Tag: data

Keeping location data accurate is a Herculean (almost Sisyphean) task. Stores go out of business and new ones take their place. Snowstorms and pandemics temporarily shutter a business. Bars stay open later on a Sunday for the Super Bowl.

It used to be that business information was updated once a year. Data companies would ship Yellow Pages overseas and people would type in the information and send it back to the companies to sell them. Consumers could buy them as CDs or DVDs to generate mailing lists.

That bar has gone up dramatically. With everything else being on their phones being real-time, consumers expect location data to be as well. Plenty of friends (including tech savvy friends) assume that what Google, Apple, OpenTable, etc. tell them is gospel.

It’s not so. I tell them to call to verify. I’ve been to plenty of “fully booked” restaurants that are actually quite empty. OpenTable charges restaurants for each reservation. That gives restaurants a disincentive to make those times available when they would otherwise be full. Bowling alleys might be full of people, but that’s because it’s league night. The typical customer can’t walk in and bowl.

I’ve been to plenty of places where Google’s “live now” data is inaccurate. (In both directions.) Here is a recent example:

The location on the map (POI for mapping nerds) shows that the McDonald’s is “Permanently closed,” but the “live” information says it is “Open”. Both things can’t be true. If you walk by (what local nerds call “ground truth”), you will find that it is permanently closed.

There are a number of ways to improve timeliness and data quality:

• Have the facilities update their information individually. Google has an extensive set of tools at Google My Business for companies to manage their profiles. Small business owners and government agencies are usually heavily pressed for time or don’t know about these tools.
• Have users submit corrections. There are a lot more users than there are business owners. I submitted the McDonald’s correction to Apple and it was updated within a few days. The challenge with this approach is that there is fraud. Competing businesses might report a business closed. People create fake emergency rooms (yes, this a real example). Yelp has been frequently bombed with reviews when a business is in the political spotlight
• Get information feeds from businesses and government. Chains could submit corrections through feeds. But even this information isn’t timely. The McDonald’s app still listed the above McDonald’s as open for at least a week after it closed. Ironically, this is one of many ways Mapquest blew it. Their initial business was store locators; they would charge businesses to put a store search on the store’s Web site. This presented a channel conflict: they didn’t want to feed it in to the consumer site because it could potentially cannibalize the store locator business. (There is precedent for doing this right: Transit agencies provide real-time data through GTFS.)
• Use anonymized cell-phone location data to predict the number of people are at a business. A key problem is that in many urban areas there is so much density that even the most advanced GPS isn’t good enough. Indoor spaces are another problem.

The best way to keep data “live” is to use real-time transaction data. In the developed world, most businesses take credit cards. A lot of cash businesses use point-of-sale systems like Square. Restaurants are using online ordering. (Disclosure: I was an early board advisor to Olo until its IPO, which powers the ordering for Google Food, as well as restaurants like P.F. Chang’s, Five Guys, Qdoba.)

In the McDonald’s example above, the store locator was inaccurate, but if you tried to order something, you were told it was closed.

There are many advantages to transaction data:

• If people are transacting, there is a very strong likelihood that a business open. If you don’t see any transactions, you can make a strong inference that the business is closed.
• You can approximate cost based on transaction value. For restaurants, you could make reasonable estimates for groups of 2, 4, etc.
• If the data source has SKU information (like from Olo or Square), you can get the full menu and the actual most popular items.
• Depending on the level of anonymity, you can determine how frequently people visit a place. Frequent visits is a good indicator of NPS.

The rapid uptake of Apple Pay gives Apple an advantage here.

Transaction data won’t tell you if the trail you wanted to hike is closed due to snow, but business search will be a lot better than it is today.