In times of economic downturns, businesses must prioritize retaining their existing customers and fostering loyalty. A recent Harvard Business Review article highlights that optimizing revenue from current customers can quickly enhance top-line performance while strengthening customer loyalty. Machine learning and artificial intelligence play a pivotal role in improving customer success and safeguarding the existing customer base. In this article, we delve into the advantages of employing ML/AI tools for customer success management and discuss why they may be the superior choice for businesses aiming to optimize their customer success strategies.
Understanding Customer Success Management (CSM)CSM is an essential aspect of any business's success, particularly during economic downturns. CSM tools aid companies in increasing customer retention, enhancing customer lifetime value (CLV), and building lasting customer loyalty. There are two main types of CSM tools available in the market today: rule-based and machine learning/AI. In this article, we compare the benefits and drawbacks of both types of tools.Rule-Based Customer Success ToolsRule-based CSM tools are software that uses predetermined rules or decision trees to manage customer interactions. These tools rely on human-created rules to determine the appropriate actions based on specific customer behaviors or triggers. For instance, if a customer has not logged into their account for a certain number of days, a rule-based CSM tool may send an automated email reminder.
Rule-based CSM tools automate specific processes and workflows designed to boost customer success. They typically use a combination of predefined rules and decision trees to determine the actions to take in response to particular customer behaviors or events. The rules and decision trees, created by humans, mimic the decision-making process of a human customer success manager.
Rule-based CSM tools offer simplicity as their primary advantage. They are relatively easy to implement and require minimal training or technical expertise. Furthermore, these tools can effectively manage straightforward customer interactions and workflows.
However, rule-based CSM tools have several drawbacks. They can be inflexible, unable to adapt to changing customer behaviors or preferences, leading to missed engagement or retention opportunities. Additionally, their accuracy and scalability are limited, as they rely on predefined rules and decision trees that may not account for the complexity of customer behavior.
Machine learning/AI customer success tools utilize machine learning and artificial intelligence algorithms to manage customer interactions. These tools are designed to analyze customer data in real-time, providing personalized recommendations and insights to customer success managers.
Machine learning/AI customer success tools analyze vast amounts of customer data to identify patterns and predict future behaviors. They employ machine learning algorithms to learn from past customer interactions, offering personalized recommendations and insights to customer success managers. For example, a machine learning/AI customer success tool may analyze a customer's purchase history and suggest additional products or services they may be interested in.
A key benefit of machine learning/AI customer success tools is their adaptability to changing customer behaviors and preferences. These tools are designed to analyze extensive data and deliver personalized recommendations and insights in real-time. Additionally, they can be highly accurate and scalable, quickly and effectively analyzing large amounts of data.
However, machine learning/AI customer success tools have some drawbacks. They can be complex, requiring significant technical expertise for effective implementation and management. Moreover, these tools may be more expensive than rule-based tools, posing a barrier to entry for some businesses.
While rule-based systems utilize predefined rules to predict churn based on specific variables like customer demographics, purchase history,and customer support interactions, they have several limitations.
First, rule-based systems are limited by the number of variables they can consider. They can only account for variables predefined in the system, potentially overlooking essential variables contributing to churn.
For example, a customer who hasn't made a purchase in the past 90 days but is highly engaged in marketing initiatives, recently contacted customer service about a new feature, and increased their product usage may exhibit upsell or cross-sell potential and is unlikely to churn. However, a rule-based system might flag this customer as at risk for churn due to their lack of recent purchases.
Second, rule-based systems cannot adapt to changing conditions. They may struggle to recognize changes in customer behavior, leading to inaccurate predictions.
For instance, if a customer has been consistently purchasing products from your e-commerce website for the past few months, your rule-based system would predict that this customer is unlikely to churn soon.
However, if the customer's behavior suddenly changes—browsing your website less frequently and ceasing purchases—the rule-based system would continue to predict that the customer is unlikely to churn since it only considers their historical behavior.
Lastly, rule-based systems struggle to handle complex data sets. They are often too simplistic to produce reliable predictions given the vast amount of data available to businesses.
