Deepti Pradeep

We first absorbed all the relevant user research available, spent a ton of time with the core PMs to understand what had been tried – what worked well and failed. We also, in parallel, looked at data and business trends with the core PMs and assessed not only biggest areas of impact but also areas that potentially had low hanging fruit – all of which were aligned to our top user needs. Aligning the vectors of the growth team with that of the core product organization, leaning on the wealth of kn ...Read More

Creative Self-Starter: Often there isn’t a set path / ready direction to get started. A strong Growth PM is able to roll up their sleeves and get into the growth tech stack and data, set direction despite ambiguity, build relationships through credibility, influence leaders, and restart with a new / different angle if required. Patiently data-driven: A strong growth PM knows both their growth model as well as user research like the back of their hand, can build strong hypotheses informed by both ...Read More

More often than not, an experiment fails. Failed experiments are the biggest sources of learning. Was your hypotheses proven wrong? Was the test design not pushing it far enough? Were there other data anomalies? Where there any positive signals in a subgroup? One needs to be careful in slicing the data by many cuts, as the validity of an AB test is largely dependent on its effect size. Cut it too much, and you might be cherry picking the data thereby losing its meaning for decision making, or wo ...Read More

Expecting glory right off the gate. The thrill of launching a first test is huge. The disappointment from reality vs. expectations might be huger. Any number of things may go wrong in the beginning. Something may be off with the test setup (dang, that missed attribute), the test might fail gloriously (think big red stat sig negative), the test may be a disappointing win (whoaaa….wait, that’s it? I was hoping 10X impact…), or something totally out of one’s control derails the test (sorry, data ou ...Read More

Start by identifying the core lever you need to move. For a B2B product, this might be new customer acquisition. For B2C, it could be daily or weekly usage metrics (e.g., WAU/MAU). Also document your hypotheses about other metrics you want to influence. Create your idea backlog by triangulating between leadership priorities, customer requests, and market gaps. Collaborate with Engineering, Design, Data Analytics, and Marketing/Sales teams to brainstorm these ideas. Implement RICE scoring (Reach, ...Read More

People stay in roles so long as they are inspired by their leader, feel valued and feel like they are growing their skills. I’ll break each of these down in terms of how I approach it. Feel inspired - Bringing a strong vision, having a clear idea on how my team can execute against that vision, eliminating barriers for them to execute, and being ready to dive into any area to ensure everything is moving along. This requires me to be high and low, and makes the team feel confident that the captain ...Read More

PLG is technically effective when a product has achieved product market fit. Running experimentation when you are still finding out the right types of audiences is not really recommended. However, you can always imbue your early product with PLG concepts through freemium / trial experiences, frictionless signup and onboarding, and in-app messaging. Even though you may not be able to run statistically significant experiments with large effect sizes, you can still bring the ethos of that through c ...Read More

Governance / air-traffic control of AB tests is essential. Running parallel AB tests on the same KPI and same audience will not guarantee clean results. If improvements done by teams are direct to production, timed before an AB test, it may not be an issue as both control and variant receive the changes thereby retaining the validity of the results. Most testing platforms come with some inbuilt conflict resolution, but I highly recommend bringing teams together in targeted forums to not only ens ...Read More

Product Growth has always used AI to some extent - we have always leveraged various types of models like propensity models for revenue optimization, predictive models to identify risk, segmentation models for personalization. This has been driven by the core goal of driving impact while driving customer delight/aha. AI-driven products and experiences only provide growth product managers with more of a platform to drive impact, faster. The core skill you need to unlock? Creativity. Think bigger, ...Read More