Revolutionizing Retail: Approach of Maneesha Bhalla in Advanced Analytics and AI

Maneesha Bhalla
Maneesha Bhalla

Contributing significantly to the field of advanced analytics within the retail sector, Maneesha Bhalla is the VP of Enterprise Data Analytics & AI at Designer Brands and a distinguished thought leader and TEDx speaker. With a rich background spanning over 20 years, Maneesha has become a trailblazer in the application of analytics to drive strategic priorities, revenue growth, and profitability.

Her leadership role at Office Depot involved spearheading the advanced analytics team, focusing on delivering actionable insights and leveraging machine learning models. Maneesha is at the forefront of utilizing analytics in marketing, particularly in the field of personalized marketing, showcasing her expertise in harnessing data to enhance customer experiences and drive business success.

Maneesha’s journey as a data leader has been dynamic, starting with a foundation in data analysis and statistics during academic years in Chemical Engineering. The journey progressed through roles at data consulting firms, emphasizing data quality. Transitioning to Target Corp, Maneesha deepened expertise in merchandise planning, supply chain optimization, and inventory management as Senior Manager in Merchandising Analytics.

In the current role as the leader of the Data and AI Centre of Excellence at Designer Brands, Maneesha combines her past experiences to build data strategy for the enterprise. She oversees data engineering/integrations, cloud data engineering, business intelligence, and AI/ML. This role allows her to leverage her career’s breadth and lead talented teams, striving to enable data-driven decision-making in the organization.

Blending Passion, Learning, and Transformative Impact

Maneesha’s fascination with the power of data led her to start as a business analyst, using data analytics and visualization tools to generate insights. Wanting to explore beyond descriptive analytics, she delved into the potential of Artificial Intelligence (AI) and Machine Learning (ML) to create predictive and prescriptive solutions. Believing in the future impact of AI/ML on innovation and transformation and to keep up with latest in this field, she pursued learning through online courses and certifications and completed her second masters in Analytics from Georgia tech recently.

This journey into the field of AI/ML allowed Maneesha to work on diverse and complex problems, including predicting customer behavior, optimizing operations, and developing data strategies. As a leader in analytics and AI, she has led and mentored teams, fostering a culture of customer centricity, innovation, and results. Passionate about AI and ML, Maneesha looks forward to continuing her career in this field, always eager to learn new skills and techniques to improve performance and deliver value to stakeholders.

Elevating Customer Engagement

In Maneesha’s role at DBI, she led a project to create a customer 360 data layer, centralizing and aggregating customer data from multiple sources for analytics and AI/ML workloads. Her team developed user-based product recommendation algorithms, resulting in increased customer engagement and retention. Tests showed an 11% increase in overall click-through rate (CTR) and a 9% increase in total recommended style demand per recipient. She also worked on a visual search proof of concept (POC) for the mobile app, meant for customers to use images for item searches.

Exploring applications of generative AI, Maneesha aims to enhance customer experiences and improve efficiency. She is investigating language-based models for personalized customer experiences and considering applications in marketing campaign content generation, product attribution enhancement, and automated product description generation.

Maneesha is also actively involved in educating leadership and peers on the capabilities and possibilities of AI/ML and generative AI. Her team has created training materials and best practice documents for running AI/ML workloads in GCP and using generative AI studio. Generating awareness and inspiring leaders on the art of the possible has been a key impact of her work, steering several initiatives and demonstrating value for the organization.

AI in Retail

Maneesha envisions the transformative role of AI in the retail industry, anticipating several key developments:

  • Personalized Customer Experiences: AI will enable retailers to deliver highly personalized and engaging customer experiences. By leveraging data and analytics, retailers can gain insights into customer preferences, behavior, and needs. Virtual shopping assistants, chatbots, and voice assistants powered by AI will interact with customers, providing tailored recommendations, offers, and support.
  • Operational Optimization: AI will contribute to operational efficiency by introducing automation, robotics, and machine learning. Retailers can automate various aspects of their operations, including warehouse management, inventory tracking, and delivery processes. This will lead to improved accuracy, productivity, and cost reduction.
  • Innovation and Differentiation: AI will empower retailers to innovate and stand out from their competitors. Through AI-driven insights, retailers can create new products, services, and business models that align with evolving customer expectations. For instance, AI can facilitate the creation of personalized and customized items, such as clothing and accessories, matching individual styles and preferences.
  • Ethical Considerations: Maneesha emphasizes the importance of addressing ethical and responsible challenges associated with AI in retail. This includes ensuring data privacy and security, avoiding bias and discrimination in AI algorithms, and maintaining human oversight and accountability. Adhering to principles of transparency, fairness, and explainability is crucial in the development and deployment of AI systems.

Maneesha sees AI as a pivotal force in driving innovation and transforming the retail industry. While it promises substantial benefits in terms of enhanced customer experiences and operational efficiency, retailers must also prioritize ethical considerations to build trust and ensure the sustainable and responsible use of AI technologies.

