This reference page lays out the logic and most of the formulas we will use to plan your local Direct Response, Retail or Brand Support Radio tests, track results, and roll out into other stations or Network buys. 

All the formulas and chart examples apply simple arithmetic to audience data that is readily available from local stations and networks.

Each section is bookmarked. Use the back-button on your browser to return to a referring page.

Media Cost Per Sale = X
Media Cost Per Customer

GM/C=X
  GM = CPI
  Set Target G = CX/M
  Set Target M = CX/G
  Set Target C = MG/X

Target Audience Demographics
Cume Quotas
Test Market Station Selection
Basic & Market Radio Pricing
Remnant Radio Blitzes & Rollout
P & Π Market Trackers
UPC/Phone Call Flighting Tool

OEF = Optimum Effective Frequency
Track EF & G
Track EF & G in Test
OEF Flighting
  Steam Speed
  Drag Response

Network Rollout

This page also serves as a reference guide to several sections of my Free Planning for ROI Excel Workbook. Bookmark this page, then send for the planner if you haven't already done so.

Media Cost Per Sale = X

Media Cost per Sale is a dynamic variable that changes constantly.  Target Media Cost per Sale is pre-set budget assigned to an incremental sale, customer, or lead.  MCPS times target Sales = Media Budget. 

MCPS can be a dollar value applied to either an average sale or to the downstream value of an incremental customer. 

You can estimate the value of each incremental customer using historical sales data.

In the graphic above, 10,000 first-time customers produce $2,500,000 in incremental revenues. Each first-timer is worth $2,500,000 / 10,000 = $250.

MCPS must be an affordable percentage of incremental revenues less variable costs, overhead and profit.  Corporate brand managers, retailers, and Direct Response advertisers commonly set different targets.

Brand Managers usually set next year's media budget as a percentage of this year's sales.  A better method is to determine an affordable Media Cost per Customer.

Retailers normally set a fixed percentage of weekly, monthly or annual sales, which boil down to a media cost per "store up."

Direct Response advertisers sell via website or in-bound telemarketers.  The total cost of that distribution channel (media + web/TM costs) is equivalent to the retail mark-up on a comparable product or service.

A typical 1-800 AMA-ZING Widget that sells for $19.95 usually includes about $10.00 for product cost, overhead, and profit.  The Breakeven MCPS is the remaining $10.00. 

You can build revenues faster if you set target MCPS at about 75% of Breakeven. See below.)

The charts below show how advertisers in different categories can set Target MCPS.

38% MCPS

Breakeven MCPS is Gross Revenues less Cost, Overhead and Profit.

Target MCPS should be set at about 75% of Breakeven.  In the chart below for a $50.00 unit sale: C|O|P is 50%, Breakeven MCPS is 50%, and Target MCPS is about 38%:

Target MCPS can yield exponential growth.

The difference between Breakeven MCPS ($25.00) and Target ($18.75) is only $6.25.  That extra cash can be plowed back into future media buys.

To see how 38% MCPS can yield compound growth, start with an initial Month 1 media budget of $10,000.  At Breakeven (50%) MCPS, the same budget churns itself from month to month, yielding flat revenues and unit sales.  At Target (38%) MCPS, each month's residual cash can be reinvested in the next month's media.

Caution: Available dollars must still be sufficient to achieve Optimum Effective Frequency against your Target Audience.  We do not set Target MCPS at, say, 10% of Breakeven and expect actual revenues and profits to soar accordingly.

Media Cost Per Customer

Retail, Direct Response, and On-line advertisers who keep track of repeat sales to the same people can easily establish an acceptable Media Cost per Customer.  An example:

Set the average sale to newbies. Assume that a decreasing or steady percentage of them buy again at the same or increasing price points.

You expect that all future newbies will generate a predictable number of unit purchases and some amount of gross revenue. Set MCPS as an affordable percentage of that revenue stream.

In the graphic below, 100 newbies generate 318 unit sales and $13,660 in gross revenue.  You set MCPS at [22%] of $136.60.

Brand Managers do not know the identity of their customers and have to guesstimate the value of a single gained or lost customer.

The method below uses simple spreadsheet logic to estimate Customer Value per $1.00 of retail or wholesale revenue for a New and an Established Brand. 

