Samuel, a sales leader of a major FMCG manufacturer in Vietnam, was faced with a challenge: servicing Vietnam’s slew of traditional trade stores with limited resources. Like many a sales leader before him, he decided to prioritize the multitude of traditional grocery stores scattered across the country through a store segmentation project that would group outlets based on claimed sales performance.
Based on the store segmentation result, his sales force was directed to prioritize stores representing the biggest sales potential. But Samuel was dissatisfied with the outcome; the classification of outlets appeared inaccurate, and staff lacked insight into which products they should push in each store.
Samuel’s dilemma is typical. Many FMCG (fast-moving consumer goods) sales teams in emerging markets are lacking in knowledge about the traditional trade landscape, from the location of the stores that represent the biggest opportunity, to the products that offer the largest potential, to the promotional activities which will yield the maximum conversion. If you don’t know the where, what and how of your market, how likely is your strategy to be successful? Claimed sales performance is not a sufficient mechanism for segmenting store performance – given the sheer diversity of Southeast Asia’s retail sector, it’s better than a one-size-fits-all approach. But it’s not enough.
In reality, an effective segmentation of the landscape requires a combination of insights across the shop, shopper and shopkeeper – adding the shopkeeper (who) into the model, and making him or her a partner in the process, enriches our understanding of the shopper touchpoints along their path to purchase.
In Southeast Asia, traditional grocery is the largest channel both number of stores and by value (sales contribution). Even in the region’s major cities such as Ho Chi Minh City or Jakarta, traditional trade accounts for more than 70% of FMCG sales. Success, then, requires a sophisticated effort directed at traditional trade outlets.
Retail segmentation works by grouping stores into homogenous clusters, and developing products that appeal to each – no differently to the way FMCG manufacturers have long segmented consumers and developed products for them. There are three typical approaches to retail segmentation: location attributes, performance attributes, and a combination of performance and physical attributes. Each approach has its benefits and limitations, and they will be felt differently based on the budget and the level of sophistication of the sales organization.
In a region as spread out and fragmented as Southeast Asia, reaching all outlets in a market is effectively out of the question, even for the largest companies with the most widely-purchased goods. As such, prioritization is crucial. As always, what not to do and where not to go is as important to a successful strategy as what to do and where to go.
Location attributes. Segmenting on location attributes means dividing stores into regions by store density, local GDP, population density, economic class or income level. High GDP per capita, for instance, makes a promising market for “mass premium” product ranges. Promising locations can then be prioritized by lower cost of reach – whether because of distance from the nearest distribution center, because of store density, or both.
Performance attributes. Whether at the total store level or individual category level within a store, performance attributes provide a clear rationale for prioritizing stores. Obviously, the basic performance attribute is profit contribution. Maximizing this – being an active “maker” of performance at a store rather than a passive “taker” of it – requires shopper insight sufficient to inform brand, pricing and in-store promotional activities efficiently. Some of what is needed – shopper profiles based on demographics, for instance, is provided by location attributes. Others – needs, psychographics, and so on – are required to help determine which products should go in which location, which promotions should be implemented, and what price points should be chosen. Understanding “shopper missions” and what shoppers are buying based on those missions, along with drivers of store choice, helps identify categories, pack sizes, product placement and other variables.
Combining performance and physical attributes. Adding tangible attributes such as the presence of defined POS material, particular brands, brand variants, and the mix of local and multinational manufacturer products to a performance rating is a particularly powerful way of segmenting the store universe.
Store segmentations can guide you to a sound retail strategy. However, a truly efficient sales outreach effort that maximizes both sales and brand building requires a knowledge of the shopkeeper, too.
A profile of the shopkeeper, similar to a store profile, can lead you to non-zero-sum solutions that win their loyalty by winning their hearts. The business needs of traditional grocery shopkeepers and owners tend to be driven by four interlinked variables – efficiency, longevity, differentiation and community. Understanding how the shopkeeper balances these variables will help your salespeople identify value-added activities that increase efficiency by reducing operation time and costs to the shopkeeper without compromising the other variables.
The typical shopkeeper’s influence on product selection cannot be overstated. The fact that that many traditional trade stores are set up to cater to shoppers who do not even enter the store is just one example of the importance of winning the shopkeeper’s heart.
Traditional trade is a complex and highly competitive channel comprising many individual outlets and it is here to stay for the foreseeable future. Too often, however, even good sales forces know only about store size, location and performance. For a truly effective segmentation, it is critical to incorporate an understanding of the shopper and the shopkeeper along with the store.
Download a complete copy of Nielsen’s Maximising Traditions - The Shop. Shopper. Shopkeeper Report here.
Editor’s note: We have updated the store universe numbers contained in the report download on November 11, 2015 to reflect updated numbers for 2015.