The brand in this case study is a premium mattress brand offering orthopedic support products. This case study highlights how GeoIQ helped the brand identify its target audience, help understand the revenue potential of a market, and eventually help open new profitable stores.
Premiumization of Indian Products
India’s consumer demography is evolving.
The unprecedented wealth generation by affluent Indians has quadrupled the purchase of high-value homes in the country. This income rise is also reflected in India’s rising per capita income. Giving birth to a receptive audience of 110 million new middle-class households in major metros, and Tier-2, and Tier-3 cities
Premium segments of customer-centric products, from automobiles to shampoos, are experiencing a surge in demand, whereas entry-level options are struggling.
These new consumers of affordable luxury exhibit a need to express their individuality through the products they purchase, necessitating physical contact between the customer and the product.
Problem Statement
The furniture brand reached out to GeoIQ with two problems:
After establishing a firm foothold in the online space, the brand wanted to expand into eight cities in India; Bangalore, Chennai, Hyderabad, Pune, Delhi, Kolkata, and Mumbai.
- Identify markets – malls & high streets to expand into
- Estimating revenue potential
Methodology
To build a fail-safe expansion strategy, two components are crucial:
- Understanding who your customer is
- Where do they live and shop
TAM Model
To gain an understanding of the target audience, we looked at the brand’s order data – both online and offline.
For TAM, we first get the store data and sales data. We then aggregate the sales data for each hex of 500 meters.
Then we would compare each hex with our existing 3,500 features to determine the characteristics that correlate with good and bad locations, i,e. locations that generate relatively more orders
Through this exercise, our model identified the significant features that stood out in catchments with a high-order density. For example, locations with large apartment complexes and high rentals.
We then applied these short-listed features across all catchment areas in the city to help us develop a comprehensive TAM map of the city
Behavior Patterns of the Target Audience
The RetailIQ AI platform identified that the brand’s target audience displayed the following characteristics,
- Affluent households with an annual income of more than ₹20 lakhs
- Capable of discretionary spending with a willingness to spend on quality-of-life upgrades
- Would use car washes or enroll their wards in supplementary coaching classes like, dance or painting
- The target audience also lives in locations with high internet speeds.
- They live near dental clinics
TAM and Market Clusters
Purchasing a furniture product is a long-term investment for most households.
Therefore, customers will consider the various options in the market before making their choice. Due to their higher levels of affluence, the customer of this brand is willing to travel from their home to the store.
With this insight into the brand’s target audience, we began identifying clusters of furniture stores in the city. Identifying these clusters was crucial for the success of the brand. The typical brand’s customer would tend to explore every shop within the cluster before making their decision.
We then evaluated the locations of every furniture cluster within the eight cities and identified profitable locations at the clear intersection of the TAM and the market clusters.
To summarize, we identified the potential target group for a brand and estimated the brand’s TAM for the entire city.
Revenue Estimation Model
After building the TAM model we developed a revenue estimation model that learns from the inferences derived from the TAM model. The revenue model utilized the brand’s existing store network of 60 stores pan-India to understand the factors impacting the brand’s revenue.
The brand’s profitable store locations had the following characteristics,
- Presence of TAM
- High retail density for home-improvement
- Spend on self-care is high
- High affluence
- Branded hypermarkets
- Presence of gyms
- Presence of branded stores in the market
But, since the data we used was from stores across India, there was a possibility of cross-contamination to occur. The features responsible for the success of a store in Kerala wouldn’t necessarily be the same responsible for the success of a store in Tamil Nadu.
Therefore, we carefully hand-picked the features that helped our model answer our questions without generating too many errors.
To summarize, we identified the locations with the presence of the TAM and then determined where the customers from these markets would shop for their furniture.
How did GeoIQ Help The Brand?
- We helped the brand identify new potential markets.
- We helped the brand determine if opening a new store affects the revenue generated at the pre-existing stores and if it affects same-store cannibalization.
- We helped them determine pincode-level marketing strategies, by co-relating the GeoIQ TAM map with their brand-page search history.
- GeoIQ helped predict the revenue for the brand at a given location
Impact
- 9 stores across 8 cities over a period of 4 months
- 30% increase in store visits
What is RetailIQ
RetailIQ is a cutting-edge AI solution revolutionizing offline expansion for the retail sector. It is built on top of extensive location data and brand data to give street-level answers to expansion problems.
RetailIQ is a powerful tool providing site recommendations to maximize success at the store level and minimize the risk of closure. It also helps identify the total addressable market, conduct competition analysis, better understand your target audience traits and their presence, and perform detailed site analysis and reports, all adding up to a successful expansion strategy.