With a self-serve product, you probably want to stay away from some of the more product-based KPIs (e.g., product adoption or health score if it's largely adoption driven) but retention, NPS, etc. are still critical metrics for Customer Success. The business has a value proposition for why it's investing in Customer Success despite the product being self-serve so it's incumbent to figure out what that investment thesis is and tie you and your team's KPIs around it.

I'd structure my thoughts tailored to the company's goals, customer needs, and product/service offerings, distinguish between leading and lagging indicators, establish the median metric numbers, and improvise as we go from time to time. 1. Understand Company Goals: I will start by understanding the company's goals. These could include revenue growth, customer retention, market share expansion, specific product adoption targets, and multi-product strategies. 2. Align with Business Objectives: Identify how we can contribute to achieving these goals. For example, if the company aims to increase revenue through upsells and expansions, we may focus on improving product adoption and identifying upsell opportunities. 3. Identify Customer Needs: It's important to monitor your customers' needs and expectations. Conduct customer surveys, interviews, and feedback analysis to identify key areas. 4. Milestones: Map the journey from onboarding to renewal/advocacy. If needed, develop the enhanced engagement model and ensure the impact is delivered from all perspectives. 5. Identify Metrics: Based on the above factors, select meaningful and actionable metrics. These may include retention rate, churn rate, NPS, product adoption metrics, expansion revenue, customer health scores, and Verified Outcomes. 6. Distinguish Leading and Lagging Indicators: Balance between leading indicators (predictive of future success) and lagging indicators (reflecting past performance). For example, while the retention rate is a lagging indicator, the product adoption rate may be a leading indicator of future retention. 7. Iterate and Improve: Continuously review and refine your customer success metrics based on feedback, changes in business strategy, and evolving customer needs. Be open to experimenting with new metrics and approaches that better align with company goals. 8. Communicate and Align: Communicate the selected metrics clearly to the customer success team and ensure alignment with their roles and responsibilities. Provide training and resources to empower them to drive success based on these metrics. By following this process, one can align with the company's goals and objectives, enabling your team to effectively drive value for both customers and the business.

This is one of the most critical components of customer success leadership both when establishing a CS function or joining a team with an already established CS org. If a CS team sits within the sales organization, there may be a natural alignment already anchoring the full team to joint business-based KPIs like churn reduction or retention/renewal outcomes. In this case, it's important to recognize how each team contributes to those shared outcomes - while the KPIs may be shared, the path to achieving the KPIs can (and likely should) differ by team. For example, CS may lean more into product adoption and customer value assurance in service to retention or renewal outcomes, whereas sales is responsible for growing the customer base or growing the renewal. If the sales and CS teams are managed separately within the organizational structure, it becomes even more key to have conversations around how CS incentivization and measurement models serve the broader organizational and business outcome goals. For example, showcasing how boosting customer product adoption through well-timed customer engagements leads to customer value and stronger renewal outcomes. One of the most effective ways to anchor teams on commonly shared KPIs is to be very specific about how the actions (inputs) lead to results (outcomes) - ensuring this narrative is reinforced consistently through the organization. It's also important to be transparent on each team (sales/CS) around how team members are measured if there are differences in accountability structures - this builds trust and confidence that while actions may differ, 'skin in the game' is present for all teams in service to business outcomes.

While I have been at HubSpot for a long time (13+ years), many opportunities have come up over time that I have had to assess new opportunities. Here are the things I tend to look at when considering a role; * Market Evolution * Size and growth potential of the market the company plays in * Stage of evolution : is this a horizon that is in its late stages or on the cutting edge? * Potential : is this a single product/one trick pony or do * Product * Do customers love their product/service? * Who are their competitors and how does their solution stack up? * Are they a leader or laggard? Have they shown product/service innovation? * Leadership and Culture * Are they proud of their vision, values and culture, even if they are against the grain? * How visual/vocal is their executive leadership? * Do people love the company? Are they staying there or leaving after short periods of time? * How do they treat failure? * Skills and Growth * Does my past experience lend well to the role? * Are there opportunities for me to build new skills? * What is my potential for impact on the business priorities? * Does this role help me build skills for the next position I hope to achieve?

