Frank Kavenik “Seven Key Factors… “

Seven Key Factors in Forecasting Signal Expansion Revenue

This article outlines seven key factors that help more accurately forecast the increased donor revenue generated from signal expansion investment opportunities.

Data-driven signal expansion is foundational to the success of a time-proven strategy that has yielded annual revenue growth of 10% to 15% for major Christian radio networks.

Signal expansion opportunities can range from:

• A small stand-alone station adding its first translator to
• A large national network upgrading major-market coverage.

By more accurately forecasting future donor revenue from signal expansion, a buyer can better determine which signal expansion opportunities to pursue.  Equally important, an analysis can determine which opportunities not to pursue.

Seven key factors affect the amount of increased donor revenue that will result from signal expansion investments.  Revenue forecasting accuracy improves from incorporating these factors into forecasting models and algorithms.

This article discusses each of these seven key revenue forecasting factors.

1. GEOGRAPHICS

A detailed analysis of geographic data is critical in forecasting donor revenue from signal expansion.  Natural terrain features such as mountains, hills, valleys, and bodies of water can significantly influence signal coverage.  Also, large man-made structures near the transmitting antenna, such as nearby tall buildings, can reduce signal coverage and therefore donor revenue.

FCC contours were historically used to forecast signal strength and coverage.  FCC contours are approximations that were developed long before the development of:

• Modern, powerful, and readily available digital computers,
• Complex signal forecasting models such as Longley-Rice, and
• Sophisticated signal analysis software systems such as V-Soft.

Today, more accurate and complex coverage forecasts are produced using modern algorithms such as Longley-Rice.  For comparison, copied below is an example of a classic FCC contour.  Also shown is a more contemporary, detailed, and accurate Longley-Rice coverage map.  Both maps are courtesy of V-Soft Communications.

CLASSIC FCC CONTOUR MAP EXAMPLE

MODERN LONGLEY-RICE COVERAGE MAP EXAMPLE

2. COMPETITON

Market competition is also an important factor in forecasting donor revenue.  More donor revenue is generated in markets with fewer competing formats and signals.

A detailed comparison of a station’s or network’s program format vs. competing stations is crucial information to quantify.  A key factor is whether listeners perceive formats to be similar (competing) or different (not competing).

In predicting future donor revenue, it is essential to compensate for the number of competing formats as well as the geographic overlap of any competing signals.

Data on competing formats and signals can be quantified to calculate the revenue reduction from each source of market competition.

3. POPULATION

Historically, revenue forecasting was based primarily upon population analysis within FCC contours.  Any reference to “Dollars-per-person” implies the use of this type of classic methodology.

More modern and accurate systems replace the classic concept of Dollars-per-person.  Important additional factors include demographics, psychographics, and donor-specific data analysis.

Also, as previously discussed, classic FCC contour signal analysis can now be upgraded with more modern and accurate signal coverage methodologies, such as Longley-Rice.

4. DEMOGRAPHICS

Demographic analysis is more accurate than classic population-based analysis.  Demographic data provides a more detailed analysis of donor characteristics than population-based analysis.

Demographics provide highly relevant data, which improves revenue forecasting accuracy over older population-based revenue forecasts.  The table below shows examples of basic demographic data.

EXAMPLES OF BASIC DEMOGRAPHIC DATA

5. PSYCHOGRAPHICS

Psychographics are more accurate than demographics in forecasting donor revenue for signal development.  Psychographic analysis is based on the cross-tabulation of demographics.  This cross-tabulation generates natural donorship clusters that are valuable analytic tools.

Incorporating the study of psychographic donorship clusters produces improved accuracy in forecasting donor revenue.

6. DONOR DATA

Powerful programs such as DonorSearch and DonorSearch AI produce highly detailed donor data.  This data relates to each unique program format and helps improve signal development revenue forecasts.

Incorporating detailed donor data into revenue forecasting models increases accuracy.

7. SIGNAL MEASUREMENTS

To know the coverage of a station with certainty, it is necessary to take actual measurements of signal coverage.  Taking and analyzing signal measurements helps more accurately forecast signal coverage and donor revenue.

• As one example, an FM antenna system, designed by a well-known consulting engineer, mistakenly reduced population coverage. Another engineer properly redesigned the antenna system to increase population coverage.
• In another case, a new station was built using a name-brand antenna. The new antenna did not produce the intended population coverage.   Using measurements, the new antenna was modified to produce the desired coverage.

Listener coverage, and therefore donor revenue,  is determined by the actual measured signal coverage, not forecast coverage.  If signal measurements show that coverage is below expectations, a potential buyer can:

• Ask for necessary repairs to improve coverage,
• Request a reduction in the asking price, or
• Choose not to pursue the opportunity further.

The map below shows signal strength measurement “dots” on top of a background color-coded Longley-Rice signal strength forecast map.  Where the color of the dots and background differs, the measurements show coverage is above or below the forecast.

SIGNAL MEASUREMENT “DOTS” ON TOP OF A LONGLEY-RICE SIGNAL FORECAST MAP

• Each colored “dot” above represents the location and value of a signal strength measurement.
• The background color represents the Longley-Rice signal strength forecast.
• The points of color differences between the dots and background indicate where the measured signal is underperforming or overperforming the Longley-Rice forecast.

SUMMARY

More Informed investments in signal expansion yield increased listening audience and donor revenue.

Powerful new data analysis tools and software systems can help improve revenue forecasting accuracy for signal expansion opportunities.

More accurate revenue forecasts help determine the best signal development opportunities to pursue (and not pursue) to grow a radio station or network.

Data-driven Signal Development is foundational to a time-proven growth strategy that has produced annual donor revenue increases of 10% to 15%.

The greater the accuracy of signal development revenue forecasts, the more successfully a station or network can grow its signal coverage, listening audience, and donor revenue.

Signal strength measurements help to:

• More accurately determine signal coverage, and
• Better forecast donor revenue from signal expansion.

NOTE:

More HisAir.net articles, by the author, on using Data Science to grow signal coverage, listening audience, and donor revenue, are at:  https://www.hisair.net/?s=kavenik

A related HisAir.net article on 18 ways to increase pledge drive revenue, by K-LOVE founder Dick Jenkins, is at:  www.hisair.net/dick-jenkins-increasing-revenue-18-ways-with-data-driven-pledge-drives  Dick’s e-mail is: dickjenkinsbiz@gmail.com

Frank Kavenik is a Christian Radio Strategic Planning Data Scientist.  He invented the BroadcastMetrics strategic planning system for Christian radio stations and networks. Frank uses BroadcastMetrics to create new strategic data and information that network leaders then utilize to increase signal coverage, listening audience, and donor revenue. Frank has completed over 900 Data Science projects serving leaders of major Christian radio networks.  He holds degrees in management, law, and engineering.

 His e-mail is:  FrankK@data-sci.biz