ETCIO spoke with eyewear retailer Lenskart to understand what the company expects from its data science team and how it fosters data science culture.
“At Lenskart, we use data to guide every business decision. To manage day-to-day activities and monthly/quarterly initiatives – if we don’t refer to the progress of metrics, we won’t be able to determine if we are going in the right or wrong direction. So we start meetings by looking at the numbers and then we dive into the stocks,” said Ramneek Khurana, co-founder and Head of Products and Technology Lenskart.
Additionally, for new initiatives, one must use consumer insight and hypothesis to take a call and move in one direction. But the results must be decided and measured with honesty to decide whether to continue/double down or set aside the initiative.
With so much use of data, how do you set clear expectations and create an environment that supports using data more effectively?
“The way we create teams is by making them part of the bottom line of the business. So, the expectation of data science is to solve the consumer problem using data science and have a significant impact on the customer experience. For example – members of the data science team in our product discovery group realized that many of our customers are not able to select the right pair of glasses because they don’t know their glasses size – and unlike shoes, t-shirts – lens size is neither standardized nor well understood by consumers. So they came up with a tool using which a customer can take a selfie to get the right glasses for them, making it easier to select products,” Khurana explained.
To manage the business – for each existing function, Lenskart tries to summarize its customer experience results into some basic KPIs that can be tracked on a weekly, monthly and quarterly basis.
Similarly, each initiative has been taken, it must return to the key objectives and business KPIs as an output metric. And also have an entry KPI definition that directly correlates with the outcome, and is a leading indicator of the completeness of the execution of the initiative to achieve the desired outcome.
“In these cases: if the input KPI is behind, we know we didn’t do enough or didn’t execute well. But if the KPI is not met, we know our assumption is incorrect and we need to change course,” he added.
As the business grows, it becomes more important to align the data science team with the business. To maintain this alignment, Lenskart engages its data science team in business reviews to identify issues that can be resolved. And the company also pairs data science team members with respective business leaders to work hand-in-hand to achieve results.
With the increasing use of data science in business, companies are restructuring and creating spaces for data-driven roles. In such cases, having strong leadership roles like Chief Data Officer or Chief Analytics Officer within the team helps to grow and learn quickly. But for Lenskart it’s a bit different.
“In our case, we believe it’s not enough to define roles in terms of designations, titles and hierarchies; it’s more important to define problem statements and build teams that have the right resources from the point of view. business, data science, operations and technology perspective to deliver on the results for the problem statement,” he concluded.