Predicting Customer Behavior Using Data Science

While there is massive potential with advanced analytics, like predicting customer behavior, your success relies on addressing these fundamentals.

Companies who obsess over customer behavior data will outperform their competitors.

That’s because the entire customer lifecycle can be optimized when you leverage behavioral data:

Acquisition: Know what products and services your customers want and how and where they want to purchase them
Engagement: Know what messages will resonate with your customers and which medium will reach them
Retention: Know who your best customers are and the types of offers that entice them

Knowing this information is not a pipe dream—advanced analytics and machine learning make it possible to better connect with your customer and grow the business.

But while there is massive potential with advanced analytics, your success relies on addressing the fundamentals.

3 Prerequisites to Predict Customer Behavior with Data Science and AI

Understanding customer behavior with machine learning is very doable, but only with the fundamentals in place. Otherwise, your efforts will fail or – even worse – guide you to make the wrong decisions for your customers, jeopardizing future business with them.

Ask yourself these three questions before you get started.

1.) What is your target or objective?

Machine learning requires a goal or target. This might be predicting sales, determining driving factors that influence particular purchases, or understanding when to target specific customers and with what messaging.

To gain real perspective on customer behavior, your focus should be on answering a question, specifically one that human analysts alone are unable to accurately interpret and/or create a repeatable process for (perhaps because of the number of inputs involved).

Without a clear objective established, it’s easy to get lost in the data or go down the wrong path. There are just too many potential variables (both known and unknown) that could influence customer behavior. If you approach your project with a concise objective, it will be easier to stay on track and ensure you’re on a clear path to answering the right questions.

2.) Is your data clean and organized?

If they you are utilizing a modern data platform like Databricks, you might be ready for next steps. Modern data architectures offer a more efficient and reliable mechanism for data integration and data cleansing which is critical for successful data science.

But if you are linking to multiple disparate sources that do not integrate with each other, or your staff goes through manual processes to build reports, you may not be ready. It’s highly likely you’re not pulling in all necessary data and that some data is inaccurate, outdated, or not properly formatted (e.g. consistent labeling, proper handling of nulls and errors, etc.). Clean, integrated data from all potential sources is an absolute necessity for data science projects.

Read: Best Practices for Excelling with AI and Advanced Analytics

3.) Is your data relevant and/or does it reflect changes in the external environment?

“Relevant data” depends on your business or your current objectives. It’s important to continually review your data and enhance it to account for changes in the external environment.

Machine learning requires inputs (a.k.a. features). For customer behavior, features may include customer traits, product traits, and usage patterns. You should regularly assess data for changes and look to new sources because customer profiles constantly change.

You also supplement your data sources with information about your customers from avenues like customer surveys or 3^rd party data. Even with the most perfect model and greatest statistical minds developing algorithms, you will not adequately interpret customer behavior without relevant inputs.

Predict Customer Behavior with Machine Learning

Machine learning helps sort through huge amounts of information about our customers and establishes a programmatic approach to predicting customer behavior—when they’ll buy, what they’ll buy, what channels they’ll buy from, if they’re likely to churn, and more.

Start by building customer profiles

In a perfect world, you would have one-to-one personalized interactions between your business and your customers. But that’s not likely, and usually not financially feasible.

This is where customer profiling comes in—or, segmenting your customers based on shared traits to more effectively target their needs. Internal and supplemental data points such as demographics, geographics, product channels, and previous purchases can be used to cluster customers. With a good grasp of customer behaviors within each segment, you can optimize your communications and offerings across the customer entire lifecycle and even anticipate their needs before they’re aware of what they want.

Apply models to customer segments to predict behaviors, like customer churn

Once you have segmented your customers, you can create a robust model to analyze each customer profile and predict behavior. As an example, we’ll demonstrate how machine learning can help predict customer churn, an area in which many of our customers are interested.

We could tell the model that we want to see a Churn Confidence level for each customer—somewhere between 0 and 1; the closer to 1, the more likely the model predicts the customer will leave. Your machine learning model would run against your data to provide a Churn Confidence Number.

We could then use this new data point, the Churn Confidence Number, in your data analytics platform to build new visualizations and do what-if analysis against other dimensions, such as customer tenure or purchase history. We can set the churn confidence thresholds to higher and lower numbers to see how churn predictions affect bottom line numbers like customer count and revenue.

Along with the Churn Confidence number, the model can provide more attribute detail for your customer profiles. You may have data that tells you who your most loyal customers are, but it’s hard to identify why. So rather than having the machine learning model spit out granular level detail and score each customer line-by-line, we can ask it to identify patterns and create more detailed customer profile groupings for us.

With clear profiles of Most Likely to Churn and Most Loyal customers, you can start to take action – Product Management can utilize this as they consider new product and package features, and Marketing will have the information needed target the right audiences with appropriate messaging.

Remember: machine learning is cyclical by nature because inputs are always changing and market factors (e.g.. supply chain disruptions) continually buying behaviors. With any machine learning project, it is important to continually evaluate data inputs and regularly test and adjust your models.

Conclusion: Getting Data Science Ready is the First Step to Predicting Customer Behavior

Data science projects have a high failure rate when the right measures aren’t in place.

Our tips to become data science ready:

Tie your business objectives to data science goals
Implement a modern data architecture to facilitate solid data collection and cleansing processes
Leverage the right data by constantly reviewing what’s being collected and enhancing where possible

With the proper planning and preparation, advanced analytics will help you know your customer better than ever.

Key Takeaways

Understanding customer behavior through advanced analytics can significantly boost business performance.
Effective use of behavioral data can optimize customer acquisition, engagement, and retention.
A successful machine learning project requires clear objectives, organized data, and awareness of external changes.
Clean, integrated data is essential for meaningful analysis and actionable insights in data science projects.
Regular updates to data and models are crucial to accommodate changing customer behavior and external conditions.
Customer profiling and segmentation enable targeted marketing and product strategies effectively addressing customer needs.
Machine learning can predict customer behaviors such as churn, allowing businesses to proactively mitigate potential loss.
Continual evaluation and adaptation of machine learning models are necessary to maintain their relevance and accuracy.