## Content marketing can be bland without original insight and commentary. Research provides that originality. But it has to be valid research or you risk losing your authority.

So how do you check your research is valid? I’ve been carrying out market research for more years than I care to remember. Fortunately, there is a formula you can use to achieve valid research and repeatable results. But before we dive into that, a word about the ‘Rule of 100’.

The Rule of 100 (Schmid, 1995) states if you get 100 responses to a survey, the ranking of answers will remain the same whether you get an additional 1,000 or 10,000 responses. The percentage difference between the first and second placed answer may change, but the top answer will remain the top answer.

I was initially sceptical of the Rule of 100, but I have seen so many examples where it is true that I now have to accept it has a place in market research. The caveat is that questions must be simple and the number of answers limited to less than 10. So if you are going to use the Rule of 100 think carefully about your questions and answers.

You should also accept that if there is just one percentage point difference between first and second place, then those positions could be easily reversed.

One of the main reasons I believe the Rule of 100 is useful in market research is sometimes it’s extremely difficult to get more than 100 answers from certain audiences.

But as a marketing scientist, I would always recommend using a formula and calculate the validity of your research. Below are 3 Tables showing how many survey responses you need for your research to be considered valid. They are based on 3 different confidence levels.

Table I - Valid research sample sizes; 90% confidence
## Valid Sample Sizes |
||||||||||||

Confidence: | 90% | |||||||||||

Error margin: | 1% | 3% | 5% | 8% | ||||||||

Population |
Sample size | |||||||||||

100 |
99 | 89 | 74 | 52 | ||||||||

500 |
466 | 301 | 176 | 88 | ||||||||

1,000 |
872 | 430 | 214 | 96 | ||||||||

10,000 |
4,036 | 700 | 264 | 105 | ||||||||

15,000 |
4,662 | 716 | 266 | 105 | ||||||||

30,000 |
5,520 | 734 | 269 | 106 | ||||||||

100,000 |
6,336 | 746 | 270 | 106 | ||||||||

500,000 |
6,674 | 751 | 271 | 106 | ||||||||

1,000,000 |
6,719 | 751 | 271 | 106 |

## Valid Sample Sizes |
||||||||||||

Confidence: | 95% | |||||||||||

Error margin: | 1% | 3% | 5% | 8% | ||||||||

Population |
Sample size | |||||||||||

100 |
99 | 92 | 80 | 61 | ||||||||

500 |
475 | 341 | 217 | 116 | ||||||||

1,000 |
906 | 517 | 278 | 148 | ||||||||

10,000 |
4,899 | 965 | 370 | 148 | ||||||||

15,000 |
5,856 | 997 | 375 | 149 | ||||||||

30,000 |
7,276 | 1,031 | 380 | 150 | ||||||||

100,000 |
8,762 | 1,056 | 383 | 150 | ||||||||

500,000 |
9,423 | 1,065 | 384 | 151 | ||||||||

1,000,000 |
9,512 | 1,066 | 384 | 151 |

## Valid Sample Sizes |
||||||||||||

Confidence: | 99% | |||||||||||

Error margin: | 1% | 3% | 5% | 8% | ||||||||

Population |
Sample size | |||||||||||

100 |
99 | 95 | 87 | 73 | ||||||||

500 |
485 | 394 | 285 | 171 | ||||||||

1,000 |
943 | 649 | 399 | 206 | ||||||||

10,000 |
6,239 | 1,557 | 622 | 253 | ||||||||

15,000 |
7,878 | 1,642 | 636 | 255 | ||||||||

30,000 |
10,682 | 1,737 | 650 | 257 | ||||||||

100,000 |
14,227 | 1,810 | 659 | 259 | ||||||||

500,000 |
16,055 | 1,837 | 663 | 260 | ||||||||

1,000,000 |
16,317 | 1,840 | 663 | 260 |

Here’s a breakdown of the key elements of valid research:

- Sample size: The number of people you need to respond to your survey for it to be valid.
- Population: The number of people in the group your Sample represents. For example, we recently calculated there are 35,000 Microsoft freelance contractors in the UK – so our Population is 35,000.
- Confidence level: This tells you how sure you can be the results are repeatable. You have a choice of 90%, 95% or 99% confidence. Most market research is based on 95%.
- Error margin: The plus or minus figure. An error margin of 5 on a result of 75%, means that between 70% and 80% of the population would choose the same answer.

As an example, if we want 95% confidence in the results and are willing to accept that the percentage response for each question may vary by +/- 3% (error margin), then the sample size would need to be 1,031 people (assuming a total audience or ‘population’ of 30,000 individuals).

So let’s assume you conduct the research and ask a question with two answers. Answer A gets 60% of the vote and answer B gets 40%. Because you accepted a 3% error margin, you can be 95% confident that if you repeated the survey answer A would get between 57% and 63% of the vote and answer B would get between 37% and 43% of the vote.

You can see from the Tables that as the Population figure rises above 15,000 people, so the additional number of respondents needed for your research to be valid narrows. So if you are not sure of the Population size of your audience just choose 15,000.

The new GDPR law means marketing consent (opt-in) will become more valuable unless you have a strong case for a legitimate interest. Inbound marketing will become the preferred method and good content can provide qualified ‘inbound’ prospects.

As B2B marketers fight to increase their inbound enquiries and marketing consent we will see further growth in content marketing. To rise above the noise, you should use valid research and comment on the results from a position of authority.