Preetam Jinka, our Co-Founder, recently spoke at the San Francisco Customer Success Meetup on March 14th.
Preetam’s talk focused on how AI can be used to streamline customer success (CS) operations and improve overall customer satisfaction. He dived into four key areas where AI can be a game-changer for CS teams:
Effortless Data Acquisition: Traditionally, extracting product usage data was a laborious task for engineers, requiring them to write and implement code. This created iteration bottlenecks and delayed insights. It also forced all other teams to depend heavily on engineering resources in order to get analytics and product usage data. AI, however, can help with getting access to data and help understand what data means without much heavy lifting. This greatly reduces and often eliminates the need for code and complex queries. It also significantly democratizes access to data.
Data-Driven ICP Identification: Pinpointing a company's Ideal Customer Profile (ICP) is crucial for targeted marketing efforts. Traditionally, this process relied on guesswork or sales data, which might not always reflect customers who experience value from the product. AI is good at detecting patterns given a large data set. Using AI, it is easy to solve the ICP challenge - analyze product usage data to identify the common characteristics of the customers who are deriving the most value from the product.
Personalized Customer Engagement: Customer success teams are tasked with engaging with customers and ensuring they achieve their desired outcomes. But manually determining the optimal engagement strategy for each customer can be time-consuming and ineffective. Here again AI can be very helpful. By analyzing historical data, AI can identify the ideal customer journey, which represents the sequence of actions that typically leads to customer success. This allows CS teams to proactively intervene and guide customers who are veering off track, ultimately improving their success rates.
AI-Powered CSM Productivity: Customer Success Managers (CSMs) often juggle multiple tools and information sources to get a comprehensive view of a customer's account. This can be a major time drain and hinder their productivity. AI can act as a central information hub for CSMs. By using natural language queries, CSMs can ask questions and receive a holistic view of the customer's account, encompassing details from support tickets, product usage data, and even company news. This eliminates the need to switch between different tools and empowers CSMs to focus on what matters most - building strong customer relationships.
In essence, AI can automate tedious tasks, generate data-driven insights, and personalize customer journeys, all with the goal of making CS operations more efficient and effective.
In other things
Will Artificial Intelligence be the "Ozempic" for customer success function? We had the privilege to attend an incredible CS leadership dinner in Atlanta, hosted by FunnelStory and the James Scott of the ATLCS community.
The conversation centered on the customer journey and what the next 2-3 years hold for our field. Here are some spicy takes that emerged:
Customer Journey: Central or Irrelevant? Some leaders see it as the foundation for understanding customer goals and driving value realization. Others, particularly in pro-serve organizations, find it less crucial.
CS as Revenue Engine: Trend or Terrible Idea? A strong push exists to make CS a revenue-owning function. Yet concerns arise about whether CS teams have the skillset to close
Automation and AI: The CS Revolution Leaders see efficiency gains through automating tactical tasks. AI-powered tools are streamlining processes (think QBR deck prep!) Can AI fully replace that human touch?
Blurred Lines: CS, Sales, RevOps Some orgs have CS and Sales sharing renewal/expansion commissions. RevOps is critical for aligning incentives and driving a focus on EBITA. Several companies have their CCOs report into the CRO, with quota responsibilities for their CS teams!
