Sprockets
HRTech, Assessment
Funding: Series A
Sprockets provides an AI-driven (LLM) hiring platform that predicts applicant success and fit for a role by analyzing behavior and language patterns, aiming to improve employee retention and reduce hiring biases.
TESTIMONIALS
I worked closely with Deanne at Sprockets and I would highly recommend her! Deanne was the point person for the company's product team and was great at calibrating with the customer service team. She always took my teams feedback and was looking to improve the customer experience through enhancements and new product releases. She is very organized and is great at keeping everyone on track to meet a deadline. She would be an asset to any company!
Jessica Tinge
VP Customer Success
HIGHLIGHTS
Implemented key functionality that drove business growth from $2M to $5M in annual recurring revenue (ARR), while also reducing customer churn by 50%. This was achieved through strategic enhancements focused on improving user engagement, increasing retention, and addressing pain points identified through customer feedback.
$2M to $5M ARR Growth
Formulated and tracked key performance indicators (KPIs) for both product and engineering teams, establishing a robust reporting system. Resultantly, engineered a 50% increase in survey conversions through strategic initiatives.
50% Conversion Increase
Orchestrated a transformative restructuring of the engineering team, yielding a remarkable 200%+ surge in production. Concurrently, reconstructed our software to align seamlessly with user workflows, optimizing operational efficiency.
200% Production Increase
Product Growth Strategy
Product Vision
User and Buyer Interview Analysis
Creative/UX Direction
Multiple 0-1 Product Releases
Execution Support
Long-Term Strategic Planning
Team Management
Go To Market Strategy
Stakeholder Management
Scalability Planning & Architecture
Data Driven Decision Making
Algorithm Planning & Analysis
OVERVIEW
The Project
The web app offers hourly workers a streamlined process to apply for jobs quickly, while providing hiring managers with instant scores indicating an applicant's likelihood of retention and performance. Utilizing Machine Learning LLM models, the system analyzes interview question responses, comparing them against top performers in a specific location to generate scores that determine the retention probability.
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Target Audience
The software targeted the hourly-worker market, specializing in convenience stores, fast food, and home health care industries. Our main clients included recruiters, hiring managers, owners, and applicants within these sectors.
Competitors
The presence of significant competitors in the space compelled the team to continuously innovate and devise new strategies to compete in a market largely dominated by Applicant Tracking Systems (ATS) and Human Resources Information Systems (HRIS). Our primary focus was on excelling in one specific area and rendering traditional ATS systems obsolete.
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User Insights
Feedback from potential buyers and existing users indicated that hiring managers often lack the time or inclination to use another app, as their days are already demanding. Hiring poses a challenge due to issues such as applicants ghosting during the hiring process and a high turnover rate, with many leaving within 90 days of employment.
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The Problem
The two major challenges identified included engaging hiring managers and increasing applicant conversions. While the initial MVP was validated using scoring metrics, the system required enhancements to reach the next level of optimization for usability and scalability, facilitating company growth. Additionally, optimizing manual white glove onboarding, addressing objections, and refining information architecture were lower priority issues that needed to be addressed in the long-term strategy.
The Solution
As a result, the rebuild was designed to enable hiring managers to make decisions through text or email communication, including responding to applicant questions and facilitating one-click or automated interviews and hiring, based on predefined score thresholds. While conversions had been steadily increasing with iterative changes, the introduction of pre-interview applications led to a significant and dramatic increase in conversions.
LESSONS LEARNED
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Lack of a CTO Early On: The absence of a CTO led to platform issues, with one engineer holding all the critical knowledge. This created a bottleneck and hindered team growth and knowledge sharing.
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Inexperienced Team: The team was too junior for a startup, requiring a complete department restructuring to address skill gaps and platform challenges.
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Burnout and Overload: The team moved quickly, but the pace led to burnout at times, highlighting the need for better workload management and support.
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Leadership Alignment: Difficulty in getting leadership to adhere to a roadmap caused delays in product rebuilds, especially when rushed features for prospective clients failed to close deals.
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Future Action: Ensure having a strong CTO partner early to provide technical leadership and a stronger voice in strategic decision-making.