* I think customers will continue to expect quick yet complex answers - our products and platforms will need to be able to keep up by offering more access and self-service options. CSMs need to be able to respond with speed and accuracy, which means they need to know the product themselves. * I see CS moving to a multi-support model using chat (both chatbot and live-hosted), AI, and large language models, with CSMs for some accounts, pod support for others. * In my opinion, for CS to remain a highly valued function to the customer, we have to make sure products are stable, data is accurate, systems are integrated, access is easy, and privacy is protected.

In my mind, I believe that it’s generally more beneficial to have the right soft skills when joining a new team, as I’ve seen it is often harder to teach than hard skills. Soft skills like communication, empathy, active listening, problem-solving, and adaptability are key to building relationships with customers and typically things that I try to get a good understanding of during interviews. Then once you have a solid foundation of soft skills, you can more easily learn the necessary hard skills, such as specific tools, processes, or product knowledge, because you’ll already know how to engage with customers effectively. That said, a balance is ideal when you’ve been on the team for a bit of time, as both sets of skills are important for success in the role. In fact, over time, some of the hard skills become a bit more important as I feel product knowledge from a tenured Customer Success Manager is extremely important.

To scale a Customer Success function effectively, it's crucial to demonstrate its financial impact and how additional headcount can amplify this effect. A simple method is to analyze renewal and expansion rates for accounts managed by Customer Success. By applying this financial model, you can determine the potential growth and retention value that an additional CSM could bring when engaging with currently uncovered accounts. This approach provides a clear rationale for expanding the team while showcasing the function's tangible contribution to the organization's bottom line.

On top of all the usual traits of being a great CSM, I think of 3 things when I think of great Enterprise CSMs: Stakeholder engagement: To me in Enterprise CS this is the ability to confidently communicate with the C*suite, the ability to multi-thread across an organisation and the ability to achieve the buy-in of multiple stakeholders towards a common objective. Change management: As an Enterprise CSM you're usually dealing with much bigger and more complex organisations, and so you can't take all the action items yourself. This means you've got to get out of the weeds and focus on the bigger picture; driving a change to meet the customer's objective. You've got to be much better at both holding your customer accountable to executing their actions AND guiding them through what they need to do to drive this change. Project management: Essentially, being great at tracking all the strategies and corresponding actions that need to be completed in order to achieve the customer's objective, as well as being able to communicate this in a clear and concise manner to the customer. Your goal here is to keep everyone on track.

A top-notch sales enablement leader has a few characteristics that make them so awesome: * A broad knowledge of sales methodologies and techniques without being attached to any... a melting-pot of best ideas will likely be best * An obsession with iteration: measure, improve, measure again, improve again, and so on * A personable nature where sellers view them as a resource and not another TPS-report-demanding 8th boss These characteristics can then lead to their core contributions: * Run a cadence of sales trainings that sellers welcome (rare) since what is taught will clearly impact them making money (which leads to that rare embrace from sellers) * Follow up on the trainings: sales managers have too many things on their plate and will want a partner in the sales enablement leader and team to surface what is working and what isn't and for whom on their team

* Model Performance Metrics: Accuracy, Precision, Recall and some other measures here. * Operational Metrics: Latency (time taken for generating responses, the lower the latency the better), Uptime and Reliability: * Business Impact Metrics: Customer Retention Rate, NPS, CSAT * Customer Feedback and Sentiment Metrics: * Feedback Scores: Collects customer feedback on AI interactions, providing qualitative insights into the effectiveness and user satisfaction. * Sentiment Analysis: Analyzes customer sentiment from feedback and interactions, helping understand the emotional response to AI-driven services. * Economic Metrics: * Return on Investment (ROI): Evaluates the financial impact of AI initiatives, comparing the benefits derived from AI tools against their costs. * Cost Savings: Measures the reduction in operational costs due to the automation and efficiency gains provided by AI * Usage and Engagement Metrics: * Adoption Rates: Tracks the adoption of AI-driven features by customers, indicating their perceived value and usability. * Feature Utilization: Measures how frequently specific AI features are used, helping identify popular functionalities and areas for improvement