Church Database Presentation

Wisdom AkpanKwaku Bodom

Gina RayNed Wang

Why Are We Doing This Project?

Rising need for better understanding of the client base

Be able to locate churches faster and more efficiently

Have a place to examine previous interactions with a church and its current relationship with Thrivent

Road Map

What is a Church?

A church is a gathering of two or more people where they meet regularly (weekly) to worship their Christian beliefs in a neutral space.

For the sake of this project, churches that didn’t agree to Apostle's Creed weren’t included

AgeTaxes Paid by MembersEducation levelEmploymentSexRaceHealth StatusImmigrant PopulationIncome Distribution

Marital / Familial Status

What Can We Predict About a Church?

Media ConsumptionPolitical BeliefsReligious BeliefsReligious ParticipationCommunity EngagementTechnology UsageFinancial Literacy

● Amount Given to Churches

● Attendance● Church Location

How Do We Make Our Predictions?

1. Build a sample set of churches from known & gathered data.

2. Utilize a model that factors sample churches and their surrounding area.

3.Make further predictions based on this model.

Places Where We Can Get Data From

Collect Names, Denominations, Locations & Attendance of Churches

Church Locators

Trusted Directories

Denominational Websites

How Can We Fill in the Gaps on Missing Information?

Add samples to denominations with incomplete sample setsSelf Reporting in SurveysSelf Reporting at ConventionsReports from Field OpsLonger Term Options

Special Church Action Team PackageOffer Services and Products in exchange for info (music, leadership conventions, renovations

etc.)

What Can a Model Do For Us?

Estimate the makeup of churches when we only know the name and location of the church by factoring

Demographics of the residents of the perimeter

Member profiles of churches with the same denomination

Known statistics of churches in our sample

Example

Example

Possible Demographic Estimations From The Model

Immigrant PopulationMarital / Familial StatusAge of Members

RaceReligious BeliefsSexMember Income

Secondary Predictions

Amount Given to Churches

Community Engagement

Education level

Employment

Financial Literacy

Health Status

Media Consumption

Political Beliefs

Religious Participation

Technology Usage

Types of Member owned Assets

Workplace Danger

Demographics estimated from the information determined by the model

Possible Features to Include

Quick facts on each individual churches and surrounding demographics

Filter or Sorting Functions

Space for FR note/collaboration

Mobile Capabilities

Capabilities to Interact with Maps

Possible Options for Upkeep

Long Term Strategies to Consider

Find Ways to address Overreporting

Offer Church Directory Software Service

Establish Relationships with Bible Colleges

Online Clergy/Religious ForumsStay Observant of Demographic/Church TrendsFundraiser Assistants and Community Aid For Churches

Possible Pitfalls

The data could have been manipulated or outdated

Not all denominations have member profiles

Relies on a One Size fits all

Our prediction method is based on generalities

Discretion of Field Agents

Relies on 3rd Party Data (Pew, Religion Census, Etc)

Potentially Expensive

Big Data