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Uncovering Hidden Signals - AI and CX with Oren Yaqobi

Last week we had a conversation with Oren Yaqobi, who leads Customer Success at Frontegg, a company specializing in identity and security solutions. Oren's insights from managing customer experience for a highly technical product were both unique and valuable. Here's what we learned:

Uncovering Hidden Signals - AI and CX with Oren Yaqobi

Last week we had a conversation with Oren Yaqobi, who leads Customer Success at Frontegg, a company specializing in identity and security solutions. Oren's insights from managing customer experience for a highly technical product were both unique and valuable. Here's what we learned:

Can you introduce Fronteg and explain your role there?

Oren: I joined Frontegg early March after spending ten years at Redis. We're focused on identity and security solutions, and I'm passionate about our product. As the leader of Customer Success, my role is to ensure our customers succeed with our highly technical offering. We're constantly learning about our customers and their needs.

How are you leveraging AI in customer experience at Fronteg?

Oren: We're integrating AI across all aspects of our CX strategy. Primarily, we use it to understand what customers want to achieve and what they actually achieve. This insight wasn't always clear before because we had to rely solely on people interpreting customer feedback, which can be biased.

Now, we're exploring AI tools that can analyze calls and provide concrete insights. These tools can tell us, 'Here's what I heard. These are the customer's goals. And these are the gaps.' They can even generate action items based on these insights.

We're also using AI for customer usage analysis and prediction, areas that are challenging to handle manually due to the large number of customers and deployments. It's helping with self-service solutions for both customers and our internal teams, and it's revolutionizing our knowledge management processes.

That sounds powerful. What challenges did you face before implementing AI in your CX processes?

Oren: Before AI, our processes were highly manual and dependent on individual organization skills. After a customer call, we had to trust team members to document everything accurately in our CRM. Even with good documentation, following up effectively and knowing what to address in the next Executive Business Review was challenging.

The biggest issue was that valuable insights from these meetings often went nowhere. They weren't actionable, even though they could have been. This led to lost opportunities and confusion. That's what we're working to change with AI.

Got it. How do you then connect customer feedback with product usage and business outcomes?

Oren: We're creating a holistic view by connecting customer feedback to usage data, revenue, and other business outcomes. But we're going further by linking this information to our product and QA teams.

For instance, I guide our QA team to focus on testing the most popular features or environments in new versions. Similarly, I ensure our product team understands how customers are actually using our product by joining calls with them and making sure the CS team represents the customers' interests internally. This approach prevents them from working in a vacuum and grounds our development in real-world data.

You’re a high level leader, how do you collaborate with other departments to enhance customer experience?

Oren: I see myself as the quarterback or the CEO of the customer. With a comprehensive view of customer needs and wants, I work to share this information effectively across the company.

I'm not afraid to bring product managers directly to customers. Recently, I connected a customer interested in our entitlement engine with the product manager owning this feature. This allows our team to hear firsthand what customers want and share our roadmap directly.

However, I'm always cautious about these interactions. There's a risk of overpromising, so I try to manage these conversations carefully. It's about finding the right balance between openness and setting realistic expectations.

What's your approach to managing customer requests and company resources?

Oren: Understanding our ecosystem is crucial. Recently, we released a great new feature, but its design wasn’t exactly what our customers wanted. We invested significant R&D efforts to address the gaps. In discussions with our CTO, I had to ask, 'How will this affect our roadmap? What's the trade-off here?'

In a startup, resources are always limited. When we commit to something new, we need to understand the full impact. I always try to uncover what kind of 'deal with the devil' we're making. What are we giving up to get this new thing? If you don't ask these questions, you might not realize the cost until it's too late.

So what did we learn?

Our conversation with Oren provided valuable insights into how a technical company approaches customer experience in the AI era. Here are the key takeaways:

1. Define and measure customer success.

  • As Oren put it, "If you don't know what success is for your customers, you can't do success." Use AI tools to objectively extract and measure these insights from customer interactions.

2. Connect the dots across the organization

  • Integrate customer feedback with usage data, product development, and QA processes to ensure customer insights drive improvements across the entire organization.

3. Be the "quarterback" for your customers

  • As a CX leader, act as the "CEO of the customer", bridging the gap between customers and internal teams. Facilitate meaningful interactions, not just information transfer.

4. Understand the "deal with the devil"

  • While pushing for customer-centric improvements, always consider the broader impact on company resources. This realistic approach helps in managing expectations and resources effectively.

By applying these principles, companies can create a more data-driven, customer-centric approach to their products and services. As Oren insightfully noted, "I love people, but they can be biased." Leveraging AI and data can help overcome these biases and drive more objective, actionable insights, at scale, ultimately leading to better outcomes for both the business and its customers.