Frank Kavenik “Growing Revenue & Coverage Using Data Science”

This article focuses on a two-step strategy using Data Science to grow network revenue and signal coverage for listener-supported Christian radio networks.  This proven strategy has helped generate 10% to 15% annual revenue growth.

The first step involves using Data Science to increase pledge drive revenue.  The second step uses Data Science to invest a portion of the increased pledge revenue into signal expansion.  The increased signal expansion then supports further growth in pledge drive revenue, which funds more signal expansion, and the growth cycle continues.

The graph below shows that annual revenue growth of 10% doubles revenue in 9 years.   At 15% yearly growth, revenue triples in 9 years.

Step 1:  Increasing pledge drive revenue.  Increased revenue results from using Data Science to ask:

1. The exact right amounts at the
2. right times, in the
3. right manner.

Step 2:  Increasing signal coverage.  Increased coverage results from using Data Science to purchase:

1. The right type of stations, in the exact
2. right locations, at the
3. right prices.

STEP 1:  INCREASING PLEDGE DRIVE REVENUE

There are two main strategies used for pledge drives.   One popular strategy is to maximize the number of pledges.  Unfortunately, this strategy reduces overall pledge drive revenue.     

In the example chart below, the decreasing black line to the left of the “green”  $900,000 peak revenue represents the strategy to increase the number of pledges at the expense of total pledged revenue.  In this example, as the number of pledges increases beyond 3,000 pledges, the total revenue drops to $800,000 and could decrease even further.

Example of Maximizing Pledge Drive Revenue

There is a second, more effective strategy utilizing Data Science to maximize the total dollars pledged.   The example above shows this “maximum pledge revenue strategy” at the green $900,000 peak of maximum pledged revenue.

Maximum pledge drive revenue results from asking the exact right amounts, at the right times, and in the right manner.

This “maximum pledged revenue” strategy generates increases in total dollars pledged, signal coverage, and a listening audience.  A portion of the increased revenue can fund further signal expansion.  Increased signal coverage then further increases pledge drive revenue, and the growth cycle continues.

A test to determine the effectiveness of a pledge drive strategy is to calculate how much the total dollars pledged are increasing in successive pledge drives.  This highly effective 2-step pledge drive and signal expansion strategy has produced a 10% to 15% increase in annual revenue.

There are a total of 18 ways to increase pledge drive revenue.  They are each discussed in the HisAir.net article by pledge drive consultant and previous K-LOVE CEO Dick Jenkins at:

https://www.hisair.net/dick-jenkins-increasing-revenue-18-ways-with-data-driven-pledge-drives/

A summary of Dick’s background is at:  https://www.linkedin.com/in/richard-jenkins-0587a79/

His e-mail is:  dickjenkinsbiz@gmail.com

Another HisAir article on using Data Science to increase pledge drive revenue is at:

https://www.hisair.net/frank-kavenik-increasing-pledge-drive-revenue-using-broadcastmetrics-and-data-science/

STEP 2:  INCREASING SIGNAL COVERAGE

The second step in the two-part network growth strategy uses Data Science to invest a portion of the increased pledge revenue into signal expansion.  This signal expansion strategy involves purchasing the right type of stations, in the exact right locations, for the right purchase price.

Depending on the type of network and the format, the right type of stations could include a combination of:

1. AM Daytime stations,
2. AM Full-time stations,
3. FM Translators, and/or
4. FM Full-power stations

The right station location could include:

1. City center
2. Suburban reach-in
3. Rural

The right purchase price is calculated using one of the five Generations of revenue forecasting models.  They are:

1. Population-based, single-contour (oldest and least accurate)
2. Population-based, multi-contour
3. Demographic-based
4. Psychographic-based
5. Donor-based (emerging newest and most accurate)

Additionally, on-site signal measurements can further increase the accuracy of station revenue forecasts.  Taking measurements of actual signal strength throughout the market coverage area helps all five models forecast donor revenue more accurately.

For example, the map below shows the forecast signal strength as the background colors on the map.  The colored dots on top of the background colors represent the locations of signal strength measurements.

The areas where the colored measurement dots differ from the background computer-forecasted colors indicate that the measured signal strength varies from the original forecast.  Compensating for these signal strength differences further improves the accuracy of the initial revenue forecast.

Map of computer forecast as background colors and actual measurements as dots.

The five generations of revenue forecasting models are each discussed in a HisAir article at:

https://www.hisair.net/frank-kavenik-5-generations-of-signal-development-models/

SUMMARY

There are several strategies for growing listener-supported Christian radio networks.  One highly effective strategy involves a 2-step approach that has yielded 10% to 15% annual revenue growth. 

The first step involves using Data Science to maximize total pledge drive revenue (not the number of pledges.)  The second step involves using Data Science to invest a portion of the increased income into targeted signal expansion.  The increased signal expansion supports further growth in both the listening audience and pledge drive revenue, which funds more signal expansion and repeats the growth cycle.

Additional HisAir articles on harnessing the power of Data Science to grow signal coverage, listening audience, and donor revenue are at:

https://www.hisair.net/?s=kavenik

Frank Kavenik is a Christian Radio Data Scientist and Strategic Planning Consultant who invented the BroadcastMetrics strategic planning system.  Frank uses BroadcastMetrics and Data Science to help Christian radio leaders increase signal coverage, listening audience, and donor revenue.

Frank has over 20 years of experience in Christian radio Data Science and strategic planning.  He has completed over 900 Data Science projects serving leaders of major Christian radio networks.   Frank holds degrees in management, law, and engineering.

His LinkedIn profile is at https://www.linkedin.com/in/frank-kavenik-jd-mm-528911/

Frank’s e-mail is FrankK@data-sci.biz