Ivailo Iliev

Lead Product Manager

Case studies

Scaling a fintech business with an ML-based automation solution

Executive Summary

In this case study, we explore how a fintech company used an ML-based automation solution to successfully transition from a sales-led to a product-led growth model and reach new market segments. By running real-time, event-based flows for individual users and continuously optimizing their effectiveness through A/B testing and machine learning, the company was able to automate much of the sales and retention process and scale their business. This case study showcases the product manager's ability to drive growth through innovative technology solutions and strategic planning.

Background

• A fintech company that operates a stocks trading platform
• Wanted to reach new market segments, specifically micro traders who deposited only $5
• Limited in their ability to do so due to reliance on sales and retention reps, who were necessary for training new users but did not scale well

Problem

• Unscalable process for training and supporting new users through sales and retention reps
• Limited ability to reach new markets, as this would mean hiring and training local sales and retention teams
• Unable to reach small customers, as retention reps were focused only on the highest paying customers

Goal

• Transition from a sales-led to a product-led growth model
• Reach new market segments, specifically micro traders who deposited only $5
• Scale the business

Metrics

• User growth
• Retention rate
• Average revenue per user
• Cost per acquisition

Product Process

• Research: The initial brief was provided as "a system that can know everything and do anything, to any customer, in real-time". As I had previous experience in building workflow solutions, I started by doing research to see if an existing solution would fit. There were some marketing automation solutions on the market that provided customizable workflows to send messages based on specific user actions. However, the company needed an integration directly with the trading platform to get real-time trading events and use retention tools and promotions. This led to the decision to build a custom solution.
• Executive alignment: I created a high-level prototype in PowerPoint to showcase how the system would interact with an end user. This was used to align stakeholders on the high-level scope, terminology, and capabilities of the potential solution, and get a "go" decision. A high-level business structure diagram helped to identify the main business components, such as flow creator, flow instances, and flow nodes.
• High-level architecture: As a product manager, I worked with architects to design the high-level architecture of the solution.
• Full scope and MVP definition: Next, we defined the full scope of the solution and identified the minimum viable product (MVP).
• Detailed product specification document: A detailed product specification document was created to specify the functional design of the MVP scope.
• Design review and work breakdown: The detailed specification was then assigned to the RnD teams. Working together with the technical team leads, we further clarified the design and broke down the work into deliverable chunks.
• Agile development process: This is when the initiative entered the agile development process. The teams did a number of POCs and presented demos at regular intervals, getting feedback and adjusting the course until the MVP was ready and deployed.
• Successful launch and retention rep training: The ML-based automation solution was successfully launched and the retention reps were trained on how to use it. The first deployed flow was a relatively simple one, but it allowed us to validate the performance of the system and resolve any issues early on.

Solution

• Built an ML-based automation solution to run real-time, event-based flows for individual users
• Provided timely actions to improve trading performance and minimize the learning curve for users
• Sent in-app communications and offered retention bonuses to users
• Conducted A/B testing and optimization to continuously improve the effectiveness of the flows
• Used a database of recorded flow instances to train machine learning models to optimize the flows
• Allowed for human-in-the-loop interactions, handled by a pilot retention team

Results

• Successfully transitioned to a product-led growth model for customer conversion and retention
• Reached new market segments, specifically micro traders who deposited only $5
• Automated much of the sales and retention process, enabling the company to scale their business
• Continuously optimized customer interactions, while applying consistent quality standards for communication with customers
• Allowed human reps to focus on more high-touch interactions with users

Removing barriers to entry for new customers on a stocks trading platform

Executive Summary

In this case study, we explore how a stocks trading platform improved their onboarding flow to remove barriers to entry for new customers and drive user adoption. By integrating automated solutions, simpler deposit methods, and in-app help interactions, as well as a custom-built, simple trading platform for new users, the company was able to improve the user experience and drive growth. This case study showcases the product manager's ability to identify and address customer pain points and drive user adoption through strategic product development.

Problem

• Complex onboarding flow with a high barrier to entry for new users
• Difficulties with KYC and identity checks. As the process was manual and time-consuming, users would often abandon their accounts
• Advanced system UI with limited in-app help interactions

Goal

• Improve the onboarding flow to make it easier for new users to get started on the platform
• Drive user adoption and growth

Metrics

• User acquisition rate
• Conversion rate
• Time to first trade
• Retention rate

Solution

• Improved the UX of the onboarding flow: By working together with the UX team, we completely redesigned the onboarding flow. This was done in a highly regulated environment and required frequent interactions with the compliance experts and external auditors, to ensure the product is kept compliant.
• Integrated automated solutions for KYC and identity checks: We researched several third-party automated solutions for identity verification. By feeding the solutions real customer data in a demo environment, we were able to select the most effective one. Other criteria for choosing the solution providers were the responsiveness of their support team and the ease of integration which the RnD teams validated in POCs.
• Provided in-app help interactions: One of the main pain points for conversion was the deposit page. We started by heavily simplifying the UX of the page, and pre-filling information as much as possible while keeping compliance and minimizing fraud. Next, we implemented in-app help and on-screen tutorials, as well as integration with a live person chat system for assistance.
• Integrated a custom-built, simple trading platform: The last major pain point in the onboarding of customers was the UX of the trading platform itself. It provided advanced capabilities for experienced traders, but new users would feel overwhelmed at first. To solve this, we integrated a partner trading platform for new users which offered a simplified UI, and then transferred them to the advanced platform once they gained knowledge.