1) Set Category Purchase Cycle in Days.  In the chart below P.C. is set at 18 days.  Therefore, the average category customer has about 20  Buying Opportunities per year.

2) Set Four Customer Categories: Exclusive, Loyal, Often, and Trial Customers who each give some defined percentage of their annual business to the Brand.  Values shown below are arbitrary.

For a New Brand:  Most sales come from Trial and "Fairly Often" customers who like the brand well enough to give it (10%) and (40%) of their annual Buy Ops.

In the chart above, 100 customers of unknown identity produce 550 sales per year, or 5.5 sales per person. Each customer is worth $5.50 per $1.00 in retail or wholesale selling price.

If Your Thing sells for $3.95 retail, the average customer is worth $3.95 x 5.5 = $21.73 per year. 

If retail markup is 50%, the wholesale value per customer is $10.86.

If you move 2,500,000 units Year I, you probably have about about 455,000 customers  (2.5MM / 5.5).

For an Established Brand: Most sales come from Exclusive and Loyal customers who like the brand so much they give it (100%) or (80%) of their annual Buy Ops. 

On average  100 Customers buy 1,710 units per year.  The average customer buys 17.1 units per year and is worth $17.10 per $1.00 of retail or wholesale revenue.

If Your Thing sells for $3.95 retail, the average customer is worth $3.95 x 17.1 = $67.54.

If retail markup is 50%, the wholesale value per customer is $33.77.

If you move 2,500,000 units per year, you probably have about 146,000 customers (2.5MM / 17.1).

Media Cost Per Customer should be set as a percentage of the annual or longer term value of an average customer.  This principle applies to Direct Response, Retail and Brand advertising.

Brand Introductions usually set a high MCPC (20-30%) in order to fund the broad reach and high Effective Frequency (3.0 to 5.0) per purchase cycle normally required to induce Trial.

A typical grocery store product invests heavily in advertising for two or three years, then recoups that investment from the revenue streams of Exclusive and Loyal customers who habitually buy the product.

Brand Maintenance & Share Defense advertisers can set a more modest MCPC (3% to 5%).  Reminder advertising requires less frequency (1.0 to 1.8) per purchase cycle at any target reach. 

Note that if an established brand loses one Exclusive Customer or Loyal to another brand or a lower priced store generic, today's loss of a single sale predicts the future loss of all (20) or (16) annual units.

Media Budgets = MCPC x Target Customers.  Given the Customer Values above and a target of (10,000) customers, the media budgets per $1.00 of retail or wholesale revenue for a New or Established brand might be:

Advertisers who use television to introduce a brand should use radio to defend it.  In spot or national buys, Radio :60s cost half as much as TV :30s.  But you need only half the Effective Frequency.  You can defend a brand in radio at 25% of the cost to introduce it in television.

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GM/C = X

In Direct Response Radio MCPS is always a function of three dynamic variables: Response Rate, Media Cost Per Thousand, and Conversion Rate - whether or not you choose to track them.  GM/C=X tracks all three variables simultaneously.

Radio is priced and sold like gasoline.  A gallon of radio is 1,000 target audience gross impressions.  CPM is cost per thousand, or cost per gallon.  You buy radio by the tank, or commercial.  Each :60 or :30 is priced according to Arbitron AQH ratings X some competitive CPM.

Since we buy radio by the gallon, it's convenient to track response by the gallon.  How many gallons of radio does it take to generate one inquiry (call, click, store up...)?  What did we pay per gallon?  How many those inquiries did we convert to sales?

G = Target Audience Gallons per Inquiry.
M = Media Cost per Gallon (CPM).
C = Conversion Rate of Inquiry to Sale.
X = Media Cost Per Sale.

Say you buy some number of commercials on three or four stations over 2 1/2 weeks.   The stations deliver 3,650,000 P25+ gross impressions for $25,200.  You field 945 calls or clicks and make 289 sales.  How would you evaluate that buy?

Typically, you'd settle for $25,200 / 289 = $87.20.

GM/C = X gives you more useful detail:

 

G, M and C are dynamic variables, so Actual MCPS (X) will vary from day to day and station to station.

GM = Cost per Call. 

