Powerlytics has a market intelligence and analytics platform that powers better decisions by using the most accurate and comprehensive business and consumer financial data available. Our consumer database and products are based on the tax returns of all 150 million households that file tax returns that cover over 200 million adults. This database includes close to 200 financial variables from the Form 1040 and all the primary schedules (A, C, D E…). While for confidentiality reasons all of the data is anonymized, it can be aggregated by Adjusted Gross Income range across many geographic areas from the US down to a Zip+4 level (on average 2-3 households). For example Powerlytics knows wealth components of income such as interest, dividends and capital gains in addition to retirement related income such as Pensions and Annuities, IRA Distributions and Social Security Benefits that could all be utilized to target retirees with certain financial attributes. Powerlytics also has a database of investible assets for every household in the U.S.
Powerlytics Use Cases in Support of Investment Management
Share of Wallet.
- Leverage Powerlytics data to understand what percent of the population that The Company has covered and moreover, understand the percent of the Zip+4’s that The Company has covered by leveraging the Powerlytics comprehensive Zip+4 tax filings
- Understand the percent of the Adult population that The Company has covered by leveraging filing status and number of filers (e.g. married filing jointly couples: wife may have a The Company account, but the husband may not)
- Understand the percent of Investable Assets of The Company customers by imputing assets with Capital Gains, Interest and Dividends (e.g. what’s our share of wallet)
- Understand the dollar amount of investable assets of Non-The Company customers by imputing assets with Capital Gains, Interest and Dividends (e.g. what’s the share of wallet that we can gain from Non-The Company customers?)
Profitable customer targeting
Create a list of most profitable customers, overlay Powerlytics consumer variables and health indexes to identify correlations In Powerlytics data. Rank zip plus 4’s across US based on likelihood of profitable customers derived from the correlated Powerlytics data.
- Full database and transformations (unusual data transformations have been found to be predictive in customer behavior, e.g. % of filers that used a paid preparer. % of filers that gave gifts to charity, non-wage income to total income, business risk score, etc.)
Propensity / Predictive Models.
- Consumer data, transformations & Health Indexes
- Financial & Non-Financial variables to buying patterns
- Geographic & time series trend analysis
- Market sizing by Zip+4, County, State, etc.
Digital Marketing (Right-Serve: right-offer, right-person, right-time).
- Out of box LiveRamp Digital Market Segments (e.g. Income between $100k-$120k with retirement income)
- Custom Digital Market Segments
- By income
- By type of income
- By employment status, e.g. retired, salaried employee, business owner/freelancer.
- By student-related expenses (e.g. Student loan interest deduction, Tuition and fees, Education credits)
- By home ownership and mortgage status
- Financial Advisors access to financial profile of customers based on zip plus 4
- Online tool for customers to benchmark how well they are doing compared to peers (income level/Neighborhood/State) in wealth metrics – interest, dividends, capital gains and other areas, e.g. charitable contributions, mortgage interest
Market size – by Z4, County, State.
- Adults by income bracket, by Z4
- Market vs. current client base
- Assess retirement plans per industry, size (annual expense, number of employees), geo
- Per employee spend – pension plan, profit sharing plan
- Understand investible assets for 150 million households based on their Zip+4
- Better align financial analyst with full investment profile of customer (customer may not provide total portfolio numbers)
- Business and Consumer Health Indexes
- Sector Analysis (Business Database – 1,000 industry sectors, by size, by location)
- Time series analysis (Consumer & Business Database)
- Micro & Macro economic trends & muni Bond Analysis