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Customer segmentation is a powerful marketing tool that helps you understand your customers better, and target them accordingly. It can help you improve your sales, increase customer retention and reduce costs associated with customer acquisition.
Statistics show that segmentation makes companies 60% more likely to understand customer challenges and 130% more likely to know their intentions. Segmented email campaigns also have higher open rates, click-through rates and conversions.
In this post, we will explore how you can use machine learning to implement effective customer segmentation for your business.
In business, customer segmentation is the process of dividing customers into groups based on shared characteristics. Segmentation allows businesses to better understand their customers and target them with specific messages that are more likely to resonate.
There are numerous benefits of customer segmentation, but here are some of the most compelling reasons to consider using it in your business.
Customer satisfaction is the foundation of any successful business. The concept of NPS, or Net Promoter Score, is based on the idea that customers can be grouped into three categories: promoters, passives, and detractors.
Promoters are customers who are extremely satisfied with your products or services and are likely to continue using them and referring others. Passives are satisfied but unenthusiastic customers who are at risk of defecting to a competitor. Detractors are unhappy customers who could actively damage your brand with negative word-of-mouth.
NPS is calculated by subtracting the percentage of detractors from the percentage of promoters. So, if you have a NPS of +30, that means you have 30% more promoters than detractors.
Since the modern customer demands a personalized experience, customer segmentation can be used to identify and target promoters with tailored messages that further improve their satisfaction.
Customer lifetime value (CLV) is the estimated net revenue that a customer will generate over the course of their relationship with your business.
There are a number of factors that go into calculating CLV, but the two most important are customer retention and customer acquisition.
Customer segmentation can be used to identify high-value customers and target them with messages and offers that encourage them to stick around. For instance, you might offer a loyalty discount to select customers who have been with you for a year or more.
Churn, also known as customer attrition, is the percentage of customers who stop doing business with you over a given period of time.
Even a small decrease in customer churn can have a big impact on your bottom line. Suppose your annual customer churn rate is 10%, and your CLV is $1,000 with 1,000 customers; that’s a loss of $100,000 per year.
Customer segmentation can be used to identify at-risk customers and target them with messages and offers that will keep them engaged. For example, you might offer a discount to customers who are about to lapse on their subscription.
By grouping customers together based on shared characteristics, businesses can better understand the needs and wants of each segment. This allows them to tailor their marketing and sales efforts to better appeal to each group, which can lead to more sales and revenue.
For example, a company that sells luxury cars might segment their customers based on income. They would then target their marketing and sales efforts towards those in the highest income bracket, as they are more likely to be able to afford the vehicles.
Customer segmentation can also help businesses increase sales by identifying potential new markets. For example, a company that sells women’s clothing might segment their customers by age. They might find that there is a large untapped market of older women who are looking for stylish clothing options.
Even a simple SaaS product can benefit from customer segmentation. By understanding the needs of each segment, a company can develop features that appeal to them. For example, a project management software might have different features for individual users, teams, and businesses.
Businesses often grapple with the question of how to allocate their marketing budget. Should they spend money on ads that target a wide audience or focus their efforts on a specific customer segment?
The answer, of course, depends on the business and its goals. But in many cases, customer segmentation can be a helpful tool for making better use of marketing budgets.
With customer segmentation, businesses can identify groups of customers with similar needs and target them with tailored messages. This can help businesses save money by avoiding the wasted exposure of advertising to people who are not interested in what they have to offer.
For example, a business selling a new type of car might use customer segmentation to target ads specifically to car enthusiasts. This would be more effective and efficient than attempting to reach a broader audience through general advertising.
Similarly, a business selling specifically to Gen Z consumers might use customer segmentation to target ads on social media platforms like TikTok, where this demographic spends a lot of time.
There are endless possibilities for how businesses can use customer segmentation to better target their marketing efforts. The key is to think about the needs of your target customer and then tailor your messaging accordingly.
When it comes to customer acquisition, businesses often face a chicken-and-egg scenario: they need customers to buy their products or services, but they need revenue to acquire those customers in the first place.
There are a number of ways businesses can use customer segmentation to improve customer acquisition. By understanding who their target customers are and what they need or want, businesses can develop targeted marketing and advertising campaigns that are more likely to resonate with potential customers and convert them into paying customers.
For example, a business that sells high-end kitchen appliances could use customer segmentation to target its advertising and marketing to individuals who are interested in gourmet cooking or who have recently purchased a home. By doing so, the business can focus its efforts on acquiring customers who are more likely to be interested in its products and who are in a position to make a purchase.