Upholding Transparency, Fairness, Privacy, and Security

The AI council at DBI plays a crucial role in fostering a responsible and ethical approach to AI adoption. The council is committed to principles and best practices that prioritize transparency, fairness, customer privacy, and security:

  • Transparency: The council ensures clarity about the utilization of AI technologies. This involves communicating how AI will be used, detailing the data collection and processing procedures, and outlining the benefits and risks associated with AI for customers, employees, and stakeholders.
  • Fairness: To prevent biases in AI systems, the council emphasizes fairness. AI models are carefully trained to exclude characteristics such as age, gender, race, ethnicity, religion, or disability. The council actively monitors and mitigates potential biases in both data and algorithms. The incorporation of explainability features in AI models aids in understanding the decision-making process and provides insights into model outputs.
  • Customer Privacy: Customer privacy is a top priority for the council. Compliance with comprehensive privacy laws is ensured, and any customer data that requires deletion from systems is also removed from AI models that might have used it during the training process. This approach aligns with industry standards and regulations.
  • Security and Resilience: Collaboration with the information security team is integral to the council’s strategy. An oversight mechanism is in place to review AI policies and the implementation of AI tools/systems. This ensures that the AI initiatives are aligned with robust security measures, safeguarding both the systems and the data of customers.

By upholding these principles, the AI council at DBI establishes a framework that not only accelerates AI adoption but also ensures responsible and ethical practices in alignment with industry standards and legal requirements.

Personalized Customer Experiences in the Data Landscape

Maneesha’s passion for the field of Data and Analytics is evident, and her vision for the future reflects a commitment to leveraging AI for the betterment of the community. She envisions contributing to the broader domain of data and AI by partnering with other firms and leaders to advocate for the ethical and safe use of AI. Her goal is to bring AI into everyday life, enhancing the quality of life for individuals, especially for the elderly and children. While she acknowledges the potential of AI in robotics, she sees it as a tool to complement human efforts rather than replacing them entirely.

At Designer Brands, Maneesha plans to harness generative AI and machine learning to personalize customer touchpoints and enhance efficiency across various functions such as supply chain, marketing, merchandising, and IT. The focus on foundational data is emphasized, recognizing its crucial role in the optimal functioning of AI applications. Investments in data foundation, including tooling, systems, and the semantic layer, are part of the strategy to ensure clean, curated, and accurate data.

The approach to leverage pre-trained models available in existing cloud platforms for fine-tuning on company-specific data reflects Maneesha’s forward-thinking perspective on the speed and scalability of AI implementation. This approach not only streamlines the integration of AI models but also sets the stage for transformative changes in how organizations can harness the power of AI, particularly in language-based applications.

Insights and Advice for Aspiring Professionals

Maneesha provides valuable advice to AI professionals and researchers aspiring to enter the dynamic technology sector. Here are key takeaways:

  • Stay Updated and Curious: Keep abreast of the latest developments and trends in AI by following experts, influencers, and publications on social media. Engage with blogs, articles, videos, podcasts, conferences, workshops, and online communities to stay informed. Cultivate curiosity and a willingness to explore new domains, tools, and techniques.
  • Develop Skills and Portfolio: Enhance technical, analytical, and creative skills in areas like programming, mathematics, statistics, machine learning, deep learning, natural language processing, and computer vision. Build a portfolio showcasing projects that demonstrate your skills, accomplishments, and contributions to the field.
  • Network and Collaborate: Recognize that AI is a collaborative and interdisciplinary field, and actively seek opportunities to network and collaborate. Join teams, groups, or clubs, participate in competitions, hackathons, challenges, and contribute to open source projects. Consider publishing your work in journals or conferences and seek mentorship from experienced AI experts.
  • Be Ethical and Responsible: Acknowledge the significant impact of AI on society and the environment, and be conscious of the ethical implications of your work. Follow principles and best practices, including transparency, fairness, accountability, privacy, safety, security, and social values alignment. Strive to use AI in a beneficial, trustworthy, and sustainable manner, respecting the rights and interests of all stakeholders.

This advice reflects Maneesha’s understanding of the multifaceted nature of AI and emphasizes not only technical proficiency but also ethical considerations and collaborative engagement within the AI community.

Charting the Data Frontier

Maneesha’s journey in the data and analytics space has been marked by a commitment to overcoming challenges and driving innovation. While the technical hurdles of advancing AI and machine learning are invigorating, Maneesha underscores the crucial nature of transforming business processes and mindsets. The transition to new technologies often encounters resistance, particularly when AI systems, viewed as “black boxes,” replace traditional decision-making based on experience. Maneesha advocates for managing these shifts by offering comprehensive training and reskilling programs.

Additionally, she emphasizes the necessity of staying abreast of regulatory changes, ensuring AI systems remain compliant. Lastly, Maneesha sheds light on the financial aspect, highlighting the importance of strategic planning to optimize costs associated with running AI systems and to achieve a positive return on investment. Through her experiences, Maneesha exemplifies the resilience and adaptability needed to navigate the complex landscape of data and analytics.