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In today’s episode I welcome Neha Mittal, a growth expert who’s worked on multiple large-scale consumer products (Twitter and Pinterest). As folks know, I love to revisit Twitter and what could have been. In this session we cover how growth strategies have evolved from the systematic experimentation era to today's AI-driven approaches. Note my conversation with Neha is very focused on consumer growth strategy - if you are curious about growth thinking for B2B products, check out my prior session with Shahzad Shaikh where he talks about PLG and his experience as Head of Growth at Asana.
We started the discussion with an overview of the user lifecycle and how it’s used to organize most growth teams at consumer companies.
We then talked about Twitter specifically and how an architecture that had been designed for massive scale supported the strategy of the growth team. The interoperability of different services enabling the growth team to get programmatic signals without much engineering work was a big unlock, and something that contrasts with the silos in many other product organizations. In fact, one reason why tension emerges between core product and growth teams at B2B companies is the inability for growth to get access to feature usage data without owning feature design and delivery.
One of the transitions Neha observed on Twitter’s growth team during her tenure was a move from experiment-driven to intuition-driven. Rather than pick one over the other, in the clip below Neha highlights when it makes sense to operate in each mode.
From there we delved into how AI is emerging as an asset for growth teams. One angle Neha highlights is content - both easier content generation and better content relevance.
But the meta takeaway about using AI tools in your growth stack is the ability to now make meaning out of unstrucured data. In the snippet below, we talk about utilizing AI to identify segments for personalization purposes. This is in contrast to the prior generation of tools, which relied on growth PMs / marketers to create cohorts based on structured user attributes - now user behavior can be modeled at a level of precision and matched with a level of relevance unheard of at consumer scale before (at least without an army of data scientists).
Over the course of our time together, we hit on several other aspects of at-scale growth strategy and emerging AI tools, including:
how Twitter’s approach to growth changed after the acquisition by Elon Musk
controls and guardrails necessary before AI can run in the wild creating content
consumer growth best practices that do and don’t carry over to B2B products
