Mani

Nikhat I. — totally agree. Operationalizing is usually the hardest part. I’ve seen teams make both approaches work, especially in smaller, more nimble companies where media and website decisions sit under a single budget owner. It becomes much more challenging in larger organizations where website and marketing decisions are split across different teams or functions. The right org structure plays a critical role in making this work.

Use MTA (like markov chain) to optimize the Experience: Decide what the user sees next based on where they are in the Markov transition matrix. For example someone seeing a product page what is the next highly likely page they will go to. Use MMM to optimize the Engine: Decide where the next $100k goes based on the saturation curves.

Thanks Nick — this is a great callout. Sorry for the delayed response. What’s really happening is we’re validating *champions*, but not the **buying system** behind them. One thing that might help is identifying the full buying group early (champion, economic buyer, procurement) and treating that as part of SQL qualification — not something discovered later. From an analytics lens, the gap is that we rarely track this explicitly. A simple leading indicator like **% of opportunities with confirmed budget authority** can tell you a lot about pipeline quality *before* deals start stalling. In a lot of cases, pipeline looks healthy — but a chunk of it was never truly closable to begin with.

Nikhat I. Yes. Guardrails are a must have given the probabilistic nature of the outputs from this technology more specifically LLMs.

I just started using Claude ..it could be a game changer for the corporate work flows.

Yes. AI Agents MIGHT become roles rather than tools. Plan but always expected the unexpected. I would say developing a framework to audit the current inventory of AI agents, then define their roles through clear responsibility mapping, permissions (read/recommend/act), coordination workflows, measurement KPIs, and risk/failure mode guardrails might help.

Got it. How about revenue in the saas industry? What leading indicators would they watch and catch revenue risks before it hits P&L based on your experience? Thanks.

Thanks Vincent. So true. GIGO. Very foundational. Being an analytics professional, I would really appreciate that. From your experience, what leading indicators companies watch for gtm launches?