Mr. Prakash will be a panelist discussing “The Ethical Dilemma: Balancing User Growth with Responsible Product Development” and leading roundtable discussions at the Product-Led Alliance (PLA) Conference on September 4-5, 2024, in San Francisco, California (US).
Sanjeev Prakash is Director of Product Management for Shipt, a subsidiary of Target that offers millions of customers a conveniently curated online grocery shopping and delivery experience. Since joining the company in late 2021, Sanjeev’s innovative fulfillment strategies and unique solutions transformed the company’s cost efficiency to save more than $100 million in under two years, driving first-ever profitability and stakeholder satisfaction.
Earlier in his career, Sanjeev served as a software developer, consultant, senior analyst, and product lead for global companies including Tata Consultancy, Siemens, Royal Bank of Scotland, and Gartner. With a bachelor’s in Computer Science and Engineering and an MBA in Finance, he brings an IT and business perspective to developing product-led growth strategies.
TechBullion spoke with Sanjeev about his three-phased approach to reducing unit economics, how he is leveraging AI to shape Shipt’s vision and strategy, and the ethical considerations raised by product-led development.
Q: Sanjeev, please introduce yourself by telling us about your background and current role. Your career has included technology development and consulting for notable businesses in diverse sectors worldwide. As an engineer, how did these positions, prepare you for a leadership role in eCommerce?
A: My name is Sanjeev Prakash. I was raised in India and have worked in various industries with global companies. I currently oversee a team of four Principal and Senior Product Managers at Shipt, leading the strategy and execution of products for Marketing Technology (MarTech) and Advertising Technology (AdTech).
In the mid-2000s, I earned a Bachelor of Technology degree in Computer Science from the prestigious National Institute of Technology in India. During my final year, I led over 80 students in a tribal education and employment campaign as part of the National Service Program. This experience was pivotal in developing my leadership skills in team-building and managing large-scale initiatives, which have been instrumental throughout my career.
After six years working in technology and consulting roles in India, I moved to the U.S. to pursue an MBA in Finance from the Simon Business School of the University of Rochester (New York). During my second year, I took on leadership roles in various clubs, mentored first-year MBA students, and was recognized as one of the top three coaches. The strong MBA curriculum and the mentorship opportunities provided me with a holistic perspective on business and people management, which led me to a position as a Senior Analyst at Gartner, a leading technology research company. Over eight years at Gartner, I rose through positions of increasing responsibility, culminating in a product lead role for a portfolio of products generating over $230 million in annual revenue. This role allowed me the unique opportunity to act as a general manager, solving business growth and new market challenges in collaboration with sales, marketing, finance, and operations stakeholders.
This blend of business and technical expertise helped me formulate strategies and develop last-mile delivery solutions when I joined Shipt in 2021. At that time, Shipt was facing a significant challenge in reducing fulfillment costs. I launched several products in collaboration with data science, engineering, and operations teams to enhance the company’s cost-efficiency. By the end of my first year, we were on the path to profitability. This success was recognized with my promotion to my current role, where I focus on building marketing products to enhance user engagement on Shipt’s marketplace, and identifying opportunities in the AdTech space to grow a significant secondary revenue stream for the company.
Q: When you joined Shipt as a Principal Product Manager, you quickly led a team of engineers, data scientists, and operations personnel in a logistics management turnaround that enabled the company to become profitable in the highly competitive grocery delivery business. Tell us about the challenge the company was facing, and how you innovated to solve it with a new fulfillment strategy.
A: The last-mile delivery space, which involves the final leg of packet delivery from a distribution center or a retailer’s location to the customer’s doorstep, faces significant challenges in terms of cost, speed, customer expectations, fraudulent behavior, compliance, and labor laws. Additionally, a couple of unique challenges for Shipt were managing the availability and expectations of independent gig workers, called Shoppers, and the demand fluctuations during peak times around holidays, versus regular times at different hours and different locations.
To manage many of these challenges, we came up with a Bundling strategy by consolidating multiple orders for delivery within a specific area or along a specific route. This consolidation of orders reduced the number of Shoppers required to deliver the orders, and thus helped in balancing the demand and supply equation. Moreover, bundling of orders reduced the number of trips needed, which lowered labor costs and decreased greenhouse gas emissions. Delivering multiple orders and packages in the same area allowed for more efficient route planning and improved the utilization of Shopper’s hours, improving their earnings and efficiency.
