How Customer Success Metrics Are Evolving in the Age of AI

How Customer Success Metrics Are Evolving in the Age of AI

There’s no hiding from AI anymore. It’s here to stay and it’s changing the way we work. 

Customer Success Managers have had a front-row seat to all of these exciting changes happening specifically in the B2B Software space. We have seen Generative AI change the way we interpret customer data, gather more accurate customer health scores, and even write out lengthy recap emails. All of this has saved CSMs tons of time so they can focus their efforts on building lasting customer relationships. 

Customer Success metrics have also started to evolve with the changes that AI is bringing. They have always given us a good idea of how strong our customer relations are. They have measured and reported on KPIs that all point to the overall customer experience. However, as CSMs become more strategic in their approach to customer relationships, they are measured differently than they were in the pre-AI world. 

Let’s dive deep into how CS professionals can not only adapt to these evolving metrics but excel in their careers as well. 

What You Will Learn

• How AI still plays a role in traditional CS metrics
• What AI is changing about metrics specifically 
• How you will be measured on customer engagement metrics
• Our predictions on what CSMs can expect from AI very soon 

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AI and Traditional Customer Success Metrics

CS metrics will always be important for several reasons. First, they provide insights into customer satisfaction and loyalty, which are key indicators of repeat business and long-term revenue. Second, they help identify areas where the product or service may need improvement. This helps lead to strategic decisions and product development. Finally, understanding these metrics allows companies to proactively address issues, reducing customer churn and fostering a positive customer experience.

This is why CS metrics aren’t going anywhere. They are the backbone of any successful business that wants to be customer-first. 

There are several key metrics that make up this scope. Here’s just a few…

Customer Churn Rate: This metric measures the percentage of customers who cancel or do not renew their subscription within a certain period.

Net Promoter Score (NPS): NPS gauges customer loyalty by asking customers how likely they are to recommend the company's product or service to others. It categorizes customers into Promoters, Passives, and Detractors, providing a clear picture of customer loyalty and satisfaction. NPS tends to not have the best reputation in the CS space. The good news is that AI is improving this. More on that later.  

Customer Satisfaction Score (CSAT): This score is derived from customer responses to direct questions about their satisfaction with a product or service, usually on a scale. It offers immediate feedback on customer satisfaction levels.

Customer Lifetime Value (CLV): CLV predicts the total revenue a business can reasonably expect from a single customer account throughout the business relationship. It helps companies understand the long-term value of maintaining good relationships with their customers.

While these metrics aren’t going anywhere, let’s talk about how AI is playing a bigger role in gathering and interpreting customer metrics. 

The Impact of AI on Customer Success Metrics

It can be tough to keep up with AI and how it seems to be affecting every aspect of our work. Customer Success metrics and KPIs are changing because of this. From our research, here is how we have seen AI change metrics and KPIs so far: 

Predictive Analytics: AI leverages vast amounts of customer data to analyze patterns and predict behavior. It can predict when a customer might cancel a subscription based on usage patterns, interactions, and feedback. With this information, Customer Success teams can manage their books of business more proactively by creating targeted strategies. These strategies will lead to better retention and increased satisfaction.

Personalization at Scale: AI is more than just predictions that create more personalized experiences. Customer Success teams can use AI to tailor interactions with each customer by providing customized recommendations, content, and solutions. One example of this would be AI-powered chatbots. They engage with customers in real-time, offering personalized assistance based on previous interactions and preferences. These new and improved chatbots can provide better Customer Success engagement metrics that can be used to determine how customers are using and engaging with your services. 

Automation and Efficiency: AI automates routine tasks. This is no longer a mystery since so many SaaS companies are stating to implement better automation thanks to the efficiency of AI. Freeing up these resources leads to more time to focus on building strategic customer relationships. Customer Success key metrics are improved as stronger, more results-driven relationships are built with customers. 

Real-time Insights: AI provides real-time insights into customer behavior, allowing for more efficient interventions when needed. By analyzing customer data, AI helps identify patterns, predict trends, and determine customer needs. Your Customer Success metrics dashboard will be much more valuable with these real-time insights coming in daily. 

