Retail Data Analytics: Social & Credit Card Data


Within Retail Data Analytics we combine social media & credit card data to see how retailers can gain key market insights, optimize footprints, & boost sales.

This post may describe functionality for an old version of CARTO. Find out about the latest and cloud-native version here.
Retail Data Analytics: Social & Credit Card Data

As the impact of the COVID-19 pandemic continues to be felt across all industries  particularly retail  there are both ongoing record declines as well as some encouraging signs of recovery depending on location.

For example in May total retail sales fell a huge 52.1% in Singapore whereas the eurozone's largest economy Germany reported a record rise of 13.9% following the easing of restrictions. As we have reported on previously these differences can be reflected by the unequal nature of the virus' spread. Regardless it has become clear that in order for retailers to survive and optimize response to the recovery they must invest in technology and explore new sources of consumer data.

Graphing showing representation of Spatial's Geosocial data for Retail Data Analytics

With the recent integration of's geosocial data in CARTO's Data Observatory  our customers are now able to uniquely explore a wealth of relationships and patterns between behavioral data and that from other categories such as financial  human mobility and demographics; unveiling new insights about a location. In this case study  we have explored how geosocial data interacts with credit card transaction data  in order to understand the relationships between social media behaviors and restaurant sales. The results live up to the hype; illuminating relevant market differences that can help retailers optimize their footprint depending on the territory.

With social media now being the speaker through which people express their feelings  opinions and interests into the world; it has also become a great platform to source valuable information about a location and how people behave when being around it. has turned all that vast amount of information generated through posts  tweets  and stories into a comprehensive set of 70+ social segments.


Becoming a Spatial Data Scientist  Retail Data Analytics

At the same time, aggregated credit card transaction patterns are key for understanding consumer behaviors and how those evolve over time and space. Geographic Insights provides highly granular metrics to measure the evolution of consumer expenditure in a retail area, allowing for validation, evaluation, and benchmarking of the sales-based dynamics of a location.

Leveraging Retail Data Analytics  we were interested not only in the relationships between credit card transactions and geosocial data  but also in how these relationships change across different markets. To investigate these questions  we selected data for the metropolitan areas of Los Angeles and Chicago to explore their commonalities and differences.

To begin we looked at credit card spend data alone in order to discover the top locations for restaurant sales.

As shown in the above maps  top performers in Chicago concentrate mainly in the downtown area. However  in LA they are spread throughout the city. It is also interesting to see how in Chicago underperformers are far away from top performers  while in LA they all mix in nearby areas.

For our analysis  we are most interested in the areas that capture the most restaurant sales. What are these areas like? Thankfully  geosocial data can help us to quantitatively answer this question. The following charts show aggregated geosocial scores for the areas where the restaurants with higher credit card spend are located in both cities.

Graphs showing aggregated geospatial scores for Chicago & Los Angeles using Retail Data Analytics

Based on the charts  you can see that the areas with the top restaurant spend share the same social segments: LGBTQ Culture  Wealth Signaling  Men's Style  and Wine Lovers. These segments are interesting; together  they tell a story of trendiness  urbanicity  and wealth.

There are  of course  some differences in the top segments. As we might expect to see in LA  Film Lovers is showing up as a top segment. These differences hint that perhaps different segments are positive indicators of restaurant success in each market. To figure this out  the best step is to look at correlations between geosocial segments and restaurant sales data. The following chart shows the 35 Geosocial Segments with the strongest correlations (for any of the cities) and with the highest differences between the two cities. They are sorted by the correlation coefficient for Chicago.

Graphs showing correlation strength of social segments using Retail Data Analytics

There is a lot of information wrapped up in this chart. A lot more than we can get into in this post. Let's focus though on two main learnings that we want to point out.

Want to carry out similar analysis?

Sign up for a FREE account

Pinpointing the social behaviors that are strongly related to restaurant sales in LA & Chicago

If one looks at the segments Film Lovers  LGBTQ Culture  and Yoga Advocates; for each of these variables  their correlations with restaurant sales are much higher in LA than in Chicago. While most segments tend to either be positive or negative for both markets  a couple actually differ. For example  Party Life has a moderately strong positive relationship with sales in LA but a negative correlation in Chicago.

The segments that performed relatively well in Chicago were focused on proximity to beautiful landmarks and landscapes. Both Sites to See and Natural Beauty had positive relationships with sales in Chicago but not LA (Outdoor Adventures also follows the pattern  but not as strongly).

These differences have serious implications for location decisions in the two cities. Based on these results  it doesn't necessarily make sense to use the same segments to choose restaurant locations in the two cities.

The progressive culture of California and LA  compared to that of the Midwest and Chicago  may affect how counter-cultural segments relate to restaurant sales

Perhaps the most interesting pattern we saw in these results was how segments related to behaviors that are sometimes seen as controversial  or even counter-cultural  seemed to perform better in LA than Chicago. Namely  the segments Body Art  Hip Hop Culture  Hipster  Party Life  and Activism all showed (to varying degrees) higher correlation with restaurant sales in LA than in Chicago.

Correlation does not imply causation. But  when viewing results like these ones  it is useful and interesting to hypothesize about why the data shows what it shows. In this case  we hypothesize that perhaps the progressive culture of LA is more broadly accepting of the listed behaviors  thereby leading to these behaviors being less likely to negatively impact restaurant sales.

Local Insights to Assess Sales Potential

The relationships between geosocial data and credit card transactions reveal that people's mindsets  interests  and attitudes correlate with the sales potential at a location. And using only demographic data as part of an analysis misses the larger picture of the uniqueness of each community. It's clear from this analysis that while restaurant sales in LA and Chicago share a number of characteristics that drive success  there are also differences. And when these differences are not or cannot be taken into account  output will likely be suboptimal.

By jointly analyzing geosocial data and financial data  it's possible to glean quantified insights that only a local would know. In Chicago  it was as easy as correlating restaurant sales data with geosocial variables to see that proximity to Natural Beauty and important landmarks are great indicators of restaurant success. This may seem obvious  but it is very difficult to quantify in other ways. Further  note that the same relationship is not nearly as strong in LA.

Want to start incorporating data from and other high-quality datasets into your models and spatial analyses?

Learn more about CARTO's Spatial Data Catalog