Q: The grocery delivery industry has some very recognizable brands. How do you develop and market software products that will differentiate your company and offer a unique value proposition for your customers?
A: The grocery delivery industry has seen significant growth and competition post-COVID, with several key players emerging as top brands. We differentiate ourselves from competitors in terms of faster delivery, excellent customer service, and integration with Target stores. We offer a personalized shopping experience for our members by allowing them to choose their preferred Shoppers, and we offer various perks to our Shoppers to keep them engaged with our platform. This customer-first approach helps us prioritize the needs of our Members, Shoppers, and retail partners, and the data-based, decision-making and experimentation mindset helps us launch the right Member and Shopper-facing products in the market.
Q: The success of your fulfillment solution propelled it to become Shipt’s flagship product, and led to your rapid promotion to your current Director role. Describe the three-phase approach you used to create this new strategy and show proof of concept.
A: The three-phase approach that I developed was inspired by the recent growth in the technology sector, and was based on my prior experience of dealing with ambiguous business problems.
In the first phase, I focused on tech innovations. We initially tested the Bundling concept using a heuristic model to generate pairs of orders and validate market clearance and cost efficiency assumptions. Success with the heuristic model led to the forming of a data science team to develop an Optimization Engine model using the Google OR tool. This model optimally paired orders to minimize shopping and delivery time.
After achieving cost savings with thousands of orders in the first phase, our goal was to scale it to millions of orders. In the second phase, I zoomed in on product experimentations where we explored various ideas based on demand data, market characteristics, and shopper behavior. We developed a simulation platform that simulated the product ideas and calculated the anticipated impact. This simulation exercise helped to prioritize initiatives based on expected impact and effort. We fostered a culture of experimentation, rolling out new ideas in select metros, monitoring key metrics, and scaling or refining ideas based on their outcomes. This approach mitigated business risks and allowed for continuous improvement.
Within a year, over 50% of Shipt orders were bundled, reducing variable costs so low that Shipt became profitable for the first time. However, as we scaled our product, we observed a growing concern from Shoppers about Bundle quality and app usability. So, in the third phase, we addressed these challenges by launching an improved app experience for Shoppers and enhancing the Optimization Engine to refine Bundle quality.
Q: How did you use artificial intelligence (AI) and machine learning (ML) in developing this product, and how is this driving your product-led growth strategy?
A: Machine learning was pivotal for us in optimizing the Bundling solution by addressing key challenges such as delivery efficiency, cost reduction, fraud detection, and customer satisfaction. The five main ways in which we utilized ML in delivering Bundling solutions were these:
- Route optimization: We leverage ML algorithms in analyzing various factors, such as delivery time windows, traffic patterns as per the hour of the day, store and delivery location, etc. to create the most efficient route.
- Bundling optimization: ML algorithms allow for dynamic bundling of orders as new orders come in, continuously optimizing delivery efficiency. Moreover, ML algorithms help us consolidate orders based on shopping time factors such as the number and type of products in the order, store type, store traffic, etc.
- Demand forecasting: We leverage prediction models to forecast demand based on historical order data, seasonality, hour and day of the week, and recent trends. This accurate forecasting of orders helps us plan delivery schedules and create optimal Bundles of orders.
- Shopper supply management: We leverage ML models to manage the supply of Shoppers for certain hours of the day and peak holiday seasons.
- Anomaly detection: We leverage pattern detection models to identify fraudulent activities, such as false delivery claims, account takeover by hackers, etc., to ensure the integrity of the delivery process.
Q: How else are you using new AI/ML and robotic process automation tools to identify new opportunities and use cases for Shipt, and how are these innovations informing your cross-functional vision and strategy for the company?
A: AI and Machine learning innovations are at the core of my product vision and strategy, significantly shaping the way we approach cross-functional initiatives. In my current role, I leverage these technologies to enhance our Marketing and Advertising products, driving efficiency and effectiveness across our operations.
Transforming marketing campaigns with AI/ML: Historically, creating marketing campaigns to offer the right incentives to our members has been a resource-intensive process. This approach often required substantial manual input and analysis to determine the optimal incentives. To address this, we are developing an ML-driven incentive platform. This platform leverages AI to analyze members’ shopping behaviors and preferences, enabling us to offer personalized promotions. By tailoring incentives to individual shopping habits, we not only improve the effectiveness of customer savings but also enhance the overall customer experience. This shift from a resource-heavy process to an AI-driven model allows us to execute campaigns with greater precision and efficiency, ultimately driving higher engagement and loyalty among our members.
