By Devashish Fuloria
India’s online retail behemoths are doubling down on their mission to expand offline to unlock scale. Seeking sustainable growth beyond the saturated demand in online and metropolitan markets, these digital brands are discovering the advantages of reaching untapped customer bases in Tier-II cities and beyond through offline locations.
However, some brands, like Lenskart, have a competitive edge over others. They’re using artificial intelligence (AI) and machine learning (ML) to enhance efficiency and expedite the process of offline expansion.
Expanding offline is more than just choosing cities that have ripe markets or finding high-street locations with the most foot traffic. It requires a deep understanding of city dynamics and deciphering the very heart beat of urban growth. It’s about understanding the ebb and flow of affluence, the rise of new hotspots and the subtle shifts in consumer behavior that come with the changing urban landscape.
This is where location intelligence takes center stage. From the initial steps of calibrating scale to gaining real-time insights, here are 5 key steps through which location AI not only streamlines the expansion process but also marks the onset of a new era in retail innovation.
Step 1: Estimating the Demand
Expansion in retail businesses whether online or offline happens with the objective of unlocking untapped demand. Building the growth plan for expansion requires an estimation of the brand’s unit economics, the capital required and the roadmap to breakeven and profitability.
This process, traditionally an excel sheet exercise, gains efficiency with AI. AI helps with precision planning and considers various parameters to optimize scale, making it a crucial first step in the offline expansion journey.
Step 2: Calibrating the Demand Potential
This is where location data analysi plays a pivotal role. Understanding the growth patterns of different cities is key for making informed decisions on where to establish a physical presence. For brands new to the offline space, the expansion roadmap is often a challenging blank slate.
While some may consider mimicking competitors, this approach might overlook crucial factors that can make or break a store. This includes demographic nuances, local affluence levels, foot traffic patterns, and even external factors like local events or economic conditions.
The choice of where to expand is more than a logistical decision with AI, it’s a narrative of neighborhoods. Residents living in affluent areas may choose to travel further for a premium shopping experience, preferring private transport over public options, even if there’s a mall less than five kilometers from where they stay.
The end goal for brands is to find areas that resonate with their desired customer demographic. By leveraging predictive analytics, brands are able gain a comprehensive bird’s eye view, which goes beyond the limitations of traditional expansion strategies to provide a base score.
This score can help brands prioritize store openings based on factors like potential margins and the likelihood of success. This is especially beneficial for brands that are new entrants in the offline space helping them allocate resources wisely and put in a phased approach that aligns with their overarching business goals.
Step 3: Store-level unit economics
Moving beyond the macro-level decisions of where to expand, the next step involves delving into the micro-level intricacies of each store’s unit economics. This step becomes paramount once regions and markets have been decided, as AI facilitates a deeper understanding of individual store potentials and the investments required.
Traditionally, this process involves sending business development teams to cities to gather on-the-ground information, who are able to provide general estimates of footfall. AI, however, fine-tunes consumer archetypes based on comprehensive data analysis. Using that it’s able to provide a more accurate estimate by analyzing the movement of the target consumer group, considering factors such as preferred modes of transportation and shopping habits.
This precision enhances the accuracy of revenue forecasts, providing brands with a clearer understanding of the store’s potential success.
Step 4: Personalized in-store experience and inventory management
While the footfall into a store is often brand-dependent, AI takes this understanding a step further by predicting consumer spending patterns based on curated target consumer data. This nuanced approach allows brands to anticipate the preferences and purchasing behaviors of their specific audience, contributing to a more personalized and effective in-store experience.
This predictive capability extends to understanding what items are likely to be more popular, helping brands make informed decisions about inventory mix, discount strategies, and even the type of products to highlight in-store.
Traditional approaches to inventory adjustments typically occur at quarterly intervals, but AI enables brands to refine strategies on a more frequent basis, even weekly. AI’s insights into consumer behavior also extend to local-level marketing. This adaptability ensures that marketing efforts remain aligned with real-time consumer preferences and market dynamics.
Step 5: Real-time analysis and strategy recalibration
The biggest competitive advantage of brands using AI for offline expansion is its ability to provide real-time analysis, enabling companies to recalibrate strategies on the fly.
Based on real-time data, AI facilitates the fine-tuning of target consumer profiles. As consumer behaviors evolve, AI algorithms adjust and refines per the changing market dynamics and consumer preference. This adaptability ensures that marketing efforts and in-store experiences remain aligned with the changing preferences and expectations of the target audience.
With AI’s real-time capabilities, brands can also adjust their strategies based on the performance of individual stores. This includes reshuffling the priority order of store openings, optimizing inventory mixes, and making operational adjustments. The ability to analyze data in real time allows for a proactive approach to addressing challenges and leveraging opportunities.
AI eliminates the need for prolonged decision-making processes and ensures that brands can swiftly respond to emerging trends and challenges. AI considers factors that might be overlooked in a human-driven process, providing a more insightful perspective for recalibrating strategies.
Overall, leveraging data and AI is integral for offline expansion because it helps businesses understand how cities grow. The growth of any locality is a function of affluence. Where you live, where you shop, everything becomes important. For a brand, it’s about looking beyond the surface and establishing its presence where it will be in harmony with the aspirations and lifestyles of its target audience.
The author is co-founder and CEO, GeoIQ
