First Look at Consumer Buying Power and Retail Market Power

Transcription

1 First Look at Consumer Buying Power and Retail Market Power Sean Howard Senior Vice-President of Research and Development Jason Norfolk Vice President of Product Management Nuno Ricardo Senior Product Manager

2 Introductions

3 Presenting today Sean Howard Jason Norfolk Nuno Ricardo

4 housekeeping Listen-only mode for attendees Questions at the end. Use the Webex Q&A Feature in your Interface Technical difficulties? Presentation deck will be available environicsanalytics.com/webcasts 4

5 Consumer Buying Power Product Overview

6 Consumer Buying Power Consumer Buying Power (CBP) from Environics Analytics estimates consumer expenditures for: 719 items in 14 categories 40 retail store types 53 Yellow Pages category headings 11 non-current consumption items CBP provides current-year estimates and five-year projections of consumer spending. The estimated values in CBP represent all consumer purchases regardless of where they were made, including out-of-state and online purchases.

7 Consumer Buying power applications Create trade area rankings by merchandise items or retail store type, to understand which areas present a greater opportunity for new retail operations. Rank merchandise items and retail store types to understand which categories attract the greatest share of consumer spending. Understand current consumer spending habits by merchandise items and services, retail store type and Yellow Pages headings. Analyze projected consumer expenditure trends five years out, based on growth rates and area demographic changes.

8 Consumer Buying power 2018: What s new Includes spending estimates for 300 new merchandise line items. Uses consumer units as the spending base. Consumption items and the hierarchy both closely align with the Consumer Expenditure Survey. Introduces 11 non-current consumption items. Improved model methodology and use of the best data sources available today.

9 Consumer Buying Power data CBP estimates are reported as aggregate consumer expenditures in dollars CBP presents average dollars spent per consumer unit

10 CBP Understanding Roll up categories 1 of 2 Consumer Units Aggregate Annual Expenditures

11 CBP data Understanding Roll up categories 2 of 2 Individual categories (i.e. Food) All Retail Stores Yellow Pages Headings Non-Current Consumption Items

12 Consumer Buying Power Methodology Overview

13 Methodological evolution Modeling Demographic file Store type-merchandise line matrix CBP 2018 CBP 2017 Multinomial logistic regression for all parentchild spending categories Heckman selection models for non-current consumption such as change in capital improvements Log-linear model for spending-to-income ratio 7-dimentional cross tabulation at the Block Group level Imputation of employees and sales is done for 2012 Census of Retail Trade for NAICS and 72 sectors Multivariate regression Grid models for Appliances and Household Repair category expenditures Table not featured by crosstabulation No imputation

14 How it was built 200+ hierarchical models 113,000+ coefficients Scored against a demographic hypercube (seven dimension cross tabulation) at the Block Group level Food Income Before Tax TOTALEXP Housing Apparel and services Transportation

15 Comparison of Control totals CBP 2018 CBP 2017 National level 100 categories of control totals from published tables from the Bureau of Labor Statistics, 2011 to National totals by H-Code using a crossreference system that maps each UCC to each H-Code. Projected to 2017, 2018 and 2023, and based on income data from Claritas Pop-Facts Premier Uses demographic data and proprietary economic time series data to forecast national sales figures that result in currentyear and five-year annual controls.

16 Creating Retail Store Type and Yellow Pages estimates A matrix assigns merchandise level expenditures to one or more retail store types and Yellow Pages headings. The assignments are created by allocating the percentages of spending on merchandise line items to retail stores, based on the dollar allocation according to the U.S. Census Bureau.

17 Retail Market Power Product Overview

18 Retail Market Power Retail Market Power (RMP) from Environics Analytics estimates supply, demand and opportunity gaps for: 119 retail store types 41 merchandise items RMP provides current-year supply, demand and opportunity-gap estimates for all variables.

19 Retail Market Power RMP provides five-year projections of demand as well as the compound annual growth rate from 2018 to 2023 for retail store types and merchandise line items. RMP variables measure total supply and demand, including sales and spending, to consumers and businesses made by national or international buyers at retail locations.

20 Retail Market power applications RMP ranks trade areas targeted for retail expansion based on total demand, total supply and opportunity gaps. RMP helps identify new sales opportunities by comparing the total supply of goods in a market to the total demand of goods in the same market. RMP can analyze the impact of competitors for any trade area to better understand market share.

21 Retail Market power 2018: What s new Includes new estimates for 42 new retail store types and two new merchandise line items. Retail store types will now nest according to North American Industry Classification System (NAICS) codes. Uses the Monthly Retail Trade Survey and Quarterly Census of Employment and Wages to calculate growth rates from the 2012 Census of Retail Trade data, which is the starting point for our estimates. Leverages a robust data imputation process to address suppressed values from the Census of Retail Trade survey.

22 Retail Market Power data The difference in RMP supply and demand is presented as an opportunity gap or surplus metric. The change between current-year estimates and five-year projections is presented in current dollars as well as an annualized compound growth rate. At the national level, supply is equal to demand for all retail store types and merchandise items.

23 RMP data Understanding Roll up categories Total Retail Trade Including Food and Drink (NAICS 44, 45 and 722) is an aggregate of all Retail Store Categories and Food Services and Drinking Places. Total Demand for Retail Trade (NAICS 44 and 45) excludes Food Services and Drinking Places. Each retail store type category such as Furniture and Home Furnishings Stores (NAICS 442) is an aggregate of its subcomponents according to its report hierarchy. Merchandise line items aggregate to Total Retail Trade Including Food and Drink (NAICS 44, 45 and 722).

24 RMP data Roll up categories Example

25 Retail Market Power Methodology Overview

26 Data Sources The major inputs of RMP are: 2012 Census of Retail Trade (CRT) Monthly Retail and Food Services report (MRTS) Quarterly Census of Employment and Wages (QCEW) Publically available county-level sales tax data Consumer Buying Power Claritas Business-Facts data RMP uses the updated 2017 NAICS codes as defined by the Office of Management and Budget.

27 How it was built - Supply Side A data imputation process has been developed to address data suppression in the CRT to: Leverage hierarchical structure of data sources NAICS and geography Apply mathematical optimization techniques to impute missing values National level data from the MRTS are used to project national control totals from Sales tax and QCEW data are used to create growth rates at the county level from Both national and county level data are applied to 2012 CRT imputed data to estimate the 2018 dollars by NAICS.

28 How it was built - Supply Side (Con t) Claritas Business-Facts data are used to allocate dollars by NAICS to the block group level. Data are scaled to match control totals based on the NAICS and geographic hierarchy. County estimates are controlled to national estimates Major or parent NAICS codes, typically two- and three-digit codes, are used to scale minor NAICS codes (four- to six-digit codes) NAICS data are converted to merchandise line estimates based on a NAICS to merchandise line conversion matrix.

29 How it was Built - Demand Side Demand side data are based on the merchandise line estimates from CBP. Merchandise line data are translated to NAICS based on a merchandise line to NAICS conversion matrix. Supply and demand are set to be equal at the national level by scaling CBP derived estimates to match the supply side dollars for both NAICS and merchandise lines. 29

30 How it was Built Opportunity gap Calculated by subtracting supply from demand 30

31 ENvision5 Demonstration Consumer Buying Power Reports and Dashboards Retail Market Power Reports and Dashboards Mapping

32 Questions?

33 Thank You Sean Howard Senior Vice President of Research and Development Jason Norfolk Vice President of Product Management Nuno Ricardo Senior