Walmart has announced a delivery service for local businesses, which should be up and running by the end of the year. It plans to use drones and self-driving cars as part of the Walmart GoLocal infrastructure. Earlier this year, Walmart invested in Cruise after previously running a delivery pilot with GM's autonomous vehicle startup. Local retailers might be able to keep using their current commerce platform and hook it into GoLocal. It's a white-label service, so deliveries won't be made by Walmart-branded vehicles.

Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look. Although many such algorithms have been around for quite some time, current machine learning, which includes the ability to automatically apply complex mathematical calculations to big data – over and over, and faster and faster – is a fairly recent development. This ability is useful in a number of vertical markets, and can already be seen to be having an impact in the retail environment. The go-to example of machine learning in this space is how major online players like Amazon and Netflix regularly offer recommendations to customers of potential products they might also like. These recommendations are determined via machine learning, which parses through previous choices made by consumers and other products they have looked at on the Web site.

The Olympic Games are an important sporting event with notable consequences for the general economic landscape of the host city. Traditional economic assessments focus on the aggregated impact of the event on the national income, but fail to provide micro-scale insights on why local businesses will benefit from the increased activity during the Games.In this paper we provide a novel approach to modeling the impact of the Olympic Games on local retailers by analyzing a dataset mined from a large location-based social service, Foursquare. We hypothesize that the spatial positioning of businesses as well as the mobility trends of visitors are primary indicators of whether retailers will rise their popularity during the event. To confirm this we formulate a retail winners prediction task in the context of which we evaluate a set of geographic and mobility metrics. We find that the proximity to stadiums, the diversity of activity in the neighborhood, the nearby area sociability, as well as the probability of customer flows from and to event places such as stadiums and parks are all vital factors. Through supervised learning techniques we demonstrate that the success of businesses hinges on a combination of both geographic and mobility factors. Our results suggest that location-based social networks, where crowdsourced information about the dynamic interaction of users with urban spaces becomes publicly available, present an alternative medium to assess the economic impact of large scale events in a city.