Unfortunately, it's difficult to estimate response rates with no specific context on your campaign, but we can walk you through some campaign response rates and discuss industry averages.

One company we've worked with is an office supplies company. They get a 1% response rate when mailing to prospects. When they target current customers, their response rate doubles to 2%.

As for industry averages, the DMA analyzed 1,122 industry-specific campaigns and determined that the average response rate for direct mail was 2.61%.

If you assume 1%, 2% or 2.61% response rates, you might be disappointed. We've seen open rates as low as 0.2%.

For some industries, that's acceptable.

If you are selling Lear Jets and 1000 postcards gets you two jet sales (a 0.2% response rate), you won't be complaining, will you?

Always be cautious when forecasting sales. A better approach is to figure out your breakeven response rate. It's a simple bit of math that asks how many customers need to buy as a result of the campaign to have the profits from those customers cover the costs of the direct mail campaign.

If the breakeven response rate percentage is very high, you might want to reconsider the campaign. Are there cheaper, better ways to get customers? Alternatively, maybe you need to try to lower the cost of doing a direct mail campaign.

Remember, you should start with small campaigns to test the response rates. This allows you to not overinvest in a bad campaign. If response rates are acceptable, increase the number you send out. If response rates are too low, you may need to tweak the offer to make it more attractive.

There's a growing school of thought on direct mail that it's much more effective to hit a smaller targeted list repetitively instead of sending to a large list with only one campaign. Keep that in mind. Failure on an initial campaign may just mean that you've got to go back at them a few times to achieve success.

Finally, always keep your expectations reasonable. It's always better to be conservative and exceed your expectations than to forecast optimistically and be disappointed.