How do we identify locations where High Net Worth Individuals live and play?
Stockout and Inventory Prediction
Client Question: How many units should we order, and when should we order to avoid stockouts?
Approach: Webster Pacific builds a tool using Google Data Studios that predicts, in real time, when each SKU (Stock Keeping Unit) will stock out. This tool pulls live inventory and sales data from the API of the client’s e-commerce tool, such as Shopify. WP then runs algorithms that consider recent 2, 4, or 8 week sales trends and uses seasonality to predict sales 26 weeks into the future. This data is then compared, by week, to the existing inventory and planned orders, by arrival date, to predict stockouts. The client can then use this information to make the right orders at the right times to avoid stockouts.
Market Prioritization and Capacity
Client Question: How many stores or studios will a Metro area support?
Approach: Webster Pacific starts by gathering data for each Metro in a particular country, including Points of Interest (POI) such as competitors or 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 a city should we locate our next retail stores?
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.
Mobile Data Analytics
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?
Wholesale Account Growth
Client Question: How do we expand our wholesale business?
Approach: WP targets the wholesalers who are carrying our client’s competitors, but not our client. To do so, WP gathers data on which wholesalers 5 to 10 of your competitors are carried. This generates a list of anywhere from 100 to over 1,000 wholesalers depending on the size of the competitors chosen. Next, we remove the duplicates to find which wholesalers carry which competitive brands. Finally, we score each wholesaler based on the number of competitor brands carried and how much E-comm you have around each wholesaler. We also provide the link to the wholesaler’s website and their phone number, as well as a map. This analysis is available for the clothing retail, furniture retail, specialty food, and specialty beverage industries.
Visit this link for an interactive map of this product for the specialty beverage industry.
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 of work here.
San Francisco & Chicago
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