Content is a key part of any marketing strategy, but it’s only effective if it’s targeted to the right audience. Customer segmentation can be used to develop targeted content that is more likely to resonate with each customer group.
Suppose you're selling fashion across a number of price points. You might use customer segmentation to develop targeted content for each price point. For instance, you would develop different content for customers who are looking for budget-friendly fashion vs. luxury fashion.
Customer support is another important part of any successful business. It’s important to have a streamlined process in place so that you can quickly and efficiently address customer issues.
Customer segmentation can be used to identify the most common issues faced by each customer group. This information can be used to develop targeted support processes that are more likely to resolve customer issues.
Making informed business decisions is essential to the success of any business. But it can be difficult to do if you don’t have a clear understanding of your customers.
Customer segmentation can be used to develop a data-driven understanding of your customers. This information can be used to make informed decisions about everything from product development to marketing strategy.
Traditionally, customer segmentation involved looking at a few high-level characteristics to bucket customers into groups. This might include criteria like age, gender, income, or location. While this information can be helpful, it doesn't provide a very nuanced or complete picture of who your customers are and what they want.
Machine learning provides a way to go beyond these basic characteristics to really understand your customers. By using customer datasets with past interactions, behaviors, and even emotions, machine learning can create models that accurately predict which customers will respond to which messages and offers.
This is a powerful tool for companies who want to create more personalized experiences for their customers. By segmenting customers based on their individual needs and preferences, companies can increase customer satisfaction and loyalty.
Machine learning can also help companies save money by identifying which customers are likely to churn and taking steps to prevent it. In addition, by understanding which customers are the most profitable, companies can focus their efforts on acquiring and retaining more customers like them.
In short, machine learning provides a more complete and accurate picture of your customers, which can be used to create more personalized, targeted, and effective marketing campaigns.
Businesses have traditionally relied on manual methods to segment their customers. This process is time-consuming and often results in suboptimal segments.
Using machine learning algorithms like the k-means clustering algorithm can find different groups more accurately, but building such machine learning models is no easy feat. Data scientists would be needed with expertise in tools like Python Pandas, Numpy, and deep learning. Even libraries that aim to make the job easier, like sklearn (short for scikit-learn) and matplotlib, require significant code and effort to implement at scale. From data analysis on dataframes to determining n_clusters and following GitHub tutorials, the manual approach involves considerable effort.
One a technical level, clustering models need to find the "k" value - the number of customer clusters. This is done with heuristics like the elbow method, or more sophisticated optimization techniques. There's also the issue of centroid initialization, which can have a big impact on the clustering algorithm's performance.
Akkio's no-code AI offers a better way. First, simply connect your historical data, wherever it may reside. You can connect a CSV or Excel file, a database, or even a SaaS application. Second, select the column that contains the customer information you want to segment. Third, hit "predict." The model is automatically built and can be deployed anywhere.
Akkio automatically handles tasks like data pre-processing, model selection, validation, hyperparameter tuning, and feature engineering. This frees you from having to code these tasks yourself or hire data scientists to do them for you.
Neural architecture search (NAS) is a key technology that enables Akkio to build optimal models for customer segmentation. NAS algorithms automatically search through a large space of potential models to find the one that works best for your data points.
When clustering algorithms are used, the platform will also automatically find the optimal number of clusters in your customer data, such that a new customer’s group can be easily detected.
Akkio's no-code AI offers a better way to segment your customer base. With Akkio, you can automatically build high-quality segments without data science expertise. This can power a number of use-cases, from market segmentation to categorizing users by spending score.
Effectively segmenting your customers is key to delivering personalized experiences and driving growth. One key is to track the relevant metrics, such as customer lifetime value, average order value, and market share. These higher-order metrics will give you a better idea of which segments are most important to your business.
Once customers have been segmented, you can boost sales through cross-selling and up-selling. Cross-selling is the practice of selling complementary products to your existing customers. Upselling is the practice of selling higher-end products to your existing customers.
Both cross-selling and upselling are effective ways to increase sales and revenue, but they can be difficult to do without a clear understanding of your customers. Customer segmentation can be used to identify customers who are likely to be interested in complementary or higher-end products and target them with specific messages.
For example, if you're a clothing retailer, you might use customer segmentation to identify customers who have recently purchased items from the sale rack and target them with messages about your full-priced items. Or, if you're an e-commerce company, you might use customer segmentation to identify specific customers who are using your free trial and target them with messages about your paid plans, or even the number of products different customers might be interested in.
If you're looking for a no-code platform to build custom artificial intelligence solutions for your business, Akkio is a great option. With Akkio, you can quickly and easily build predictive models using your own data - no coding required! Start your free trial today.