the near future.
Here are some resources to help you transform the way your organization thinks about data, knowledge sharing, your organization:
This book is about the types of transformations that occurred at Google after implementing OKRs and how they continue to use OKRs to measure business success.