Analyze market share shift from a target company to another set companies across digital and physical channels to track how users of the target business are interacting with other businesses. This share shift shows how the target company is gaining/losing traction against a defined set of benchmark companies over time across both digital and physical channels.
Share shift is defined as the number of users in one business (the “base business”) that also used other named businesses (a pre-defined set of “destination” businesses) within a specified “look-forward” period. A user is defined as a person that visited a physical location or had a digital session. Share shift is reported as a percentage. Customers provide 10 businesses to track (as the “base business”) against other 10 businesses that users may visit (“destination businesses”) and can also provide the specified look-forward period (last day, last week, last two weeks, or last month).
This intelligence-set enables the measurement of churn against a defined peer-set over time as it eliminates “double-counting” the same user visiting multiple businesses (and only counts the user once). This provides an accurate measure of churn, and can be calculated:
* From physical locations to aggregate
* From digital properties to aggregate
For the avoidance of doubt, this dataset does not include user level data.
* Physical Visits available for: 10/1/18 – 4/30/20
* Physical Visits Brands: Costco, CVS, Home Depot, Kroger, Lowes, Target, Walgreens, Walmart, Whole Foods
* Digital visits available for: 3/1/19 – 4/30/20
* Digital visits brands: Costco, CVS, Home Depot, Kroger, Lowes, Target, Walgreens, Walmart
* Source – Carrier CRM, Carrier Network Location, App Location
# Use Cases
* Investment Analytics and Alternative Data
* Market Share and Company Performance Models
* Competitive Benchmarking within Sectors (i.e., Retail) or Categories
* Supply chain planning for Brands and Retailers
# Key Benefits
* Integrated Dataset Understand Growth, Usage, Engagement to identify early indicators of recovery. Single dataset provides unified view of consumers (demographic, location, web, and mobile app behavior).
* Consumer Behavior Across Channels Measure consumer behavioral changes with in-store and online visits, engagement, and churn, over time (in any store, on any site, or by key demographics).
* Persistent Panel Track trends over time to identify what “sticks” in New Normal based on consistent panel of ~35M users.
* Macro-View Gauge activity across businesses and sectors for complete market view or use granular data to measure which localities are recovering first.
# Verticals
* Financial Services
* Retail
* CPG
# About TruFactor Intelligence
* Privacy and Security TruFactor meets the most rigorous standards of security, privacy, and compliance. TruFactor has collaborated with consumer advocacy groups and incorporated guidelines from multiple federal agencies to define industry-leading practices for data governance. Please see details at https://trufactor.io/data-privacy-and-security
* Carrier-based Combines data from mobile telecommunication carriers, SDK partners, and first-party application partnerships to deliver an integrated and uniform view of consumers across physical and digital behavior. Data sources are evaluated against robust criteria of Accuracy, Density, Variety, Density, Lineage, Consent, Scale, and Retention.
* Intelligence-as-a-Service The TruFactor platform converts raw signals into application-ready consumer intelligence via: Data Ingestion and Anonymization, Transformation and Structuring, and AI and Optimization Engine.
# Data Schema
Data Format: CSV
Schema
Column|Description|Example
—-|—–|—-|
date|Date of event|20190927
business_name_base|Name of base business|Walgreens
ticker_name_base|Ticker for associated base business (if available)|WBA
channel_base|Represents channel of visits to base business – visits to physical locations or digital sessions|Physical, Digital
channel_dest | Represents channel of visits to destination_set | PHYSICAL, DIGITAL
lookforward_window|Time period for defining users of destination business (visited the location/property in the next day, next week, next two weeks, etc.)|Daily, Weekly, Bimonthly, Monthly
destination_set|List of businesses included in destination set |’walmart’, ‘cvs’, ‘costco’
share_shift|Value from 0 to 1 that represents in percentage how many people who either visited or had a digital session with the “base business” who also visit or had a digital session with the set of businesses in the “destination set”|0.45
For additional information, please contact <clientservices@trufactor.io>
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| Heapery Attribution License | Heapery-Private |
| Category | Geospatial |
| Data Schema | |
| Support Contact Email Address | support@heapery.com |
| Data Format | |
| Version number | |
| Language | English |
| Data Size | |
| Last Updated Date | 8/11/2020 |
| Refund Policy | Dataset is free and provided as-is |
