While there is massive potential with advanced analytics, like predicting customer behavior, your success relies on addressing these fundamentals.
That’s because the entire customer lifecycle can be optimized when you leverage behavioral data:
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.
Before you start diving into building machine learning models, my advice is to slow down.
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?
A lot of customers ask if we can help them solve problems through machine learning and data science. It depends. If they are utilizing a modern architecture (typically in the cloud) to access and review their data, then they 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.
3.) Is your data relevant and/or does it reflect changes in the external environment?
This is a tricky one. “Relevant data” could be somewhat subjective and might depend on your business or your current objectives. But due to all of the changes prompted by COVID, a lot of information you collected and analyzed in the past may no longer be relevant. 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. But in the current state of the world, features like usage patterns may have changed dramatically. You should regularly assess data for changes and look to new sources because customer profiles constantly change.
While easier said than done—because not everyone can be Amazon, Netflix, Walmart, or Apple collecting new data in a real-time way—you can (and should) always generate new information about your customers from avenues like customer surveys or 3rd party data. Even with the most perfect model and greatest statistical minds developing algorithms, you will not adequately interpret customer behavior without relevant inputs.
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. Think about customer churn models built prior to COVID—they’re likely obsolete because the features changed dramatically. With any machine learning project, it is important to continually evaluate data inputs and regularly test and adjust your models.
Data science projects have a high failure rate. At Analytics8, we practice ethical data science, which means helping our customers recognize when they need to take a step back from a machine learning project if they don’t have the right measures in place.
There are our tips to become data science ready:
With the proper planning and preparation, advanced analytics will help you know your customer better than ever.
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