Improved Customer Support: AI-powered chatbots and virtual assistants improve customer support by handling common queries, freeing up human agents to focus on complex tasks. Going back to the chatbot example, having a more automated support system will result in better insights that the CS team can use to gauge customer satisfaction and how they are utilizing the product or service. 

AI-driven Customer Engagement Metrics

Customer engagement success metrics are on the rise. With AI automating a lot of the customer journey, companies need to have better insight into how their customers are engaging with their products. Tools like Salesforce Einstein, Zendesk Explore, and Hubspot Service Hub are examples of tools that help CSMs around the world better understand their customer engagement metrics. Having this window into the usage and overall engagement of your customers will help you know how you can deliver better results for your customers. 

As mentioned earlier, metrics like Customer Health Score (CHS), Net Promoter Score (NPS), and Customer Lifetime Value (CLV) aren’t going anywhere. 

But AI is improving them.. A lot. 

The amount of data AI is able to analyze is almost incomprehensible to the human brain. Customer behavior, product usage patterns, past feedback, trends in customer loyalty and satisfaction, purchase behavior, and engagement levels all play into accurately predicting these metrics. 

Here are some other engagement metrics that you should plan on seeing in the very near future if you haven’t already: 

Sentiment Analysis: AI tools can sift through customer feedback, support interactions, and social media mentions to gauge customer sentiment. This real-time insight helps teams to adjust their strategies and communications accordingly.

Engagement Score: AI can calculate an engagement score by analyzing various interactions a customer has with your organization. This includes website visits, social media interactions, and product usage. This comprehensive view can help teams understand how engaged customers are and what actually drives their engagement.

Personalization Effectiveness: AI can track how personalized content and recommendations affect customer behavior and satisfaction. This metric helps teams to refine their personalization strategies for better engagement and conversion rates.

Predictive Customer Journey Mapping: AI can predict the paths customers are likely to take, identifying potential drop-off points and opportunities for engagement. This allows teams to create more effective customer journeys.

The Future Landscape of Customer Success Metrics

To repeat what’s already been said, Customer Success metrics aren’t going anywhere. They are key to long-term business growth because they give any organization a window into the overall satisfaction and success of their customers. With AI doing its thing, it’s important to understand that portions of traditional customer outreach and conversation will be automated. 

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With this automation, metrics of engagement and success will evolve. 

With all the information that’s been discussed so far, what can we expect the future of CS metrics to be like? 

For starters, personalization will be more scalable and tracked more effectively. Yes, customers want results but the human-touch approach should not be overlooked. According to Salesforce research, 66% of consumers expect companies to understand their unique needs and expectations, and 52% expect all offers to be personalized. 

Because of this, metrics will likely evolve from generic satisfaction scores to more nuanced indicators of personalized engagement and value realization. Imagine metrics that can predict customer needs before they even articulate them. This will result in more proactive support and tailored success plans for each customer. 

Going off of that, future metrics will increasingly focus on predictive analytics. These analytics will play an important role in identifying potential churn risks or upsell opportunities before they fully manifest. This could involve complex algorithms that assess customer health scores by integrating usage patterns, support interactions, and even sentiment analysis from various communication channels.

One thing we are already seeing is software platforms becoming more integrated and connected thank to AI. 

As this continues, Customer Success metrics will reflect this complexity. We'll see metrics that account for a customer's interaction across multiple platforms and services, providing a more holistic view of their journey. This approach will help companies understand the broader context of customer experiences, leading to more effective cross-platform strategies.

And with these advancements in software, more ethical privacy considerations will need to be implemented. Future metrics will need to balance the benefits of personalization and prediction with the priority of protecting customer data and ensuring transparency.

There are lots of exciting changes on the horizon. 

The best thing any Customer Success team can do right now is analyze the current metrics that are being tacked. From there, make sure they are aligned with your plans for growth and automation. If you are planning on implementing AI into your processes, assessing how they will affect your metrics for success will be a great place to start to ensure nothing is missed in the customer journey. 

Best of luck!