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Improve your marketing performance with audience segmentation

The first step to any successful marketing campaign is knowing who your audience is and having visibility about its different types of behavior, which can be achieved through customer segmentation. Customer segmentation is a process of dividing customers into smaller groups based on shared characteristics, such as age, gender, location or income level as well as behavioral and psychological profiles. Doing this can help you to better understand their needs, wants and pain points to create more personalized and targeted content that will resonate with each segment.

How customer segmentation and Machine Learning can help your business

Effective customer segmentation can have a number of important benefits for your business. These benefits include:
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Predict Churn
Latest machine learning algorithms can help you reduce customer churn by identifying which customers are at risk of leaving your brand. Once you have identified which customers may not bring repeat business, you can then take steps to reduce their churn rate. For example, if you know that customers in a certain segment are more likely to churn, you can target them with special offers or personalised services.

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.

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Lower Acquisition Costs
By understanding which segments of the market are most valuable to your business, you can focus your marketing and sales efforts on these groups rather than wasting time and money targeting those who are unlikely to convert.

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.

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Increase Sales
In order to maximize sales, businesses need to understand their customers and identify which groups are most likely to make a purchase.

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.

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Personalized experience
Segmentation can help you build better relationships with your customers by giving them a more personalised experience when they engage with your brand. When you take the time to understand their specific needs and pain points, they’ll appreciate your effort and be more inclined to stick around long-term.

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 a number of ways. One way is to use clustering algorithms to group together customers with similar characteristics.

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.

We bring your attention to customers who are at risk of leaving, allowing you to take corrective action and thereby lowering your acquisition costs.  

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.

1. Recency

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.

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 maximise your digital marketing budget by understanding each segment’s CLTV and improving customer retention.