Client Question: How many stores, restaurants, or studios will a Metro area support?
Which sidewalks and which retailers within a shopping center receive the most foot traffic?
Who are our main competitors, where are they located, and what does that tell us about the market?
Approach: Webster Pacific starts by gathering data for each Metro in a particular country, including Points of Interest (POI) such as competitors and complementary businesses, existing ecomm or website traffic, demographics, and income characteristics. This data is backtested against existing metros in which the client is already present to build a theory about which metrics are most predictive of a market being at capacity. WP then combines the existing market data with the potential market data to estimate how many of the client’s locations can fit in any given Metro.
Client Question: Where within the city should we locate our next store?
Approach: Webster Pacific begins by choosing plausible neighborhoods based on proximity to other competitive and complementary POI. For example, a designer fashion store may want to be proximal to Whole Foods Market and other designer fashion stores, but far from mass market retail. Next, we gather data on each neighborhood, like proximity to existing ecommerce orders, quality of retailers present, proximity to target customers, etc. Finally, we create a model using the collected data, which scores every neighborhood studied.
Client Question: Which sidewalks and which retailers within a shopping center receive the most foot traffic?
Approach: Webster Pacific collects mobile data “pings” and counts the number of mobile devices present in every store and on every sidewalk. Additional analysis includes cross-shopping, to understand which devices go into multiple stores. For example, which other stores do mobile devices visit after visiting Nike?
Client Question: Who are our main competitors, where are they located, and what does that tell us about the market?
Approach: WP studies competitive schools using web research, web scraping,
and interviews. Studying the education landscape for both private
and public schools can give insights to relevant market
size/capacity, pricing, curriculum, etc. WP has extensive experience studying supply across 5 continents, in 40+ markets around the world.
Client Question: How do we identify locations where High Net Worth Individuals live and play?
Approach: WP starts by harvesting data which indicate where High Net Worth Individuals (HNWI) reside and spend their time. This could be home listings > $5M, exclusive golf and yacht clubs, designer shopping centers, or restaurants that offer Dom Perignon. We then develop algorithms that sift through these thousands of data points to quantify the “luxuriousness” of towns, neighborhoods, or even places. Some news coverage of this type work here.