Results

• Successfully removed barriers to entry for new customers
• Improved the user experience for new users
• Drove user adoption and growth

Improving retention rates with an AI-powered CRM

Executive Summary

In this case study, we explore how a retention team used an AI-based CRM system to more effectively target and communicate with their customers, resulting in improved retention rates. By utilizing AI algorithms to choose the right customer to call at any given moment, the team was able to target the customers more effectively, which drove customer engagement and retention. This case study showcases the product manager's ability to leverage AI and machine learning to drive business outcomes and improve customer retention.

Problem

• Traditional CRM systems were limited in their ability to segment and target customers based on specific characteristics and behaviors
• Choosing which customer to call was left to the retention rep's discretion, which led to inconsistent results and missed opportunities
• This resulted in generic, mass communications that were not always relevant or effective in driving retention

Goal

• Improve retention rates by more effectively targeting and communicating with customers
• Prioritize the best opportunities to call customers in order to upsell or prevent churn

Metrics

• Retention rate
• Customer satisfaction
• Average revenue per customer
• Customer lifetime value

Solution

• Built an AI-based CRM system that could prioritize which customer to call at any given moment, based on real-time events and characteristics, and historic behaviors of the customer.
• The system was able to choose the customer with the highest potential to prevent negative and reinforce positive outcomes and put them automatically on a call with the best agent available to support them.
• Used machine learning algorithms to continuously optimize and improve the accuracy of customer targeting prioritization. We used the senior retention reps' knowledge and experience to train the system, by allowing them to skip calls that were not relevant.
• Prioritized calls for the retention agents, automatically dialing the next right customer based on events and customer metadata

Results

• Successfully reached the right customers with targeted, personalized communications
• Improved retention rates through more effective communication and customer engagement
• Demonstrated the value of using AI and machine learning to drive business outcomes. In this case, AI systems are not replacing human-to-human interaction, but can greatly assist and optimize the work of experts, by making split-second decisions based on vast amounts of data.

Driving customer upgrades with a targeted outreach program

Executive Summary

In this case study, we explore how a software company successfully drove upgrades among customers using outdated versions of their Managed File Transfer gateway. By implementing a personal outreach program and tracking upgrade status, the company was able to address issues with supportability, upgradeability, and security, as well as help customers stay up to date with new features and improvements. This case study showcases the product manager's ability to develop and execute a successful upgrade strategy and improve the overall customer experience.

Background

• A software company that provides a Managed File Transfer gateway to enterprise customers
• Customers have permanent licenses and can download and install whichever release they choose
• Many customers were using old versions that were more than 10 years old
• This caused issues with supportability, upgradeability, security, and awareness of new features and improvements

Problem

• The company could not provide patches for old versions
• The upgrade path was difficult, with many steps required to reach the latest version
• Third party libraries were not updated and had vulnerabilities
• Customers were unaware of new features and improvements

Goal

• Drive upgrades among customers using outdated versions of the Managed File Transfer gateway
• Address issues with supportability, upgradeability, and security
• Help customers stay up to date with new features and improvements

Metrics

• Percentage of customers using unsupported versions who completed an upgrade
• Time to upgrade (e.g. average number of days between outreach and upgrade)
• Customer satisfaction with the upgrade process
• Number of security vulnerabilities addressed through upgrades
• Customer adoption of new features and improvements
• Average revenue per customer after upgrade

Program Management Process

1. Identify the specific versions of the Managed File Transfer gateway that were unsupported and needed upgrading.
2. Develop a list of customers using these outdated versions.
3. Determine the best approach for reaching out to these customers, including personal outreach by account managers and generic outreach to all customers.
4. Create a plan for tracking the upgrade status of each customer.
5. Execute the outreach program and monitor the progress of upgrades.
6. Analyze the results of the program and make any necessary adjustments.

Solution

• Ran a program to notify and upgrade all customers on unsupported versions
• Personal outreach by account managers
• Generic outreach to all customers
• Tracking of upgrade status

Results

• Successfully drove upgrades among customers using outdated versions
• Improved supportability, upgradeability, and security for these customers
• Helped customers stay up to date with new features and improvements
• Improved the overall customer experience

Transitioning an enterprise server product to a modern lifecycle

Executive Summary

In this case study, we explore how a product manager successfully aligned a software company's move from a yearly release with a 3-year general support cycle to a single version with monthly updates with internal stakeholders and customers. By addressing concerns about update quality, frequency, and the need for patches, the product manager was able to smooth the transition to a modern product lifecycle and improve the overall customer experience.