Enhancing Advertising Effectiveness with ML: In the advertising domain, relevancy is key to customer engagement and satisfaction. We are harnessing ML algorithms to improve the targeting and placement of our sponsored products. By analyzing vast amounts of data, including user behavior, preferences, and purchasing patterns, our ML models can predict which ads will resonate most with specific customer segments. This ensures that our advertising efforts are not only more relevant but also more effective in driving conversions and revenue. The continuous learning capabilities of our ML systems allow us to adapt to changing customer preferences in real time, maintaining high levels of relevancy and engagement.
Leveraging AI/ML in our marketing and advertising strategies has tangible benefits for our business. Improved campaign efficiency and ad relevancy lead to higher customer satisfaction and retention, directly contributing to revenue growth. Moreover, the ability to quickly adapt to market changes and customer preferences gives us a competitive edge in the fast-paced digital landscape. As we continue to explore new AI/ML applications, we remain committed to driving innovation and delivering exceptional value to our customers and stakeholders.
Q: The rapid deployment of AI in marketing and product-led growth strategies is raising broad ethical concerns about algorithmic bias in technology, data privacy, user experiences, and social responsibility. In September 2024, you will be one of the experts addressing these issues on a panel at the PLA Conference. How should marketers, product managers, and engineers be approaching these problems, and how are you mitigating these risks at your company?
A: As AI technologies become more integral to business operations, marketers, product managers, and engineers must take proactive steps to address ethical concerns, particularly around algorithmic bias, data privacy, user experiences, and social responsibility.
Algorithmic bias refers to unfair discrimination that occurs when the algorithm’s outcome indicates subconscious or conscious prejudices against a certain group of people. Generally, there are three usual suspects for the source of algorithmic bias: underrepresented or biased training data sets, algorithmic design bias, and outcome interpretation bias. For example, suppose a resume filtering application is designed to select candidates based on historical data of successful hires. In that case, the model might create bias against applicants from certain schools or specific demographic groups.
Addressing algorithmic bias is crucial to ensure fairness and equity in ML-based systems as it can lead to legal and ethical issues for the organizations. There are three main ways we try to mitigate this type of risk. First, we analyze and understand the training data to ensure it’s not underrepresented. Second, use techniques and tools to detect and quantify bias in algorithms. Third, we have established clear guidelines and ethical standards at the company level for any AI development.
Data privacy is a major concern as ML models often use vast amounts of personal data. Protecting this data to retain customer trust and complying with regulations is critical for any organization. We take a customer-first approach to anonymize personal data and regularly update privacy policies to ensure compliance. Taking consent from users and incorporating user insights to refine algorithms help improve user engagement and satisfaction.
Q: As Director of Product Management, how much are you involved in training and mentoring younger product managers, and how do these ethical issues factor into your team members’ education?
A: As Director of Product Management, I am deeply involved in the training and mentoring of younger product managers. This is a critical aspect of my role, as it ensures that our team is not only proficient in the technical and strategic aspects of product management, but also deeply aware of and committed to ethical practices.
We have a robust onboarding program that introduces new product managers to our company’s values, processes, and tools. I facilitate regular sessions on the latest trends and best practices in product management and AI ethics. I personally mentor several team members, providing career guidance, offering feedback and support to enhance their technical and leadership skills, and discuss real-world scenarios and guide them on how to navigate ethical dilemmas in product management. I encourage my team to work closely with the legal and compliance team for any data-related product ideas to ensure our products are compliant with data privacy laws and best practices. I also participate as a speaker at business school events to share industry insights and inspire future technology and product management leaders.
I aim to develop not only skilled and strategic product leaders, but also conscientious professionals who are committed to building fair, transparent, and socially responsible products. This holistic approach ensures that our team is well-equipped to navigate the complex ethical landscape of modern product management and AI development.
Q: After getting an academic foundation in computer science and engineering, nearly a decade later you pursued an M.B.A. in Finance, and two years later—while employed at Gartner, you earned the CFO Award of Excellence and Planner of the Year Award. How does your dual education and skill set make you uniquely positioned for various aspects of your roles, such as problem-solving, cross-functional collaboration, and communicating with corporate stakeholders?
A: My journey began with a strong technical foundation during my engineering course, where I was regularly exposed to various unstructured problems. This experience honed my ability to tackle complex issues in a structured manner. Also, my association with various club activities taught me early lessons in team-building and leadership. These experiences collectively provided me with a solid grounding in technical expertise, problem-solving abilities, and cross-collaboration skills, which were pivotal as I advanced in my career in software engineering and consulting roles.
