Changelog & Future Improvements

Releases

Expanded merchant coverage to 25,000 merchants with continous growth planned for future updates
New merchants can be found in CONSUMER_SPENDING_MERCHANT_PRODUCT_TIMESERIES and CONSUMER_SPENDING_MERCHANT_PRODUCT_RETENTION_TIMESERIES

Expanded granularity to include point of interest level data
Added product time series data
Data is available in CONSUMER_SPENDING_POI_TIMESERIES, CONSUMER_SPENDING_MERCHANT_PRODUCT_TIMESERIES, CONSUMER_SPENDING_MERCHANT_PRODUCT_RETENTION_TIMESERIES

Expanded product granularity to include zip code level data.
Added customer retention data
Data is available in CONSUMER_SPENDING_MERCHANT_RETENTION_TIMESERIES, CONSUMER_SPENDING_INDUSTRY_TIMESERIES, CONSUMER_SPENDING_MERCHANT_TIMESERIES

Added spend by geography for states and top 100 CBSAs. Added online/offline spend breakdown
Expanded the granularity of values in the CONSUMER_SPENDING_TIMESERIES table to include breakdown by geography. Geographies are grouped both by purchaser primary location (i.e. where the spender lives) and purchase location (i.e. where the physical store is located). The data covers US states and the top 100 core-based statistical areas (“CBSAs”) as measured by population.
- Purchaser geographies are represented in the PURCHASER_PRIMARY_GEO_ID and PURCHASER_PRIMARY_GEO_NAME fields
- Purchase location geographies are represented in PURCHASE_LOCATION_GEO_ID and PURCHASE_LOCATION_GEO_NAME fields
- PURCHASER_PRIMARY_GEO_ID and PURCHASER_PRIMARY_GEO_ID can be used to join the data with Cybersyn’s geography tables such as the GEOGRAPHY_INDEX based on the GEO_ID field
Added purchase channels covering online and offline spend.
- Purchase channels are reflected in the CHANNEL field in the CONSUMER_SPENDING_ATTRIBUTES table and included in the VARIABLE_NAME values in the CONSUMER_SPENDING_TIMESERIES table
Note that data for “purchase location” geographies only includes offline spend.

Added aggregations by NAICS codes and by 4-5-4 retail months
Expanded the CONSUMER_SPENDING_TIMESERIES and the CONSUMER_SPENDING_ATTRIBUTES tables to include aggregations by NAICS (North American Industry Classification System) and 4-5-4 retail calendar months.
Deprecated MCC_CODE, MCC_CODE_DESCRIPTION and MART_VARIABLE fields in the the CONSUMER_SPENDING_ATTRIBUTES table and replaced them with AGGREGATION_TYPE and AGGREGATION_VALUE.The newly added fields can be used to filter to a desired level of aggregation including to the NAICS, MARTS Segment, and MCC levels.
Additionally, the following fields were added in anticipation of upcoming dataset expansions. Note that these fields only contain total values for now. Future iterations of the product will include more granular cuts of data.
- Added COMPANY_NAME, PURCHASER_PRIMARY_GEO_ID, PURCHASER_PRIMARY_GEO_NAME, PURCHASE_LOCATION_GEO_ID, and PURCHASE_LOCATION_GEO_NAME to CONSUMER_SPENDING_TIMESERIES
- Added CHANNEL to CONSUMER_SPENDING_ATTRIBUTES

Expanded values in the CONSUMER_SPENDING_TIMESERIES table to include nominal estimates. New measures include estimates for revenue ($), transactions (#), and average order value ($). These newly added variables build on the existing year-over-year (%) estimates for those measures already in the table.
Nominal values can be used to measure market share or compare average transaction amounts across retailers. Because these estimates are based on a panel of consumer spending, they are not meant to be projections of the entirety of US spending but they are accurate as a measure of relative spend. For example, the sum of all Chipotle spend will not equal the company's actual spend but Chipotle's market share relative to McDonald's should be correct.
The newly-added variables in the timeseries table include matching variables in the CONSUMER_SPENDING_ATTRIBUTES table with the new variables being MEASURE values of Revenue, Transactions, and AOV.

Added the calendar_index table which compiles common calendars into a single table. Each calendar type has a unique CALENDAR_ID, which allows users to select which calendar type they want to use. Individual periods within each calendar type include period start and end dates.
The calendar_index currently includes regular calendar periods (days, weeks, months, quarters, and years) and 4-5-4 retail calendar periods (4-5-4 retail months, quarters, and years).
The 4-5-4 retail calendar is a standardized accounting and reporting calendar system used by many retailers, where each fiscal year is divided into 13 weeks, aiming to align with seasonal variations and facilitate more accurate financial comparisons.

Added year-over-year estimates for company-level spend, transaction, and AOV to the CONSUMER_SPENDING_TIMESERIES. Company-level information is identified with the company_id field.
The company_id is a unique identifier assigned by Cybersyn to each company and is joinable to the COMPANY_INDEX, which provides company names and other helpful identifiers such as CIK, LEI, PermID, and more. Note that when the company_id is null, then the row represents data for all companies.

We note known issues and planned future improvements. If you would like to submit a bug report or feature request, email us at support@cybersyn.com.