For example, consider an online retailer serving customers in multiple countries and selling a wide range of products. The business needs to analyze a considerable amount of data—customer demographics, purchase histories, browsing patterns, and more—to predict churn. A rule-based approach would struggle to account for all the various aspects of churn in such a complex data environment.
Conversely, AI-based solutions excel at predicting churn. These systems employ machine learning algorithms to scan large data sets and identify patterns that are difficult for humans to detect. This means they can consider a wider range of factors and adjust as needed. Sophisticated algorithms allow AI to analyze complex data sets and discover patterns and connections that a rule-based system might miss. AI-based systems can also evolve over time and acquire new skills. The system's predictions can improve as more data is collected, enabling companies to continuously enhance their ability to predict churn. According to a McKinsey study, machine learning models are up to 30% more accurate than conventional rule-based models in predicting churn. A separate IBM study found that machine learning algorithms reduced churn rates at a telecom company by up to 54%, compared to just 33% using a rule-based approach.
A prime example is Netflix, which uses AI-based technologies to predict which movies or TV shows a user will likely enjoy. With this data, Netflix creates personalized recommendations for each user, reducing churn.
In conclusion, rule-based systems are insufficient for churn prediction today. AI-based solutions are more suited for this task as they can account for more factors and adapt to changing circumstances. Businesses looking to reduce churn and retain customers should consider AI-based systems, like those provided by Churned.
AI-based customer success tools offer the significant advantage of learning and adapting over time in addition to their accuracy and scalability. As the system processes more data, it can improve its accuracy and efficiency in predicting churn. This means businesses can continuously refine their customer retention strategies and decrease churn rates.
However, AI-based systems have their challenges. Developing and implementing an AI-based customer success tool requires extensive expertise in data science, machine learning, and AI, which may be difficult for smaller businesses or those without a dedicated data science team.
Moreover, AI-based systems need a substantial amount of data to function effectively. Insufficient or poor-quality data can result in inaccurate or unreliable predictions. AI-based systems can also be susceptible to bias if the training data is biased, leading to inaccurate predictions and negatively impacting customer retention efforts.
In terms of cost, AI-based systems may require a more significant investment compared to rule-based systems. The complexity of the technology and the need for specialized expertise may increase the cost of development and implementation. However, the potential benefits in terms of accuracy, scalability, and adaptability may outweigh the costs in the long run.
In conclusion, both rule-based and AI-based customer success tools have their advantages and limitations. Rule-based systems are simpler and less costly to develop, but their accuracy and scalability may be limited. AI-based systems, on the other hand, are more complex and may require more resources, but they offer greater accuracy, scalability, and the ability to learn and adapt over time.
Businesses seeking to leverage the benefits of AI-based customer success tools without incurring prohibitive costs or complexity should consider Churned. Churned aims to bridge the gap between complex AI and SaaS, B2C-subscription and e-commerce, making AI accessible for all. Our team of skilled experts, including a professor in data science, has developed a powerful AI engine that integrates seamlessly with clients' IT systems. By analyzing patterns and behaviors of their clients' customers, Churned can make accurate predictions about their health and risk of churning.
Churned's AI engine allows businesses to offer a unique proposition to their clients. Unlike many competitors who rely on rule-based systems, they use advanced machine learning and AI to deliver unparalleled insights into customer behavior. Our tool is easy to implement and "plug and play," meaning that it can be integrated seamlessly with clients' existing CRM, marketing, and support systems.
Churned primarily serves three industries: SaaS, B2C subscription, and e-commerce, with their tool mainly used by customer success and marketing teams. By providing real-time insights into customer health and risk, Churned helps clients lower their churn rate, increase customer lifetime value (CLV), and boost net revenue retention (NRR).
While AI-based customer success tools may have their downsides, Churned's unique approach to AI and developing the tool makes it accessible for all. Our focus on making AI accessible to a wider public sets us apart from the competition. With Churned, companies can leverage the power of AI to drive their business forward and achieve sustainable growth.