Scaling without compromising: Blending digital and human touchpoints

Scaling operations is often misunderstood as compromising quality for efficiency gains. 

At Propel24, Ciara Conlon, Senior Manager of Customer Onboarding at MongoDB, shared valuable insights on scaling customer onboarding programs to meet the evolving needs of a growing customer base. She talks about MongoDB's journey and key principles for scaling functions effectively in a dynamic marketplace.

In this session, she discussed: 

• Imperatives for adapting to a changing landscape
• How to scale customer onboarding responsibly
• Using data to optimize customer onboarding

From growth-first to profitability and sustainability: Adapting to a changing landscape

The current economic climate has shifted from prioritizing growth at any cost to focusing on profitability and sustainable growth. This means businesses must operate more efficiently with existing resources rather than simply increasing headcount.

Customer expectations have also evolved. Modern customers demand personalized, timely support without constant human interaction. They expect easy setup and onboarding to realize value from their software investments quickly.

Similarly, internal team dynamics are changing. Failing to adopt new technologies and evolving ways of working can lead to admin overhead, disengagement, burnout, and a lack of innovation. It is crucial to enable team members to focus on strategic tasks they excel at and enjoy, such as engaging with and serving customers.

There is an urgent need to adopt data-driven and personalized service delivery methods to meet customer expectations and thrive in the competitive marketplace.

Scaling customer onboarding responsibly

Scaling the onboarding program to support a growing customer base responsibly requires doing more for customers without simply increasing headcount. 

The goal is to maintain high-value delivery while optimizing efficiency and accelerating time to value, even as the customer base expands. This is a significant but common challenge in today's business environment.

Using data to optimize customer onboarding

Initially, MongoDB’s onboarding model focused on human interaction at the higher end of the revenue scale, with self-serve options for lower tiers. They identified opportunities to support self-serve customers better and improve scalability without simply increasing headcount. 

They aimed to serve customers more efficiently, especially those with higher risk profiles, and to offer more flexible onboarding options. To achieve this, they needed to optimize their onboarding methodologies to maintain value delivery and accelerate time to value. 

Here is how MongoDB used data to guide their decisions, focusing on customer profiles, time-to-value, and behavioral patterns.

1. Customer profile

The customer onboarding team at MongoDB analyzed customer profiles to identify areas of higher and lower risk. They found that customers migrating data from another source or those with less knowledge of MongoDB tended to have more challenges and longer launch times. 

Using risk cases from the previous year, they gathered insights into customers who consistently delayed timelines or deployed suboptimally, indicating a higher risk of churn. This data helped the onboarding team assign higher risk profiles to specific customer segments and tailor their onboarding approaches accordingly.

2. Time-to-value

Time-to-value was a critical metric for MongoDB, as it highlighted which customers were achieving success quickly and which were experiencing delays or abandoning projects. The analysis revealed that customers with high experience with MongoDB launched twice as fast as those with low knowledge. 

This insight provided confidence that experienced customers could potentially succeed with less human interaction, allowing MongoDB to allocate resources more effectively.

3. Behavioral patterns

The onboarding team monitored whether customers engaged with their content, responded to outreach, and valued webinars or workshops. These insights helped the MongoDB team gain perspective on what different types of customers valued, allowing them to tailor their onboarding programs to meet these preferences. 

With the help of behavioral data, the onboarding team could better nurture customers during the critical early stages, ensuring they received the right level of support and resources to succeed.

Implementing changes based on the insights from the data

Based on their data analysis, the onboarding team implemented two major changes to optimize their onboarding process and better support customers. These changes focused on leveraging insights to segment customers more effectively and direct them to optimal onboarding experiences. Here's a deeper look into the best practices they implemented:

1. New risk intervention motion

The customer onboarding team at MongoDB introduced a new risk intervention motion for lower-risk customer segments. This approach aimed to reduce the need for human engagement while still providing adequate support.

They improved customer coverage and value by intervening when customers showed signs of non-progression or unhealthy product signals. This allowed the team to strategically redirect time to more complex risk mitigations and interactions that customers valued.

2. Additional support for higher risk profiles

For higher-risk customer segments, the onboarding team created high-value activities tailored to their specific needs. For instance, customers with less experience with the product received tighter technical enablement early in their journey. 

The capacity saved from the new risk intervention model was used to fund these additional high-value activities, ensuring that higher-risk customers received the support needed to succeed.

Key takeaways from the session

Scaling an onboarding program can be daunting, but focusing on key principles can guide the process effectively. These key principles will help you create a scalable and efficient onboarding model that meets customer needs and optimizes resource use.

1. Use data-driven insights

Data should be the foundation of any scalable onboarding model. Segment customers by risk and complexity, understand their profiles, and analyze their time to value and behaviors. This data-driven approach allows for informed decision-making and ensures that the onboarding process is tailored to meet the unique needs of each customer segment, enhancing the overall effectiveness and efficiency of the program.

2. Take a customer-centric approach

Prioritize customer needs and preferences when designing your onboarding process. Recognize that while check-ins and updates are valuable for tracking timelines and identifying risks, they may not always align with what the customer wants. Tailor interactions to provide maximum value and encourage engagement, ensuring that the customer experience remains positive and productive.

3. Be strategic about resource allocation

Efficient resource allocation is crucial for scalability. Assign routine, low-impact tasks to digital tools or a risk intervention model to improve efficiency and customer experience. Prioritize human-led engagements for complex, high-impact scenarios where relationship management, complex risk mitigation, and trust building are essential. This strategic approach ensures that resources are utilized effectively, focusing human efforts where they are most needed.

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