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Tillster, Inc. is a global leader in powering digital ordering, delivery, couponing, and loyalty solutions, across web, app, kiosk, and call center platforms. The company empowers restaurant brands that are looking to increase their revenue, achieve operational efficiencies, and better engage the guests.
Enabling nearly 50 million digital orders per year, Tillster offers the added dimension of integrated marketing and data mining, including a proprietary AI and machine learning tool. Tillster serves more than 100 global and regional brands, including Burger King, Baskin-Robbins, Freddy's Frozen Custard & Steakburgers, Godfather's Pizza, Jollibee, and Pollo Campero, with teams spread across North America, Latin America, EMEA, and APAC.
Farzana Noorzay is the Head of the data science, analytics, engineering, and data products teams at Tillster. She has become the best leader because she had very similar roles in the last two positions. She is responsible for the data products, including actionable dashboards used by decision-makers to define the course of action for promotions based on consumer behavior trends. Tillster also leverages AI products to help understand consumers and provide appropriate incentives, messaging, and shopping cart recommendations. Of course, the backbone of these products is the data infrastructure, data pipelines, algorithms, models, and calculations. Farzana enjoys being responsible for the products that affect clients as well as the restaurant consumers. Farzana's career has always revolved around data from when she was an intern at the County of Santa Barbra as a college student. She built a payroll management system with MS Access 2.0, where she found it very rewarding and even believed it was fun to build something and see it in use. Farzana has worked in many industries such as entertainment, healthcare, fintech but always remained vigilant about staying very close to data engineering.
Early in her career, Farzana was responsible for building data and server architecture. As a result, understanding the base level of data products was valuable in helping her lead and guide decisions now.
"It is essential to understand each aspect that affects the products, from the infrastructure to the algorithms and models, the software development process, the users and personas, and the business case," believes Farzana.
Farzana spends time understanding the user, their needs, limitations of the tech stack, the ecosystem, and the industry. Then she tries to introduce relevant features that can solve a specific need or gap. She is very collaborative, so she discusses proposed solutions with an internal team from various levels in the organization and then completes the market research to confirm that there is a market need. However, most of the time is consumed with resolving a tech debt or meeting some baseline needs while developing solutions.
When Farzana worked for health insurance companies in the early 2000s, AI had a very different term (actuary) and was left to the statisticians who analyzed risk. Today AI is integrated into many parts of Tillster products and services. She thinks that it has come downstream and become a vital part of almost every aspect of technology. Consumers and users almost expect it. "This is an exciting and welcome change. Cloud computing has enabled big data and AI to that sounded like science fiction in the early 2000s," adds Farzana.
Over her career, Farzana has learned many valuable lessons within her field. In turn, she feels every data science leader should be naturally curious with a thoughtful approach to getting at the most critical metrics and indicators. They should ask provocative questions that go against the grain instead of finding proof for their hypothesis. "It takes a certain level of exposure to and expertise in an industry to be successful. It is advisable to spend time learning about the scenarios, risks, and drivers of the industry," said Farzana. For example, when she worked for a company where she led the data science products for large live venues. She spent her time learning about the operations, the business partnerships, and the players of that industry. Farzana had never thought about what it took to run a stadium that could host 20,000 guests for a four-hour game until that point. This taught her that the technical approach for data science must be sensitive to real-world scenarios. And thereby, stresses that emerging data science leaders consider the differences between the data science needs of a stadium and the data science needs for building wealth management tools or healthcare messaging. "The data science leader should possess adaptability and curiosity," concludes Farzana.
Early in her career, Farzana found it hard to take on leadership, authority, and decision-making. She shied away from roles that required more authority because she needed to hone her skills before feeling more comfortable in setting a direction. After several years when she served in individual contributor roles, Farzana found herself stuck. "It was as if people perceived me as someone who could not lead," she explained. Farzana regretted her earlier decision not to accept her leadership roles. Eventually, after years of dedication and experience, she was able to get her career moving towards higher levels of management, and she is grateful for that.
Farzana has two pieces of advice for the emerging leaders— first, she insists everyone keep learning and stay dedicated to personal growth and skill development at every stage in their career. Second, she asserts that it is critical to evaluate the buy versus build notion. In some cases, it may be best to buy or partner with an organization compared to building a product from scratch. "People tend to enjoy putting things together and building from the ground up, but sometimes it's more strategic to use work that someone else has done in the area and take it further or integrate it into the workstream," she adds.
Farzana believes Tillster is on a clear path to rise to the top of the industry and be the catalyst for smarter technical enablement of the food and beverage industry. In terms of data, it has a wonderfully strategic data culture that starts from the top, and the CEO drives this culture herself. Her leader, Hope Neiman, the CMO, does a fantastic job by setting a culture of producing high-quality market-relevant data products. Farzana regularly performs a deep-dive analysis of the data, and with leadership like this, Farzana and the team are inspired to help fuel their vision. It provides a targeted approach to outcomes-focused menu building, food ordering and operational efficiencies, and incentives and loyalty programs that can be mapped directly to revenue and profits. The industry has been trending toward faster ordering through various channels, with in-store kiosks becoming the most sought-after ordering channel in Q3 of 2021 across the globe.
