Customer success is a critical part of any business. It involves building strong relationships with customers to ensure their needs are met and they achieve their desired outcomes. However, managing customer success is no easy task. It requires a proactive and holistic approach to meet customers' outcomes, constantly assisting and guiding them toward success. In this white paper, we will discuss the critical challenges of customer success and how to overcome them with automation and machine learning.

Challenges of Customer Success:

1. Unpredictable Customer Behavior: Customer behavior can be unpredictable, creating challenges for CS managers. Many business factors might impact customers' behavior directly or indirectly, and keeping an eye on everything is nearly impossible. Identifying the impacting factors, monitoring those, and acting upon them at the right time becomes a mission for CS professionals.

2. Customer not finding value: This is the biggest challenge for CS. The exciting part is that multiple parties are contributors to enable this challenge, but CS folks have to solve this. It has a direct impact on CS teams’ supreme goal, “retention.” This scenario arises due to promising vs. delivery, product limitations, and mismanaged CS execution. It's important to find a balance that meets both the customer's goals and the business objectives.

3. Incomplete or Inaccurate Data: One of the biggest challenges in customer success is working with incomplete or inaccurate data. Even though we all have realized the value of data, it takes a lot of energy to become data mature. Until we capture the right data at the right time with the right frequency, all the strategies are bound to fail. A lack of data maturity can make it challenging to identify patterns or trends, which can result in missed opportunities.

4. Surprise events & failed strategies: CS is a highly human-involved practice, bringing many rule books and guideline-based strategies. Too often, CS leaders deploy their strategies without validating them. This leads to events like surprise churn and a need for clarity on what's working and what's not. Building a business outcome-driven strategy, validating them in advance, and acting dynamically seems idealistic in traditional CS process

How Automation and Machine Learning Can Help:

Automation and machine learning can help customer success managers overcome these challenges in several ways.

1. Rely on outcome-driven business indicators: Automation and machine learning can extract all possible correlations, curate the right indicators and validate them against outcomes. Moreover, it can uncover correlated metrics which might be indirectly impacting customer behavior.

2. Capture relevant logs and build data-driven stories around customers' objectives: Customers won't buy your subjective story of ROI. you have to ensure your product team captures the correct logs, linking them to actions performed and, finally, to objectives. Audit to analysis should pass through the objectives-deliverables-outcomes flow. Automation and machine learning setup can help you avoid transitional usage tracking and subjective story.

3. Evolving and dynamic data maturity: Business data maturity is a broad term and cannot be achieved in a very short span of time. However, it needs to be nurtured efficiently over a period of time. Business needs to understand what data points matter to them, what relationships need to be developed, and how it has to be captured & monitored. Continuous assessment of the state of data and actions can help.

4. Predict & Proactive Strategies: Automation & Machine learning can help with correlation analysis, simulate prediction for a given strategy, monitor the deployed strategy, assess the tracked metrics & KPIs, identify potential issues, and proactively address them before they become significant problems.

Conclusion

Customer success is critical to the success of any business. However, managing customer success can be challenging, especially unpredictable customer behavior, lack of customer value, incomplete or inaccurate data, and guidelines-based strategies instead of data-driven strategies. Automation and machine learning can help overcome these challenges by collecting and analyzing data more efficiently, streamlining the customer journey, predicting customer behavior, and identifying growth opportunities. With the right tools and resources, customer success managers can effectively guide customers toward success, leading to customer retention and a winning business strategy.

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