In the chart above, CPC = $6.90 x 3.862 = $26.65 = $25,200/945. 

Long-term average G can often be projected to future buys, as can C. 
M tends to vary, depending on station selection, day part, and negotiation.

If any three of four variables are known, the fourth can be easily derived using simple algebra:

GM/C = X
CX/M = G
CX/G = M
MG/X = C

Therefore, you can set Target response rates, cpm, and conversion rates to achieve any Target MCPS. It is virtually impossible to do so using the typical Cash In : Cash Out method.

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GM = CPI

All Direct Response and most Retail advertisers track Cost Per Inquiry.

Suppose you spend $10,000 on one or several radio stations. You field 400 calls, clicks, or store ups.

Your CPI is clearly $10,000 / 400 Inquiries = $25.00.

Suppose that $10,000 buys 2,850 gallons of radio (1 gallon = 1,000 Gross Impressions).

    Your response rate (G) is 2,850 gallons / 400 calls = 7.125.

    Your CPM (M) is $10,000 / 2,850 gallons = $3.51.

    7.125 x $3.51 = $25.00.

Therefore, GM = CPI.

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Set Target G = CX/M

Given stable C, M and Target X, I set Target G for new Direct Response Radio creative or for a new station or destination program using the formula G=CX/M.

If budget permits, I G-test new creative in local markets, before adding it to the national campaign. I always set Target G when negotiating buys on different formats (e.g. Country or Jazz) whose response rate is unknown. 

If (G) is within normal limits, we proceed. If (G) is impossibly low, I either try for a lower spot cost, yielding lower CPM, or pass on the buy. The table below shows an impossibly low G for a proposed Network Program renegotiated to a lower price.

Set Target Retail G = X/M

In any local retail or brand support test, track Gross Impressions (in 000's) per incremental up, purchase, or unit sale. 

Clearly Media Cost per Sale (X) = GM. 

If G = .125 (125 TA GIMPS per + unit) and local CPM is $9.50 then
X =  .125 x $9.50 = $1.18.

If target X is $.75 and network CPMs are $2.50, then
rollout Retail G = $.75/$2.50 = .300.

Set Target M = CX/G

Given stable C, G and Target X, I set Target M for a new Direct Response Radio station or program using the formula M=CX/G. This method is useful in three distinct settings.

NETWORK ROLLOUTS.

M=CX/G is the preferred method for translating local spot radio test results to network rollouts. 

In test, reliable values for G & C may be ascertained, but the practical cost of local radio is too high to achieve Target X.  The chart below demonstrates how acceptable G, C, and X can be achieved in network vehicles at a lower M.

PARITY ROLLOUTS.

This is also the preferred method for bidding on remnant bourses or opening discussions with media reps of stations with formats similar to those of our benchmark stations (e.g. News/Talk and Sports).

If (M) is within normal limits, we proceed. If (M) is impossibly high, I either try for a lower spot cost at or about Target M, or pass on the buy. The table below shows an unacceptably high (M) for for a proposed Network Program renegotiated to a lower price.  Note that G may be somewhat lower than normal, but possible in a known format.

CHANGE MCPS.

If the Target Media Cost per Sale (X) changes and Response & Conversion rates are stable, we can quickly reset Target (M).

MCPS might increase if we switch from a percentage of initial sale to percentage of lifetime customer value. That yields a higher Target (M), which can open up new media opportunities.

MCPS might decrease if the product needs to sell at a lower price. That yields lower Target (M), which can close out all radio outlets other than network or satellite.

Set Target C = MG/X

Given stable G, M and Target X, I set the target Conversion rate (C) for a new Direct Response Radio initiative using the formula C=MG/X.

This is the preferred method for negotiating a buy where the absolute price per :60 is less flexible.  The table below shows an impossibly high (C) of 26% for for a proposed Network Program host-endorsement campaign. 

In the first round of negotiation, I set Target G at the 12-Week average and Target C of 18%.  That yields a bid price of $225 per :60.  In the second round, the media rep requires a somewhat higher spot cost, which requires a lower (G). That in turn requires an Executive Decision.

TARGET AUDIENCE DEMOGRAPHICS

In Direct Response Radio, we always use stations and programs that deliver weekly cumes in which your TA (Target Audience) and QA (Qualified Audience) comprise a high percentage of total.

