Don't unleash a generic AI agent on your entire database. To get high response rates, segment contacts into specific sub-personas based on role, behavior, or status (e.g., churn risk). Then, train dedicated sub-agents or campaigns for each persona, allowing for true personalization at scale in batches of around 1,000 contacts.

The 'MQL death cycle' is over. Forward-thinking marketing organizations should align around Net Annual Recurring Revenue (Net ARR) as their ultimate measure of success. This metric, which combines new customer acquisition with retention, forces a focus on the entire customer lifecycle and proves marketing's contribution to sustainable business growth.

For consumption-based models, simple size-based segmentation (SMB, Enterprise) is insufficient. Stripe and Vercel use a two-axis model: company size (x-axis) and growth potential (y-axis). A small company growing at 200% YoY is more valuable and warrants more sales investment than a large, stagnant one.

Instead of viewing them as separate efforts, businesses should link customer retention and acquisition. By unifying data to better re-engage existing customers via owned channels like email and SMS, brands increase lifetime value. This, in turn, reduces the long-term pressure and cost associated with acquiring entirely new customers.

Everyone obsesses over Net Revenue Retention (NRR), but Gross Revenue Retention (GRR) is the real indicator of product health. GRR tells you if customers like your product enough to stay, period. A low GRR signals a core problem that expansion revenue in NRR might be masking.

The current AI hype cycle can create misleading top-of-funnel metrics. The only companies that will survive are those demonstrating strong, above-benchmark user and revenue retention. It has become the ultimate litmus test for whether a product provides real, lasting value beyond the initial curiosity.

While individual AI companies see slightly lower retention than SaaS, Stripe's data reveals customers often churn from one provider directly to a competitor, and sometimes switch back. This indicates the problem being solved is highly valued, and the churn reflects a rapidly evolving, competitive market, not a lack of product-market fit for the category itself.

An LLM analyzes sales call transcripts to generate a 1-10 sentiment score. This score, when benchmarked against historical data, became a highly predictive leading indicator for both customer churn and potential upsells. It replaces subjective rep feedback with a consistent, data-driven early warning system.

Because AI products improve so rapidly, it's crucial to proactively bring lapsed users back. A user who tried the product a year ago has no idea how much better it is today. Marketing pushes around major version launches (e.g., v3.0) can create a step-change in weekly active users.