In the ever-evolving world of talent acquisition and career development, I had the opportunity to design a comprehensive growth strategy for Talentbay, a platform that bridges the gap between university talents and teams. I was a first growth hire for Talentbay and I helped define growth from strategic perspective, but also was hands-on with trying different tactics and building playbooks.
How the product worked
🤳 Students swipe in the mobile app for interesting teams
👩🏼💻 Teams search for talents in the WebApp
🔮 Matching works on the basis of Machine Learning model using profile information
👫 After a confirmed PING (request) they communicate via the talentbay chat
Talentbay's Network-of-Network Hypothesis
Using the atomic network Framework proposed by Andrew Chen in his book Cold Start Problem, I created a hypothesis about how Talentbay grows and how it can leverage Double-sided Network Effects.
1. There Were Two Principal Components of the Marketplace:
TEAMS (The Hard Side of the Network): Represents companies, startups, and organizations scouting for talents. This side is more challenging in terms of acquisition and retention.
TALENTS (The Easy Side): Represents a pool of potential hires, predominantly students from various disciplines, which is easier to attract and engage due to its vastness.
2. There Were several Levels of Networks:
a. Atomic Network:
The foundational level of the network. It postulates a threshold of users on both sides within a given category and geolocation for optimal marketplace operation. By achieving this "atomic" number, both supply (teams) and demand (talents) gain significant value, ensuring stability.
b. Category-based Networks:
Here, the two main components are subdivided into categories, forming 'sub-networks' within the larger structure. For instance, categories could include fields like Software Development, Digital Marketing, or Human Resources, among others. These are just examples and can be replaced with any field or discipline, highlighting the platform's versatility. This level was especially important for remote roles.
c. Geographic Segmentation:
This is where the framework focuses on localized networks based on different geolocations like cities. This local segmentation ensures users from one region derive value from its local market that is relevant for them.
d. Network of Networks:
The culmination of the framework. By having interconnected atomic and category-based networks, Talentbay establishes a vast and intertwined "network of networks." This interconnectedness results in a ripple effect: growth and activity in one network can stimulate growth in another, thereby amplifying the overall value of the platform. Which unlock Cross-Side Network effect and Brand as a Macro Loop.
3. Cross-Side Network Effects:
This showcases the mutual benefits for both components:
From TEAMS to TALENTS: An increase in teams enriches the value for talents, solidifying the platform's reputation.
From TALENTS to TEAMS: A richer talent pool augments the experience for teams, elevating the platform's allure.
In essence, Talentbay's Network of Networks Framework adopts a strategic, tiered approach to segmenting its user base. From atomic networks, through categories and geolocations, and culminating in a vast interconnected ecosystem that helped inform a playbook on how to spin each new market and how those markets grow to find traction and become sustainable.
Talentbay's Growth Model
On top of defining the Marketplace-related framework for how Talentbay's growth I've also mapped and build its Growth Model based on the Growth Loops framework from Reforge and Product-Channel-Market-Fit framework to make sure we grew with the right channels.
Linear Channels:
- For Teams:
Brand SEO, Events & Conferences, PR.
- For Talents (Students):
Brand SEO, Guerrilla Marketing (targeting students).
Acquisition Loops:
- Paid Marketing: Targeting both teams and talents.
- CGCD (Company Generated, Company Distributed Content):
A powerful acquisition strategy for teams, leveraging targeted content marketing paired with Inside Sales.
- UGCD (User Generated Company Distributed Content):
A potent acquisition loop for talents: When teams create profiles/jobs, this content is indexed for search engines, organically boosting SEO and drawing students to the platform.
- Referrals (For Students):
Incentivized Referrals: Rewards for students who bring in new users.
Organic Sharing: Encouraging satisfied students to share TalentBay within their circles.
Habit Loops:
- Your Week on TB: A recap feature for both teams and talents, capturing their past week's engagement on TalentBay to maintain platform relevance.
- Chat Notifications:
Pushing in-app, push, and email alerts about new chat messages to pull users back into the app and sustain active engagement.
- Social Loop:
For Students: Alerts about new teams and job listings.
For Teams: Updates on talents relevant to their search.
- Build Network Loop:
For Students and Teams: Notifications on connection requests to foster and nurture networking on TalentBay.
The Data Backbone
One of the first challenges I tackled at Talentbay was designing a robust data pipeline. Understanding that data is the lifeblood of any growth strategy, I ensured that we had a great data pipeline in place. This allowed us to make fast iterations on both the product and marketing fronts. The pipeline served as the foundation for all our growth initiatives, enabling real-time feedback and analytics.
The North Star Metric
I have identified The North Star Metric that guided all our initiatives as the "Number of messages sent per user." This metric was chosen as it directly correlated with user engagement and provided us with a clear focus. All contributing metrics and experiments were aligned to optimize this particular metric.
Optimizing the Onboarding Experience
All the investors we talked to told us we need to make sure we don't have a leaky bucket problem on neither side of the platform. I took a deep dive into optimizing the onboarding experience for both sides. For talents, the focus was on creating a network early in their careers and gaining practical experience. For teams, it was about building a fast applicant pipeline and getting valuable insights from academia. By streamlining this process, we were able to significantly improve the initial user experience, setting the stage for long-term engagement. Combining our North Star Metric (that was also our retention metric) and working backwards from successful users experience, I've been optimizing new user experience for Setup moment, Aha moment, and Habit moment.
Introducing Rapid Experimentation
In a fast-paced environment, the ability to quickly validate hypotheses is invaluable. I introduced a rapid experimentation process that could run with and without developer help. This involved categorizing experiments into different levels based on their complexity and the resources they required. The Minimum Viable Experiment (MVE) approach was implemented, allowing us to test and validate our growth hypotheses in the most efficient way possible.
Product-Led Growth Pipeline
Understanding that the best leads are those that are already engaged with your product, I designed a Product-led Growth (PLG) pipeline. This pipeline was aimed at better qualifying leads within the product for inside sales outreach. By focusing on in-product behavior and engagement metrics, we were able to identify high-value leads that were more likely to convert, thereby making the sales process more efficient, and marketing and product taking care of user engagement for those who didn't need human intervention.
This multi-faceted growth strategy for Talentbay not only improved user engagement, enabled PLG, but also made our sales and marketing efforts more targeted and effective. It's a testament to the power of a well-thought-out, data-driven approach to growth.
If you're looking for someone who can bring a comprehensive and strategic approach to Growth: