Improve your marketing performance with audience segmentation
How customer segmentation and Machine Learning can help your business
Customer segmentation can also help you identify the root causes of churn. For example, if you notice that customers in a certain geographic location are not making repeat purchases, you can investigate why this may be the case (e.g. are you using a different supplier in this location? Is the product hard to find in stores?) and take steps to address the issue.
Once you have identified your target segments, you can create targeted messages that speak to their pain points and send them deals that are far more likely to resonate with them than generic ones. For example, if your data reveals people who prefer vegan foods and live active lifestyles are most likely to click on your ads for a meal prep service, your company can focus its ad budget on this group rather than wasting money on ads that will not 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 outdoor adventure winter clothing may target ads toward women in colder climates who regularly go hiking.
How Machine Learning can be useful for customer segmentation
Another way to use machine learning to create customer segments is to build predictive models that identify which customers are likely to purchase certain products or services. This can be done using a variety of features such as past purchasing behaviour, web browsing history and social media activity.
Predictive models would also be able to analyse customer sentiment and create segments of customers likely to leave the brand through analysis of online reviews, social media comments and repeated browsing of competitor 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 analyse 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 personalised 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.