PLG 0-1
Company Overview
Bolster AI uses AI to automate the detection, analysis, and takedown of threats across lookalike domains, social media, app stores, and the dark web. Our solutions safeguard organizations of all sizes from startups to big companies in varies industries such as Roblox, LinkedIn, Dropbox, and Uber. Backed by Microsoft's venture fund.
Our most common personas include teams in Security Engineer, Chief Information Security Officer, SOC Analyst, Security Engineer, and additionally Legal and Marketing teams that are also involved in brand protection.
I was the 1st Growth hire building Product-Led Growth (PLG) strategy and roadmap from scratch, focusing on new user acquisition, activation, engagement, and monetization/pricing for our community freemium product CheckPhish.
Context
Two main use cases definitions:
URL Scanner: Determines if a link is phishing, clean, or a scam. This feature attracts both professionals and personal users.
Typosquat Domain Monitoring: Identifies lookalike domains impersonating a brand.
Problem
The original CheckPhish product saw organic traffic success for URL Checker use case to identify if a link is suspicious, spam, or clean. However,
The product didn't communicate the full value of our Enterprise offering for comprehensive AI-powered detection and remediation across web monitoring, dark web, app stores, and social media
The primary user base was focused on URL Checker use cases, missing opportunities for broader engagement with our full suite of enterprise services
PLG Strategy
Objectives:
Develop and launch an end-to-end experience for the "Lookalike Domain" protection feature as part of a Product-Led Growth (PLG) strategy, aligning with our main value proposition for the Enterprise product
Optimize web storytelling and SEO to attract and convert visitors searching for “Lookalike domain” solutions
Drive user acquisition, activation, and monetization by offering valuable, un-gated experiences and implementing strategic in-app onboarding
Improve website visitor to user acquisition with new messaging and feature
Acquisition Phase
Goal:
Help web visitors with immediate value by allowing them to try the "Lookalike Domain" protection feature through un-gated experience without signing up as a hook
Offer value of continuously monitor for new visitors to sign up
Implementation:
Users can enter a domain to check for lookalike domains and receive instant results
Encourages users to sign up to monitor additional domains and access continuous protection on new changes
In-App Onboarding and Continuous Monitoring
Goals:
Help users understand the value of continuous domain monitoring through an intuitive onboarding process
Encourage users to upgrade for full access to comprehensive web monitoring, which includes advanced ML/LLM model training and customization for each domain or brand beyond lookalike typos results
Implementation:
Converted users get instantly dashboard results if they choose to sign up to continuously monitor that domain
New registered users gets empty state where they get guided onboarding to set up domain monitoring for their brand
Users are provided with a limited set of results, highlighting the need for upgraded plans for comprehensive monitoring
In-app prompts and messages guide users towards upgrade flow
User Feedback & Iteration
User Feedback Loop:
Conducted user interviews and surveys post-launch to gather insights on areas for improvement and valuable feedback for enhancing product marketing language
“My first impression was positive as I found similar domains including 3 I registered myself to prevent phishing. Seeing a high-risk item on dashboard immediately is crucial for me. It’s fantastic from a security perspective to have this process automated and visible on a dashboard”
“The Instant results is exactly what I am looking for. ”
Impact
User Acquisition and Activation:
Increased conversion rate from website visitors to signed-up users from 2% to 6%
Achieved a 60% activation rate for the new feature, indicating strong user engagement
Business Growth:
Double the user base since introducing domain lookalike monitoring product
Contributed growth to support company's Series B funding round
Supported revenue growth by driving pipeline increase from freemium users within the first three months of launch
Learnings for Backlog Improvements:
There is more work to be done! Leveraged user feedback to prioritize backlog improvements, enhancing user experience and adding requested features aligned with PLG motion, influencing a traditional sales-led enterprise company and stakeholders
Pricing Feedback: Jump from Freemium to full Enterprise was too expensive; users were willing to pay for lighter, self-serve versions to get 1) full results and alerts and 2) advanced features with automated detection and recommendations
Trial Period: Users desired easy access to trials with full results for evaluation
Collaboration Needs: Noticed multiple sign-ups from the same company, indicating a need for collaboration features and growth loops
