Few people, including advertising professionals, know how to calculate the expected response from a newspaper ad. A newspaper circulation of 200,000 doesn’t mean 200,000 people will be contemplating your ad.
Here are the variables you need to consider when calculating an expected response from a newspaper ad.
1. YOUR TARGET
Every product has a demographic that represents its prime target. Let’s say, for the sake of this example, that your target demographic is adults, 25 – 54
2. MARKET SIZE
If you are located in a city of 1.5 million people and the 25 – 54 demographic represents 40%, then your target population is 600,000.
3. DURATION BETWEEN PURCHASES
Since people don’t buy every product they use every day of the year, the duration between purchases is important to consider. Let’s say people buy your product one time per year on average. Then, your available market in any month is 1/12 of 600,000, or 50,000. Of course, you’ll have to take into account the seasonal fluctuations for your product and apply the same logic.
4. NEWSPAPER CIRCULATION
If your local daily newspaper has a 40% penetration in your market, calculate that against your available 50,000 market to reach 20,000 possible qualified exposures to your message.
Not every subscriber reads every page every day. Studies have shown that if you get 10% of the subscribers to read your ad, that is a very generous number. Applying that calculation to our example reduced the number of targeted customers to 2,000.
6. YOUR MARKET SHARE
Unless you have an exclusive monopoly in your market, you have competition with some customers who are loyal to them. If your overall market share is 10%, you can apply that to the remaining targeted customers, leaving 200 as your reasonable expectation.
7. RESPONSE RATE
Assume a 2% response rate from your ad.
Your expected response (customers making a transaction) will be 4.
These numbers can vary wildly. However, the chain of logic remains constant. The offer, size of ad, weather, lack of or heavy competition, time of year and numerous other influences can have a bearing on final results. But, this model is useful to illustrate the realities of advertising response.