My Free Planning for ROI Excel Workbook asks you to plot your current and target customer profiles according to standard Media Demographics.

The first table below distributes 25,000 CURRENT and 65,000 TARGET customers across a typical TA demo break out. Left hand numbers show national population and percentages of each demo. The next two tables gather additional demographic criteria that define your QA.

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BASIC & MARKET RADIO PRICING

Radio commercial prices are based on Arbitron ratings and adjusted according to local demand for specific programs, days, and day parts. There are twenty basic prices quoted for standard day parts.

Agencies charge the gross rates (100%)and pay the net (85%), as do some local retailers and others who buy their own media. This section describes the basic logic of radio pricing.

Cume Quotas:  INQS/CUME

The Cume Quota is the set of individuals within a station's weekly cume who are actively shopping or interested in your category and who are willing to buy via phone, web, retail etc.

In some categories, the market-wide cume quota is well-known.  The number of cars and light trucks that change hands each week equals about 2% of the adult population.  Many retailers figure 1% to 2% of the local population shops for furniture, washing machines, etc. each week.

In many categories, especially those in which Direct Response advertisers operate, weekly cume quotas are unknown.  But they can be inferred!

In the graphic below, the Blue box represents the weekly cume of WXYZ-AM.  The Yellow box represents the Weekly Cume Quota.

Cume Quotas may refresh themselves very slowly or very quickly.  People who buy gold, CD collections, or 1-800 AMA-ZING widgets are always in the market.  People who get rear-ended on the way home from work enter the Personal Injury Attorney category instantly.

If, say, 2% of the entire P25+ population will shop your category this week, it's likely that 2% of the P25+ listeners to WXYZ-AM will do so as well.  If half of them are willing to buy over the phone, then your Cume Quota on WXYZ-AM is 1.0% of the station's P25+ weekly cume.

Over time the Cume Quota may stay static, refresh itself slowly, or turnover completely each week.  Some shoppers may stay in the Cume Quota for several weeks, until they finally buy something from somebody. 

Massive media weight cannot increase the size of the Cume Quota on any one station.  It's better to buy broad & thin.  Run Totsie 1.5s on three or four stations than Totsie 4.0s on one.

In the short run, INQs/CUME is usually a very small percentage.  So why do we care? 

KEY CONCEPT: The same creative will likely produce similar percentage results on stations of similar format and known CUMEs.  

In the Optimum Effective Frequency Range chart above, the creative attracted 126 Inquiries from Spot 1 through Spot 44, when we closed out OEF. 126 INQS /250,000 CUME = .05%.  During the entire test, 48 spots attracted 131 INQS. 131/250,000 = .05%.

We would expect, then, that for similar stations, we'd hope to attract Inquiry from a similarly tiny sliver of cume.  We'd be surprised if a similar station format produced much larger Weekly Cume Quotas - no matter how many spots we ran!

We'd also expect similar Weekly Cume Quotas in other markets, and in national network buys. One station test does not predict national response as well as G (Gallons/Inquiries), but INQS/CUME is a useful tool.

TEST MARKET RADIO STATION SELECTION

In Direct Response Radio I always try to test new creative under ideal circumstances.  If your budget permits only one or two stations, I'll select those that best reach your Target Audience.  AM News/Talk & Sports formats tend to pull better than FM Music formats, but good creative will stand out in any station format. 

I assemble a preliminary buy-list of stations based on TA and QA Cume Rankings.  Then I select the station or stations that offer the lowest QA CPM and/or the highest QA density.  In the chart below, I'd test on WABC or WDEF or both.

In Diary markets about 70% of any station's weekly cume are P1's, that is they Prefer or give more listening hours to that station than to any other.  P1 GIMPS are the lion's share of all GIMPS.  P1's generate most of a station's response. 

In PPM markets, only about 58% of the cume are P1s.  (Personal Program Meters increase station cumes by about 20%.  PPMs count people who hear a station infrequently or involuntarily.)

In diary markets, I add 70%-of-QA cume totals down a ranker list until I reach about 100% of the available QAs in a market. Those are all the stations you'll ever need.  (In PPM markets, I add 58%-of-QA cume totals.)

