Cultural diversity in India is complex, and with increasing urbanisation, there is a movement of communities from their native locations to non-native urban centres. Retailers, with help from technology, can cash in on this opportunity by creating localised assortments catering to various cultures and food tastes.
To make this article palatable for thought to the unfamiliar, Dosa is a pancake made of fermented rice batter and a staple dish in the south of India. So what does an innocuous dish from the southern part of India have to do with retail and technology? A lot, it seems. The dynamism of the Indian economy has resulted in the creation of many modern urban centres. Increased rate of urbanisation that was traditionally the forte of tier one cities is now being actively witnessed in tier two and three locations as well. The rate of urbaniaation is fueled not just by intra-state inflow of citizens but also interstate movements. The end result is that, we now have many urban centres with an appreciable degree of cultural diversity of people originating from non-native locations. Diversity that is further characterised by varied food, entertainment and lifestyle preferences.
The behaviour of people belonging to different cultures in a non-native environment can have a different tendency which is referred to as the acculturation factor. Some are highly un-acculturated, which means that the members live close to and socialise only with members of their community. Some are partially acculturated, which implies that they have partially adapted to the non-culture, but still maintain their native roots when it comes to food and lifestyle preferences. There are also people who are highly acculturated, which imply that they have fully integrated with the new culture and do not identify themselves with their culture of origin. Different cultures will have varied degrees of acculturation. The partially acculturated population comprises a significant percentage of the non-native population in many of the urban centres. Many of these citizens shop extensively at supermarkets, often finding it difficult to find merchandise of their cultural preferences.
Non-native consumers at any level of acculturation often turn to exclusive community mom and pop stores for their shopping needs. These stores are often only limited but exclusive assortments catering to the needs of the specific community. The freshness of merchandise, level of pricing attractiveness and availability of promotional offers will be missing from many of these community retail stores. Yet, consumers flock to these outlets due to the exclusivity and convenience of merchandise availability.
For organised retailers, localisation has mostly been about broadly sending more of summer wear to one region and winter wear to another. Creating localised multicultural assortments requires a high degree of granularity in merchandise planning and execution. Of course, it is also essential to maintain a fine balance between prohibitive supply chain execution costs and maintaining merchandise availability. This would obviously depend on the size of the multicultural population under consideration.
In order to effectively retail to culturally diverse consumer segments, organised retailers need to focus on comprehending and building demographic profiles of their stores. These can be achieved in many ways such as qualitative research through in house CRM data, store surveys, consumer intercepts in stores, consumer intercepts in locations of community interests, insights from consumer goods suppliers, etc. The census and population data which is available at a locality level in India is hugely beneficial and can also serve as ideal inputs into creating store demographic profiles. The census data is a gold mine for local demographic information and can be effectively leveraged directly or through data aggregators. The demographic profiles will be built as approximate percentages of predominant communities of the total population in a defined geographic area.
Parallel to building the store demographic profile, the retailer should also build assortment profiles which comprises of the merchandise offering for each targeted community nationally. These assortment profiles are created by researching the food, lifestyle and shopping habits of the communities being addressed by the retailer. A significant amount of the data related to the assortment profiles can be derived from primary research done by consumer goods companies. The final store merchandise profile will be based on a combinative decision based on store demographic profile percentages and the assortment profiles.
Obviously, technology needs to be leveraged optimally to plan and build multicultural localised assortments. Robust merchandise assortment planning and flexible store clustering applications will need to be used to provide the inputs into store and assortment profiles. Traditional store clustering tools that provide limited parameters with basic demographic attributes will fall short of expectations. Retailers need to be sure that the attributes used to cluster stores are able to support multiple levels of demographic parameters including the store community profiles. The store clustering solution should be well integrated with the merchandise planning applications so as to effectively build assortments for each store profile clusters.
The resultant merchandise matrix is a complex proposition and will need to be effectively percolated to all activities of retail execution.The localised merchandise matrix will be in addition to the standard assortments that are offered at the retail outlets throughout the country. The ratio of standard to localised assortments will depend on the store demographic profile. The localised assortment profile will need to be integrated well with purchasing, space planning, pricing and supply chain systems. These are to ensure that the assortment strategy flows down well into the execution stage.
Another important aspect of implementing localised assortments is measuring the performance of the assortments. Big data technologies provide the ability to create highly optimised methods of storage of customer segments, assortment profiles, pricing and sales data. These data can be used to create product level models to be used by in memory analytics which can churn out decision enabling information in real time.
Additionally, a high degree of ingenuity is also required to bind and harness multiple technology applications to plan, devise, execute and analyse the local assortment strategy.