For a business to be successful and sustainable in the long run, it needs to adequately identify its customer base. While the prospect of appealing to everyone seems attractive, the reality for most companies is different, and having the wrong customers can even present challenges to the operational viability of the organization. By leveraging machine learning (ML), businesses can gain tools to make informed decisions about which customers to pursue, and then, focus on nurturing this customer base by creating alignment with the company’s long-term goals. Here are five steps to help you do that. #1: Define the problem and objectives For an ML algorithm to be effective, you need to clearly articulate the problem it needs to solve, and the specific objectives that the model should achieve. Here’s a practical application. An ML algorithm can analyze customer purchasing and spending patterns, and determine whether the cost of acquiring and servicing a particular customer segment outweighs the financial benefits that this customer segment generates. In simple words, sometimes, a company can lose money by servicing some customers, and not be aware of it. ML can help decision-makers identify those scenarios. There are various reasons why this can happen. For instance, if there is a specific group of customers that constantly engages in complaints, or that requires extensive support, this might overstretch the resources of the organization, and negatively impact the overall customer experience that the company is able to deliver. By integrating ML, you can identify patterns in customer interactions, pinpoint those who may be overly demanding or frequently unhappy, and decide to sever ties with them. The same goes for identifying potential high-risk customers that could cause harmful financial losses in the future. #2: Identify relevant data sources The goals you establish for your ML integration will also determine what ML algorithm—classification, regression, clustering, or a different one—you will use. Based on this, identify the type of data you need. Data collection sources can range from internal databases and CRM systems to APIs that can help you pull data from public databases. Also, you can extract valuable patterns and insights by analyzing logs and transaction records. However, don’t limit yourself to structured data. Other places where you can find valuable unstructured data include reviews and audio recordings—for example, from monitoring customer service calls. Keep in mind that, as your ML model evolves, you might require new insights, and this will prompt you to collect additional data to ensure your model meets your goals. #3: Categorize data for optimal segmentation Once you have the necessary data, you need to categorize it adequately to ensure that the segmentation process meets its intended outcomes. There are various categories that you can use for this, depending on your type of company and the service you offer. Here are some options. Spending Patterns: Do your customers pay on time? Do they pay up-front, or tend to defer payments over time? If they pay by credit card, how many cards do they use? All of this is valuable information that gives you feedback about your customers' financial habits. Purchasing Behavior: Are they the type of customers that opt-in for extra services? What kind of plan have they chosen? If they have the option to leave a tip—like in the case of delivery drivers—do they use it? Compliance Patterns: This category can give us information about customers who lose or damage products due to negligence or poor oversight. Whenever they submit a claim, we can categorize them based on whether they filed a police report and followed up with it, or in the case repairs were needed, if they attended their maintenance appointments as planned. Loyalty: There’s a reason why many companies divide customers into loyalty brackets. This is not only to reward them for their ongoing allegiance, but also, to understand their behavior and identify patterns. By looking at your top loyalty tier, you can uncover insights that show you why your customers are staying for so long. Complaints: This is very useful, as it helps you identify if there are any recurrent themes behind ongoing complaints and whether these are justified or not. If they are, it gives you suggestions for improvement. If they are not, it can pinpoint you to those customers that you might be better off without. #4: Assess the model’s performance on the test set, and finetune when required To maximize effectiveness, testing is key. Once you run the model using your test set, which is the dataset that you gathered specifically for this purpose, see whether the model is giving you the results you desire. Basically, you are comparing whether the model’s predictions are accurate, and if they are not, you need to determine how far off they are from reality to see how you need to finetune your model. Here, you would be looking at metrics like accuracy, precision, and recall, however, these will vary depending on the ML algorithm that you used. Based on these results, you can make the necessary adjustments. Sometimes, the changes might involve changing the algorithm altogether, while other times, you might need to incorporate additional data or alter some of the model’s parameters. All in all, finding the right model is a similar process to determining the right business model for your company. You test what you built, and if it works, you keep going, and if it doesn’t, you need to iterate until you find the right fit. With ML, it is pretty much the same. Don’t be afraid to go a few steps back and restructure how your ML model works. This is much better than staying put with a model that has been proven not to work, and that will only cause you to waste money. #5: Implement monitoring mechanisms to track the model’s performance over time If you’ve made it to this step, it means you’ve found a model that works. However, the fact that it works now doesn’t mean that it will always be the case. To ensure that your ML model remains relevant and effective, you need to incorporate monitoring mechanisms that can track how the model is faring, and whether any adjustments are needed. Why is this important? First, because patterns change over time, and when the change surpasses a level of significance, your data can quickly become outdated. Second, external factors can cause your priorities to shift, and your model will need to evolve in order to give you the right solutions. Which industries could benefit the most from ML-driven segmentation? While effective customer segmentation can benefit almost every business, some industries can particularly benefit from implementing this approach. This is because there are sectors where competition is fierce, providing customer service requires a lot of resources, and staunch customer loyalty is required to succeed. From my personal experience, one example is subscription services, which can benefit by focusing on those clients that have a high retention potential—which reduces both CAC and churn. Foodtech, in which customer satisfaction is paramount and positive engagements can bolster word-of-mouth, a main engine for referrals, can also benefit substantially from adequate ML-driven segmentation. Last, but not least, companies in the financial services arena, especially those firms that focus on high-net-worth individuals and building long-term relationships, can leverage this process to center their efforts on those customers that matter the most.