How should coffee shops adopt online delivery effectively?

There’s no doubt that online ordering and delivery services had revolutionized the fundamental logic of F&B industry, even for the most offline experience focused business: coffee shops.

A Shanghai-based specialty coffee shop brand which owns 20+ stores across the city is seeking growth. During the past few years, the brand has built a solid consumer base of white collar office workers and accumulated well-acknowledged brand leadership. But it is now facing more severe competition from emerging players, and a more sophisticated market landscape.

The challenge: difficult to assess online delivery effectiveness

To win out, the brand embraces digital transformation, adopting measures including online ordering and delivery. However, after rolled out for a few months, the performance wasn’t all that impressive comparing to their strong offline business.

The brand decided to work alongside CVI to find out how to improve their strategies. And here are some of their initial concerns and questions:

“We’d like to know who ordered our coffee online, and whether or not they were previously our offline customers? ”

“The delivery platforms we worked with share little information about how they deliver to our customers. How well exactly is our delivery territory?”

“Adding online services means more store staff and adjustment of workflow. How do we prioritize in-store experience versus online orders and optimize operations at store level?”

These questions are not unique to this particular coffee shop brand, but instead, they are commonly asked by many business operators who are struggling to adopt delivery services.

Understanding who is ordering? From where?

We took a few months of online ordering data from the stores, examined and plotted on maps. We realized that the coffee delivery “hot spots” were prime offices roughly 1-km away from the stores.

Delivery destination hot spots

As shown on the maps, delivery hot spots concentrating at prime office buildings suggests that the delivery TA are white collar workers with higher spending power. This is coherent to the brand’s offline customers.

But the 1-km range, instead of 3-km (the maximum distance that platforms generally deliver), says something more interesting:

1. Since no one would walk a 1-km distance to buy a coffee, it’s safe to say that most of the online orders are NOT coming from previous offline customers who work in close proximity, thus delivery service does help to gain customers beyond their previous reach.
2. 1-km, rather than 3-km, seems to be the optimal delivery distance for coffee. Although the platforms promise a 3-km delivery range, longer distance and travel time tend to result in poor product experience: a cold hot-latte or a melted iced-americano. When it comes to the optimal delivery distance, it’s more of a product experience issue, rather than a technical one predominated by the delivery platforms.

Packaging of the delivery products should also be considered as part of the consumer experience.

Understanding the actual delivery territories?

Left to right: delivery destination points; outlining the data points; the actual delivery catchment area

Contextualizing the delivery destinations data in geographical space allows us to trace the actual catchment area for each store. By doing so, we realized:

Highly overlapped delivery catchment areas in downtown.

1. Most of the delivery catchment areas were overlapped because current stores are concentrated in the same commercial districts. Staying close to foot traffic, where shopping malls and offices are clustered, makes perfect sense for offline business, but often less effective for online delivery. In the case of this brand, more than half of the stores had a catchment that is over 90% overlapped with other stores. This means the current network has limited overall coverage and there’s potential cannibalization between stores.
2. While some catchments fulfill the 3-km radius reachability, some only reached one-sixth of the theoretical coverage. The limitation may have to do with the store’s location constraints, such as less accessibility due to sparse road networks, less TA in the surrounding area, geographical barriers like rivers and highways, etc.

The size of delivery catchment area varies due to location constraints.

Conclusion

1. The idea of ‘optimal delivery distance’ is essential when planning for delivery services. However, the distance is not only determined by the capacity of the platforms, but also by the nature of the product and the optimal consumer experience. Ask this while planning your delivery strategy: how far would be too far for our customer to experience our product at their best conditions? If the answer is shorter than what delivery platforms could offer, consider improving your packaging strategy such as isothermal bags, or simply roll out products that are more suitable for long-distance delivery.
2. Reviewing the characteristics of delivery hot spots — type of buildings, functions of the neighborhood, spending power in the region, etc. — gives you better insights on consumer profiles. Monitor and plot them on the map constantly, and use it to inform your marketing strategy, product differentiation, and menu design.
3. One can use a simple circle or isochrone to draw the ‘theoretical’ delivery catchment area of a store, but only until you plot your actual delivery data on the map would you be able to examine the ‘actual’ ones. Comparing the two — where it should cover vs. where it actually served — is the key to improve your online delivery strategy, and it should be revisited from time to time to keep track of your delivery effectiveness.
4. Online delivery and offline experience are two different beasts, but they could compensate each other if set up smartly. Stores that are in the busiest districts don’t guarantee good delivery orders, while low foot traffic stores may cover a vast delivery audience. A brand should consider using location intelligence to deploy its strategy more dynamically, and make the most of its network and locations.

This article was produced by CVI, a data analytics company with a mission to help retail businesses make better decisions. At CVI, we take a citizen-centered and data-driven approach to build technology tools and to formulate strategies that empower city-shapers. If you are a food service operator, a retailer, a city planner or an urban design enthusiast and are interested in harnessing the power of location intelligence, give us a holler at info@cvi-tech.com.