An excellent way to test new creative for a reasonably well-known local brand is to buy a Totsie 1.0 on three stations that share 30% to 50% of their listeners. We get EF 1.0 among half of each station's unduplicated cume, and EF 1.0 to 3.0 among half the people who listen to two or three stations on the buy-list.

A remnant radio blitz distributes a reasonable budget thinly across 4 to 10 stations during a short flight of one to three weeks.  You hope to build broad awareness among listeners who patronize one or more of the stations on your list, generate initial sales from new customers, and feed them into your back-end pipeline.

My remnant brokers supply estimated Cost per GRP.  I have local DMA Target Audience population on file.

You select a launch market using P & Π or historical sales data.  Determine your expected weekly sales in that market and set a reasonable MCPS for incremental sales.  Plan to track sales by DMA ZIP cluster shipments or area code calls.

If you have sales numbers for the past year or so, convert the numbers to a chart.  A tight sales curve indicates a small but reasonably loyal number of customers who may re-order if you just remind them to do so.  A jumpy sales curve implies the opposite.  Compare Cleveland and Richmond to Charleston below.

The red lines on the Cleveland and Richmond charts came from carefully planned two-week remnant blitzes.  Twenty three other markets will similarly tight sales curves produced similar one to two week spikes.

The frenetic sales curve in Charleston (and 30 other markets) mitigated against even trying remnant. 

Initial Blitz Planning & Tracking.

To plan the Cleveland blitz, for example, we set out some benchmarks and targets.

We expected 24 sales per week in Cleveland @ $75.00 average and hope to increase unit sales by 5. We were willing to spend 85% of the average first time purchase ($25).  120 x $25 x 85% = $2,550 per week in gross media.

My broker quoted $65 per P25+ GRP in Cleveland. There are 2,602,200 P25+ in the Cleveland DMA, so I estimated a market-wide CPM of $2.50 ([2,602,200/100,000] ≈ $2.50).  Therefore, our budget should buy about 39 GRPS and 1,020 gallons on four to ten stations  ($2,550 / $2.50 = 1,020). 

At an aggressive conversion rate of 25% we needed 480 Inquiries to acquire 120 customers (120/.25 = 480).  Therefore, Target G was 2.130 (1,020/480 = 2.130).  

National network G, over several years, had been in the 1.450 to 1.750 range, so 2.130 seemed doable in Cleveland, given its tight 2-year sales curve. 

We instructed the broker to buy about 15 x :60 per week, Mon-Fri, 8a-6p, on as many stations as our budget permitted.  

A week after the first blitz ran, my broker reported that $2,175 was actually placed on seven stations.  We also recorded 134 incremental customers and $3,386 incremental revenue at $25.27. 

Our actual multiple was 5.6 (134/24 = 5.6).   We probably ran 870(000) gross impressions in Cleveland ($2,175/$2.50 = 870).  We did field 536 inquiries (134/.25 = 536) for an actual G of 1.610 (870/536 = 1.610).   MCPS was only 64% ($2,175/$3,386)!  Here again is what happened:

Note that weekly sales stayed well above the 18-month average for several weeks and that the sales curve resumed its characteristic tightness.  We had reminded existing customers to buy again, and attracted several new customers into the pipeline.  Three months later we ran another reminder blitz with similar, although slightly less dramatic results.  We concluded that four remnant blitzes per year are sufficient to fish out the market wide quota of prospects in Cleveland.

Conservative initial planning can produce better than expected initial blitz results.  Accurate tracking and well-time sustaining flights can protect the established base, encourage reorders, and attract new customers.  You build your business at an ever-diminishing MCPS.

An established multi-SKU brand, such as a website or chain of stores, can use the ratio of population to sales to track individual markets against themselves over time, to select a test market, or to distribute a fixed budget judiciously over several markets.

P = 000 Persons per Dollar of Revenue

Problem: You wish to invest $25,000 in media in one or more of the following five markets whose weekly sales over the past 8 to 10 weeks are similar.

 

The first thought might be to distribute the media with gross sales.  Phoenix would get the most support, Hartford the least.  However, when you compute P, using local P25+ DMA populations, a different scenario emerges.

