Data for mom-and-pop shops
Why and how can independent F&B operators use data analytics
If you think running a restaurant is hard, try thinking about what it takes to operate an independent restaurant in a post-Covid world. Imagine being that restaurateur who had to furlough her team because she didn’t have enough business to support them. Imagine having to deal with upset suppliers, inflexible landlords, strict sanitary authorities and unsettled customers. And imagine having to do all of that on your own because you are an independent operator.
According to the National Restaurants Association about 1 in 6 restaurants in the USA have closed since the pandemic started, leaving about 3 million people jobless. The situation isn’t dissimilar in China. CVI estimates — based on data collated from Dianping — that in Shanghai alone some 50,000 restaurants went out of business between the beginning of the outbreak and May 2020. Simply put, 15% of all food and beverage businesses in the city have closed since the beginning of the sanitary crisis. And yet, in spite of the obvious difficulties, millions of people still make a living running a mom-and-pop shop. For most of them, it is their only source of income.
It is hard to objectively rank the challenges restaurant owners face when starting and operating their businesses; after all high labor costs, supply chain inconsistencies and fierce competition all pile up onto a mountain of obstacles they have to deal with regularly. But there is one high stakes decision all brick-and-mortar operators have to make at some point: choosing where to open shop. Location can make or break a restaurant project. And as cities open up again and consumers cautiously restart wining and dining out, where a restaurant is located will bear more importance than ever before.
How can independent restaurant operators make better decisions when it comes to site selection? Can mom-and-pop shops use data to, at least partially, level the playing field with larger, wealthier and more powerful competitors?
What is wrong with how independent restaurateurs choose locations?
Most F&B entrepreneurs will tell you location scouting isn’t fun. Traveling the city right and left, exchanging messages with far too many brokers and being hard-balled by landlords isn’t for the faint of heart. And the worst part is that despite endless hours spent trying to find the perfect location, there is never guarantee of performance.
The truth is that the tactics independent restaurant operators use to select new locations have obvious flaws. Unfortunately those tactics are forced on them by the status quo and there is not much they can do about it. The commercial real estate brokerage industry isn’t designed to make independent operators successful. Here is why:
- Brokers are not transparent: The first step in the journey to finding a new location is connecting with brokers. The real estate brokerage business needs information asymmetry to thrive. Brokers have the upper-hand over prospective tenants simply because they control information and it is up to them to decide how much or how little to disclose. It isn’t in the broker’s best interest to transparently disclose how much other tenants are paying in rent, whether their businesses are performing or if a given location is really a good fit for your business. Brokers are not transparent — especially with small and independent operators — because they are incentivized to close deals, not to make retailers successful.
- Landlords are biased: Assume a broker has introduced you to an opportunity in a new shopping center that just opened downtown. The next step will be to meet the developer. Developers will overwhelm prospective tenants with compelling data about footfall, they will make bold claims about the site’s appeal and the neighborhood’s bright future. Because landlords are incentivized to make you their tenant, their arguments are likely to be biased and self-serving.
- Operators are subjective: Assume you’ve survived the brokers and the landlords. If a prospective tenant is interested in the site, he or she is likely to want to do his or her own research. For most operators this means walking the site: qualitatively assessing how busy the area is, speaking to a few fellow restaurateurs and grabbing a bite at one of the busier shops. While there is nothing wrong with this approach, at best it only informs the future tenant’s intuition. Taking a high-stakes decision off the base of a subjective assessment like the one described above is simply not robust enough.
In sum, mainstream site selection tactics are unsuccessful because the information tenants are basing their decisions on is incomplete, biased and subjective.
Using data to make site selection more robust
We believe that access to data intelligence should be democratized. That if small businesses have access to more and better data-analytics tools, we’d all benefit from it. Independent operators will be more resilient, cities will be more diverse and consumers will have access to more urban experiences. That is why we launched Haodi, a free data-analytics tool that helps independent retailers make better site selection decisions. The tool uses multiple data sources to robustly and unbiasedly answer the top 3 questions all operators have when opening a new store: Is the area busy? What is the competition like? And, how are other businesses performing? Here is how it works:
Is the area busy? Most operators would rely on their observational skills to make this judgment. They would sit at a coffee shop, stare attentively at an intersection and assess if the area is busy. Not fundamentally wrong, but far from robust.
