The SaaS Growth podcast: Rebuilding SaaS Marketing in the AI era

This episode focuses on Sara Archer, Chief Revenue Officer at ChartMogul — a subscription analytics platform used by over 6,000 SaaS companies. Sara joined in 2018 as employee number one on the commercial side, couldn't build a forecast for the CEO on day two because the CRM data was useless, and has spent seven years inheriting more responsibility — from rebuilding the sales stack to now running both sales and marketing as a unified function. She believes demand generation is the hardest non-technical problem in SaaS and that most marketing systems that look healthy on paper are actually dead weight.ChartMogul built and shipped a revenue recognition product at the customers' request. The sales team dreaded every demo. Support tickets piled up. The product domain — accounting compliance — didn't match anyone's expertise. They decommissioned it. Years later, they built CRM capabilities inside ChartMogul instead, and the difference was immediate: it was fun to sell, customers adopted it naturally, and it made sense as an expansion lever. The lesson wasn't "don't build a second product" — it was that choosing the wrong problem set splits a small engineering team across two intellectual domains and erodes the quality of everything.On the AI front, Sara's team uses it heavily for data analysis — turning 25 raw sales call transcripts into an objection report in hours, compressing case study production from a week to two and a half hours. But they tried an AI email response tool for product questions and shut it off. It could answer the technical question but couldn't understand why the customer was asking — what business problem sat behind the query. Sara calls this "layers of theory of business" that AI can't yet replicate. She also flagged the "AI tourist" phenomenon from ChartMogul's data: users who sign up for AI products experimentally, with no intent to recur, inflating churn rates across the category.Sara shares what actually shifted their results — and what she'd rebuild from day one:↳ What separates the 3.5% of SaaS companies that reach $20M — adaptability and willingness to reinvent, not a better strategy↳ Why her first move at ChartMogul was rebuilding the CRM — and whether that was the right call or just her comfort zone↳ The revenue recognition product mistake — how a second product split engineering focus and created a domain expertise gap↳ How to know it's the wrong product: sales dreads the demo, support tickets take longer, and retention drops↳ When to move from founder-led sales — and why you should always hire two reps, not one↳ Why adding sales at low price points creates friction — and how to reverse-engineer the buying process instead↳ AI for commercial teams — case studies in 2.5 hours, automated call coaching twice a day, and objection trend reports from raw transcripts↳ Where AI fails in sales — it answers the question but doesn't understand the business reason behind it↳ The "AI tourist" problem — why experimental signups inflate churn and what to separate in your metrics↳ Why she dismantled marketing that looked healthy — conferences, content, panels — because it wasn't moving trial numbers↳ Pricing as a muscle — review it every two to three months, even if the answer is "do nothing"↳ The simplest scaling advice: listen to three customer calls and the problem becomes obvious

They entered the US market with no brand, no partners, and no margin for mistakes. Instead of chasing “one more channel”, they spent a year running brutal research, 150+ calls per quarter. The conclusion was uncomfortable; the product sold, but there was no scalable way to grow sales.So they rebuilt the product into an AI agent. One that connects directly to real business data, explains what’s happening across the funnel, and takes action inside ad platforms.This episode is also about becoming an AI-first company in practice. Every team, marketing, product, analytics, and sales, works inside Cursor as a shared system. Knowledge base, onboarding, strategy, and daily decisions live in one place and default to AI.This episode focuses on Eveline Ogorodnikova, Head of Marketing at Elly Analytics and Plurio. As the first marketer in the team, Eveline led Elly through four years of market expansion, failed scaling attempts, and a strategic shift from analytics dashboards to an AI-native performance marketing agent.Eveline shares what actually worked, what didn’t, and how to run an AI-first company day-to-day:Why they made 150+ calls per quarter, and still couldn’t scale revenueHow interview funnels outperformed classic demo funnels in outboundWhy dashboards stopped working, and why “answers + actions” became the only scalable modelHow the team rebuilt performance marketing around an AI agent that optimizes campaigns based on revenue, not clicksWhat it really means to build an AI agent that connects to trusted business data, explains changes across the funnel, and takes action inside ad platforms without human micromanagementHow every team works inside Cursor as an operating system: onboarding, strategy, research, decisions, and context all live in one placeWhy Cursor replaced onboarding docs, internal wikis, and most sync meetingsHow AI changes roles inside the company, fewer manual operators, more strategic thinkersWhy AI-first companies still need humans, but in completely different jobs than beforeIf you’re building a complex B2B SaaS, selling to marketers or founders, and feel stuck between “the product works” and “growth doesn’t scale”, this episode will hit close to home.Watch on YouTube: https://www.youtube.com/@Digital-HunchOr listen wherever you get your podcasts.Learn more about Elly Analytics: https://ellyanalytics.com/Connect with Eveline on LinkedIn: https://www.linkedin.com/in/eveline-ogorodnikov/Follow Renata Zinnatullina on LinkedIn: https://www.linkedin.com/in/renatazinnatullina/Visit our site: https://digital-hunch.com