In Phoenix, P = 3,377(000)/$2,200 = 1.535.  You get one dollar of revenue from every 1,535 persons.  Like G (000 Gross Impressions per Inquiry), the lower the P the better.  In San Antonio, you need only 907 persons to produce a dollar of revenue.

Resort the five markets by P.  San Antonio is clearly the hottest market of the five shown.  Since new creative should always be tested under optimum conditions, San Antonio would be your best place to test Real People Radio, or even new Urgent Announcer Copy.

To allocate your fixed budget of $25,000 among the five markets, use Π.  (PI is short for P Index.  Excuse the pun.)   Simply index each market's P value against the total.  You can also use this method to index all 250 DMAs against national sales.  Hot markets will index above 100.  Slow, below.

San Antonio indexes at 136 against the five-market P of 1.233.  Phoenix indexes at only 80.  The sum of the indexes is 521.  San Antonio's 136 is 26% of 521 so San Antonio should get 26% of your $25,000 budget.  Phoenix only 15%. 

In practice, you'd test San Antonio first, track incremental sales by DMA ZIP-Cluster, then apply positive results to each successive market.  (See Remnant Radio Blitzes for media modeling.)  If your new creative fails in San Antonio, it probably won't fare better elsewhere.  If it succeeds, roll out down the list.

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A cursory review of daily/hourly phone inquiries or sales by UPC may reveal that much of your current sales volume occurs during certain days of the week and hours of the day.  If so, test 1-800 or retail brand support radio on those days and during those hours.

You can derive peak unit sales hours from UPC data or your own phone bills.  The Double-Average method hones in on your peak hours:

Plot unit sales in some benchmark number of stores by day and hour. A 60-day or 180-day summary is an excellent baseline. Weekly reports will show any recent changes in same-store sales. Compute overall average unit sales or calls per hour.

Group I: Select all above average hours. Compute Group I average. 

Group II: Select all above-average hours in Group I.

The graphic below shows total unit sales from 8am to 11pm, Monday-Sunday for an average week.   

Hourly average sales Mon-Sun, 8a-11p: 106.
Boldface hours are above that average.
Boldface (double) average: 188.
Highlighted blocks are above the double average.

In a retail radio test market we might run Real People Radio while most people are driving to the stores.  We'd use the Cume Ranker Method to select the top three or four stations in our demo, and then buy a Totsie 1.5 to 2.5 on each. 

To the extent traffic managers will permit, we'd distribute GIMPS following the same pattern as unit sales.   Run Cluster Bombs during peak hours.

The graphic below distributes 250,000 Gross Impressions (250 Gallons) across the days and day parts highlighted in Group II above. 

Tuesday and Wednesday are optional radio days.  The Thursday-Friday spots would run in afternoon drive.  The Saturday-Sunday spots would run in the 12N-6p day part.

Track unit sales changes for two consecutive or three alternate purchase cycles. Compute overall G (000 Gimps per incremental unit sale).  Note significant changes in Group II time blocks.

BENCHMARKET REFERENCE.strong> If possible, compare results to sales in one or two similar markets that get no radio support.  Plot baseline UPC data for the benchmarkets in advance, note changes during the test market, and assume the same changes would have occurred in the test market absent radio support.

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OEF = Optimum Effective Frequency

Effective Frequency is the approximate number of times that half or more of the cume audience of a program, station or day part have heard a Radio commercial during a flight.  Most response comes from loyal listeners to a station, not from people who tune in sporadically or who happen to hear a station in a 7-11.

Effective Frequency is not "average frequency."  The latter is derived by Arbitron from beta-binomial distributions of the AQH, which includes people who tune to a station infrequently or involuntarily.

EF is a useful tool that can be derived arithmetically from two numbers any rep can supply: the weekly cume audience in a day part, and the AQH.

EF is a function of two variables: Turnover (Δ) = Target Audience Cume/AQH and N = Number of Spots run in a flight. 

As a general rule of thumb, N = Turnover.

The exact formulas vary slightly, depending on whether or not Arbitron uses PPMs (personal program meters) or diaries to gather audience data.

PPM Markets

To achieve a target EF, buy N spots such that: N = Δ x .96.
To determine EF after n spots have run: EF = n / Δ x 1.03.