In contrast, a data-forward approach to answering the same question would involve using mobile phone pings. In most developed countries smartphones are ubiquitous. Telecom companies are able to use simple triangulation methods to pinpoint how many devices — a proxy for how many people — are in a given place at a given time. And in case you didn’t know, it is perfectly legal for them to sell that data.
Knowing how many people congregate in an area might be a good indication of total footfall, but knowing if they are the right demographic from your business is what you really need to know. A number of data sources can be used as proxies to get to those levels of granularity. For example, one could look at what type of real estate is found in the area. Assume you are going to start an up-market quick-service healthy eatery. As an operator you would want to have a large concentration of premium office buildings around your site, because health-conscious and time-poor clients are more likely to work there. Sourcing this type of data is possible thanks to the proliferation of online real estate brokers (think owners.com in the US or lianjia.com in China), who advertise their available leases on the internet.
What is the competition like? This is yet another question usually answered with intuition. Seasoned restaurateurs have flair — a combination of experience and vision. The most experienced ones are able to tell if a site has the right tenant mix after a simple walk-though. However, for novices or for operators entering a new city banking on flair alone is just too risky.
Restaurant review sites — like Yelp in the USA or Dianping in China — offer plenty of data that operators should exploit to make data-informed business decisions. By parsing data from those platforms, operators can know how many competitors are in their area, what type of restaurants are most abundant, what is their average price and even what are the most popular dishes in the neighborhood. Ultimately it is up to the operator to interpret the data, but when making high stake decisions, more information is always better than less.
How are other businesses performing? This is probably the hardest question to answer. Most businesses are quite protective of their performance numbers, so even close colleagues would be reluctant to openly speak about their revenue.
F&B operators looking to adopt data intelligence can look towards on-demand food delivery providers for useful data. As more and more consumers opt to order-in, each one of their meals becomes a data point. Companies like Uber Eats and Meituan aggregate the number of orders each restaurant gets through apps and websites. Some, like China’s Eleme, go even further by displaying the number of transactions at an SKU level. On-demand delivery data is a useful proxy for the general business performance of a neighborhood. In addition it can also indicate relative demand for a specific product or restaurant type.
From data-inputs to business decisions
One of the main reasons why independent businesses do not use data-intelligence is because it is hard for them to make the leap between data-supported insights and business decisions. At the most basic level, there are at least 2 very practical ways any mom-and-pop restaurant can use data intelligence to improve their site selection.
Choosing between 2 or more sites: Deciding where to open shop is all about making choices. Operators will visit dozens of sites before making up their mind. Data-intelligence is a very powerful tool to make comparative assessments and objectively rank your options by the metrics that matter the most to your business.
Negotiating your lease price: Operators who use data-intelligence will be in a much better place to negotiate with brokers, developers and landlords. Having unbiased data about the site’s profile will empower prospective tenants to seek deals that are fair and make sense for their business.
Conclusion
Historically independent retailers, wether in F&B or otherwise, have always winded up with the short end of the stick. Unable to benefit from economies of scale, with not strong bargaining power over their suppliers and with smaller marketing budgets than their larger counterparts, it has always been an uphill battle for survival. Things have only gotten worse for them in 2020, and we’d be naive to assume that the independent restaurateurs will come out unscathed from the pandemic. Thousands of businesses will close shop and millions of people will lose their livelihoods. Data-intelligence won’t magically solve all the problems of independent retailers, we are lucid about that. But we see Haodi as a modest step in the right direction to democratize access to data-intelligence for small and independent food and beverage businesses.
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.
Haodi is one of CVI’s technology products. It is a free digital tool that helps restaurant operators use data inputs to make better site selection decisions. You can start using Haodi for free on WeChat MiniApps, or write us an email if you want to learn more.