Background

• A software company that provides a Managed File Transfer (MFT) gateway
• Previously used a yearly release with a 3-year general support cycle
• Wanted to move to a single version with monthly updates

Problem

• Customers were worried about the quality of updates, which might introduce bugs
• They were worried about the need to update too frequently, which would require downtime
• Research and development (R&D) was worried about the need to provide patches on top of updates

Goal

• Align the new product lifecycle with internal stakeholders and customers
• Smooth the transition to a single version with monthly updates

Metrics

• Customer satisfaction with the new product lifecycle
• Time to update (e.g. average number of days between the release of an update and the time it is adopted by customers)
• Percentage of customers adopting the new product lifecycle
• Number of reported bugs or issues related to updates
• Customer retention rate
• Average revenue per customer after the transition to the new product lifecycle

Product Process

1. Gathered feedback from internal stakeholders, including R&D and customer-facing teams, about their concerns and needs related to the new product lifecycle.
2. Conducted customer research to understand their concerns about updates and the new product lifecycle.
3. Developed a plan for addressing these concerns, including measures to ensure update quality, minimize the need for downtime, and provide support for patches.
4. Presented the plan to internal stakeholders and obtained buy-in.
5. Communicated the new product lifecycle to customers and provided support and resources to help them understand and adapt to the changes.

Results

• Successfully aligned the new product lifecycle with internal stakeholders and customers
• Smoothly transitioned to a single version with monthly updates
• Improved the overall customer experience through better communication and support for the new product lifecycle

Managing trade-offs with a framework for prioritization

Executive Summary

In this case study, we explore how a research and development (R&D) team at a software company implemented a framework for prioritization to effectively manage trade-offs between competing priorities. By implementing a scorecard to prioritize new feature requests, grouping together defects and new features in the same area to minimize testing, and prioritizing release scope, the R&D team was able to effectively balance the needs of customers, strategic initiatives, and engineering improvements.

Background

• A software company's R&D team
• Needed to effectively manage trade-offs between competing priorities

Goal

• Implement a framework for prioritization to effectively manage trade-offs between competing priorities

Metrics to improve

• Time to market for new features and defects
• Customer satisfaction with the speed and quality of defect resolution
• Percentage of customer requests that are prioritized and addressed in a timely manner
• Time spent on unplanned work (e.g. defects and emergencies) as a percentage of total time
• Productivity of the R&D team (e.g. number of features delivered per unit time)
• Quality of released features (e.g. number of reported defects per feature)

Product Process

1. Gathered input from internal stakeholders about the priorities and trade-offs they faced.
2. Developed a scorecard to prioritize new feature requests based on factors such as customer demand, strategic importance, and technical complexity.
3. Categorized defects and grouped them with related new features to minimize testing efforts.
4. Prioritized the scope of each release based on the needs of customers, strategic initiatives, and engineering improvements.
5. Obtained buy-in from internal stakeholders on the new framework for prioritization.
6. Trained R&D team members on using the framework and provided ongoing support as needed.

Solution

1. Implemented a scorecard to prioritize new feature requests.
2. Grouped together defects and new features in the same area to minimize testing.
3. Prioritized release scope, including new features, work on ongoing strategic initiatives, customer defects, and engineering improvements.

Reaching new markets with a container-based SaaS offering

Executive Summary

In this case study, we explore the product discovery phase of a project to create a container-based software as a service (SaaS) offering for a Managed File Transfer (MFT) gateway. By addressing the challenges of deploying and configuring the product for each customer individually, the company aims to enable the deployment of many mid-sized enterprises in a multi-tenant cloud deployment and reduce the time it takes to onboard new customers.

Background

• A software company that provides an MFT gateway to enterprise customers
• Each customer is installed and configured on a separate deployment, requiring a lot of time and pre-sales resources (about 2 months) and a lot of cloud resources to run a separate virtual machine (VM)

Problem

• Long time to deploy new customers
• High resource requirements for individual deployments

Goal

• Create a container-based SaaS offering that can be hosted on AWS and deployed to many mid-sized enterprises in a multi-tenant cloud environment
• Decrease the time it takes to onboard new customers

Metrics to improve

• Time to onboard new customers
• Number of new customers onboarded per unit time
• Resource utilization of the container-based deployment compared to individual deployments
• Customer satisfaction with the onboarding process
• Average revenue per customer after the transition to the container-based SaaS offering
• Retention rate of customers using the container-based SaaS offering

Product Process

1. Conducted research on container-based deployment solutions and their potential benefits for the MFT gateway.
2. Created a high-level prototype in PowerPoint to communicate the concept and potential benefits to internal stakeholders.
3. Worked with architects to design a high-level architecture for the container-based SaaS offering.
4. Defined the scope of the minimum viable product (MVP) and identified the key features and capabilities that would be included.
5. Created a detailed product specification document for the MVP scope.

Results

• Further development and testing of the container-based SaaS offering
• Implementation of the MVP and rollout to new customers
• Continuous iteration and improvement based on customer feedback