DIARY Markets

To achieve a target EF, buy N spots such that: N = Δ x 1.11.
To determine EF after n spots have run: EF = n / Δ x .911.

The ratios between cume and AQH in various day parts tend to be similar across all formats and all markets. Hence the number of spots necessary to achieve EF 1, 2, 3... tend to be similar.  The chart below shows typical cume, AQH and & Δ for any station, as well as N for different target EF.

To test Direct Response copy for a first-time radio advertiser as inexpensively as possible I can simply buy 16 spots in the Mon-Fri 10a-3p day part on a big New Talk AM station, then flight them in Cluster Bombs based on whether the brand is new, unique, or well-established.

TRACK RESPONSE RATE & EF RANGE

Optimum Effective Frequency Range is comprised of two values: Entry EF and Exit EF.

Entry EF occurs when we've run enough spots to provoke a surge in response (G drops below average). Exit EF occurs when response from Active Shoppers begins to taper off (G rises above average).

When I test new copy on a benchmark station I plot DAILY G (Gallons / Inquiries) and note EF & G for the entire flight and Entry and Exit EF.

To test new copy for a new advertiser in a highly cluttered category (e.g. a gold seller, tax relief specialist, dating site, etc.) I might buy a 13-day Totsie 3.0 on a station, i.e. a number of spots sufficient to generate an EF of 3.0.

The base numbers for the station might be:

The average commercial delivers 15,500 gross impressions or 15.5 gallons.  Over the course of two and half weeks we track daily gallons, G and EF. 

Average G over 13 days was 5.679 (744 gallons / 131 Inquiries).

On the first Thursday, G drops below the average to 4.133.  We had run 19 spots by then, so entry or minimum EF was 1.2.

On the last Monday, G rose above average to 7.750.  We had run 44 spots by then, so exit or maximum EF was 2.7.

In subsequent buys on the same or similar stations, we'd buy a Totsie 2.7 at a negotiated CPM based on known values of G, C and MCPS.

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OEF FLIGHTING

The objective of OEF Flighting is to achieve steady response from each series of commercials we run on any one station or program.

We can set an accurate Effective Frequency range and thus the number of spots to run during a typical two to three week flight.  (See above.)

How often should we run such a flight?  Once a month?  Every six weeks?

Ideally, we'd like to run often enough to retain awareness, but infrequently enough to allow the Cume Quota to refresh itself.

One way to set the length of a flight and the interval between flights is to estimate Steam Speed.

STEAM SPEED AXIOM:  The longer the time takes the average person to move from emotional Room Temperature to Steam Heat, the longer the time frame in which he or she might respond to a compelling commercial.
The shorter, the shorter.

SLOW STEAM Category.

Our QUOTA of Active Shoppers takes longer to achieve Steam Heat.  They are in the market longer.  N commercials needed to achieve a target OEF Range (or EF ≈ 1.0+)can be spread out over several days.  The intervals between flights on the same station or in the same program can and should be several weeks, unless you have a huge MCPS and can afford saturation buys.

FAST STEAM Category.

Our QUOTA of Active Shoppers takes little time to achieve Steam Heat.  They are in the market only briefly. (People who get rear-ended on the way home from work enter the Personal Injury Attorney market instantly!)

N commercials needed to achieve a target OEF Range (or EF ≈ 1.0+) should be run in only one or two days.  The intervals between flights should be shorter - once a week even.

In a typical one to two week test market, the advertising captures response from only one QUOTA of Steam Heat Shoppers.  We can infer the Steam Speed of that QUOTA from DRAG. 

Drag is measureable response that limps in during the hours, days or even weeks after you go off the air.  Drag can also help up estimate Steam Speed. 

Long Drag implies Slow Steam Speed. The average prospect takes several days, weeks, or even months to move from disinterested Room Temperature to 100º in your category. 

The problem you solve is important, but many prospects are in no hurry to solve it.  Response can drag in for several days or weeks.  

Short Drag implies Fast Steam Speed. The average prospect enters your category quickly and is in a hurry to solve a problem.  They hear, they call.  But few will think about it for long.

DRAG INQ RATIOS = Steam Speed.  If budget permits, we can run two single-station blitzes a week or two apart.  If there are significant differences in the total INQs attracted by two single-day blitzes, we can also infer that the available supply of Steam Shoppers has either diminished, refreshed itself, or increased.

