Improve your marketing performance with audience segmentation
The first step to any successful marketing campaign is knowing who your audience is and gaining visibility into their different behavior patterns, which can be achieved through customer segmentation. Customer segmentation is the process of dividing customers into smaller groups based on common characteristics, such as age, gender, location, or income level, as well as behavioral and psychological profiles. This can help you better understand their needs, wants, and pain points in order to create more personalized and targeted content that will resonate with each segment.
How customer segmentation and machine learning can help your business
The latest machine learning algorithms can help you reduce churn by identifying customers who are at risk of leaving your brand. Once you’ve identified which customers may not be bringing retention, you can then take steps to reduce their churn. For example, if you know that customers in a certain segment are more likely to churn, you can target them with special offers or personalized services.
Customer segmentation can also help you identify root causes of churn. For example, if you notice that customers in a certain geographic location are not making repeat purchases, you can research why this may be the case (e.g., do you use a different vendor in that location? The product is hard to find in stores?) and take action to resolve the issue.
Once you have identified your target segments, you can create targeted messages that speak to their pain points and send them offers that are far more likely to interest them than generic offers. For example, if your data reveals that people who prefer vegan foods and active lifestyles are most likely to click on your ads for a meal preparation service, your business can focus its advertising budget on this group rather than to waste money on ads that won’t be seen by or appeal to other segments.
By understanding the needs and wants of each customer segment, businesses can create targeted marketing campaigns that are more likely to result in a sale. For example, a business selling winter clothing for outdoor adventure might target ads to women who live in colder climates and who hike regularly.
How machine learning can be useful for customer segmentation
Machine learning can be a powerful tool and can be used to create customer segments in many ways. One way is to use clustering algorithms to group customers with similar characteristics.
Another way to use machine learning to create customer segments is to create predictive models that identify customers who are likely to purchase certain products or services. This can be done using a variety of features such as past purchase behavior, web browsing history, and social media activity.
Predictive models would also be able to analyze customer sentiment and create customer segments likely to leave the brand through the analysis of online reviews, social media comments, and repeat browsing competing sites as opposed to your website.
Important metrics we use to understand your customer
RFM, CLTV and Churn Rate are all important factors to consider when trying to understand your customer base better. By understanding how these factors contribute, you can more easily identify which customers are most valuable to your business and take steps to improve customer loyalty and increase retention rates.
Now, sifting through all the above data in order to build predictive models and create customer segments is an enormous task. In fact, it can take anywhere from months to years depending on the size of your business and the amount of historical data you have. Based on machine learning technology, Indaru can analyze data quickly, identify trends and create highly accurate customer profiles for your business.
Using ML to create customer segments is a more scalable solution than manual modeling because as new data is retrieved, the model is continually updated. For example, if your business grows from a customer base of 50 000 to 650 000 in a year, AI technology is capable of handling and sorting through the additional data dynamically.
RFM, or recency, frequency and monetary value, is a method of measuring customer loyalty. It takes into account how recently a customer has made a purchase, how often they make purchases, and how much they spend on each purchase. This information can help you identify which customers are most engaged with your brand and what kinds of things they are interested in purchasing.
2. Customer lifetime value
CLTV, or customer lifetime value, is a measure of how much revenue a customer is expected to generate over the course of their relationship with your company. This metric can help you identify which customers are most valuable to your business and create personalized messaging to keep them loyal to your brand.
3. Churn rate
It costs 5x more to acquire customers than it does to keep them, so churn rate is an extremely important metric in helping you maintain high levels of customer satisfaction. In addition, churn rate can be compared to growth rate to determine whether your business is actually growing. If the churn rate is higher than the growth rate, your business is in fact shrinking and urgent corrective action is needed.
How Indaru can help with your audience segmentation
Indaru has a large and diverse team of data science and digital marketing professionals with extensive experience in audience segmentation and data analysis. We use machine learning to create dynamic and robust customer segments in order to help you maximize your digital marketing budget by understanding each segment’s CLTV and improving customer retention.