Six years into my career, I decided to broaden my horizons with a Master of Business Administration degree program. I found the MBA program at the University of Rochester’s Simon Business School to be transformative, as it provided me with a comprehensive understanding of various business disciplines, including finance, marketing, strategy, sales, and pricing. This holistic perspective, combined with my technical and problem-solving skills, empowered me to approach new and unfamiliar challenges with greater confidence and strategic insight.
Upon completing my MBA, I joined Gartner, where I led several long-range planning projects that were critical for developing the company’s long-term strategy for growing new business and improving customer retention. Leveraging my dual expertise in technical and business domains, I successfully developed and executed strategic plans that significantly enhanced client retention by over three points within just one year. This notable achievement was recognized with the prestigious CFO Award of Excellence and the Planner of the Year award at Gartner in 2015.
In my current role as a Director of Product Management, these experiences have uniquely positioned me to oversee and drive various aspects of product leadership. My engineering background provides a deep technical understanding that is crucial for guiding product development and innovation. Complementing that expertise, my business education equips me with the strategic vision necessary to align product initiatives with broader business goals, manage cross-functional teams effectively, and navigate the complexities of market dynamics.
Throughout my career, I have developed a balanced skill set that blends technical acumen with strategic business insight. This unique combination enables me to lead with a comprehensive perspective, ensuring that both technical execution and business strategy are seamlessly integrated. It allows me to tackle multifaceted challenges, foster cross-functional collaboration, and drive impactful product innovations that contribute to the company’s long-term success.
Q: How do you see the tech industry evolving, and how important do you think it is for engineers and data scientists to understand business and financial concepts, and social responsibility concerns? What will be the greatest challenges in the next 3-5 years? What career guidance do you give to young engineers and students?
A: The tech industry is going through a rapid evolution, driven by advancements in Artificial Intelligence, Machine Learning, cloud computing, and the Internet of Things (IoT). These technologies are transforming how businesses operate, innovate, and interact with customers.
AI and ML are becoming central to new product ideas, operation efficiency, and customer experience. The adoption of cloud services is enabling companies to scale rapidly and reduce costs. IoT is expanding the connectivity of devices, leading to the creation of smart environments. This connectivity is generating new data sources and opportunities for innovation. The data-driven decision-making is becoming a key differentiator for businesses. Data scientists and engineers are at the forefront of this transformation, developing models and algorithms that drive insights and strategic decisions.
As the industry continues to evolve, it is becoming increasingly important for engineers and data scientists to have a comprehensive understanding of business and financial concepts, as well as social responsibility concerns for several reasons:
- Alignment with Business Goals: A deep understanding of business objectives allows engineers and data scientists to align their technical solutions with the strategic goals of the organization and contribute directly to the company’s success.
- Resource Allocation: Financial acumen helps in making informed decisions about resource allocation, project prioritization, and cost management. It enables technical teams to justify investments and demonstrate the return on investment (ROI) of their projects.
- Innovation and Value Creation: Understanding market trends, customer needs, and competitive landscapes enables engineers and data scientists to innovate effectively. They can develop products and solutions that create value for both the company and its customers.
- Ethical AI: Engineers and data scientists must ensure that AI and ML models are designed and deployed ethically. This includes addressing algorithmic bias, ensuring fairness, and preventing discrimination.
- Data Privacy: Protecting user data and ensuring privacy are paramount. Compliance with data protection regulations such as GDPR and CCPA is essential for maintaining user trust.
I see three main challenges in the next 3-5 years of technology evolution:
- Regulatory Compliance: Navigating the evolving landscape of tech regulations will be a significant challenge. Companies will need to stay ahead of regulatory changes related to data privacy, AI ethics, and cybersecurity.
- Talent Shortages: The demand for skilled engineers and data scientists will continue to grow, leading to potential talent shortages. Companies will need to invest in training and development to bridge the skills gap.
- Cybersecurity Threats: As cyber threats become more sophisticated, protecting systems and data will require continuous innovation in cybersecurity measures.
Navigating a career in the rapidly evolving tech industry can be both exciting and challenging for young engineers and students. By staying current with the latest technologies, enhancing their problem-solving skills, being adaptable to the change in the industry, and being mindful of ethical and social responsibilities, young engineers and students can position themselves for successful and impactful careers in the tech industry.
