👩💻 2-min PMM Insights: Zola's NPS Surveys, Retool's Real-Time Support, Starburst Data on Process Change, FB's Daily Metrics
September 2023, Part 2 release
📈 4 MICRO [PRODUCT MARKETING] CASE STUDIES
[1] Monitor user experiences and build a robust customer support mechanism to ensure early customer delight and supercharge sales.
Retool's pivot to inbound sales meant onboarding multiple early customers to a product with a "lot of bugs." Using a smart Slack automation system, the company could track user activities and intervene in real-time to offer support when users hit errors.
[2] Lean on a nuanced selling motion with great storytelling if your prospects are buying a 'process change' from you.
The CRO of Starburst Data suggests first identifying if your prospects are buying a process change (ex: devs changing the way they build code) vs. a product change (ex: upgrading iPhone models). A process change requires your sales team to educate before selling, plus it also deserves a lot of documentation, community focus, videos, and digital assets.
[3] Send monthly NPS surveys and focus on feedback over time from detractors to shape your future product strategy.
Zola (an online wedding registry) has better NPS scores than Amazon and the retail industry benchmark. The company favors detractors partly for their straightforward feedback and uses their comments to build 'themes' over time to improve its product.
[4] Don't use DAU/MAU as a default metric to gauge engagement; instead, align with the behaviors of your core user base.
DAU/MAU is a great metric to measure daily engagement in ads-driven social platforms like Facebook. Typical SaaS products like Salesforce, Dropbox show 'low frequency, episodic' use. A better engagement move would be to focus on your valuable hardcore users and see how you can produce more of them.
📚 1 BOOK & TOP 3 INSIGHTS
“The AI Dilemma: 7 Principles for Responsible Technology” by Juliette Powell and Art Kleiner
[1] There are 4 perspectives to consider for Triple-A (autonomous, analytic, and AI) systems: engineers, society/activists, government/regulators, and corporations. You may relate better to one or more perspectives. None of them are wrong, but they highlight the possibilities and issues that may surface to find solutions that work for everyone.
[2] Study these 3 factors if you're developing an AI system that generates text, images, or other content to prevent the spread of false or manipulative information: (i) the people who might use the system to mislead others, (ii) the way these people may act, and (iii) the new types of propaganda that might proliferate.
[3] Every company should hold critical discussions at 4 key moments during the R&D process for any AI system: "(i) when the model is being constructed, (ii) when people are given access to it, (iii) when content is disseminated, (iv) when end users recognize the impact their digital tools are having on their beliefs and behaviors."
🧠 5 CURATED MARKETING THINK PIECES
[1] Why do users prefer certain designs? Insights from the landscape theory
[2] To Build Value in Tech, Build Different
[3] How to do product positioning (using abstractions)
[4] KPIs for Developer Relations Teams. You can’t optimize what you can’t measure!
[5] How we learn to recognize problems