Quick Service Restaurants thrive on walk-ins. Even though these businesses have partnered with food delivery services for online orders, the overheads and order ticket sizes are the two challenges that come along with it. With hyperlocal targeting, brands in this model can identify specific catchments around their facility with a higher density of their target audience.

In this study, let’s see how a Quick Service Restaurant could identify granular catchments where their target audience is present and substantially improve footfall.

The brand has 110 restaurants in 30 cities and they have witnessed similar patterns across them with varying impact and intensity. They have noticed that a majority of their user base continues to order online, which negatively impacts their PnL.

Problem Statement

• The higher margins charged by delivery partners reduce the overall revenue
• The average store engagement per user is > 45 minutes. When users order online, the average spend is reduced as compared to what they would spend at the store. The average spend at the restaurant is 1.3X higher than online spends
• Lower repeat propensity of first-time users from online channels
• Additional overheads and costs associated with delivery set-ups

Therefore it is more lucrative to have a larger ratio of in-store customers as compared to the online user base. What the brand already knows is that its omnichannel audience is cohesive. Therefore, the target audience is clearly defined, now the need is to identify granular catchments where the TG resides in close proximity to the store location.

The Solution

The delivery data is parsed to get the addresses of the users that have ordered online. This gives an understanding of the catchment area where the target users reside. Once the catchments are identified, the machine learning capabilities are utilized to understand the common location attributes among all the locations.

The attributes could be something like the presence of colleges, presence of tech parks, a population density of more than 15,000 per sq. km., presence of gated societies, presence of gyms, sports complexes, recreational centers, presence of government offices, and so on.

One can also build a funnel of insights and add levels to the attributes considered as success metrics. For example, if the presence of colleges and institutes is an attribute of the location where TG is present, then we can further add information such as what is the fee bracket of these colleges. If the presence of tech parks is an attribute, we can look into the size of the company (small, medium, or large-scale enterprise), average employee strength, and other factors. This would help further understand the characteristics of suitable locations to pursue.

Based on the identified attributes, the model recommends more look-alike locations that could be targeted for:

• Running discounts and promotional campaigns for in-store purchases
• Hyper-local targeting digital marketing initiatives in these smaller catchments rather than considering larger, less relevant geographies
• Hyper-local targeting OOH and brand activation campaigns
• Identify new locations where footfall and revenue could be maximized

Conclusion — The Intelligence is All-Encompassing

Once you know the attributes of the location where your target audience will be, there’s no end to the list of problems you can solve. You can enrich your expansion models with this data to hit the right spots and maximize footfall and revenue.

Businesses can also target all their marketing budget and efforts (OOH, activations, and digital) onto these catchments and maximize ROI by driving more footfall.

Adding levels of insights on top of the data for these catchments will further help streamline GTM strategies, and help businesses to make pin-pointed decisions.

All in all, when you dive deeper into what location data and intelligence have to offer, there’s no room for ambiguity, the strategies and decisions are pinpointed, and predictions are more accurate.