How Analytics Has Made Retail ‘Personalised, Frictionless, and Faster’
The retail industry may appear simple from the outside but has tremendous depth and science under the hood, and this is what drew Venkat Raghavan, the associate director and global head of analytics at Tesco Business Services, to this domain. Analytics India Magazine caught up with Raghavan to understand the field better and discuss what role analytics plays here. AIM: What are the main responsibilities of an analytics leader? Venkat Raghavan: I believe the core purpose of analytics leaders is to shift organisations from gut-based decision-making to data-driven decision-making. This is a significant cultural transformation, especially for large organisations. Keeping this in mind, I would like to highlight three key responsibilities of an analytics leader: Create access to new possibilities to improve decision-making:  Analytics leaders need to nurture and create an environment where decision-making can become faster, deeper, and data-led. Decision-making based on Data Analytics is a “new” form of management that allows us to respond more effectively to these threats and opportunities. Build the capability base to deliver these possibilities: Now that we are laying down the platform of possibilities to improve decision-making, we now have to build the capabilities to execute those possibilities. These capabilities are created through a combination of skills, platforms, and internal and external partnerships. Analytics leaders must define the DNA of talent and create the right environment and enablement for them to be successful. This includes a strong talent value proposition, skilling programmes, and the right toolset for them to unleash their capabilities at work. Deliver tangible economic value addition to the organisation:  The arcane aspect of analytics is no longer acceptable. It cannot just be used to provide insights into the business. Analytics leaders need to evolve to ensure that the insights derived from analytics play a critical role in the organisation’s growth. We need to strive to establish a straight-line connection between the analytics investments and measurable financial impacts around sales, profit, and cash. AIM: What is the role of data science in the world of retail technology? Venkat Raghavan: Until 2010, technology was focused on improving systems to enrich the experience for customers and colleagues. For example, in retail, the role of technology predominantly hinged around creating a process of searching and shopping for customers or buying, stocking, and transacting for retailers. Post-2010, there has been a paradigm shift towards making the experience more personalised, frictionless, and faster—thanks to the critical role that data science played in this transformation. It has derived the retail-driven data from the system, analysed the patterns and behaviours by answering the pertinent questions, and hence built actionable insights. In the last five years, the relationship between the functional system and data science has become more symbiotic, as illustrated in the diagram below: AIM: What are the challenges faced while implementing innovations in technology products? Venkat Raghavan: Introducing technological change or bringing in a new set of innovations present different challenges to the organisation. One of the key challenges lies in striking the right balance between speed and perfection as a process to scale up. An organisation is expected to ensure business continuity and secure information and intellectual property; on the other hand, it has to facilitate the execution of tech innovation and ensure a speedier go-to-market. It is a tough choice between speed and quality. The second challenge lies in deciding between an organisation’s ‘build’ versus ‘buy’ strategy. The COVID-19 pandemic has accelerated the business adoption of digital technologies as never seen before. This need for speed has raised the dilemma of which technology to buy from the market and which one to build in-house. While the traditional view has always been not to reinvent the wheel, the truth is that every resilient business is like a snowflake and keeps changing. The technology or software you bought was tailor-made for a particular problem. It may not be able to customise itself adequately for future challenges. Last but not least is the challenge of precision versus scale. When an organisation begins experimenting with digital enhancement, automation, or artificial intelligence, one truth quickly emerges; transformation is more than just developing new technological solutions and plugging them into the existing environment. To reach their full potential, new technologies need the processes to be redefined and simplified, data flow to be rewired, and the orchestration of work between humans and machines to be re-engineered. This involved a high degree of precision that could help the project or a technology implementation reach its desired outcome. However, organisations also have to ensure that the implementation of innovation is scalable and not saturated in the vicious cycle of improvisation to attain a near-perfect model. AIM: How can retailers leverage the power of data engineering? Venkat Raghavan: Advancements in data engineering have been the single biggest contributor to the analytics and science revolution we are seeing around us today. However, it is a hard-hitting fact that as much as 90% of organisational data is still dark and hidden, as they lie in unstructured formats and systems. Therefore, data engineering still has significant ground to cover to reach its full potential. Also, as opposed to the common belief that data engineering focuses on building storage systems for data, the field serves three critical purposes—data pipelining and storage, compute environment for data scientists, and productionalisation of data science algorithms for scale consumption. It is important to see this holistic value proposition of data engineering to extract its full potential. Data pipelining and storage: An organisation, especially in the retail sector, is likely to deal with a massive amount of consumer and supplier data. To analyse all that data, there is a need for a trusted and comprehensive view of the entire data set. When the data resides in multiple systems and services, it needs to be combined in ways that make sense or an in-depth analysis. Data flow can be unpredictable, and the problem gets magnified as the breadth and scope of the role data plays increases. Hence, a strong data pipeline strategy is required to eliminate most of the manual steps from the process, enable a smooth, automated flow of data from one stage to another, and make the end datasets accessible to consumers. Compute environment for data scientists: Data science is constantly getting more extensive with respect to the nature of problems and the heterogeneity of datasets and algorithms used to solve them. The open-source revolution is creating better possibilities for data scientists to experiment and evolve the craft now more than ever. Therefore, organisations need to constantly improve the computing environment by focusing on the right toolset and processing power to enable data science to unleash new possibilities for organisations. Productionalisation of Data science algorithm for scale consumption: Analytics and science will play a huge role in improving products and services by understanding customers and their needs. These potentially focus on real-time hyper-personalisation of customers, optimising inventory, demand forecasting, the effectiveness of promotions, and their impact on sales. AIM: How has digital transformation strategy changed the company’s top-notch products? Venkat Raghavan: Digital transformation is a never-ending journey, where every step forward accelerates your move towards future steps. We set up our Digital Transformation Centre of Excellence to enable accelerated automation and optimisation of business processes, leverage opportunities offered by new-age technologies, and ensure effective rolling and deployment of these technologies through effective change management. One of our digital transformation initiatives is the introduction of chatbots to enable self-service capabilities for our customers, colleagues, and suppliers. We have also built technology innovations that enhance the experience for our customers both in-store and while shopping online. From providing the right range of products in the stores, enabling data-driven models for space utilisation, helping with planograms and display to improving the store colleague experience, product lifecycle management, and traded planning by using the cloud, blockchain, mixed reality [AR/VR], mobile development, IoT and Computer Vision. Our technology team is also instrumental in developing other solutions such as robotic picking, urban fulfilment centres, and ‘Whoosh’, the new superfast food delivery solution for customers.
We need to strive to establish a straight-line connection between the analytics investments and measurable financial impacts around sales, profit, and cash.
We need to strive to establish a straight-line connection between the analytics investments and measurable financial impacts around sales, profit, and cash.
["Interviews and Discussions", "retail", "tesco"]
["data science", "artificial intelligence", "AI", "chatbots", "Snowflake", "tesco", "computer vision", "RAG", "Aim", "analytics", "retail", "R", "Interviews and Discussions"]
["AI", "artificial intelligence", "computer vision", "data science", "analytics", "Aim", "RAG", "chatbots", "Snowflake", "R"]
I am a technology journalist with AIM. I write stories focused on the AI landscape in India and around the world with a special interest in analysing its long term impact on individuals and societies. Reach out to me at shraddha.goled@analyticsindiamag.com.