When making location-related decisions, mostly real estate related, retailers use all data available to understand if that community will be a good, profitable location for their business. However, before Spatial there was no way to capture the organic emotions, personalities, and behaviors that best describe a community. Spatial turns conversations on social media into behavioral segments that can be measured around any location.
This data can be provided directly to clients that can analyze the data’s impact on business performance. We do this with large retailers that have in-house analytics teams and third party analytics companies (like Intalytics).
Spatial also performs analytics for retailers. Retailers share their performance data for existing locations and we analyze the activity on social media that has the biggest impact on performance. Retailers then use our results to evaluate new locations, understand their core customers, and drive location-based marketing efforts. Read more about our process here: Payless Case Study.
We quantify all of the behaviors and personalities people express on social media. Then we can identify which of these segments predict business performance. For any new location a client of Spatial’s evaluates, we deliver a report along with scores indicating to what degree their business would over or underperform there and why.
No. We partner with analytics firms (such as Intalytics) to incorporate our data into their comprehensive models. If we do not currently partner with your analytics firm, we build a separate analytics model to capture all of the variables in social data that affect your company. We then deliver reports and visuals to help you compare and incorporate this information with your overall site selection and real estate processes.
Although Spatial uses data social media as an input, we are a location intelligence company. We don’t manage social media or analyze you or your customers’ online presence.
We can and we have provided this service for clients. However, our philosophy is to remove as much bias and preconceived expectations from our analytics process. Ideally, we first identify the behaviors that actually impact your business performance and then the ethnography (social science) team at Spatial helps build an understanding of the behaviors we discovered and why they matter.
Legacy psychographic data takes information (like surveys, demographics, or purchase data) and turns them into psychographics by making assumptions or drawing conclusions about what that data could mean about their personality. Psychographics created from surveys are not organic and will always contain some bias, have low completion rates, and only get answers to the questions the survey creator thinks to ask.
Spatial’s data is completely organic, and derived from the conversations people are actually having in a community about what they are doing and how they feel about what they are doing. The source is true personality data.
The simplest answer is no. A representative population for a study is often around 15% or less. Social media participation by those over the age of 50 is 21% (Pew Research). Although more of the raw data may come from younger generations, the filtering process of the raw data ensures that we capture all relevant signal to understand the actual community in an area.
Spatial has full U.S. coverage (even suburbs and small towns) as well as major international cities in Europe, the Americas, Africa, Europe, and Asia. We can quickly add custom coverage for clients anywhere in the world (other than China) as part of the terms of our agreement.
No. Spatial’s goal is to understand locations through the behaviors of people that spend time there. We don’t need personal user information to create our insights and strictly follow all requirements by our data partners regarding personal information.
Primary Data sources:
New data pulled in every day, primary impact on segmentations, scores, topics, insights, etc.
Secondary sources:
Primarily used for initial training, validation and filtering of primary data sources.
