The WeathScore is a brand new score that was designed and built by our WealthEngine data scientists. The score itself is easy to read. Yet at the same time, the system is working hard behind the scenes by running complex evidence-based calculations. The result is a comprehensive score that helps you prioritize profiles with a greater depth than ever before. This opens up an entirely new dimension in wealth analytics and targeted fundraising!
This article will provide a detailed overview of how to read this score, as wall as how it is calculated – and why that makes it so special.
- It is available on individual profiles and can be used to filter your results in MyProfiles. The WealthScore will soon be available across the entire platform: it will be appended to screening files, available for examination in WEAnalyze, accessible through the public REST API, and query-able as an attribute in WEProspect.
This article will cover:
- Definition and Use Case
- Calculating the Score
- Why start with the P2G?
- Why add modeling?
Definition and Use Case
The WealthScore is arguably the most robust of WealthEngine’s ratings and scores. It is a numeric score that rates a person’s overall financial health, considering all forms of modeled assets (e.g. financial wealth, property value, physical assets, etc.) and liabilities such as mortgages, auto loans, and revolving credit. It provides an easy 360-degree view to accelerate the most effective prioritization and targeting of prospects.
The WealthScore is assigned to each profile in the range 1-100 and takes into account multiple wealth signals for modeling. The higher the number the better.
|Excellent||90 - 100||Major Gifts priority prospects/portfolio assignments|
|Good||70 - 89||Major Gift secondary prospect/portfolio assignments|
|Average||40 - 69||Annual Fund base prospects/mailing segmentation/Planned Giving prospects|
|Fair||1 - 39||Low-end direct mail|
- Note: Records such as net worth and some types of debt are private information. For this reason, we calculate those attributes using complex models that our data scientists have built using a truth file.
The WealthScore is one of multiple scores and ratings that are assigned to profiles - each of which has its own benefits. In this context, you can think of the WealthScore as your tool for deep segmentation. It takes some of our other scores and then digs even deeper.
For example, let's say that you have 100 potential donors who have a P2G of 1/0. This is a wonderful problem to have, but you do now need to determine which of those individuals you should be contacting first. You can check the WealthScore to quickly figure that out!
- Note: for information on the P2G, check out our detailed P2G Guide.
Calculating the Score
The WealthScore, as it is being generated for each profile, has two key steps in its calculations that highlight its purpose and value:
- The P2G offers a starting foundation by initially dividing people into categories.
- Complex models utilize the Wealth Ratings and other key attributes to segment people even further from there.
In other words, the WealthScore builds upon WealthEngine’s existing Propensity to Give score and Wealth Ratings. The P2G score incorporates all the data in the profile, whereas the Wealth Ratings only apply to the more traditional wealth and assets information.
It provides an easy-to-read, comprehensive measurement that takes full advantage of our existing scores and combines their strengths. Using the WealthScore, you can now differentiate and prioritize profiles with a greater level of detail than ever before.
Let’s dig a little deeper:
Why start with the P2G?
The Propensity to Give has historically been WealthEngine’s key overall score, presenting the most complete picture of your prospect.
The P2G is unique in that it incorporates all the data in the profile. It will consider the strength and predictive value of specific data source matches (e.g. a tie to a family foundation) as a way of identifying top prospects. In this way, the P2G is not limited to traditional wealth and assets information. This is important because some wealthy people (even philanthropically inclined ones) may have managed their finances and estates so as to make it look like they don’t own anything. The P2G is specifically designed to catch those individuals.
Why then add modeling?
The P2G score, while incredibly valuable, is a relatively straightforward rules-based classification system. It does not utilize the more sophisticated machine learning techniques of the Wealth Ratings.
After all, if you have screened a large file, or pulled a large group from the Prospecting area of the platform, then you may have found that you had a large number of people who all had the same P2G score. Not all of those individuals are equally promising as prospective major donors. However, you still want to make sure that you are allocating your resources correctly and prioritizing the best of your best! To account for this, our data scientists have built five complex new models that utilize key attributes such as: Age; Total # of Real Estate Properties; State Political Donations; Total Net Worth; Total Income; Total Debt; and Total Real Estate Value.
Those Machine Learning techniques will further slice and dice your list so that you can truly focus in and maximize your fundraising:
P2G 1/1 - 1/5
P2G 2/1 - 2/5
P2G 3/1 - 3/5
- Note: for more information on how the P2G itself is calculated, check out our detailed P2G Guide.
To illustrate this, let’s say that you have a Jane Smith and a John Doe.
- Jane Smith has a P2G of 1/4. That means Model 2 will be used to calculate her WealthScore.
- John Doe, on the other hand, has a P2G of 3/1. As a result, Model 4 will be selected.
Once the correct model has been applied to the profile, the WealthScore is generated based on those additional calculations - this is the number between 1 and 100. Remember, the higher the number the better!
Keep in mind that some of these attributes (such as Net Worth, Total Debt, etc) are statistically derived, as we cannot see into anyone’s bank accounts or tax filings. Instead they are based on insights we have gleaned using a truth file. Jane Smith doesn't necessarily have $5,000 in debt - but based on our complex and extensively-researched algorithms, her profile closely resembles people who do.
At the end of the day, the WealthScore takes predictive analytics to the next level by combining the best parts of both the P2G and modeled scores like the Wealth Ratings. It applies state-of-the-art machine learning techniques across all the features associated with a profile, taking visible wealth and assets into consideration, but without being limited by them.