BACK TO TOP

A successful rollout from local to national radio requires simple arithmetic. The key values to check are Target M (CX/G) and EF.

Network CPM is typically much lower than those of spot radio.  A local test that fails to achieve Target X because of high M may do so handily in a network rollout.

Destination programs attract loyal audiences, so program turnover is also lower than that of spot stations.  Fewer spots are required to achieve entry or exit EF.  A national cume quota refreshes itself more slowly, too.  So fewer Totsies per year are required to fish out the pool.

We apply the test OEF Range to any destination program.  If the range for a test on WXYZ is 1.2 to 2.7, we'd expect similar response from a buy on Hannity, Limbaugh etc. In a local test, though, it may take several days to achieve entry EF (1.2).  The first commercial that runs on a network program achieves a 1.0 against hundreds of thousands of prospects!

We can also apply the Response rate achieved in the test during OEF range.  WXYZ-AM's News Talk format reaches the same type of people that Sean, Rush, etc. reach nationally.

Test market conversion rates can be applied, too, unless the existing in-bound TM staff cannot handle a surge in call volume, or the advertiser's web servers cannot handle a surge in UV1s.

The simple arithmetic used to gross up local results to national targets are summarized in the charts below.  First, the raw numbers used to buy a 13-day Totise, then the results.

Network Rollout Math.

First, set OEF values for G,M, C and X.   The long rectangles summarize what happened from the first Monday airing to the last Monday, when G rose above average.

In the WXYZ-AM test above, from EF 0 to EF 1.2 to EF 2.7 those values were:

   G = 5.413.
   M = $11.29.
   C = 35.1%.
   X = $174.03.

Second, set Target M.

If Target X is $80.00, target M = CX/G = .351 x $80 / 5.413 = $5.19.

In most cases, satellite channels and network destination programs offer CPMs of under a dollar to $4.50.  We just won't buy any pricier programs for the time being.

Third, determine the price of a 1.2 to 2.7 Totsie on any program. Destination program turnovers are lower (in the 5 to 7 range), so we might need only 6 or 8 insertions to reach the entire program cume once or twice.  A :60 on Rush might cost $7,500, but we do achieve a de facto EF of 1.0 against over a million P25+ every time the commercial runs.

There are no Arbitron ratings for Satellite (Sirius/XM) radio, save for some 2009 cume audience estimates.  I impute an across the board turnover to set Mon-Fri 6a-8p AQH of cume/17.1.  More spots are required to achieve imputed entry  EF, but the low spot prices and CPM ranges on S/X ($.96 to $2.00) and the upscale new-car-owner profile of S/X subscribers usually pay off the investment in a modest flight fairly quickly.

Broad national network news feed services such as Citadel/ABC offer attractive CPMS, but suffer from high turnover (12.5 to 20.5). At $5,000+ per :60, we'd need $60K to $75K to achieve entry EF, and several hundred thousand dollars to achieve exit EF.

Given the Radar ratings of, say Sean Hannity, we can apply our target values for OES, G,G,C and X to the program's numbers.  The CUME, AQH and $$$ values below are approximate.

Hannity's M ($4.21) and our test values for G and C deliver a projected GM/C=X of $64.86, which is far enough below Target MCPS ($80.00) to warrant a test.

We needn't run the 7 spots required for EF 1.2 all at once though. A better plan is to run 2 :60s in the same program, and another :60 the following day.  We get a real frequency of 2 against one day's AQH and real frequency of 3 against a fair number of P25+. 

Track calls, clicks, conversion.  Verify MCPS.  Buy more spots.

OEF FLIGHTING

We want to attract maximum response from Active Shoppers, then spread out successive flights to give the program's Cume Quota time to refresh itself.

A unique brand with a slow steam speed and a low MCPS, such as Dinovite, can run profitably once every four or five weeks on an appropriate station, destination program or satellite channel.

In the chart below, the GREEN boxes represent the hour-long blocks of a good network destination program such as Sean Hannity, Laura Ingraham or Rush Limbaugh. The RED bars are your commercial.  If we buy several programs that share audiences, we can build EF nationwide rather quickly.