Twinkle Bandla

Before AI, lifecycle management was a lot more manual. It mostly meant keeping track of key milestones, collecting feedback when we could, and stepping in to fix things when risks started showing up. It worked — but it was reactive and often time-consuming. Now with AI in the mix, a lot of those heavy lifts are automated. We can spot patterns in data, get real-time alerts, and know exactly when something needs attention — without having to go hunting for it. One such example is around  driving a ...Read More

This is an interesting question and needs a good perspective on how AI and humans can interact effectively. It’s going to be a collaborative and interactive framework where AI and humans work together. You have to define guidelines for the AI—what to monitor, how to monitor, and when to alert. Defining these steps plays a crucial role when you set up the workflow. For instance, let’s take some examples of how CSMs can effectively use AI: Risk Management: Define guidelines and thresholds for your ...Read More

Thanks for the interesting question. Performance assessment on any new rollout of AI models plays a crucial role. Think from the perspective of a new agent/AI feature that is launched in your product. It's important to assess it from three areas: Accuracy: How accurate are the responses? Get instant feedback from users (based on thumbs up and thumbs down). Let me give you a simple example: if you have launched an AI feature that scans and reads all the feedback from customers and categorizes it ...Read More

Driving experience through AI is interesting as well as becomes challenging if not integrated with the right data sources and right rules defined. Today, in the world, there are different sources through which we get the sentiment/feedback data captured. User-level interactive feedback can be captured on the efforts involved to use the feature. That is real-time analysis and instant feedback through in-app engagement. Capturing sentiment and cross-functional alignment through NPS and CSAT helps ...Read More

The Role of Customer Success Professionals Is Becoming More Efficient and Data-Driven. I would like to answer this question by relating to the CSM core activites and how AI can influence , boosting efficiency. Category 1: Customer Relationship Management Preparing for and participating in customer meetings Tracking customer updates Handling customer communications Now imagine: AI automatically summarizes customer history and interactions, enabling CSMs to prepare for meetings in minutes instead ...Read More

I want to refer to an interesting topic here on how AI complements resource management. It is a continuous process of reskilling and upskilling resources to best fit the organizational structure in a scalable and efficient way. Let me talk about complex product lifecycle management, where CS teams play a crucial role in customer success. Let’s talk about technical customer success teams, which are gaining a lot of importance in the market, where customer success is more than just relationship ma ...Read More

AI-driven insights always work hand in hand with data-driven solutions. The more you train the model with quality data, the more accurate the insights AI can provide. Today, insights often reside in different systems. That’s why it’s important to have a centralized location to integrate data and get a holistic view of your customer. Let’s talk about the different types of data—support ticketing data, customer interactions, user utilization data, customer communications and so on.If you can conne ...Read More

It all starts with building an efficient process for launching and managing milestones throughout your customer journeys — primarily through 1:M initiatives such as email campaigns, webinars, and office hours. Let’s break each one down Email Campaigns How effectively you engage with customers through emails depends on sending the right content to the right audience at the right time. Measure: Email engagement (open rate and click rate) — this reflects the efficiency and relevance of your outreac ...Read More

The key to assessing long-term impact is understanding and connecting the leading and lagging indicators. This question has two aspects: How to measure the success of AI-driven insights on customer health scores How to measure the impact of AI-driven insights on customer success at the organizational level Impact on Customer Health Scores Customer health management starts with defining the right metrics that help assess customer health and understanding how AI insights are providing the right al ...Read More

Interesting question! AI has influenced many strategies—especially around building smarter playbooks for risk mitigation, driving cross-functional collaboration, and evaluating resource utilization to reduce operational costs. Let me give you a simple example: Imagine if a resource (let’s say, Resource A) spends 6–8 hours on routine manual tasks like monitoring customer health and scanning communications for risk signals. If AI can automate that work using data, the CSM can instead focus on exec ...Read More