Hook

a few things to look out for when trying to track down the imposter in your ai stack (+ a blog post if you want to know more about the unfortunate phenomenon known as ~companies slapping the name of agent onto any old copilot~: https://lnkd.in/gmB_hxZZ)

I had SUCH fun on the The Customer Success Pro™ live podcast last week, talking all about renewal prediction and forecasting - a part of CS very close to our hearts at Hook. It struck me that so many CS teams still need help with forecasting and so I spent a bit of time thinking about my views on how it's done best.... What it takes to renew and grow a customer is different in every company and for every CS team. This means that the data inputs for your forecast need to reflect that. For some companies, log ins do = renewals (you lucky things 😜 ) ... but for most, it takes a lot more than just that to guarantee a renewal, so we can't forecast purely based on whether the customer has logged in. So how do you build a forecast you can trust? Firstly, leverage data to find out what customer behaviour drives renewals in your customer base (we use ML for that here, but you can leverage your internal data to find this insight) . Then use this data to start the foundation of your forecast. For a lot of companies this is their health score - it is for me too. But where a lot of folks go wrong is using a health score that's not rooted in data,so your forecast is immediately inaccurate. ^ Now for your scaled segment or for your annual forecast, actually this data-driven health score forecast might be enough. But for me for in Q and next Q forecasting, especially in a managed CS segment, I add the following (because this is what it takes to renew a customer in my world): ✅ Is this customer achieving value? Based on their reason for buying. ✅ Have we quantified that value and shown it back to the EB? The problem for CS leaders when layering this kind of input into your forecast becomes that everyone's perception of whether or not a customer is achieving value is different. (Same applies to other subjective inputs). So here you've got a couple of options: 1) Put a solid framework in place that outlines a universal view on what exactly classes as value delivered (in my case) and how to weight that into your forecast - to drive consistency. 2) Leverage AI to quantify some of this stuff and turn it into a data point. More on how we're helping customers with number 2 another time 😎

Sadly, there's no crystal ball for revenue forecasting. But AI might be the next best thing. Yesterday at The Customer Success Pro™ Live, Natasha Evans, Alec Gillespie and Rob Zambito shared where AI genuinely helps them with revenue forecasting: 👉 Machine learned health scores that have signals layered on TOP to get an accurate read on both historical behavioural patterns and customer sentiment. 👉 Retrospectively understanding the patterns and trends within forecasting (allowing you to go back after the fact to see what your forecast got right or wrong, so accuracy compounds quarter over quarter). Pair these AI features with the fundamentals: real renewal conversations with the right stakeholders, a genuine read on value realisation...and that's when the forecasting magic happens. Thanks Anika Zubair for having us!

This Thursday, our VP of Customer Growth, Natasha Evans, is taking the stage with Alec Gillespie and Rob Zambito to talk all things revenue forecasting in CS! If you're in or around London this week, make sure to rsvp: https://luma.com/k8718x8p

Just asking for 'more visibility' or 'more account data' won't get you the insights that your CS team actually need. Each data request has to be structured around a specific business outcome to make sure it'll drive results instead of just sitting in a dashboard. Want to know how to make those requests? Get our brand new guide ➡️ 'What Data to Fight for as a CS Team' 🔗 https://lnkd.in/eDwTbTAN

Asking for data points without a real reason why you need them is the best way to end up data-rich and insight-poor. Our new guide breaks that cycle: 1️⃣ The 3 different types of data you can ask for and what each is best at 2️⃣ The framework for ensuring each data point is linked back to real CS outcome 3️⃣ How to frame your request to get a yes, every time Get your free copy here 🔗 https://lnkd.in/eDwTbTAN

Building automations for your CS motion without doing the groundwork first is a bit like packing for a trip without knowing the destination... ...it won't end very well. At Hook, the 'what to automate' conversation was approached a little something like this: 1️⃣ Mapping specific jobs to either automations, agents or humans, across the entire customer lifecycle. 2️⃣ Defining the specific outcome you want to drive with your automations, and figuring out what's currently stopping you from achieving that outcome. 3️⃣ Answering your discovery questions (examples available in our free guide below!) 4️⃣ Building and iterating (over and over) You can watch the webinar back to see the full process and understand how Hook's customer team used automations to eradicated busywork AND drive real business outcomes. Recording ⏭️ https://lnkd.in/e58ukSfN Guide 📖 https://lnkd.in/eY_dz6vp

If your day looks a little something like this… Razzle-dazzling at 9:30 Photosynthesizing at 11:45 Crystallizing at 14:15 We’ve got something for you 👀 Hook’s MCP is now live. (connects to Claude, ChatGPT, and more) Find out more here 👉 https://lnkd.in/dFMs4Uwh

A career in Business Transformations & Customer success allows you to engage with people who are building the future. Recently, I had the opportunity to evaluate Hook —an amazing AI-powered Customer Success Platform and I came out genuinely impressed, not just by the product, but by the thinking behind it. Rather than just discussing dashboard or reporting capabilities, it was more centred on questions every Customer Success leader is asking today: - How to scale Customer Success intelligently? - How to move Customer Success from reactive to proactive through predictive intelligence? - How does operating models incorporate AI to enable Customer Success teams to focus on more strategic customer conversations? It was exciting to see how these ideas are being translated into practical workflows, looking beyond traditional health scores to understand leading indicators, renewal confidence, expansion potential, and customer value creation. Conversations like these reinforce my belief that the future of Customer Success will be defined less by CRM administration and more by intelligence, prediction, and business impact. Thank you, Charlie McKay and Finn Clubb, for the engaging discussion. I look forward to see and recommend - how Hook continues to shape the future of AI-powered Customer Success. #CustomerSuccess #AI #MachineLearning #BusinessTransformation #EnterpriseSaaS #CustomerExperience #PredictiveAnalytics #StrategyExecution #CSLeadership #AgenticAI #sixhats.co