I was given a unique opportunity in 2018, managing a team of over 150 post-sales professionals who were responsible for retaining and growing thousands of customers by providing a better user experience with clear ROI. Little did I know that this opportunity would evolve into a thrilling customer success adventure. I’m Ori Entis, co-founder and CEO of Staircase AI and I’d like to share our story (so far).
I have worked for many years as a cross-functional leader, with demonstrated experience in Engineering, Product Management and Customer Success. Following some fruitful and insightful post-sales leadership stints in various companies, it was only a matter of time before I co-founded my own company with Lior Harel. Here’s a glimpse into our journey.
Traditional customer success strategies are flawed
Around 5 years ago, I was working for a leading SaaS MarTech vendor that was growing exponentially. After managing the product team, I was asked to lead the global post-sales team of around 150 employees – customer success pros, support representatives, technical account managers, data scientists, and more. We had access to a formidable CS tech stack and I was expecting to hit the ground running.
Moving from product to a direct interaction with our customers was an incredible opportunity. I felt that safeguarding our customer base revenue and expansion was one of the most important roles in the company.
I initially thought that having access to the CRM, CS platform, product analytics tools, and a capable BI team should give me a clear picture of what’s going on. But unfortunately the data was often stale or incorrect.
We had to compensate by constantly meeting the relevant stakeholders to align, share information, and decide on next steps. At times, I would have to ask 5-6 employees from different departments to join a meeting to get the true 360 of the customer. This manual approach was simply not scalable, especially from my CS leadership standpoint.
My eye-opening moment
The business had lost a few key customers and it was becoming evident that a new approach was needed. I started spending time with my team, while also engaging directly with customers, to try to uncover the blind spots. I soon realized that product analytics tools are not silver bullets because they are lagging indicators that don’t really flag at-risk accounts and don’t help avoid churn.
I reached my eye-opening moment when I understood that customer success is all about the relationships. Also, I realized that you need to break down all communications and interactions to truly understand relationships.
Old tech stacks don’t promote customer intelligence
Building strong relationships with key stakeholders was proving to be extremely valuable time and again. We started understanding when organizational changes or technology evaluations were about to happen with our accounts. This gave me and my team the opportunity to be proactive, tweak our playbooks accordingly, and mitigate risks before they caused damage or churn.
But all of this was done manually and wasn’t a sustainable working pattern.
What about the tech stack I mentioned earlier? CS solution vendors and my team members had conflicting opinions about the benefits of the tech stack that was in place at the time. This issue had to be resolved to achieve sustainable growth.
The CS solution vendors continued to sound confident about their offerings. They kept promising a 360 view of customer accounts along with customizable triggers and alerts to help build a dynamic post-sales strategy. But my team had a different opinion. They complained about manual data logging dependencies, being overwhelmed with the logistics, and the “loud” nature of the alerts.
It had become safe to assume that the existing tech stack wasn’t working.
Thinking outside the box is key
I then started running some experiments. For example, I told my team to manually analyze a sample of their email communications with customers in the onboarding phase. They were soon able to identify areas that were causing friction, customer frustration, team inefficiency, and overall slower time-to-value (TTV). These customer insights proved to be extremely valuable.
Both product and service models started working better. TTV was reduced by 25%!
The solution was becoming clear – analyzing customer-vendor communications. It turned out to be an accurate indicator of customer engagement, sentiment, trending topics, relationship strength and more. Combining all of that with product telemetry was a real game changer. But there was still a glaring problem – my team had to do all of this manually. These time consuming processes were impossible to perform on a daily basis.
Staircase AI is born
Back in 2020, there were no tools that could analyze human interactions for B2B use cases and identify key incidents accurately in real-time. Traditional tools simply aggregated various communication channels into one location, but it was still very hard to sift through thousands of emails, tickets, and chat records. This is when Lior Harel and I decided to co-found Staircase AI.
Lior, co-founder and CTO, and I soon noticed that major advancements were taking place in the Machine Learning (ML) world, namely NLP and deep learning. We envisioned a system that could understand what’s happening with customers, without pressing CS teams into mundane tasks like manual data logging. The ultimate goal was to predict churn risk and growth opportunities automatically and accurately.
The rest is history.
Staircase AI has now become a force to reckon with in the CS space. Its automated customer intelligence platform is all about breaking down millions of interactions to get a better understanding of what’s going on in accounts – relationship changes, sentiment fluctuations, churn risks, and new growth opportunities. Improving NRR requires a proactive approach. Staircase AI is here to help you do just that.
